6
Assessing Forecasts of Mortality, Health Status, and Health Costs During BabyBoomers' Retirement

Ronald D. Lee and Jonathan Skinner

The U.S. 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). Many of these elderly will be in nursing homes; Schneider and Guralnik (1990) predict ''there may be two to three times as many individuals aged 85 years and above in nursing homes in 2040 as there are individuals aged 65 years and above in nursing homes today!" Combined with projected increases in the population of disabled elderly is the rapid growth in health expenses per elderly person. The Health Care Financing Administration (HCFA) forecasts that nearly one-third of gross domestic product will be spent on health care by 2030 (Burner, Waldo, and McKusick, 1992). Auerbach and Kotlikoff (1994) predict that future generations will be required to pay in taxes 82 cents per dollar of income to support currently legislated Social Security and Medicare benefits. It is possible, of course, that the elderly might be expected to pay more out of pocket. But calculations by Bernheim (1994) suggest that, if anything, most baby boom families are saving too little for their retirement.

Ronald Lee's research for this paper was supported by National Institute on Aging grant AG11761-01A1. The authors are grateful to David Cutler, John Bound, Alan Garber, Bert Kestenbaum, Nancy Maritato, S. Jay Olshansky, Joshua Wiener, and panel members for helpful suggestions.



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Assessing Knowledge of Retirement Behavior 6 Assessing Forecasts of Mortality, Health Status, and Health Costs During BabyBoomers' Retirement Ronald D. Lee and Jonathan Skinner The U.S. 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). Many of these elderly will be in nursing homes; Schneider and Guralnik (1990) predict ''there may be two to three times as many individuals aged 85 years and above in nursing homes in 2040 as there are individuals aged 65 years and above in nursing homes today!" Combined with projected increases in the population of disabled elderly is the rapid growth in health expenses per elderly person. The Health Care Financing Administration (HCFA) forecasts that nearly one-third of gross domestic product will be spent on health care by 2030 (Burner, Waldo, and McKusick, 1992). Auerbach and Kotlikoff (1994) predict that future generations will be required to pay in taxes 82 cents per dollar of income to support currently legislated Social Security and Medicare benefits. It is possible, of course, that the elderly might be expected to pay more out of pocket. But calculations by Bernheim (1994) suggest that, if anything, most baby boom families are saving too little for their retirement. Ronald Lee's research for this paper was supported by National Institute on Aging grant AG11761-01A1. The authors are grateful to David Cutler, John Bound, Alan Garber, Bert Kestenbaum, Nancy Maritato, S. Jay Olshansky, Joshua Wiener, and panel members for helpful suggestions.

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Assessing Knowledge of Retirement Behavior An alternative view is much more optimistic about retirement prospects for the baby boom generation. Disability and morbidity will continue to become more compressed, leading to healthier years later in life (Manton, Stallard, and Liu, 1993a; Manton, Corder, and Stallard, 1993) as well as to a secular increase in the average retirement age. The Social Security tax base may be buoyed by immigration and increased fertility rates. The economic demands of higher health care costs will be offset by productivity gains and higher income levels; projections from the ICF-Brookings model, for example, anticipate the percentage of elderly (65+) requiring Medicaid coverage for long-term care to decline by 2018 (Wiener, Illston, and Hanley, 1994). 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. Americans can afford to grow old. And they will grow old gracefully" (Farrell, 1994, p. 68). Figuring out which of these two scenarios is correct is clearly crucial for forming policies to prepare for the next century. If the retiring baby boom generation will drag down the American economy by 2020, then government policies designed to smooth the projected health and Social Security costs are likely to be most effective now, while the baby boomers are nearing the peak of their earning capacity. Conversely, a government program designed to save against a nonexistent crisis can disrupt the saving and retirement plans of the generation it was designed to help. In this paper, we attempt to identify the major factors that account for these very different predictions, and we suggest how these discrepancies can be reconciled. We focus on data requirements that may be useful, or even necessary, to piece together the puzzle of how health and mortality trends will affect retirement income security in 2020. We also stress, however, that much of the work that remains to be done is not simply gathering or linking more data. Instead, the task of reconciling the two divergent views of baby boom retirement must involve more consensus about the interpretation of the data or more generally developing modeling strategies that are more likely to hold long-term predictive power. For example, as we show below, a large part of the difference in projections of health costs depends on alternative assumptions about the extent to which the relative price of health care will rise over the next 40 years. Projections of this type are based on past data, but it is not clear whether the past 25 years reflect a long-term trend or a transition to a new steady state in which medical care prices are stabilized. Improvements in mortality do not result from the passage of time, but rather from the influence on biological processes of changes in health care interventions, lifestyle choices, medical technology, epidemiological processes, and so on, and the evolution of these is not well understood (see Warner, 1993). Similarly, changes in income and health care costs depend on many influences, including policy decisions to be taken in the future, that are difficult to predict

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Assessing Knowledge of Retirement Behavior over the long run. In other words, many of the problems to be surmounted involve the modeling or interpretation of the existing data, rather than shortfalls in the data themselves. These problems are more intractable (and divisive) than simply collecting better data and relate fundamentally to the intrinsic difficulties in forecasting very complex economic systems. We focus on four issues related to projecting how mortality, health status, and health costs will affect retirement income over the next 30 to 50 years. The first general issue is how rapidly mortality will decline. There are a wide range of mortality projections. Which statistical approaches hold the greatest promise for long-term projections necessary to maintain the financial viability of the Social Security system? We suggest a number of research approaches, using existing data, that may improve our ability to assess the predictive power of competing models of mortality. Finally, we discuss how mortality projections of specific ethnic groups, or of the very elderly, can be improved. The second issue is, what will be the health-disability status of the elderly in the next 30 to 50 years? There is considerable debate about the growth in the number of disabled or frail elderly as the consequence of many more people living past age 85. This is a crucial question both for baby boomers setting aside resources for future illness and for future Medicare and Medicaid expenditures. Despite the controversy over the future progress of morbidity and disability, there is surprising agreement in projections of the nursing home population in 2020. Since a consensus doesn't necessarily mean that these predictions are correct, we also consider some possible strategies for better measuring long-term changes in patterns of disability. The third issue is a related question: given the predicted health-disability levels, how will per-person costs for a given health-disability level evolve in the next 30 years? Will health care costs continue to grow at historical rates, or will they converge to a rate commensurate with wage growth? Determining which of these scenarios is correct is crucial in deciding whether baby boomers will enjoy a plentiful or a strapped retirement. The answer is not likely to be found solely by extrapolating the past 30 years of information. We argue that structural predictions require better information about whether changes in health care costs are the consequence of economic and political policies or are generated by a residual called "technological progress." This question cannot be resolved simply by collecting more data, but must come through improved modeling strategies. 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? Such a question requires information not just about the elderly population, but about the working population in 2020 as well. If disability does decline over time, would retirement ages be extended, providing more earned income and placing less strain on households' nest eggs? Finally,

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Assessing Knowledge of Retirement Behavior most of the projections relate to aggregate or per capita spending or utilization. But it is very likely that different socioeconomic groups would fare differently under predicted changes in income and in health spending. We know that people with lower socioeconomic status have substantially higher rates of disability. We also know that income growth for this group has been lagging behind aggregate growth rates (or even declining). Yet the nexus of disability, income, and wealth accumulation, especially for lower income households, is not well understood. HOW RAPIDLY WILL MORTALITY DECLINE? To determine whether the Social Security and private pension funds held by the baby boom generation are actuarially sound, it is necessary to have good forecasts of mortality so that one knows how long participants in a plan are likely to draw benefits. A distinct but related problem for a pay-as-you-go pension program is that one also needs forecasts of the population in the ages that qualify for benefits. Forecasting the elderly population decades ahead requires forecasting the mortality not only of the elderly but also of the younger adults who, if they survive, will become elderly. For long-term forecasts, one must deal with the entire age distribution of mortality, from infancy on up. We consider both problems here. 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. In this paper, we will largely ignore the immigration issue and focus on forecasts of mortality. The reader is also referred to a critical review of the topic by an interdisciplinary group convened by the National Academy of Sciences/Institute of Medicine whose views are summarized in Stoto and Durch, 1993. Mortality Decline in the United States During the 20th Century The pace of mortality decline in the United States during the 20th century has varied, as shown by Table 6-1, and there is one subperiod, 1954–1968, in which the age-standardized male death rate actually rose.1 Overall, the figures in the table do not give a strong impression of either an accelerating or a decelerating rate of decline. However, it is useful to consider a hypothetical population in which each age-specific death rate declines at its own constant exponential rate. Life expectancy would rise at a slowing pace because death rates at younger ages would approach zero, and increasingly the deaths averted would be those of the elderly, who would not live many more years in any case. Thus there is a built-in tendency for life-expectancy gains to decelerate even if each age-specific rate continues to decline steadily. Nothing, of course, says that death rates cannot begin to decline more rapidly at older ages, but unless there is a break with

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Assessing Knowledge of Retirement Behavior TABLE 6-1 Annual Rate of Decline in Age-Adjusted U.S. Death Rates, by Sex for Selected Periods Period Males Females 1900–1936 0.8 0.9 1936–1954 1.6 2.5 1954–1968 -0.2 0.8 1968–1990 1.5 1.4 NOTE: This is the rate at which the crude death rate would have declined for a population with the age distribution of the 1990 U.S. population subject to the age-specific death rates of each period. SOURCE: Social Security Administration (1992). historical trends, life-expectancy gains are bound to slow. Indeed, life expectancy increased by about 18 years from 1900 to 1944, but by only about 10 years from 1944 to 1988; Lee and Carter (1992) forecast it to rise by only 6.5 years in the 44 years from 1988 to 2032. Comparisons of Forecasts Many people rely on mortality projections by the Bureau of the Census and/ or the Office of the Actuary of the Social Security Administration (SSA). These agencies generally forecast less rapid declines in mortality, and smaller gains in life expectancy, than do other recent mortality forecasts available today. Their forecasts are roughly consistent with the views of some authors, such as Fries (1980, 1989) and Olshansky, Carnes, and Cassel (1990), who argue that future life expectancy is bounded at around 85 years for the general population (sexes combined). Other authors argue that life expectancy as high as 100 years may be obtainable in the not too distant future. For example, Manton, Stallard, and Tolley (1991) calculate a lower bound to attainable future life expectancy of 95 or 100 years, if people were to adopt optimal lifestyles, and also claim that such levels are already exhibited by some special subpopulations with particularly healthy lifestyles, such as Mormon high priests. Ahlburg and Vaupel (1990) foresee the possibility of such high life expectancies by 2080 by extrapolating the rapid rates of mortality decline that obtained in the United States in the 1970s. Obviously a U.S. life expectancy of 95 or 100 years would have important implications for pension systems of all kinds. In work described more fully below, Lee and Carter(1992) use extrapolative

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Assessing Knowledge of Retirement Behavior time series methods combined with a simple model of the age distribution of mortality to forecast that life expectancy will rise to about 86 years by 2065 (with a 95% probability interval of 81 to 90 years), or to 84.3 by 2050. These forecasts implied a life-expectancy gain that was twice that forecast by the Census Bureau and SSA at the time, and is still twice as great as the SSA forecasts and substantially higher than those of the Census Bureau. These large differences in forecasted levels of mortality lead, of course, to correspondingly large differences in forecasts of the number of elderly. For example, point forecasts for the number of people over age 85 in 2050 vary from 18 million in the Census projections to 41 million by Manton, Stallard, and Tolley. Furthermore, the forecast by Manton, Stallard, and Tolley lies far outside the high-low bracket given by Census. The SSA forecasts consider trends in 10 groups of causes of death. 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. The ultimate rates of decline are fully in effect about 25 years into the forecast. (This discussion is based on Social Security Administration, 1992.) The forecasts that result from this approach imply a sharp slowing of the rates of decline of mortality at all ages, relative both to the previous two decades and to longer run historical trends, measuring from the start of almost any decade back to 1900. Table 6-2 shows the difference between long-run historical trends and the rates of decline assumed in the SSA forecast. These differences are great at the youngest ages, where they imply that the SSA death-rate forecasts would be about five times as high as the simple trend-extrapolated rates. The differences diminish with age and are least for rates at 65+. For men in these ages, the difference is negligible, but for women it is considerable. More detailed calculations show that at the younger old ages, in the 60s and 70s, the SSA forecasts rates for females that are 60 percent to 70 percent higher than simple trend extrapolation would suggest. If instead we compare the forecasts to the average rate of decline from 1968 to 1988 for the total age-adjusted death rate, 1.49 percent per year for males and 1.56 for females, the contrast with the SSA forecasts is even greater. There is nothing intrinsically wrong with forecasting that mortality will decline more slowly in the future than it has in the past, and SSA evidently believes it is right to do so, based on its cause-specific analysis. Other Methods of Forecasting Mortality Change From a demographic point of view, the problem of forecasting mortality has two aspects that may be usefully separated conceptually, and which are in fact often separated procedurally. First, one must deal with the complexity of the age

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Assessing Knowledge of Retirement Behavior TABLE 6-2 Average Annual Rate of Decline in Mortality for Base Period Versus SSA Forecast, by Age and Sex, Percent per Year Age Group 1900–1988 (base period) 1988–2066 (forecast period) Forecast Rate - Base Rate Ratio of Forecast to Trend Extrapolation in 2068 Male 0–14 3.25 1.21 -2.04 4.9 15–24 1.54 0.65 -0.89 2.0 25–64 1.09 0.71 -0.38 1.3 65+ 0.52 0.54 0.02 1.0 Total 0.95 0.60 -0.35 1.3 Female 0–14 3.39 1.24 -2.15 5.3 15–24 2.52 0.61 -1.91 4.4 25–64 1.59 0.61 -0.98 2.1 65+ 0.95 0.55 -0.40 1.4   SOURCE: The first two columns of data are taken directly from Table 4 in Social Security Administration (1992:9). The third column is the second minus the first. The last column is calculated as exp(-78* entry from previous column). It represents the ratio of the SSA forecast of mortality levels in 2068 to the death race in 2068 that would result from extrapolating the historical trend from the period 1900–1988. distribution of mortality, somehow reducing the dimensionality of the problem so that it is not necessary to forecast the many age-specific rates separately and independently. Second, one must forecast the level of mortality and decide how the level is to be characterized and measured. 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. We will first discuss the problem of age distribution and then that of level. In recent work, there have been two approaches to dealing with age distribution. In the functional parametric approach, a complicated nonlinear function of age with up to nine parameters is fit to the age profile of mortality for a given year or series of years. Changes in the level of mortality then come from changes in the parameters. In practice it may be desirable to hold most of the parameters fixed and capture changes over time through variations in just three key parameters (see McNown and Rogers, 1989, 1992). Forecasts of level and age distribution are obtained by modeling the sample period variations in these key parameters and then forecasting them. A different approach uses so-called relational methods. In this approach, a

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Assessing Knowledge of Retirement Behavior standard age profile of mortality is established, representing a central tendency in the shape of the age distribution. New mortality schedules representing different levels of mortality are then generated by some transformation of the standard, where the transformation may be characterized by one or two parameters. The Lee-Carter (1992) method is of this form. In it, the model: is fit to the sample period age-time-specific mortality rates, mx,t. Here exp(ax) can be viewed as the standard schedule, and the coefficient bx describes how this standard is transformed to generate new age schedules of rates when kt, the index of mortality level, varies. Time series methods can be used to model the sample period variation in kt, and the estimated model can then be used to forecast kt. From these forecasts, forecasts of mx,t can be recovered using the equation. In most applications of this model, kt is well modeled as random walk with drift. Gomez de Leon (1990), using exploratory data analysis on Norway's extensive historical mortality data, selected this model from among a variety of simple models to represent patterns of change, based on a variety of criteria including goodness of fit. This model makes some strong assumptions. If the model fit perfectly in the sense that all errors ex,t were zero, then any age-specific death rate could be expressed as a linear function of any other, and the correlations among them would all be unity. Autocorrelations for each rate would equal the autocorrelation of kt. 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 kt, while Figure 6-2 plots life expectancy since 1900 and its forecast derived from that of k. It is notable that whereas the time path of historical life expectancy is decelerating (the gains in the first half of the period were twice as great as those in the second half of the period), the trend in k is roughly linear, and the gains in the two subperiods were equal. Also note that while rates of mortality decline reported in Table 6-1 were quite variable across subperiods, the decline in k, which indexes the log of the level of mortality, appears quite regular. The Lee-Carter forecasts foresee substantially larger gains in life expectancy than do the SSA forecasts: about 10 years versus about 5 years. We have seen that the SSA forecasts assume a substantial slowing of mortality decline. The Lee-Carter forecasts come close to assuming that historical trends will continue, although this is more nearly true for some age groups than for others. For ages over 60, for complicated reasons, the forecasts published in Lee and Carter (1992) are for more rapid decline than the average rates for 1900 to 1987 and come closer to the average rates of decline for 1930 to 1987. Both the Lee-Carter and Rogers-McNown approaches draw on time series analysis and ARIMA type models for forecasting the level of mortality. Another

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Assessing Knowledge of Retirement Behavior FIGURE 6-1 Comparison of mortality forecasts to 2065, based on data from 1900–1989 (dots) and from 1933–1989 (solid), with 95 percent confidence bands. NOTE: Both forecasts are (0,1,0). The forecast from 1900 has a dummy for the influenza epidemic; see text. approach has been to develop a standard trajectory for life expectancy based on the historical record for many populations and to incorporate the pronounced tendency for life expectancy to rise more slowly when it is at high levels than when it is at low levels. This approach has often been used quite successfully by international agencies. While there is certainly room for further work on extrapolative models (see, e.g., suggestions made below), current work appears to be pursuing logical directions, and there is no reason to expect that major new initiatives in this area would have a high payoff. 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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Assessing Knowledge of Retirement Behavior FIGURE 6-2 U.S. life expectancy and forecasts (95 percent confidence intervals with and without uncertainty from trend term). NOTE: The forecasts employ a (0,1,0) model with an influenza dummy estimated on mortality data from 1900 to 1989. The 95 percent confidence intervals are shown with and without uncertainty from drift. ity is to apply extrapolative methods to cause-specific data, which permits relevant medical and biological outside information to be introduced to some degree, as is done by SSA. The development and estimation of explicit statistical models relating disease, disability, and mortality to individual behaviors and risk factors, as will be further discussed later, is another possibility drawing on deeper information. Still another approach is to consider the history of mortality change in terms of epochs of progress against particular kinds of diseases and, in so doing, to identify the likely future direction and pace of progress. David Cutler has suggested the following illustrative periodization for U.S. mortality since 1850: (1) 1850–1880: Primitive medical knowledge and poor sanitary conditions yield high mortality, particularly for children. (2) 1880–1920: Rapid mortality declines reflect improvements in water supplies and general

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Assessing Knowledge of Retirement Behavior sanitary conditions. (3) 1920 to 1950: Continuing dramatic progress against infectious diseases result from the development of antibiotics and other effective treatments. (4) 1950–1970: The end of dramatic gains against communicable diseases and slow progress against chronic and degenerative diseases result in more slowly declining mortality. (5) 1970-present: Reductions in cardiovascular mortality result from life-style improvements and more effective treatment. What does this approach suggest for future mortality trends? There are a number of possibilities: (1) Ever more money is spent on limited-applicability major surgery for cardiovascular diseases, and modest reductions occur in mortality largely owing to lower levels of smoking. (2) Expensive treatments are curtailed with little effect on mortality, and cost growth slows. (3) The genetic revolution leads to earlier diagnosis and treatment of disease, leading to substantial mortality improvements and slower growth in health costs. (4) Dramatic progress is made against chronic and degenerative diseases through behavioral lifestyle changes, through new genetic interventions, and through progress in treating cancer, with uncertain implications for costs. (5) Newly emerging infectious diseases such as AIDS, new antibiotic-resistant strains of old diseases, and diseases caused by worsening environmental conditions lead to rising mortality, with uncertain effects on costs. This range of possible future scenarios shows the difficulty with the approach. A case could be made for each. How is the forecaster to choose among them? The record since 1900, as summarized by the mortality index plotted in Figure 6-1, shows a surprisingly regular pattern of decline, despite such major breakthroughs as the development of antibiotics. Forecasts using exactly the same method, but with starting dates ranging from 1900 to 1960, yield virtually identical forecasts of mortality and probability bounds for the United States (see Lee and Carter, 1992); a starting date of 1970 yields forecasts of more rapid mortality decline, but starting in 1980 gives results quite similar to those from earlier starting dates. This consistency suggests that historical periods of relative progress and stagnation tend to average out and do not lead to turning points in the underlying trend of mortality. Evaluating the Uncertainty of Mortality Forecasts Considerable progress has been made in describing the uncertainty of mortality forecasts in recent years. In a notable paper, Alho and Spencer (1990) 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. In interpreting these results, it is important to keep in mind that these bounds refer to the probability distribution for mortality in any given single year, not for the general level of mortality over the forecast horizon. Lee and Carter (1992) provide probability intervals describing the un-

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Assessing Knowledge of Retirement Behavior share of health spending accounted for by out-of-pocket costs (including private insurance premiums) might affect the indirect as well as the direct costs of health care for baby boom retirees. Finally, we consider data issues in measuring out-of-pocket expenditures. Most projections of health costs assume a relatively stable fraction of government and private cost sharing. For example, Wiener, Illston, and Hanley (1994) predict an increase in the number of nursing home patients who spend more than 40 percent of income and assets on nursing home care, from 36 percent in 1993 to 39 percent in 2018 assuming current Medicaid policy. This predicted amount is quite modest, especially given the 25-year horizon. While not directly comparable, recent evidence on overall out-of-pocket expenses suggests that the burden of health care costs for the elderly has been rising substantially in the past several decades. Between 1977 and 1987, the percentage of elderly households (age 65+) who spent at least 20 percent of their income on out-of-pocket expenses rose from 7.0 percent to 10.6 percent (Taylor and Banthin, 1994). These figures, based on the National Medical Expenditure Surveys (NMES) in 1977 and 1987, do not even include nursing home expenses, which are likely to be substantial for a small fraction of the population. The mix between out-of-pocket and government or private insurance payments affects the share of retiree health benefits that are actually paid by the cohort of retirees. Suppose, for example, that the out-of-pocket expenditure share declines, with the government picking up the shortfall. As a consequence, tax revenue needs to be larger to pay for the additional government share of health expenditures.19 The increased share of government spending and taxation would then transfer resources from younger taxpayers (who foot the higher government share through their tax payments) to older generations (who benefit from the increased government spending for their medical bills). By contrast, increases in private health care spending would be more likely to be borne by the recipients, either directly through out-of-pocket expenses or indirectly through higher insurance premiums. 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. Despite the importance of out-of-pocket expenditures for assessing prospects for retirement security, we know surprisingly little about their characteristics from current data sources. Covinsky et al. (1994), for example, reported that of 2,661 seriously ill patients who survived their hospital stay, 31 percent lost all or most of their savings as a consequence of their illness. However, this is a very specific sample of ill patients, which makes inferences about the prevalence of catastrophic expenses difficult. The NMES is cross-sectional, and cannot provide a good picture of how these costs evolve over time. For example, one might

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Assessing Knowledge of Retirement Behavior expect that out-of-pocket health expenses would pose a greater problem to future retiring baby boomers if catastrophic costs tended to be persistent. In other words, if households are randomly struck with out-of-pocket expenses equal to 20 percent of income, they can smooth consumption by drawing down assets or other contingencies. However, if only a few households are subject to 20 percent health expenses in every year, then these families would be subject to considerable financial distress. But there are few if any longitudinal studies of out-of-pocket health expenses. Feenberg and Skinner (1994) did use truncated panel tax data to infer the time series properties of out-of-pocket expenses in 1968–1973, but their study was limited to people who itemized in each year. More recent longitudinal data with detailed out-of-pocket expenses would be extremely useful. Another problem with existing data is the weak link between nursing home and noninstitutionalized cost data. Both the 1977 and 1987 surveys, for example, exclude all nursing home costs from their measure of out-of-pocket expenses (although the 1987 study did make some efforts to statistically link the nursing home data with the noninstitutionalized data). Hence there is no data source that provides a general picture of the overall risk in out-of-pocket health care costs. The Distribution of Retirement Income Most of the estimates for future health status and health costs have focused on aggregate or average levels. Few have accounted for the fraction of people who would be deemed disadvantaged because of poor health and low levels of income and wealth. Wiener, Illston, and Hanley (1994) are notable for providing measures of the number of nursing home patients expected to be receiving Medicaid or the percentage incurring very high costs relative to income. They can provide these projections because their model simulates a large number of "people" through disability and income generators, allowing a detailed description of the distribution of health and income realizations. Some additional factors may be necessary to capture changes in the distribution of retirement income security. Projections typically assume a homogeneous growth in wage rates among all individuals. Yet wage growth rates of lower educated people are falling behind the average growth rates, and in many cases are even negative in real terms (see Levy and Murnane, 1992). 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. Similarly, socioeconomic status tends to be strongly correlated with levels of disability. For example, Figure 6-7, taken from House et al. (1990), shows the number of chronic conditions as a function of age for the lowest and highest socioeconomic class. At ages 55 to 64, for example, the average number of

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Assessing Knowledge of Retirement Behavior FIGURE 6-7 Average number of chronic conditions by socioeconomic status and age. SOURCE: House et al. (1990:App. A). chronic conditions for the upper socioeconomic status group is less than half the number of conditions for the lower socioeconomic group. Of course, some part of this correlation may be the consequence of poor health conditions reducing earning capacity (Preston and Taubman, 1994). The authors attempted to partially correct for this by requiring both low income and low education attainment for the lowest socioeconomic status, and both high income and high education for the highest socioeconomic status. Still, disentangling the correlation between poor health levels and income is an important research topic that can be potentially addressed with longitudinal data on past earnings and current disability patterns. Lower income households tend to face substantially higher levels of disability, laggard growth in income, and low levels of wealth relative to their income (Hubbard, Skinner, and Zeldes, 1995). 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). Another shortcoming of most projections involving health care costs is that either assets are assumed to grow exogenously (Wiener, Illston, and Hanley, 1994) or saving rates are assumed to be held constant (Auerbach and Kotlikoff, 1994). Yet changes in the composition and magnitude of health care costs (particularly those out of pocket) may be expected to affect the accumulation behavior of households. For example, in Hubbard, Skinner, and Zeldes (1994, 1995), uncertainty about out-of-pocket health costs causes most people to save more in response to additional risk. In their model, however, those with a reasonable

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Assessing Knowledge of Retirement Behavior chance of becoming eligible for Medicaid in the event of a bad health shock (or Aid to Families with Dependent Children [AFDC] in the event of a bad shock to earnings) save less because of asset-based means testing. Because welfare programs typically restrict eligibility to people with less than $3,000 in assets, any personal saving above that amount is effectively taxed at a 100 percent rate in the event an individual requires long-term Medicaid or AFDC assistance. In their simulation model, such asset-based means testing programs reduce saving for lower income households. In sum, projections about retirement security often focus on average levels of health care expenditures within quite broad demographic groups. However, the design of appropriate public policy is probably more concerned with the retirement outcomes of a specified group of people, perhaps those in the bottom quintile or quartile of the income distribution. Hence developing methods for predicting the financial security of specific demographic groups may be beneficial for policy purposes. It may be possible to learn more about the correlation among saving, income growth, disability, and health care use with the ongoing project to match up data from the Panel Study of Income Dynamics with Medicare data. HRS and AHEAD, with their linked data of health, assets, and income, can provide a better picture of the group of elderly who are likely to receive the lowest levels of income and assets, and experience (perhaps) 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. Whether longer life expectancy will strain Social Security and private pensions, or whether rising health care costs and many frail elderly will place a large burden on the economy, would be useful to know now, when there is still time to prepare for potentially large government and private expenses in the future. In this paper, we have examined a number of issues related to predicting health and mortality in the next century and have identified why different approaches to prediction or estimation have sometimes yielded such different estimates. In some cases, the controversies about predictions can be addressed by better use of existing data sources or by additional data collection. An underlying theme of this paper is that many of the real questions about retirement income security cannot be answered simply by collecting more data or by running more complex estimation procedures. In the case of mortality, the crucial question is whether the observed trend in mortality rate reductions will be sustained for the next 50 years. By the same token, the fundamental question about health care costs is whether cost increases from the past 30 years might be expected to persist until the year 2030. Determining the answers to these ques-

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Assessing Knowledge of Retirement Behavior tions is difficult and requires conceptual and theoretical advances as well as more and better data. NOTES 1.   However, these dates were presumably chosen not randomly but to maximize the variation, and so the marked contrasts in rates should be interpreted with caution. 2.   In the forecast for low gains in life expectancy, basic death rates are assumed to stay constant at their 1990 levels, and these are increased by AIDS death rates, which are assumed to increase linearly to 2005 and then to remain constant throughout the forecast period (Bureau of the Census, 1993:xi). Therefore the impact of pessimistically projected future increases in AIDS mortality on life expectancy can be calculated by comparing this projected life expectancy with that in 1990. 3.   These are based on personal communication with Bert Kestenbaum. 4.   Individuals, of course, might live longer but the average across individuals would be bounded in this way. 5.   Kaplan (1991), in a study of Alameda County, concluded that age-specific prevalences of some chronic conditions might be expected to rise over time. 6.   Zedlewski and McBride (1992) predict 3 million nursing home residents in 2010 and 4.3 million in 2030, so a midpoint of about 3.6 million in 2020 would be roughly consistent with the estimates in the text. 7.   Obviously, average levels of disability in nursing homes are higher than levels among the disabled in the community. Still, there is a 35 percent chance that a widow with two children and 5 to 6 activities-of-daily-living limitations will have as her primary caregiver one of her children (Soldo and Freedman, 1994). 8.   Guralnik (1991) shows that people tend to be more impaired in the 2 or 3 years before death and that the costs of such impairment of those about to die increase with age. Therefore, as mortality declines and deaths are increasingly shifted toward older ages, the costs arising from this impairment prior to death will also tend to rise. Note, however, that in the transition from one mortality regime to another, there will be a countervailing tendency for deaths to drop below their steady-state value and therefore for costs to diminish. 9.   Also see National Research Council (1988) for a detailed discussion of data sources and limitations. 10.   Research by Alan Garber and Thomas MaCurdy has focused on combining data from the NLTCS and the National Nursing Home Survey, although the units of observation in the two samples are obviously different. 11.   We focus here on direct medical costs. A more general definition of costs would include the indirect costs of lost work and informal caregiving. 12.   Since expenditures are price multiplied by quantity, one can make inferences about the average use of health care services among demographic groups from cost data if one is willing to assume that the price ''indexes" facing the different demographic groups are similar. In other words, when we use cost data to make inferences about relative health services use, we are making the assumption that if people aged 65 and above have expenditures that are twice those of people under age 65, this older group is receiving twice the real quantity of health care services. 13.   Utilization per capita apparently comprises both changes in visits per person in a given age group and changes in the overall distribution of different ages. 14.   Some part of the increase in relative prices can also be traced to the increased demand for real health services as the age composition of the population changes. 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. 16.   Of course, even an increase of 8 cents per dollar of lifetime income (37 cents versus 45 cents)

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Assessing Knowledge of Retirement Behavior     is a substantial increase in the tax burden of future generations and is caused largely by the pay-as-you-go nature of the current Social Security and Medicare programs (see Kotlikoff, 1992). 17.   Analysis by the Census Bureau of the past performance of its forecasts, along lines pioneered by Stoto, (1983), does give some idea of the probability coverage to assign to the forecasts of population size, but these cannot be applied to any other demographic measure except population growth rates, and certainly not to dependency ratios. 18.   Because some cancellation of variations will occur over the 5-year interval, this probability interval will be narrower in percentage terms than the corresponding interval for a single year or for a point in time. 19.   Of course, other government spending could be cut to pay for the higher share of government health spending. But were the share of government health spending lower, taxes could also be lower, if other government spending programs were held constant. REFERENCES Ahlburg, D., and J. Vaupel 1990. Alternative projections of the U.S. population. Demography 27(4):639–652. Alho, J.M. 1992. The Magnitude of error due to different vital processes in population forecasts. Pp. 301–314 in D. Ahlburg and K. Land, eds., Population Forecasting, a special issue of The International Journal of Forecasting 8(3). Alho, J.M., and B.D. Spencer 1985. Uncertain population forecasting. Journal of the American Statistical Association 80(390):306–314. 1990. Error models for official mortality forecasts. Journal of the American Statistical Association 85:609–616. Alter, G. 1990. Old Age Mortality and Age Misreporting in the United States, 1900–1940. Paper presented at the annual meetings of the Population Association of America, Toronto, Ontario. Auerbach, A.J. and L.J. Kotlikoff 1994. The United States' Fiscal and Saving Crises and Their Implications for the Baby Boom Generation. Report to Merrill Lynch & Co. (February). Bennett, N.G., and S.J. Olshansky 1994. The Impact of Adjustments to Official Mortality Schedules on Projected Age Structure and the Future of Age-Entitlement Programs in the United States. Unpublished manuscript. Department of Sociology, Yale University, New Haven, Conn. Berg, R.L., F.E. Browning, J.G. Hill, and W. Wenkert 1970. Assessing the health needs of the aged. Health Services Research 5(Spring):327–331. Bernheim, B.D. 1994. The Merrill Lynch Baby Boom Retirement Index. Merrill Lynch & Co. Bloom, D., and S. Glied 1992. Projecting the number of new AIDS cases in the United States. Pp. 339–366 in D. Ahlburg and K. Land, eds., Population Forecasting, A Special Issue of the International Journal of Forecasting 8(3). Bureau of Census 1993. Population Projections of the United States, by Age, Sex, Race, and Hispanic Origin: 1993–2050. Current Population Reports, P-25-1104, by Jennifer Cheeseman Day. Washington, D.C.: U.S. Department of Commerce. Burner, S.T., D.R. Waldo, and D.R. McKusick 1992. National health expenditures projections through 2030. Health Care Financing Review 14(1):1–29.

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