RIchard W. Johnson, Simone G. Schaner, Desmond Toohey, and Cori E. Uccello
The Urban Institute
This report was prepared under contract between the U.S. Department of Health and Human Services (HHS), Office of Disability, Aging and Long-Term Care Policy (DALTCP) and the Urban Institute. For additional information about this subject, you can visit the DALTCP home page at http://aspe.hhs.gov/_/office_specific/daltcp.cfm or contact the ASPE Project Officer, John Drabek, at HHS/ASPE/DALTCP, Room 424E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201. His e-mail address is: John.Drabek@hhs.gov.
The authors are grateful to Jim Robinson of the University of Wisconsin-Madison for generously providing us with his actuarial model of long-term care services use, Jon Bakija of Williams College for generously providing us with his federal and state income tax calculator, and Marc Cohen, John Drabek, Amy Finkelstein, Mark Pauly, and especially Peter Kemper for valuable comments on an earlier draft. The views expressed here are those of the authors and should not be attributed to the U.S. Department of Health and Human Services or the Urban Institute, its board, or its funders.
Long-term care spending is expected to soar in coming decades as the population ages. Enhanced private insurance coverage of long-term care needs might ease the looming crisis. Raising private insurance coverage rates would increase the pool of funds set aside to finance future services and would reduce reliance on public resources. Enhanced private coverage could also protect families from catastrophic long-term care costs. Some policymakers have proposed expanding tax incentives for private long-term care coverage to stimulate demand.
To assess how different tax break proposals for private long-term care insurance might affect private coverage, we need better information on the decision to purchase long-term care insurance and the sensitivity of the purchase decision to price changes. This report describes the results of our efforts to model private long-term care insurance coverage and simulate policy reforms.
Like traditional medical insurance, private long-term care insurance is a financial contract whereby the insurer agrees to provide covered benefits in exchange for regular premium payments by the policyholder. The cost and adequacy of policies vary by the types of services they cover, when they start paying benefits, how much they pay, and for how long.
Insurance companies generally price policies as a function of age at issue date, health status, and the comprehensiveness of the plan. Most insurers classify applicants into three broad health categories: preferred, standard, and substandard. Most applicants qualify for standard rates, although according to one estimate 15 percent are denied coverage because of health problems. Policies are guaranteed renewable, and premiums remain fixed over the life of the contract. However, rates can rise for an entire class of policyholders if insurers can demonstrate that their costs exceed premium revenue, and rate increases have been common in recent years.
There are two principal challenges in modeling the decision to purchase private long-term care insurance, particularly the effect of prices on take-up rates. First, at any age uncovered individuals who live in the same state generally face the same premiums, making it difficult to observe how coverage rates vary with price, controlling for age and state of residence. Second, covered people deciding whether to renew their policies face lower premiums than otherwise identical people without coverage deciding whether to purchase a policy for the first time. This relationship could bias the estimated impact of price on the coverage decision.
The analysis addressed these complexities by estimating hazard models of time to purchase private long-term care insurance as a function of the net benefit that individuals expect to derive from the policy. The net expected benefit is the difference between what policyholders expect to receive in benefit payouts from the plan over their lifetimes, in present value terms, and what they expect to pay into the plan in the form of premiums. The measure, which accounted for state-level fluctuations in premiums and Medicaid eligibility rules, varied widely across individuals. Respondents were dropped from the sample once they purchased coverage, eliminating the correlation between premiums and past purchase decisions. Observing purchase decisions over an extended period, beginning when respondents were relatively young, reduced the censoring problem that would otherwise result if we modeled only cases that had not yet purchased coverage at relatively old ages (and thus had revealed their reluctance to obtain coverage).
Data came primarily from the Health and Retirement Study (HRS), a nationally representative longitudinal survey of older Americans. The survey asks respondents whether they have any long-term care insurance (excluding government programs) that covers nursing home care for a year or more or some at-home personal or medical care, and when they purchased the policy. Follow-up questions added in 2002 ask whether the respondent had already described the plan to the interviewer, such as when reporting traditional health insurance plans, and if so to identify the plan. After reclassifying respondents as lacking private long-term care coverage if they described their private long-term care insurance policies as traditional health plans, Medicaid, or Medicare, we found that coverage rates in the HRS were consistent with industry estimates.
The sample consisted of person-year observations between 1992 and 2004 on adults ages 51-61 in 1992 who did not have coverage in the previous year. We observed respondents every other year. We restricted our sample to respondents likely to satisfy long-term care insurers underwriting restrictions and thus able to purchase private coverage. We dropped respondents who reported any activity of daily living or instrumental activity of living limitations, kidney problems, history of stroke, or cognitive impairment, and who were not living in nursing homes. In 1992, about 5 percent of respondents were dropped from the sample because they reported one of these health problems. The share increased over time as the sample aged, rising to 32 percent in 2002. Overall, 17 percent of the person-year observations were dropped because of health problems or nursing home residence. The final sample consisted of 32,242 observations on 6,991 respondents.
The net expected benefit of coverage significantly increased the likelihood of taking-up private long-term care insurance coverage, although the impact was modest.
Every $1,000 increase in the net expected benefit of coverage would raise purchase probabilities by about 2.3 percent.
Under the assumption that premium changes have the same effect on take-up rates as changes in expected out-of-pocket payments for services, this result implies a price elasticity of demand for private long-term care insurance of about -0.75.
Take-up rates also increased with age, education, health status, and the self-assessed probability of using nursing home care in the next years. They declined with number of children, perhaps because children help with their parents home care or help finance nursing home costs.
Creating additional federal tax incentives for the purchase of private long-term care insurance would modestly boost take-up rates.
Granting a full tax deduction to all itemizers, even those who spend less than 7.5 percent of adjusted gross income on medical expenses, would raise take-up rates by about 3 percentage points, from 14 percent to 17 percent, increasing the number of adults with coverage by about 21 percent.
Take-up rates would rise to 19 percent if all taxpayers could fully deduct premium expenses from income subject to federal income taxes, representing about a 36 percent boost in the number of older adults with coverage.
The impact of tax incentives on private long-term care insurance would be concentrated among high-income taxpayers. Tax breaks would have very little impact on coverage rates for adults in the bottom half of the income distribution.