Assessing Knowledge of Retirement Behavior (1996) / Chapter Skim
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6 ASSESSING FORECASTS OF MORTALITY, HEALTH STATUS, AND HEALTH COSTS DURING BABY BOOMERS' RETIREMENT
6 ASSESSING FORECASTS OF MORTALITY, HEALTH STATUS, AND HEALTH COSTS DURING BABY BOOMERS' RETIREMENT
Pages 195-243
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From page 195... ...
economy may soon stagger under the weight of the elderly baby boomers, who are expected both to live much longer than earlier cohorts of the elderly and to fuel continued growth in health care costs. Recent projections of life expectancy suggest that the Social Security Administration may be under considerable strain to support the nearly threefold growth by 2040 in the number of people over age 65 (Lee and Carter, 1992~.
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Another projection of long-term health care costs predicts that nursing home expenses, as a fraction of median income, will actually decline by the year 2030 (Zedlewski and McBride, 1992~. As a recent Business Week cover story concluded, "The elderly are more vital than before.
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Finally, we consider how these various factors might be expected to affect retirement income after Social Security payments are received and after health costs are spent for the baby boom generation in the coming 40 years. For example, how might extended life span affect the ability of the Social Security Administration to pay benefits?
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Population forecasts for the age group 65+ over a time horizon of 65 years evidently depend primarily on forecasts of mortality, and fertility does not enter in. However, to some degree they depend on forecasts of the rate and age distribution of immigration as well: in 1990, 8.6 percent of the elderly population was foreign born, according to the Census bureau.
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population subject to the age-specific death rates of each period. SOURCE: Social Security Administration (1992)
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At the start of the forecast period, the death rates from each cause are assumed to continue to decline at the exponential rate observed during a 20-year base period. These initial rates of decline are then merged into ultimate rates of decline that are assumed for each of the cause groups based on an assessment of various factors believed to influence the rate of decline for each cause in the long run.
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For example, Statistics Canada first prepares a forecast of life expectancy at birth, taking this as the measure of level, and then determines how to allocate death rates by age in a manner consistent with the prior forecast of life expectancy. For other approaches, such as modeling health status as the outcome of dynamic disease processes and changing risk factors, this distinction is less useful.
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Lee and Carter construct probability intervals for the mortality forecasts generated in this way, and for forecasts of period life expectancy. Figure 6-1 plots base period estimates and forecasts for Kit, while Figure 6-2 plots life expectancy since 1900 and its forecast derived from that of k.
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The real question, we believe, is whether these extrapolative methods currently yield the best possible forecasts, or whether other models, incorporating more structural information about the complicated biological processes leading to disease and death, might yield superior forecasts. One possibil
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(2) 1880-1920: Rapid mortality declines reflect improvements in water supplies and general
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develop probability intervals for the SSA mortality forecasts. They find that below age 20, the SSA high-low bounds are narrower than empirical 95 percent probability intervals; from 40 to 64, they are wider; and for other ages, they are about equal to the empirical bounds.
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In sum, it is best to view the Lee-Carter probability intervals on mortality forecasts as lower bounds on the uncertainty of the forecasts. One possible reason for slower mortality decline in the future is the emergence of new diseases such as AIDS.
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These ex post standard errors have been widely used to attach prospective probability intervals to population forecasts, although some strong assumptions are evidently required to do so. The Stoto intervals have been compared with those arising from Lee and Tuljapurkar's (1994)
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His analysis, and new estimates of mortality rates in extreme old age, are for 1987 only. He finds that rates based on the HCFA data files are reasonably accurate until age 97 or so, but that after that they increasingly understate death rates calculated more carefully from the master-beneficiary-role files.
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procedures should be used to estimate death rates for the older population back to the earliest date feasible, possibly to the late 1960s. These death rates should routinely be calculated on an annual basis.
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mortality data even for death rates at ages 70 to 79? These are important questions, since researchers seeking results applicable to the United States, and frustrated by the poor quality of U.S.
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They thus confirm the direction of the differentials found by the Census Bureau. It is possible that the Medicare data discussed earlier could be useful in this context.
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HEALTH STATUS PROJECTIONS AND THE COMPRESSION OF DISABILITY The burden of the baby boom retirement cohort will depend not just on their total number, but also on their health. In this section, we consider the difficulties in forecasting whether people in the next century can look forward to an active, healthy retirement or a relatively frail and inactive one.
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Extrapolating such self-reported trends is problematic. Another easily measured indicator of health status is the body mass index, the ratio of weight to height.
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used Census Bureau data to show that the percentage of men aged 45 to 64 reporting they were unable to work rose from 6.3 percent in 1970 to 9.7 percent in 1980 and
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If the debate over compression is not entirely resolved, most forecasts of nursing home patients the most obvious symptom of endemic disability in the elderly population do not exhibit a great deal of variation, even for projections
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suggest about 2.6 million for the middle Census Bureau projection for 2020 or about 3.2 million for the high Census projection.6 The close similarity of these predictions does not mean that they are accurate. Even to the extent that more sophisticated prediction models account for trends in levels of disability, there may also be changes in the demand for nursing home patients if the underlying health status is held constant.
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However, the modeling requirements are also substantially larger because they require making inferences about transitions among disability levels for, say, the individual who avoided having a stroke in 1995 because of improvements in medical treatment or lifestyle. Is this person systematically different from the average individual in the same age group?
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The National Nursing Home Survey provides no information about people who aren't admitted to nursing homes and provides only a cross-sectional snapshot of nursing home admissions.~° The second potential problem with existing data sets is nonresponse. We have argued above that long-term longitudinal panels are necessary to elicit lowfrequency changes in the compression of disability over time.
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Predicting Real Health Care Services by Demographic Group The first step is to use current data to get a benchmark of how real quantities of health care services might be expected to evolve over time with changes in the age composition and in disability levels. In general, health care expenditures rise by age and by disability or health status (e.g., Manton, Stallard, and Liu, 1993a; Chulis et al., 1993~.
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Official predictions by HCFA of national health expenditures, expressed as a ratio of gross domestic product, to the year 2030 are presented in Figure 6-3. Predicted growth is sub stantial, with overall national health spending reaching 26.5 percent of gross domestic product by 2020 and 32 percent by 2030.
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Of that increase, approximately half is attributed to a change in the relative price of health care for physician and inpatient care, and less than one-third to nursing homes. Although inpatient
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, the growth in health care costs will be attenuated. One component of the increase in predicted real health services, particularly for nursing home care, is the increased total demand for medical care as the baby boom generation ages.~4 Do countries with more rapidly growing elderly populations experience more rapid health care costs?
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For evaluating the retirement prospects of the baby boom generation, it matters crucially whether one views the 30-year trend in health spending as a transition to a new steady state or as a trend destined to continue (until gross domestic product is exhausted!
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The view that technology is driving health care costs certainly receives some support from the evidence on continued growth in costs, despite cost-cutting programs such as the prospective payment system. However, McClellan (1994)
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HOW WILL CHANGING MORTALITY, HEALTH, AND HEALTH COSTS AFFECT RETIREMENT INCOME SECURITY? One of the primary issues facing policy makers is how changes in mortality and health status and health costs might be expected to affect the income security of baby boom retirees.
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Yet strong fluctuations like the baby boom and baby bust have occurred in the past, and they lead to more extreme outcomes than the scenarios. Also, a little thought will verify that a scenario-based forecast cannot possibly yield probability intervals that are consistent among the different items that are forecast since variations in annual numbers or single age groups tend to be offsetting when the numbers or groups are summed to form larger population aggregates broader age groups, or total population size, for example.
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Drawing on results in Tuljapurkar (1990) , Lee and Carter derive and calculate analytic results for a quartic approximation to find both the expected value of the forecast and the probability intervals.
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Nor, it is important to add, is there any way to attach any kind of probability to these brackets, or to those of SSA.17 To a first approximation, and other things equal, payroll tax rates for Old Age, Survivors and Disability Insurance benefits must vary in proportion to the old age dependency ratio. It is because of the projected increases in this ratio that in the 1980s it was decided to begin to raise payroll taxes in order to accumulate
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, the old age dependency ratio will increase by 34 percent by 2020 and by 98 percent, a virtual doubling, by 2050 (see Figure 6-5~. These figures are only slightly higher than those of the Census Bureau and SSA, and they are daunting.
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The stochastic population forecasts shown in Figures 6-4 to 6-6 do not simply produce new scenarios based on upper and lower probabilistic forecasts of the vital rates. Some researchers have made population forecasts of this kind (McNown and Rogers, 1992~.
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How can the probability intervals for demographic quantities be combined with estimates of uncertainty for forecasts of economic variables? Health care costs?
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Uncovering how work and retirement will evolve in the long term is a difficult problem. The current trend appears to be towards earlier retirement, not later retirement, suggesting that the secular changes in underlying health status that might be expected to delay retirement in the future are currently being offset by other factors, perhaps related to disability insurance among others.
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Of course, health insurance provided by employers for their retired employees may entail some redistribution across cohorts, as rising health care costs of retired employers are implicitly paid through higher insurance costs of current workers. Still, the prospects for intergenerational transfers are much more pronounced in the public than in the private sector.
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It is likely that this group would face a very difficult retirement in the face of rising health care costs. The government Medicaid burden might also be substantially larger than forecast, given the likelihood of many households falling short of a sufficient level of income and wealth to support nursing home costs.
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This is the group most likely at risk from higher levels of out-of-pocket health care costs or cutbacks in Social Security payments. However, there are no projections for this group that account for the correlation among lower wages, poor health, lower wealth levels, and lower life expectancy (Preston and Taubman, 1994~.
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the greatest levels of disability. CONCLUSION The economic well-being of the baby boom generation during retirement will depend crucially on the evolution of mortality rates, disability, and health care costs.
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15. As Getzen notes, variation in increases in health care costs across countries may have swamped the effects that were due to population changes alone.
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and L.J. Kotlikoff 1994 The United States' Fiscal and Saving Crises and Their Implications for the Baby Boom Generation.
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1990 Empirical DEA Models to Fit and Project Time Series of Age-Specific Mortality Rates. Unpublished manuscript.
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Lee, R.D., and S Tuljapurkar 1994 Stochastic population forecasts for the U.S.: Beyond high, medium and low.
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1992 Medical care costs: How much welfare loss? Journal of Economic Perspectives 6(3)
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Schneider, E.L., and J.M Guralnik 1990 The aging of America: Impact on health care costs. Journal of the American Medical Association 263(17)
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Moran 1993 Global budgeting in the OECD countries. Health Care Financing Review 14(3)
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