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to approximately 80 percent. This assumes all current users of formal care, and approximately two-thirds of eligible nonusers, would participate in the program.
Although the evidence from the demand for nursing home care literature suggests that use would increase a great deal, one should probably not use the literature estimates directly for five reasons. First, data from some studies are old and may not reflect current behavior, and the ability to generalize from others is limited. 13
Similarly, the decrease in the out-of-pocket cost of institutional care will increase utilization of nursing home services. In modeling the proposal it was assumed that the reduction in the price of nursing home care would increase utilization 20 percent.
Second, these studies are cross-sectional; therefore, the range of price elasticity estimates may reflect true differences in the populations sampled or simply differences in specification.
Third, according to recent surveys, the overwhelming majority of disabled elderly want to stay out of nursing homes.
A fourth issue is that price elasticities become less relevant when the nursing home bed supply is constrained by government regulation.
Finally, the program recommended by the Commission contains both home care and nursing home care provisions. Assuming that home care and nursing home care are to some extent substitutes, neither the nursing home induced demand effect nor the home care induced demand effect may be as large as the studies discussed might suggest.
Estimating the Cost of Long-Term Care for the Nonelderly
Data on the long-term care needs of the nonelderly are limited to estimates of the size of this population. 14 Precise estimates of severity of the disabilities, as well as current long-term care use or coverage from existing public programs, are not available. Therefore, the estimates of the net federal costs of providing long-term care services to the nonelderly were based on simple proportions based on the net per capita costs of the elderly applied to the nonelderly. Specifically, the net federal costs of providing home health care to the nonelderly were based on the per capita costs of the eligible elderly applied to the total number of nonelderly estimated to have any limitation in activities of daily living. Costs for the nonelderly population were estimated as 75 percent of this cost. Some of these individuals would not qualify as severely disabled, and others were expected to be receiving federal assistance through Medicaid, Medicare, or another public program.
Based on the estimate of the effective reduction in the price of care facing consumers, use of home care services is assumed to double under the Commission's proposal, and the probability of use is adjusted accordingly. Currently about 40 percent of those who would be eligible for care under the proposal use paid home care services at a point in time. In modeling the proposal it was assumed that this number will increase
To estimate the net federal cost of the nursing home portions, the net federal cost of the elderly population was simply increased by the current proportion of nonelderly nursing home residents.
Notes to Appendix E
who require active human assistance, standby assistance, or assistance from special equipment
1. Prepared by David L. Kennell, Lisa Alecxih,
and Dhiren Patel of Lewin/ICF and Joshua M. Wiener of The Brookings Institution.
2. In the model, a Monte Carlo simulation method
ology is used to simulate changes in an individual's status by drawing a random number between zero and one and comparing it to a fixed probability of that event occurring for an individual with a given set of sociodemographic characteristics. For example, the annual probability of death for an 85 year old noninstitutionalized female is assumed to be 0.03 (that is, three out of every 100 women age 85 who are not in a nursing home are expected to die each year). If the random number drawn by the model is less than or equal to 0.03 for this 85-year-old woman, then the individual is assumed to die in that year. If the number drawn lies between 0.03 and 1.00, then the individual is assumed to continue to live during that year.
8. Raymond J. Hanley, Lisa Maria B. Alecxih,
Joshua M. Wiener, and David L. Kennell, “Prediction Elderly Nursing Homes Admissions: Results from the 1982-84 National Long-Term Care Survey,” Research on Aging 12 (2) (June 1990): 199-228; Korbin Liu and Kenneth Manton, “The Characteristics and Utilization Patterns of an Admission Cohort of Nursing Home Patients," The Gerontologist 23 (1) (February 1983): 92-98; William Weissert and William Scanlon, "Determinants of Institutionalization of the Aged,” Project to Analyze Existing LongTerm Care Data, Volume III (Washington, D.C.: The Urban Institute, 1983): 1-19; Leticia Vincente, James A. Wiley, and R. Allen Carrington, "The Risk of Institutionalization Before Death." The Gerontologist 19 (4) (August 1979): 361-367; A.S. Kraus, R.A. Spasoff, E.J. Beattle, D.E.W. Holden, J.S. Lawson, M. Rodenburg, and G.M. Woodcock, “Elderly Applicants to Long-Term Care Institutions," Journal of American Geriatric Society 14 (3) (Fall 1976): 117-25; Jay N. Greenberg and Anna Ginn, “A Multivariate Analysis of the Predictors of Long-Term Care Placement," Home Health Care Services Quarterly 1 (1) (Spring 1979): 75-99; Erdman Palmore, "Total Chance of Institutionalization Among the Aged,” The Gerontologist 16 (6) December 1976): 504-7.
3. Harold Fullerton Jr., “Labor Force Projections
1986-2000," Bureau of Labor Statistics Monthly Labor Review 110 (9) (September 1987): 19-29.
4. George Silversti and John Lukasiewicz, “A
Look at Occupational Employment Trends to the Year 2000," Bureau of Labor Statistics' Monthly Labor Review 110 (9) (September 1987): 46-63.
5. David L. Kennell and John Sheils, Documenta
tion of the Pension and Retirement Income Simulation Model (PRISM) (Washington, D.C.: ICF Incorporated, September 1986).
9. Marc A. Cohen, Eileen J. Tell, and Stanley J.
Wallack, “The Lifetime Risks and Costs of Nursing Home Use Among the Elderly," Medical Care 24 (12) December 1986): 1162; Lawrence G. Branch and Alan M. Jette, "A Prospective Study of the Long-Term Care Institutionalization Among the Aged," American Journal of Public Health 72 (12) (December 1982): 1374.
6. This occurs in every year except 1979, when the
model simulation starts. In this year, a disability status is simulated for all persons age 65 and over, based on the prevalence rates estimated from the 1982–84 NLTCS. Sixty percent of individuals receiving Disability Insurance program benefits at age 62 are simulated to continue to be disabled when they reach age 65.
10. We would like to acknowledge the direction and
support of the assistant secretary for planning and evaluation, HHS, in the modeling work.
7. In the 1982-1984 NLTCS, disability was defined
as the inability to conduct any of the activities of daily living or instrumental activities of daily living due to a health condition which had or would endure for 90 days or more. The measure used in the model is based upon the control card in the NLTCS and therefore includes persons
11. Barry Chiswick, “The Demand for Nursing
Home Care: An Analysis of the Substitution Between Institutional and Non-Institutional Care," Journal of Human Resources 11 (3) (Summer 1976): 295-316; Alvin Headen, "InsuranceInduced Demand and the Hazard of Nursing Home Entry," North Carolina State University and Duke University Center of Demographic Studies, Faculty Working Paper No. 152, 1989
; John Nyman, “The Private Demand for Nursing
Home," Journal of Health Economics 8 (2) (June 1989): 209-31; John Nyman, "Analysis of Nursing Home Use and Bed Supply: Wisconsin, 1983," Health Services Research 24 (4) October 1989): 511-37; and, William Scanlon, "A Theory of the Nursing Home Market,” Inquiry 17 (1) (Spring 1980): 25-41.
resa Fama and David L. Kennell, “Should We Worry About Induced Demand for Long-Term Care Services?" Generations XIV (2) (Spring 1990): 37-41.
13. Chiswick, "Demand for Nursing Home Care";
Nyman, "Nursing Home Use: Wisconsin"; and,
12. For a more detailed discussion of the literature
on induced demand for long-term care see, The
14. Estimates prepared by Pepper Commission staff.
This appendix illustrates in several ways the many combinations of revenue sources possible to finance the Commission recommendations. First, Table F-1 lists potential revenue sources, the amounts of revenues that could be raised from each source, once fully phased in, and how well each source meets the criteria established by the Commission. These criteria specify that any revenue package must be progressive, must require contributions from all citizens, and must grow at a rate sufficient to support growth in the recommended benefits.
F-1 through F-6 present tables and graphs to illustrate how each would affect taxpayers in different income groups. The distributional tables and the estimates of total revenue, were provided to the Commission by Price Waterhouse. The goal was to conform as closely as possible to specifications for such estimates by the Joint Committee on Internal Revenue Taxation. Some of the revenue sources, which cannot readily be distributed across taxpayers, are not included in the tables.
Finally, Exhibit 1 shows the increase in tax liabilities from the options that would arise for eight prototypical households. These are not necessarily representative households, but were chosen to highlight impacts on a broad range of different types of taxpayers. The descriptions of the households note the assumptions necessary to estimate tax liabilities from the various sources.
Since a financing package may be composed of taxes from several sources, Table F-2 offers six options to illustrate the range of effects on taxpayers. Each option is large enough to support the Commission recommendations once fully phased in; in practice, its components could also be phased in, since all of the revenues will not be needed at once. Each option also draws upon at least one major revenue source, such as the income tax or the payroll tax, with other smaller revenue sources designed to help meet the criteria for an acceptable overall package. Two of the options assume that part of the revenues could be obtained from a realignment of current budget priorities—drawing upon reductions in other federal spending. These optional packages illustrate that it is possible to raise the necessary revenue from many different combinations of sources.
Together, these materials underscore that there are many possible combinations of revenue sources that could achieve the goals of the Commission. The Commission does not endorse any particular option or the particular elements of any option shown here. Other revenue sources or combinations that are not included might reasonably be considered. The objective in presenting options is simply to illustrate alternative approaches to raising the revenues needed to put the Commission recommendations into effect.
To help clarify the impact of these various options, several pieces of information are presented. Figures