3


Demographic Trends

Around the world populations are aging. This is a relatively new demographic phenomenon because for most of human history populations were young and lives were short. Population aging is largely caused by two demographic trends. Most obviously, people today are living longer than before. A second and less obvious cause of population aging is a decline in the birth rate. With lower birth rates, younger generations are smaller relative to older generations, thus raising the average age of the population. There are other demographic processes that affect aging, including migration, but they generally play a smaller role.

This chapter will examine these trends in the United States, starting with improvements in life expectancy and their implications for the individual life cycle. Later sections of the chapter will discuss population aging and why it matters.

LIFE EXPECTANCY AND THE INDIVIDUAL LIFE CYCLE

Life Expectancy at Birth

U.S. life expectancy at birth started improving in the eighteenth century, reaching 47.3 years in 1900, 68.4 years in 1950, and 78.2 in 2010 (Arias, 2011; Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds, 2011). Increases were most rapid in the first half of the twentieth century, when infectious diseases were brought under control, greatly improving survival of children. In contrast, increases in life expectancy since 1950 have been due mostly to declines in adult mor-



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3 Demographic Trends Around the world populations are aging. This is a relatively new de- mographic phenomenon because for most of human history populations were young and lives were short. Population aging is largely caused by two demographic trends. Most obviously, people today are living longer than before. A second and less obvious cause of population aging is a decline in the birth rate. With lower birth rates, younger generations are smaller relative to older generations, thus raising the average age of the population. There are other demographic processes that affect aging, including migra- tion, but they generally play a smaller role. This chapter will examine these trends in the United States, starting with improvements in life expectancy and their implications for the indi- vidual life cycle. Later sections of the chapter will discuss population aging and why it matters. LIFE EXPECTANCY AND THE INDIVIDUAL LIFE CYCLE Life Expectancy at Birth U.S. life expectancy at birth started improving in the eighteenth century, reaching 47.3 years in 1900, 68.4 years in 1950, and 78.2 in 2010 (Arias, 2011; Board of Trustees, Federal Old-Age and Survivors Insurance and Fed- eral Disability Insurance Trust Funds, 2011). Increases were most rapid in the first half of the twentieth century, when infectious diseases were brought under control, greatly improving survival of children. In contrast, increases in life expectancy since 1950 have been due mostly to declines in adult mor- 32

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DEMOGRAPHIC TRENDS 33 tality as cardiovascular disease became more manageable. The rise of 9.3 years in the United States between 1950 and 2006 was substantial, but most other countries in the world achieved more rapid improvements over the same period. Figure 3-1 compares trends in life expectancy for the United States (black line) and eight other large high-income countries (Australia, 90 Li and Lee 85 80 U.S. SSA TPAM 2011 Years 75 70 65 60 1950 1975 2000 2025 2050 Japan Germany United States (SSA) Australia Spain United States (TPAM 2011) Italy France United States (Li and Lee) Canada United Kingdom FIGURE 3-1 Life expectancy at birth in selected countries, and alternative projec- tions for the United States to 2050. SOURCES: United Nations (2011); Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance 3-1.eps Trust Funds (2011); Li and Lee (2005); and Technical Panel on Assumptions and Methods (2011).

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34 AGING AND THE MACROECONOMY Canada, France, Germany, Italy, Japan, Spain, and the United Kingdom). The United States ranked at the top of this group of countries in 1950 but dropped to last place in 2006 (United Nations, 2011). Why does the United States now rank so low in international life ex- pectancy comparisons? This question has drawn the attention and concern of researchers and policy makers. The current situation is especially surpris- ing given that the United States spends far more on health care than any other country. In response to these concerns, the National Research Council (NRC) appointed a committee of experts in 2008 to investigate the reasons for this divergence between the United States and other high-income coun- tries. In its final report the committee reached several conclusions (National Research Council, 2011): A history of heavy smoking and current levels of obesity are playing a substantial role in the relatively poor longevity performance of the United States. (p. S-4) The damage caused by smoking was estimated to account for 78 percent of the gap in life expectancy for women and 41 percent of the gap for men be- tween the United States and other high income countries in 2003. (p. S-2) Obesity may account for a fifth to a third of the shortfall of life expectancy in the United States relative to the other countries studied. (p. S-2) What are the implications of these conclusions for future trends in life expectancy? Mortality will likely continue to decline as further progress is made in medicine, biotechnology, public health, nutrition, access to medi- cal services, incomes, and education. However, substantial disagreement exists among analysts about how rapidly future improvements will occur (Bongaarts, 2006; Wilmoth, 1997 and 2001). At one end of the spectrum of opinion are pessimists (Carnes, Olshansky, and Grahn, 1996; Olshansky et al., 2005), who believe that the most advanced countries are close to a biological limit to longevity. A very different opinion is held by optimists (Oeppen and Vaupel, 2002), who expect life expectancy at birth to continue to rise very rapidly, reaching over 100 years later this century. Most projec- tions by researchers and government agencies fall between these extremes (Lee and Carter, 1992; Li and Lee, 2005; Tuljapurkar, Li, and Boe, 2000; Bongaarts, 2006). The best-known U.S. projection is the one used by the Social Security Administration. The 2011 Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Federal Disability In- surance Trust Funds (commonly known as the Trustees Report) projects life expectancy to reach 82.2 years in 2050, up from 77.7 in 2006. In 2010 the Social Security Advisory Board appointed the Technical Panel on Assumptions and Methods (TPAM) to assess the assumptions and methods used in the Trustees Report. The TPAM made a number of

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DEMOGRAPHIC TRENDS 35 recommendations, including a significant revision of mortality projections. The conclusion that increases in life expectancy will likely be more rapid than is currently assumed in the Trustees Report is based on an analysis of potential future trends in smoking and obesity (Technical Panel on As- sumptions and Methods, 2011). The TPAM noted that the slow pace of improvement in life expectancy over recent decades was due to the impact of smoking and obesity and that these behavioral effects will likely continue to depress U.S. life expectancy. However, after rising for decades, indicators for smoking and obesity have now plateaued. According to the 2011 NRC report mentioned above: After 1964, when the Surgeon General's Office released its authoritative report on the adverse effects of cigarette smoking, the increase in smok- ing slowed, stopped and eventually reversed in the United States. (p. 5-4) Recent data on obesity for the United States suggest that its prevalence has leveled off and some studies indicate that the mortality risk associated with obesity has declined. (p. S-4) The TPAM therefore assumed that the adverse impact of these behav- iors on life expectancy will remain at, or close to, current levels rather than rise much further in the future. Taking these trends into account, the TPAM expects U.S. life expectancy to reach 84.5 years in 2050. This estimate is close to a widely used and respected projection made by Li and Lee (2005) but above the 2011 Trustees Report assumption of 82.2 years. As shown in Figure 3-1, the future pace of improvement is more rapid than assumed in the Trustees Report but still slightly slower than the pace achieved by other high-income countries in past decades. The projections of life expectancy, population aging, and other de- mographic indicators used in the present report are based on special pro- jections made by the committee to incorporate the higher trend in life expectancy recommended by the Social Security TPAM (see Appendix A). Life Expectancy at Older Ages Remaining male life expectancy for those aged 65 in 2010 equaled 17.5 years, but it dropped to 10.8 years for those aged 75 and to 5.7 years at age 85 (Table 3-1). At all ages women have more years remaining than men. The committee's projections indicate an ongoing upward trend in remaining life expectancy at these older ages as well, with life expectancy at 65 reach- ing 22.2 years for males and 24.1 years for females in 2050. It is notewor- thy that Japanese females had already achieved in 2009 the life expectancy that U.S. women are not projected to reach until 2050 (Organisation for Economic Co-operation and Development, 2011). The declines in remain-

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36 AGING AND THE MACROECONOMY TABLE 3-1 Years of Remaining Life at Older Ages in the United States: 1950, 2010, and 2050 Age Gender 1950 2010 2050 65 Male 12.8 17.5 22.2 Female 15.1 19.9 24.1 75 Male 7.9 10.8 14.2 Female 9.0 12.5 15.6 85 Male 4.5 5.7 7.6 Female 5.0 6.7 8.5 SOURCES: 1950 and 2010 from Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds (2011); 2050 from special projections prepared by the committee (see Appendix A). ing life expectancy as people age are important for the later discussion of public support systems such as Social Security and Medicare. Longer Life and the Individual Life Cycle As discussed later in this chapter, population aging results in part from longer life and in part from lower fertility. Figure 3-2 plots the average number of years spent in the three main life cycle phases: (1) total not working (during childhood or adult years prior to age 60); (2) working (defined as being in the labor force); and (3) retired (over age 60 and not in the labor force). The sum of the years spent in these three phases equals the life expectancy at birth, as plotted in Figure 3-1.1 As longevity rises over time, people spend more time in retirement. Between 1962 and 2010 the average time spent in retirement rose by 5 years (from 10 to 15 years), while life expectancy rose by 8 years. During this period, years in the labor force increased modestly but years not work- ing declined slightly. These trends are largely attributable to the increasing labor force participation of women (see Chapter 5). In 1950, only 10.6 years were spent retired, but by 2050 the years in retirement are projected to reach 20, nearly doubling, while working years rise from 31 to 40 and nonworking years remain nearly constant. The U.S. population is devoting increasing years to retirement both in absolute terms and as a proportion of life. As shown in Figure 3-3, the pro- portion of life spent in retirement rose from 15 to 19 percent between 1962 and 2010 and is expected to reach 24 percent in 2050. The ratio of retired 1Estimates are based on labor force participation rates by age through 2010 provided by the Office of Critical Trends Analysis of the Social Security Administration. Labor force participa- tion rates are held constant from 2010 to 2050.

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DEMOGRAPHIC TRENDS 37 90 84.5 78.1 80 70.1 Retired 70 60 63.0 64.4 59.5 50 Years Working 40 30 28.8 20 26.1 26.4 Not working 10 (<60) 0 1950 1975 2000 2025 2050 FIGURE 3-2 Years lived retired, working, and not working, 1962-2050. SOURCES: Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability 3-2.eps Insurance Trust Funds (2011) and projections by the committee. 60 50 Retired yrs/ working yrs 40 Percent 30 Retired yrs/ life expectancy 20 10 0 1950 1975 2000 2025 2050 FIGURE 3-3 Retired years as a proportion of working years and of life expectancy, 1962-2050. SOURCES: Board of Trustees, Federal Old-Age and Survivors Insur- 3-3.eps ance and Federal Disability Insurance Trust Funds (2011); and projections by the committee.

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38 AGING AND THE MACROECONOMY to working years also grew from 35 to 41 percent between 1962 and 2010. By 2050 this proportion is projected to be 52 percent, which implies that the average individual would then work 2 years for every year in retirement. Adjusting the Life Cycle As discussed in later chapters of this report, the costs of public support for health and pension benefits to the elderly will be difficult to bear given the rapid pace of population aging. Among the adaptations that are being considered is an increase in the age at retirement with full benefits, because the costs of this public support decline as the age of eligibility rises. It is useful to consider two simple demographic calculations to put this adapta- tion in context. In the first, we ask until what age a person would have to work in 2050 in order to have the same number of years in retirement as someone who retired at age 65 in 2010. The answer is 70.2 years. Another useful calculation would be how many more years of work will be needed in the future to keep the ratio of retired years to working years constant at the 2010 level.2 As shown in Figure 3-4, a retirement age of just 60 years would have yielded the same ratio in 1950 as in 2010. Based on the committee's projections, a rise of 4 years (from 65 to 69) would hold this ratio unchanged between 2010 and 2050. This scenario involves a smaller rise in the age at retirement and would allow some in- crease in years spent in retirement. Socioeconomic and Geographic Variations in U.S. Life Expectancy The preceding discussion focused on the average life expectancy in the United States and other countries. In addition to between-country variation in life expectancy there is substantial within-country variation, e.g., among racial and ethnic groups, among states and counties, and among groups with different levels of education and income. Table 3-2 presents life expectancy for whites and blacks in 2008. White life expectancy at birth exceeds black life expectancy by 5 years (75.9 vs. 70.9) among males and by 3.4 years (80.8 vs. 77.4) among females. By age 65, these racial differences have declined to 1.8 years for males and 1.0 year for females. Analyses of ethnic differences usually find mortality among Hispanics to be lower than among whites. This so-called "Hispanic para- dox" is probably due to a selection for good health among immigrants from Latin America and a tendency of Hispanic immigrants to return to their 2For this simulation, working years are calculated as years lived between age 20 and retire- ment age in a stationary population with current life expectancy. Retired years equal years of life remaining after retirement. Age at retirement is set at 65 in 2010.

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DEMOGRAPHIC TRENDS 39 75 70 Age 65 60 55 1950 1975 2000 2025 2050 FIGURE 3-4 Hypothetical retirement age required to keep the 2010 ratio of retired to working years constant through 2050. SOURCES: Board of Trustees, Federal 3-4.eps Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds (2011) and projections by the committee. country of origin when they become ill (Elo and Preston, 1997; Markides and Eschbach, 2011). Large mortality differences due to education level are found in the United States as well as in other countries. As shown in Table 3-3, life expectancy at age 25 in 1998 was 7.6 years less for males with less than 9 years of schooling compared to males with 13 or more years of schooling. Among females the difference between these two groups was 4.9 years. At age 65 these differences narrowed but remained a substantial 3.4 years for males and 2.5 years for females. A more recent analysis of trends through 2008 found that adults with fewer than 12 years of education in 2008 had life expectancies similar to the U.S. average in the 1950s and 1960s TABLE 3-2 Years of Life Expectancy at Birth and at Age 65 for Whites and Blacks, 2008 Difference White Black (White - Black) At birth 78.4 74.3 4.1 Male 75.9 70.9 5.0 Female 80.8 77.4 3.4 At age 65 18.7 17.5 1.2 Male 17.3 15.5 1.8 Female 19.9 18.9 1.0 SOURCE: U.S. Census Bureau (2012).

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40 AGING AND THE MACROECONOMY TABLE 3-3 Years of Life Expectancy at Ages 25 and 65 by Educational Attainment, 1998 Years of Schooling Difference 0-8 9-12 13+ (13+) - (0-8) At age 25 Male 47.0 47.5 54.6 7.6 Female 52.9 53.6 57.8 4.9 At age 65 Male 14.9 15.1 18.3 3.4 Female 17.9 18.3 20.4 2.5 SOURCES: Data from Hummer and Lariscy (2011); Molla, Madans, and Wagener (2004). (Olshansky et al., 2012). By combining education and race, the study showed large and growing differences in life expectancy between whites with 16+ years of schooling compared to blacks with fewer than 12 years of education. Geographic differences in mortality are also well established. Life ex- pectancy in 1999-2001 was highest in Hawaii and Minnesota and lowest in the District of Columbia and Mississippi (Table 3-4). Differences at the county level are even larger than among states (Ezzati et al., 2008). The literature has proposed a range of factors that may be responsible for or contribute to these mortality differences. Generally, disadvantaged groups or populations smoke more, are more obese, exercise less, live more stressful lives; have less access to health care services; have fewer social resources and lower status occupations; live in neighborhoods with poor housing, high levels of pollution, and relatively high crime rates; and have less income and education (Centers for Disease Control and Prevention, 2011; National Research Council, 2004a, 2004b). Differences in genetic endowment may also play a role (Christensen and Vaupel, 2011). Despite the often significant correlation between these explanatory factors and mortality, it is difficult to disentangle the complex causal pathways and quantify the true determinants of mortality differences. TABLE 3-4 Years of Life Expectancy at Birth and at Age 50 for Selected States/Areas, 1991-2001 District of Difference Columbia Mississippi ..... Minnesota Hawaii (Hawaii - D.C.) At birth 72.3 73.6 79.0 79.7 7.4 At age 50 28.0 31.4 31.4 32.4 4.4 SOURCE: Wilmoth, Boe, and Barbieri (2010).

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DEMOGRAPHIC TRENDS 41 These mortality differences have potentially important implications for the design of policies to address the adverse consequences of aging. In particular, raising the full retirement age for all retirees leads to a larger proportional reduction in expected years of retired life for disadvantaged groups than for advantaged groups. The committee believes that such a differential impact would be undesirable. POPULATION AGING Population Age Distribution The older population in the United States is on the threshold of a boom. The population aged 65 and over will increase substantially between 2010 and 2030, reaching 72 million in 2030, more than twice the level (35 million) in the year 2000 (He et al., 2005; Vincent and Velkoff, 2010). Figure 3-5 shows broad changes in the nation's age distribution from 1950 to 2010 and projected changes through 2050. This graphic highlights the growing share of people in older age groups and the corresponding decline in the share of the population under age 30. As discussed in more depth later in this chapter, the United States is aging less rapidly than most other high-income countries and may be relatively better able to cope with the pressures of demographic change. Another view of changing age distribution is provided by Figure 3-6, which plots U.S. population size by age for 1975 and 2000 and as projected to 2025 and 2050. In 1975 a large proportion of the population was be- tween 10 and 30 years of age. This group is often referred to as the baby boom generation, because it consists of the survivors of the large number of U.S. births between 1945 and 1965. As this generation ages, its presence in the age structure leaves a visible bulge that reaches ages 35-55 in 2000, ages 60-80 in 2025, and ages 85+ in 2050. The population aged 90+ rises more than 17-fold between 1975 and 2050. Over past decades, the baby boom postponed population aging as it moved through the labor force ages. But now it is ushering in a new period of very rapid population aging as it moves into old age. The baby boom generation retires during the first quarter of the twenty- first century, causing a steep increase in the number of recipients of Social Security and Medicare benefits (Figure 3-7). The number of people reaching age 65 each year rose modestly between 1950 and 1980 and then fell for several years as a result of low birth rates during the Depression years of the 1930s. But between 2000 and 2025 the annual number of those turning age 65 is expected to more than double, from 2.1 to 4.3 million (the mod- est reduction after 2025 is due to the end of the baby boom in the 1960s).

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42 AGING AND THE MACROECONOMY 100 90 80 70 60 Percent 50 40 30 20 10 0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Age Group 90+ 60-74 30-44 0-14 75-89 45-59 15-29 FIGURE 3-5 Percent distribution of the population by age, 1950-2050. SOURCES: Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability 3-5.eps by the committee. Insurance Trust Funds (2011) and projections Providing pensions and health care for this wave of new retirees will be a challenge. Demographic Drivers of Aging The amount of aging the U.S. population will experience in the future depends on trends in mortality, fertility, and migration. In general, the lower the levels of fertility, mortality, and migration, the older the population will become. It should be stressed that if infant and younger-adult mortality rates remain relatively low for a prolonged time, as has been the case in most developed countries for many decades, changes in life expectancy at older ages become inceasingly important to changes in overall life expec- tancy. The demographic assumptions on fertility, mortality, and migration underlying the population projections in this report are discussed next.

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DEMOGRAPHIC TRENDS 51 25 65+ 20 15 Percent 10 75+ 5 85+ 0 1950 1970 1990 2010 2030 2050 FIGURE 3-12 Share of population aged 65+, 75+, and 85+, 1950-2050. SOURCES: Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability 3-12.epsby the committee. Insurance Trust Funds (2011) and projections Age Dependency Ratio The age dependency ratio (ADR) is the ratio of population aged 65 and over plus those under 20 ("dependents") to the working age popula- tion (ages 20-64). Figure 3-13 plots estimates of the ADR for the United States from 1950 to 2010 and projections to 2050. The ADR fluctuates substantially over time but shows no clear long-range trend. It reached its 1 0.8 Total 0.6 Youth 0.4 Retiree/worker 0.2 Old 0 1950 1975 2000 2025 2050 FIGURE 3-13 Age dependency ratios, 1950-2050. SOURCES: Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust 3-13.eps Funds (2011) and projections by the committee.

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52 AGING AND THE MACROECONOMY peak value, 0.94, in 1965 then declined to its minimum, 0.67, in 2010 and is projected to rise again to a new peak, 0.85, in 2037. To explain this trend it is useful to examine the two components of the ADR: the old age dependency ratio (OADR, population 65+/popula- tion 20-64) and the young age dependency ratio (YADR, population <20/ population 20-64). As seen in Figure 3-13, these two components show very different trajectories over time. The YADR peak in 1965 was responsible for the first peak in the ADR, and the future rise in the OADR is responsible for the projected second peak in the ADR in the 2030s. As a result of these opposing trends in the OADR and YADR, the composition of dependents shifts from mostly under age 20 in the 1960s to nearly even between old and young dependents in 2050. Although widely used, this ratio has a key flaw: It implicitly assumes that all people aged under 20 and over 64 are "dependents" and that all people aged 20-64 are "working." These assumptions are at best an ap- proximation of reality, and the quality of this approximation changes over time both because of changes in actual economic behavior and because of changes in underlying health. Retiree to Worker Ratio The retiree/worker ratio (RWR) can be considered an improved version of the old age dependency ratio. The numerator of the RWR consists of the number of retirees (instead of the population 65+) and its denominator consists of all people in the labor force (instead of the population aged 20- 64). The RWR typically exceeds the OADR by a small amount because the number of retirees exceeds the population aged 65+ and because the num- ber of workers is somewhat smaller than the population aged 20-64. The trends over time in the two indicators are similar, as shown in Figure 3-13, where the dashed line represents the RWR. Support Ratio Unweighted Support ratios differ from dependency ratios in that the supporters (or workers) are in the numerator and the dependents (or consumers) are in the denominator; these measures are therefore inversely related to dependency ratios. The simplest support ratio is the proportion of the population that is working. The numerator consists of everyone in the labor force5 and the denominator equals the entire population, all of whom are consumers. The 5"Labor force" is defined in Chapter 5.

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DEMOGRAPHIC TRENDS 53 unweighted support ratio (SRU) rose from 1962 to 1980 then plateaued until 2010, but it is expected to decline by 2050 (Figure 3-14). The dis- advantage of this measure is that it assumes that workers of all ages have equal incomes and that the same amount is consumed by people of all ages. Weighted The weighted support ratio (SRW) is a more sophisticated measure that improves on the unweighted version by allowing incomes of workers and consumption levels to vary by age. Specifically, the age patterns of consumption and labor income discussed previously (see Figure 3-10) are applied to the population by age to calculate the SRW. The ratio depends on the base year age profiles of consumption and labor income that are used. These are held constant to isolate the effect of changing population age distributions. It is a hypothetical "other things equal" calculation, not an attempt to project what the future ratios of labor income to consump- tion will be. Trends and projections of SRW are presented in Figure 3-14 (top line), based on the labor income and consumption profiles of 2007 combined with each year's population age distribution. The SRW is higher than the SRU mainly because income substantially exceeds consumption among 1 Weighted 0.8 0.6 0.4 Unweighted 0.2 0 1950 1975 2000 2025 2050 FIGURE 3-14 Unweighted and weighted support ratios, 1962-2050. SOURCES: Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disabil- ity Insurance Trust Funds (2011); 3-14.eps Lee and Mason, 2011; and projections by the committee.

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54 AGING AND THE MACROECONOMY workers.6 However the pattern of change in the SRW over time is similar to that of the SRU. Table 3-5 summarizes estimates of six indicators in 2010 and 2050. These aging indicators differ because they are differently defined. Their absolute levels will not be examined here because there is little to be gained from a discussion of the differences. Instead the committee focuses on the projected trends (last column), which anticipate the future impact of popu- lation aging. These results lead to two main conclusions regarding trends to 2050. First, the U.S. population will likely age substantially, as indicated by the 64 percent rise in the population aged 65+, the 81 percent rise in the OADR, and the 71 percent rise in the RWR. Second, the economic impact of this aging is cushioned by a decline in youth dependency. The net effect of these demographic trends is best captured by the SRW, which is projected to decline 12 percent by 2050. This means that, other things being equal, consumption per capita will be 12 percent lower than it would be without population aging.7 Adapting to Population Aging As noted throughout this report, adapting to future population ag- ing might involve a rise in the age at retirement. Such an increase would counteract the projected adverse changes in most of the above indicators. To illustrate, Figure 3-15 plots the age at retirement (conventionally set at age 65) required to keep the OADR constant at 0.22. This calculation indicates that the age at retirement would have to rise from 65.0 in 2010 to 73.3 years in 2050 to prevent the OADR from increasing. A separate calculation indicates that a similar increase in age at retirement will keep the SRW constant. It should be emphasized that Figure 3-15 represents a purely hypothetical exercise to illustrate the magnitude of changes in age at retirement needed to keep this dependency ratio unchanged. Such a large change in age at retirement is likely to be politically unacceptable, and it is not the committee's intention to recommend it. It is worth noting that this increase in the age at retirement of 8.3 years is larger than the rise of 4.0 years needed to keep the ratio of retired to working years constant over the individual life cycle (see earlier discussion of Figure 3-4). The reason for this difference is that the rise in the age at retirement plotted in Figure 3-15 compensates both for the projected rise in life expectancy and for population aging resulting from fertility decline 6The support ratio is typically less than unity because consumption is funded in part from sources other than labor income, such as asset income. 7That is, consumption per weighted consumer will decline by 12 percent, other things equal.

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DEMOGRAPHIC TRENDS 55 TABLE 3-5 Summary Indicators of Population Aging, 2010 and 2050 Percent Change, Indicator 2010 2050 2010-2050 Aged 65+ (%) 13.0 21.3 64 ADR .67 .84 26 OADR .22 .39 81 RWR .26 .45 71 SRU .51 .47 -8 SRW .78 .68 -12 SOURCES: Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds (2011) and projections by the Committee. and migration changes. In contrast, the life cycle calculations summarized in Figure 3-4 compensate only for rising life expectancy. GLOBAL PATTERNS OF AGING Population aging is occurring in most countries because life expec- tancy has risen and fertility has declined. Aging is most pronounced in high-income countries (i.e., Europe, North America, and Japan), where the median age of the population rose from 29 to 39 years between 1950 and 2010 (United Nations, 2011). United Nations projections expect this me- dian to reach 48 years in 2050. Populations in the developing world (Asia excluding Japan, Latin America, and Africa) are generally younger, with 75 70 Age 65 60 1950 1975 2000 2025 2050 FIGURE 3-15 Retirement age required to keep old-age dependency ratio constant at its 2010 level. SOURCES: Board of Trustees, Federal Old-Age and Survivors 3-15.eps Insurance and Federal Disability Insurance Trust Funds (2011) and projections by the committee.

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56 AGING AND THE MACROECONOMY a current aggregate median age of 27 years, but aging is also proceeding rapidly and the median is projected to reach 37 years in 2050. Figure 3-16 plots past estimates and projections of the proportion aged 65+ for each of the world regions. Large regional differences are apparent. In 2010, proportions 65+ in Europe (16.2) and North America (13.2) are substantially higher than in Latin America (6.9), Asia (6.7) and Africa (3.5). By 2050 these proportions are expected to have risen further, reaching 26.9 percent in Europe and 21.6 percent in North America. The steepest increases are projected for Asia and Latin America, where levels will more than double and reach above today's European levels. Africa will also age, but slowly, and will remain the youngest region. Figure 3-17 compares the United States with other high-income coun- tries. By mid-century the proportion aged 65+ is projected to reach 21 percent in the United States and substantially higher in other rich countries (over 30 percent in Germany, Italy, and Spain and 36 percent in Japan). The reason for this difference is the relatively high fertility in the United States and the low fertility in Germany, Italy, Japan, and Spain. In addition, the United States is expected to have higher mortality and migration rates. Other high-income countries therefore face more pronounced aging than does the United States. UNCERTAINTY IN POPULATION PROJECTIONS It is obvious that many assumptions are required for a population pro- jection. Painful actions such as raising the retirement age might be taken 30 Europe 25 North America 20 Percent 15 Latin America 10 Asia 5 Africa 0 1950 1975 2000 2025 2050 FIGURE 3-16 Share of the population aged 65+ in five world regions, 1950-2050. SOURCE: United Nations (2011). 3-16.eps

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DEMOGRAPHIC TRENDS 57 40 Japan 35 Spain 30 Italy 25 Germany Percent 20 Canada 15 France 10 United Kingdom 5 United States 0 1950 1975 2000 2025 2050 FIGURE 3-17 Share of population aged 65+ in eight high-income countries, 1950- 2050. SOURCE: United Nations (2011). 3-17.eps now to ameliorate the consequences of projected population change far in the future. How certain can we be that the projected changes will actually occur and that action is needed now? To answer this important question, we need an indication of the uncertainty in the projections. Demographers and statisticians have mainly used four different meth- ods to assess the uncertainty of population projections (see National Re- search Council, 2000, for a detailed examination of both the accuracy of past projections and the uncertainty of population forecasts). The tra- ditional method, which can be called "scenarios," is familiar to all: The projections are made in high, medium, and low variants, based on expert opinion about how high or low each of the key inputs--fertility, mortal- ity, and net immigration--might be. This is certainly helpful, but there are difficulties with this approach. It seems to assume that if fertility (for example) is higher than expected in the first year of the projection, then it will also be higher in every subsequent year, and this assumption rules out the kinds of fluctuations that have occurred in the past. Because of this, the scenario method invites us to believe that if we just wait for a few years it will become clear whether the population is evolving according to the high or the low scenario, and uncertainty will be reduced. But this interpretation is mistaken. After a few years, a new set of scenarios would again feature similar high, medium, and low variants. Construction of the scenarios also requires deciding whether to com- bine the high fertility assumption with a low mortality assumption or a high mortality assumption, and likewise for migration assumptions. This

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58 AGING AND THE MACROECONOMY decision is essentially arbitrary, and however it is done, inconsistencies in the high-low ranges will result (see Lee, 1999).8 A second approach, called ex post analysis, analyzes the past record of success of forecasts prepared by an agency as a guide to the uncertainty of future forecasts. If the forecasting method has not changed too much over time, this method can be very useful. An unusually careful ex post analysis of the United Nations projections was provided by the National Research Council (2000). A third approach might be called "random scenarios." It assumes a cer- tain probability distribution of the true outcome in relation to high and low bounds provided by experts. Given this distribution, a process like the one described above can be used to generate possible future paths for each vital rate (Lutz, Sanderson, and Scherbov, 2004; Tuljapurkar, Li, and Boe, 2000). A fourth approach is based on time-series analysis, which combines demographic methods with well-established statistical methods to model, analyze, and forecast historical data on fertility, mortality or migration (Lee and Tuljapurkar, 1994). The models capture not only the trend but also the typical patterns and degree of persistence of fluctuations. One can draw random numbers that, combined with the models, generate one possible version of the future of a particular rate--say fertility--that is consistent with the typical past patterns (Lee, 1999; 2011). In the same way, possible futures can be generated for mortality and net immigration. Then this set of randomly generated fertility, mortality, and migration outcomes can be used to generate a possible future trajectory for the population and its age distri- bution, say up to 2050. By repeating this process with a new set of random numbers, another possible future is generated. After 1,000 such repetitions, it becomes clear which outcomes are most likely and which are less likely, and it is possible to derive a probability distribution. This method produces not only a probability distribution of outcomes for a given year, but also a distribution of trajectories. Such an approach is valuable because some outcomes of interest, such as the projected Trust Fund balance for Social Security in a given year, depend not only on the demography of that par- ticular year but also on the whole demographic trajectory leading up to that point, with all its ups and downs. In fact, the Social Security Trustees have included in their annual reports a stochastic forecast of this sort for the system's finances. Figure 3-18 shows a probabilistic forecast of the OADR based on a stochastic version of the committee's single-sex population projection, for 8In effect, the scenario method assumes that projection errors in each component are per- fectly correlated over time (always too high or always too low), and that errors in the different components are always perfectly positively or negatively correlated with one another (if fertil- ity is high, then mortality or immigration is low, for example). Neither assumption is correct.

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DEMOGRAPHIC TRENDS 59 0.45 0.40 0.35 0.30 0.25 2010 2020 2030 2040 2050 FIGURE 3-18 Old-age dependency ratio as projected by the committee, the Census 3-18.eps Bureau, and the Social Security Trustees, 2010-2050. The central black line is the bitmap median committee forecast. Therewith vector is a 50 percenttype probability that the ratio will lie between the green dotted lines in any year and a 95 percent probability that it will lie between the green dashed lines. The solid purple line is the 2008 Census Bureau projection. The red diamonds indicate the high, intermediate, and low variants of the 2012 Social Security Trustees projection. SOURCES: Donehower and Boe (2012), U.S. Census Bureau (2008), and Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds (2012). which 1,000 stochastic trajectories were created (see Appendix A for a description of the method used). The solid black line is the median in each year of the 1,000 random trajectories. The inner dotted green lines indicate quartiles, so there is a 50 percent chance that the future outcome will lie between them in a given year. The outer dashed green lines represent the upper and lower 2.5 percent bounds and define the 95 percent probability interval for the OADR in each year.

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60 AGING AND THE MACROECONOMY Figure 3-18 also plots the most recent Census Bureau projection of the OADR as a purple line. It is just at or below the lower 25 percent bound of the committee forecast, perhaps because the life expectancy forecast in the Census Bureau projection is lower than in the committee's. The Census pro- jection does not come with a range. Figure 3-18 further shows the OADR as projected by the Social Security Trustees in its 2012 high, intermediate, and low-cost variants, all indicated by diamonds. The intermediate projection is very close to the committee's median through 2030, and then transits to the Census Bureau projection at the lower 25 percent of the committee's range. The Trustees' low-cost scenario is slightly below the lower 2.5 percent bound for the committee's forecast, while the high-cost scenario rises above the committee's upper 2.5 bound before dipping below this bound around 2040. The Trustees' projection is centered a bit lower than the committee's owing to less projected gain in longevity (life expectancy of 82.2 years in 2050, versus the committee projection of 84.5 years, as discussed earlier). The Trustees do not assign a probability to the range for the OADR, but Figure 3-18 suggests that its probability coverage is about 95 percent. This is the probability that the OADR in any given year will fall between the high-cost and low-cost brackets. This does not mean, however, that there is a 5 percent chance that the OADR would generally lie outside this range in every year between 2010 and 2050. That probability would be far lower, because a typical trajectory of the OADR would wander around within that range, with offsetting upward and downward variations. This last point can be seen clearly in Figure 3-19, which shows a probabilistic forecast of the weighted support ratio based on the age profiles for 2007 shown earlier in Figures 3-10 and 3-11. It uses the same 1,000 stochastic trajectories and plots 20 of them for illustrative purposes. The trajectories often can be seen to fluctuate rather than to be persistently high or low. As in Figure 3-18, the solid black line is the median projection. The dashed blue lines indicate quartiles (so there is a 50 percent chance that the future outcome will lie between them in a given year), and the dashed green lines represent the upper and lower 2.5 percent bounds and define the 95 percent probability interval. The value of the ratios has been adjusted so that the ratio in 2007 is 1.0. That is, the plotted values show the ratio relative to the ratio in 2007.9 The expected decline in the support ratio between 2010 and 2050 is 12 percent (the same as reported earlier in this chapter), and there is a two- 9Of course, the age profiles of labor income and consumption will change over the next four decades, and any attempt to project their future levels would involve substantial uncertainty. However, the support ratio for future years is calculated using the baseline (2007) age profiles so as to isolate the effects of demographic change. Therefore the uncertainty surrounding future values of the age profiles is irrelevant. For a discussion of the construction and use of support ratios in this context, see Cutler et al. (1990).

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DEMOGRAPHIC TRENDS 61 1.00 0.95 0.90 0.85 2010 2020 2030 2040 2050 FIGURE 3-19 Projected weighted support ratio with probability bounds and 20 illustrative stochastic trajectories, 2010-2050. The central dark line is the median 3-19.eps committee forecast. There is a 50 percent probability that the support ratio will lie between the blue dashed bitmap withyears lines in future vector type and a 95 percent probability that it will lie between the green dashed lines. The figure shows 20 trajectories. The actual projection is based on 1,000 sample paths. For the method used, see Appendix A. SOURCE: Donehower and Boe (2012). thirds chance that the decline will be between 9 percent and 16 percent, and a 95 percent chance that it will be between 5 percent and 19 percent. We can conclude from the stochastic approach in Figures 3-18 and 3-19 that it is virtually certain that the U.S. will experience substantial population aging and that the support ratio is virtually certain to fall in the coming decades. The expected decline of 12 percent translates into an average yearly rate of decline of one-third of 1 percent (0.33 percent).