Many older Americans are retired and rely on different parts of the U.S. retirement system for their financial security. The U.S. retirement system is often described as being composed of Social Security, employer- sponsored pensions and retirement savings plans, and individual savings. In addition, older Americans may work past traditional retirement ages or phase into retirement. Social Security’s Old-Age and Survivors Insurance program is the foundation of the U.S. retirement system and provides benefits to retired workers, their families, and survivors of deceased workers. In 2018, about 53 million retirees and their families received $844.9 billion in Social Security retirement benefits, according to the Social Security Administration. However, Social Security is facing financial difficulties that, if not addressed, will affect its long-term stability. If no changes are made, current projections indicate that by 2034, the retirement program Trust Fund will only be sufficient to pay 77 percent of scheduled benefits. Employer-sponsored pensions include DB plans, which generally promise to offer a monthly payment to retirees for life. Employers also sponsor defined contribution (DC) plans, such as 401(k)s, in which individuals accumulate tax-advantaged retirement savings in an individual account based on employee and/or employer contributions, and the investment returns (gains and losses) earned on the account. Participants in both DB and DC plans receive certain tax preferences provided the plans comply with requirements outlined in the Internal Revenue Code (IRC). For fiscal year 2018, estimated tax expenditures related to retirement plans and savings amounted to about $188 billion. The Employee Retirement Income Security Act of 1974 (ERISA) outlines minimum standards and requirements that must be met by most private sector employer- sponsored retirement plans; it does not, however, require any employer to establish, or continue to maintain, a retirement plan. Assets rolled over from employer-sponsored DC plans when individuals change jobs or retire are the primary source of funding for individual retirement accounts (IRAs). Over the past 40 years, private sector employers have increasingly moved from offering DB plans to offering DC plans. While DC plans offer more portability, some financial risks—such as poor investment returns, decreases in interest rates, and increases in longevity—have shifted from the employer to the employee, with important implications for individuals’ retirement planning and security. Individual savings are any other non-retirement plan savings and investments. Home equity is an important asset for many households. Other sources of savings or wealth may include amounts saved from income or wages, contributions to accounts outside of a retirement plan, non-retirement financial wealth that is inherited or accumulated over time, and equity from other tangible assets such as vehicles. Wealth: For analyses in this report, we defined wealth as net worth, i.e., assets minus debt. Assets could be financial (e.g., savings accounts, stocks, bonds, retirement accounts) or nonfinancial (e.g., the value of any houses or vehicles). Retirement accounts include defined contribution plans, such as a 401(k), or individual retirement account (IRA)s. Net worth is a measure often used by researchers studying retirement security. Present value of future income from Social Security and defined benefit pensions: Older Americans may also have other future retirement resources, not included in net worth, such as the present value of benefits expected from defined benefit (DB) pension plans and Social Security. These present value estimates could be included in a broader definition of economic resources or wealth, and we were able to produce estimates of these additional retirement resources to supplement our analysis of the distribution of income and wealth among older Americans over time. While all estimates produced using survey data are subject to some uncertainty, our present value estimates for these additional retirement resources are also subject to additional uncertainty that arises from using another data source—the Financial Accounts of the United States—to create a measure of aggregate defined benefit entitlements; having limited information about lifetime earnings in the Survey of Consumer Finances; and making assumptions about life expectancy, real discount rates, and retirement ages, which are unlikely to hold for all households. Data limitations prevented us from producing this broader measure of retirement resources for our analysis examining the distributions of income and wealth as a cohort of older Americans aged. Income: For analyses in this report, we defined household income as the sum of income across all sources, including wages and salaries, Social Security benefits, traditional pension benefits from defined benefit plans, withdrawals from retirement accounts, and income from any other sources, such as interest on financial assets or benefits from social safety net programs such as the Supplemental Nutrition Assistance Program (SNAP). See appendix I for more information on our definitions and the methods used to produce estimates of wealth, the present value of future income expected from Social Security and defined benefit plans, and income. Older Americans may also have wages or salaries from working longer as they transition to retirement. According to data from the Bureau of Labor Statistics, more older Americans are working. From 1989—the earliest starting year for our analyses—to 2018, the labor force participation rate for Americans aged 55 or older increased from 30 percent to 40 percent. In addition, some older Americans may receive income from financial assets, such as interest or dividends, and from other benefit programs, such as Social Security Disability Insurance. The number of older Americans is increasing faster than the population as a whole. In 1990, about 52 million, or around 1 in 5, people in the United States were aged 55 or older. By 2030, that number is expected to be about 112 million, or around 1 in 3. The aging of the baby boomers— that is, people born between 1946 and 1964—as well as increasing longevity and lower fertility have contributed to this trend. The oldest baby boomers turned 55 in 2001 and the youngest are turning 55 this year. In addition, average life expectancy for those ages 65 or older has increased significantly over the past century and is projected to continue to increase. For example, a man turning 65 in 2030 is expected to live, on average, to age 85.0, an additional 5.3 years compared to a man who turned 65 in 1980, who was expected to live, on average, to age 79.7. A woman turning 65 in 2030 is expected to live, on average, to age 87.3, an additional 3.5 years compared to a woman who turned 65 in 1980, who was expected to live, on average to age 83.8. Since life expectancies are averages—some individuals will live well beyond their life expectancy— longer life expectancies, combined with the possibility of living well beyond life expectancy, mean that people must now prepare for the potential for more years in retirement with greater risk of outliving their savings. Disparities in income and wealth among older households have become greater over the past 3 decades, according to our analysis of 1989 to 2016 data from the SCF. For our analysis, we divided older households in the data into five groups, or quintiles, based on income or wealth. Each year of data in our analysis used a different set of households. Therefore, each quintile includes different sets of households over time. In other words, the households in the top 20 percent in 1989 are not the same households as those in the top 20 percent in 2016. While the households included in the SCF are different for each year of data we used in our analysis, we were able to examine how the distribution of income and wealth across older households changed over time. We found mostly higher income and wealth across all quintiles over time, disproportionately so for the top quintile. For example, we estimated that average income of households in the top 20 percent in 1989 was about $242,000. In 2016, estimated average income of households in the top 20 percent was about $398,000, which is about 64 percent higher (see fig 1). In comparison, estimated average income of households in the bottom quintile—bottom 20 percent—was about $9,000 in 1989. In 2016, estimated average income of households in the bottom 20 percent was about $14,000, which is about 55 percent higher. We found similar results when we analyzed changes in median income. Our findings were similar when we analyzed changes in wealth (defined as net worth). Estimated average wealth of households in the top 20 percent was about $2.1 million in 1989. In 2016, estimated average wealth of households in the top 20 percent was about $4.6 million, which is more than twice as high. (See fig. 2.) In comparison, average wealth of households in the bottom 20 percent was similar over time from 1989 to 2013. In fact, in both 2010 and 2013, estimated average wealth of households that were in the bottom 20 percent in either of those years was negative, meaning that those households, on average, had more debt than assets. (See text box for discussion of how recessions during the time period of our analysis could affect retirement security.) Within the top quintile, a disproportionate share of income and wealth is held by the top 1 percent compared to the next 19 percent. (See figs. 3 and 4 for average income and wealth of households in the top 1 percent.) For example, we found households in the top 1 percent in 1989 had estimated average wealth that was about $13 million more than estimated average wealth for households in the next 19 percent (about 10 times as much estimated average wealth). By 2016, households in the top 1 percent had about $34 million more in estimated average wealth compared to households in the next 19 percent (about 13 times as much estimated average wealth). Social Security is the foundation of retirement security in the United States, and along with income from traditional DB pensions, can be particularly important for older households with lower wealth. As discussed in the text box above, some older Americans will expect future income from Social Security, DB pensions or both. We analyzed the present value of these sources for two subsets of older Americans: 1) those who expect future income from Social Security but not DB pensions, and 2) those who expect future income from both Social Security and DB pensions. On average, households with lower wealth, and that expect future income from Social Security but not DB pensions, may receive a significant income stream from future Social Security benefits, according to our analysis of SCF data (see fig. 5). The bottom 20 percent have little in wealth, on average, but the estimated present value of future Social Security benefits provides them relatively significant financial security in retirement. On the other hand, for the top two quintiles, wealth was the most important retirement resource, as households in the top quintile have wealth that, on average, far exceeds the estimated present value of benefits provided by any future Social Security or pension benefits. We found similar results for households with lower wealth and that expect future income from Social Security and DB pensions. While the lower quintiles may have little in wealth, on average, they may expect to receive a significant income stream from future Social Security and DB pension benefits (see fig. 6). Wealth was the most important financial retirement resource for the top two quintiles, on average. While disparities remain, the present value of future income expected from Social Security and DB pensions mitigate these disparities to some extent for those households that expected such income, as illustrated by the examples below. Estimates for all older households in 2016 that expect future income from Social Security but not DB pensions: Households in the top quintile had, on average, about $6.1 million in assets, about 272 times as much as the bottom quintile, which had estimated assets of, on average, about $22,000. When looking at a broader definition of retirement resources (assets plus the present value of future income from Social Security), we estimated that the top quintile had, on average, $6.6 million in these resources, about 27 times as much as the bottom quintile, which had, on average, about $241,000. Estimates for all older households in 2016 that expect future income from Social Security and DB pensions: Households in the top quintile had, on average, about $3.2 million in assets, about 61 times as much in assets as the bottom quintile, which had estimated assets of, on average, about $52,000. When looking at a broader definition of retirement resources (assets plus the present value of future income from Social Security and DB pensions), we estimated that the top quintile had, on average, about $4.3 million in these resources, about 8 times as much as the bottom quintile, which had, on average, about $535,000. Recent research has theorized that benefits expected from Social Security “ a long way” to explaining why having little in DC accounts and future income expected from pensions does not necessarily translate into dramatic changes to living standards as people retire. In particular, the progressivity of Social Security, meaning Social Security benefits replace a higher percentage of pre-retirement earnings for lower-earning households, could be helpful for these households, especially in the absence of other resources, such as retirement accounts. Income and wealth were consistently lower over time for older households headed by someone who was a racial minority, single, or hadn’t attended college, according to our analysis of 1989 through 2016 SCF data. (See fig. 7 for an example using the middle quintile.) We found these disparities existed across all quintiles and all years (see fig. 8 for another example, this time using the top quintile). Generally, the largest disparities from 1989 to 2016 were between 1) households in which the head had not attended college and households in which they had and 2) coupled households and single women. These results are consistent with our prior work, which found that women age 65 and older had less retirement income, on average, and live in higher rates of poverty than men in that age group. Disparities were also sizeable for households headed by someone who was white and non-Hispanic compared to those headed by a minority. There are multiple reasons why households headed by someone with at least some college education may have more wealth in retirement. Most notably, those with more education may have access to higher-paying jobs and be able to save more. Our review of the literature identified several other theories to explain this association. These include (1) education increases awareness about the need to save, (2) highly- educated individuals may have more financial education and achieve higher rates of return on savings, (3) those with more education may be willing to work longer, and (4) highly-educated individuals may have wealthier parents and thus may have received larger bequests. Our prior work has explored how recent trends in marital patterns and saving for retirement, among other factors, can negatively affect retirement security for minorities, women, or those who are single. The percentage of households with retirement accounts was higher across all wealth quintiles in 2016 compared to 1989, and it was disproportionately higher for the top quintile, according to our analysis of SCF data. In 1989, the percentage of households with retirement accounts—amounts in DC plans and IRAs—ranged from 4 percent of the bottom quintile to 65 percent of the top quintile (see fig. 9). By 2016, 11 percent of households in the bottom quintile had retirement accounts compared to 86 percent of households in the top quintile. These increases reflect the transition to more employers offering DC plans, among other factors. Further, the percentage of households in the bottom quintile with retirement accounts had not returned to its pre- recession rate. As discussed earlier, households with less wealth may be more reliant on income from Social Security and DB plans. Further, we found the amount in retirement accounts was often low, particularly for the lower quintiles. In 2016, 89 percent of the households in the bottom quintile had no retirement accounts, and another 10 percent had account balances of less than $50,000 (see fig. 10). In comparison, over half the households in the middle quintile had retirement accounts, and almost all of these households had less than $200,000 in their accounts. Older Americans may rely on resources other than those discussed above for financial security in retirement (see fig. 11), and these “non- retirement assets” remained important over the time span of our analysis, regardless of their value relative to retirement account balances or the present value of future income from Social Security or DB pensions. Home equity. We estimated that over 80 percent of households in each of the top four quintiles of the wealth distribution owned a home in each year of our analysis. However, the home ownership rate for households in the bottom quintile in each year of our analysis was consistently much lower than for the other quintiles–ranging between 18 and 32 percent. Further, the home ownership rate for households in the bottom 20 percent in 2016 (19 percent) was significantly lower than the home ownership rate for households in the bottom 20 percent in 2007 (28 percent), the starting year for the most recent recession. In 2016, the estimated average amount of home equity of households in the bottom quintile was about $2,000, and $50,000 for the second-from-the-bottom quintile, compared to about $118,000 for the middle quintile, about $208,000 for the fourth (or second-from-the- top) quintile, and about $559,000 for the top quintile. According to researchers, most households appear to treat a house as a source of reserve wealth that can be tapped in the event of a substantial expense, further pointing to the importance of home ownership for many older Americans. Vehicles. A majority of households in each quintile of the wealth distribution owned a vehicle across all years in our analysis, although the bottom quintile had ownership rates that were disproportionately lower. However, despite this, we estimated that vehicles provided higher value, on average, relative to other non-retirement assets for households in the bottom quintile from 2010 onward. For example, in 2016, the estimated average value of vehicles among households in the bottom quintile was about $7,000 in 2016, compared to estimated average values of less than $2,000 in home equity and about $3,000 in all other non-retirement assets. All-other non-retirement assets. For the top quintile of households, the average value of these “other assets”—which included stocks, bonds, and other savings outside of retirement accounts, among other things—was more than average home equity or the average value of vehicles over the period of our analysis. Estimated average wealth in this other assets category was about $3.3 million in 2016 for the top quintile. Individual income sources and debt were also important factors in older households’ financial security. Researchers have examined the importance of income sources for households and found Social Security is more important for households with lower incomes, while older households with the most income tend to have a diverse range of income sources, such as earnings from financial assets and income from DB plans. We found that debt could have a substantial effect on households’ financial security, particularly for the bottom 20 percent. For example, in 2010 and 2013, average net worth for this group was negative because debt was greater than assets. A substantial number of older Americans born from 1931 through 1941 lived into at least their 70s or early 80s, according to our analysis of data on a cohort of people born in these years. (See text box and app. I for more on how we analyzed Health and Retirement Study (HRS) data on this cohort.) However, this same cohort faced disparities in longevity. Further, our analysis, as well as that of other researchers, found income and wealth each have strong associations with longevity, as do certain demographic characteristics, such as gender and race. However, even among those with multiple factors associated with a shorter life, such as having lower mid-career earnings and not having attended college, a significant proportion from our cohort were alive in 2014, when they were in their 70s or early 80s. Taken all together, individuals may live a long time, even individuals with factors associated with lower longevity, such as low income or education. Those who live a long time and have little or nothing in DC account balances or pension benefits may have to rely primarily on Social Security or safety net programs. Analyzing Income, Wealth and Longevity We examined the association of income and wealth with longevity in a nationally representative sample of Americans born from 1931 through 1941. Throughout this analysis, our references to “older Americans” and “households” apply to that specific subset of older Americans born from 1931 through 1941 and their households. The Health and Retirement Study (HRS) began in 1992 and first surveyed these individuals when they were 51 to 61 years old. The same individuals have been re-interviewed every 2 years since, provided they continued to participate in the survey, and the most recent complete data is from 2014, when those who were still alive were 73 to 83 years old. We were able to measure deaths over a period of 22 years (1992 through 2014). Every 2 years, the HRS attempted to measure whether the original respondents were still alive, but these longevity data were incomplete because some of the original respondents declined to participate in later waves of the survey. Once these respondents left the survey, their actual longevity could not be followed. Therefore, we used survival analysis to estimate the proportion of individuals in the1992 sample alive in 2014. Survival analysis accounts for survey respondents with complete or incomplete longevity data and allowed us to estimate the chance of death by any given time in the observation period. Most importantly, our analysis assumed actual longevity from 1992 to 2014 of the individuals in our analysis did not have a systematic relationship with whether the original HRS respondents continued to participate in the study except that leaving the study implied a later death. We believe this assumption to be reasonable for the purpose of our analysis for two reasons. First, a small percentage (8 percent) of the original respondents dropped out of the survey, so that the impact of any longevity differences among the population who dropped out would likely have been small. Second, while some baseline characteristics of respondents do appear correlated with non-response over time, the population that dropped out of the study does not appear to vary significantly from those completing each wave, except for race and ethnicity. We conducted this analysis, at the individual level, for HRS respondents in 1992, and any spouses or partners also born in 1931 through 1941. Additional details and caveats to this analysis are available in appendix I. We broke the sample into quintiles based on their income or wealth. To determine an individual’s place in the income distribution, we measured mid-career household earnings using administrative records from the Social Security Administration that are linked to the HRS data. Specifically, we defined mid-career household earnings based on average annual earnings reported to the Social Security Administration for years when the survey respondent we identified as the household head was ages 41 to 50 as well as the earnings of their spouse or partner during those years if the respondent was part of a couple in 1992. This measure of earnings provides a relatively stable indicator of the household’s labor market experience, compared to using a single year of earnings, which could be unusually high or low. For wealth, we used the household’s initial net worth in 1992, including any balances in defined contribution accounts or individual retirement accounts, but excluding second homes, which HRS did not consistently capture in all years. In both instances, the sample was broken into quintiles. For additional details on our methodology, see appendix I. Overall, an estimated 63 percent of the individuals in our sample were alive in 2014 (ages 73 to 83), and greater levels of income and wealth were associated with greater longevity in our analysis of HRS data. For income, an estimated 52 percent of individuals from households in the bottom quintile of the mid-career earnings distribution were alive in 2014, compared to an estimated 74 percent of individuals from households in the top quintile. (See fig. 12.) The percentages by wealth quintile were similar. Other researchers have similarly found that greater levels of income and wealth are associated with greater longevity. For example, a researcher at the Social Security Administration has established that men with higher earnings had seen greater gains in longevity than those with lower earnings. Understanding the association among income, wealth, and longevity is complicated because of relationships among the characteristics, as well as their relationships with demographic characteristics (see text box). Besides income and wealth, several demographic characteristics were also associated with longevity in our analysis of HRS data, and these relationships have also been noted in other researchers’ studies. Women tended to live longer than men: Women had greater longevity through 2014, with an estimated 69 percent living to at least ages 73 to 83 compared to an estimated 58 percent of men. Non-Hispanic whites and Hispanics tended to live longer than blacks: For Hispanics, an estimated 68 percent lived to at least 2014, as did an estimated 65 percent of non-Hispanic whites, compared to an estimated 52 percent of non-Hispanic blacks. More educated individuals tended to live longer than those with less education: An estimated 75 percent of college graduates lived to at least 2014, compared to an estimated 65 percent of those who graduated from high school and an estimated 50 percent of those with less than a high school diploma or GED. Individuals who self-reported being in good health tended to live longer than those who reported being less healthy: Among those who self-reported being in excellent health in 1992, an estimated 78 percent lived to at least 2014, compared to an estimated 31 percent of those who reported being in poor health. Income, Wealth, and Demographics Are Interrelated The relationships of income, wealth, and demographics with longevity are complex because of interactions among these characteristics themselves, which make it difficult to determine the direction or extent of causality. For example, there are many potential interactions among educational status, income, and wealth. Higher levels of education could provide access to better job opportunities, increasing income. Education could contribute to greater financial literacy and better financial decision making, increasing wealth. Having access to wealth could make it easier to attain additional education. While income, wealth, and education all are associated with longevity, it is difficult to interpret their individual associations with longevity because of their possible interactions with each other. We estimated that individuals whose households were in the top two quintiles (top 40 percent) of the mid-career earnings distribution were more likely than their counterparts in the bottom 60 percent to be alive in 2014 (ages 73 to 83) in an analysis controlling for race and ethnicity, gender, age, education level, and initial self-reported health status on entry into HRS in 1992. In a similar analysis, we found that individuals from households in the top quintile (top 20 percent) of wealth in 1992 were more likely to be alive than their counterparts in the bottom four quintiles. Our findings are consistent with the work of other researchers who also controlled for such factors. However, such observational studies are only able to demonstrate that a statistical association exists between two characteristics. For example, one study that found a strong association between income and life expectancy specifically notes that unmeasured factors likely affect the association. Similarly, we cannot determine from our analysis the extent to which income or wealth causes differences in longevity. Even among individuals with characteristics associated with decreased longevity, a substantial proportion of older Americans lived at least into their 70s or early 80s, according to our analysis of 1992 to 2014 HRS data. For example, we constructed three scenarios to illustrate how longevity varies for those with different mid-career earnings and education. Among those in the “bottom” scenario–those individuals who had no college education and were from households in the bottom 20 percent of the earnings distribution–an estimated 50 percent were still alive in 2014 (see fig. 13). We estimated that the corresponding percentages for our “middle” scenario and “top” scenario were 65 percent and 80 percent, respectively, of individuals still alive in 2014. Thus, even among those with education and earnings associated with lower longevity, a significant proportion, 50 percent, were still alive in 2014, and these individuals will need to provide for themselves through their remaining years. We also analyzed a subset of our bottom scenario that included those who had no college education and were from households in the bottom 20 percent of the earnings distribution and whose self- reported health status was fair or poor. While the percentage of the individuals who survived was lower, an estimated 39 percent were alive in 2014, which is a substantial proportion. Most individuals have the potential for an unexpectedly long life, including individuals with demographic characteristics associated with lower longevity, income or wealth. In addition, individuals may face major expenses as they age. For example, several experts we spoke with noted that health care costs can pose a particular challenge at older ages. Taken all together, individuals may live a long time and face financial challenges in their later years, including those with less income and wealth. For example, of the individuals in the bottom group of our scenarios illustrating the effects of earnings and education on longevity, an estimated 50 percent were still alive in 2014. Should these individuals not have DC accounts or have little in them, or should they have little to no DB pension benefits, they may have to rely primarily on Social Security (which itself faces financing difficulties) or safety net programs. Using HRS data and following the same households over time, we examined how income and wealth distributions changed and found that, in general, disparities in income decreased while disparities in wealth persisted among a cohort of older Americans as they aged (see text box for more information on our analysis). Households with the top 20 percent of mid-career earnings saw larger drops in income than households in other mid-career earnings groups, decreasing income disparities overall. During the same time period, the amount of wealth held by most households remained steady and wealth disparities persisted. We also found important differences in the distribution of income and wealth among households by race and ethnicity and education level. Analyzing Income and Wealth for Households Over Time We analyzed Health and Retirement Study (HRS) data to estimate how income and wealth distributions changed as a particular cohort of older Americans aged over time. We analyzed income, wealth, and select financial resources for the same group of survey respondents (heads of households) or their spouses or partners who responded to the survey in 1992 and were still alive and responded in 2014, which is the most recent year for which the data are complete. We defined wealth as net worth. Data limitations prevented us from producing estimates of the present value of future income expected from Social Security or defined benefit pensions. The heads of households we analyzed were from the original HRS cohort and were born in 1931 to 1941. If neither the head of household or the spouse or partner interviewed in 1992 was still alive in 2014, their household was not included in our sample. As a nationally representative longitudinal survey, the HRS allows us to follow the same set of Americans from their 50s through the remainder of their lives; these household heads or their spouses or partners had reached their 70s or early 80s by 2014, allowing us to estimate how income and assets changed for the households as they progressed through retirement. We are reporting medians, as our analysis indicated that means were not consistently reliable. Appendix VI contains additional figures examining how assets and income changed for households headed by individuals in HRS’ “War Babies” cohort, who were born from 1942 through 1947. For our analysis, we divided older households in the data into five equally sized quintiles, or earnings groups, based on the number of households and their mid-career household earnings. We defined mid-career household earnings based on earnings reported to the Social Security Administration for years when the survey respondents were ages 41 through 50, as well the earnings of their spouses or partners during those years if the respondents were part of a couple in 1992. For more on our analysis, see appendix I. As described in the textbox above, our analysis included households in which either the head of the household or their spouse or partner were still alive in 2014, and table 1 shows the race and ethnicity and education level of the household head, as well as the composition of the household. As discussed in the previous section, certain demographic characteristics, such as being a minority or being less educated, are associated with a shorter life. However, not everyone with these demographic characteristics will have a shorter life. As the table below shows, there are households in which the head had at least one of these characteristics and lived into his or her 70s or early 80s. We analyzed HRS data and found that household income declined as heads of households born from 1931 through 1941 and their spouses or partners aged, with decreased earnings from work contributing to the decline as people retired. Those households that had the highest mid- career earnings—those in the top earnings group—experienced the largest declines in income from 1992 when the heads of household were ages 51 to 61 to 2014 when the surviving heads of household or their spouses or partners were ages 73 to 83 (see fig. 14). For example, estimated median income for the top earnings group decreased by 53 percent, from about $121,000 in 1992 to about $57,000 in 2014. In comparison, for those with the lowest mid-career earnings—those in the bottom earnings group—estimated median income declined by 36 percent, from about $28,000 to about $18,000 over this same period. The decrease in income disparities may reflect the shift from work-related earnings to Social Security as the largest source of income for households in the top 20 percent, indicating the possible transition from working to retirement. More specifically, in 1992, 94 percent of households in the top mid-career earnings group had work-related earnings, which contributed the largest amount to their income. By 2014, only 25 percent of the top earnings group still had work-related earnings, and Social Security provided the highest median value of all income sources. Among households in the bottom mid-career earnings group, 68 percent had work-related earnings in 1992, and 15 percent continued to have work-related earnings in 2014. Similarly, work-related earnings provided the greatest source of income for these households in 1992, and Social Security provided the highest median value of all income sources for these households in 2014. However, concerns about retirement insecurity for those with lower earnings may remain. Social Security is progressive, meaning it replaces a higher percentage of income for those with lower earnings, but the formula for calculating Social Security benefits provides a higher benefit amount to those with higher lifetime earnings. In addition, those households with higher mid-career earnings maintained relatively higher income in retirement, perhaps due to their having higher levels of other types of non-wage income after retiring. For example, in 2014, a significantly greater percentage of households in the top two earnings groups had income from employer-sponsored retirement accounts compared to those in the bottom earnings groups, although households may not be consistent in how they spend down these funds. We analyzed HRS data from 1992 to 2014—when heads of households were in roughly their 50s to when they were in their 70s or early 80s—and found that for most households, the level of wealth was relatively consistent as they aged, and disparities in wealth persisted over time. As shown in figure 15, wealth remained relatively steady for households in the bottom three mid-career earnings groups over the time period we examined while households in the top two mid-career earnings groups experienced larger fluctuations in wealth. More specifically, households in the top two earnings groups saw their wealth increase overall from 1992 to 2014. However, while wealth increased from 1992 to 2006, this was followed by declines in wealth from 2006 to 2014. Looking at the overall time period of our analysis, wealth disparities persisted between households in the top earnings groups and households in the bottom earnings groups. For example, in 1992, households in the bottom 20 percent had estimated median wealth of about $93,000 while households in the top 20 percent had estimated median wealth of about $432,000, a difference of about $339,000 (or the top had about 4.6 times the median wealth of the bottom). In 2014, households in the bottom 20 percent had estimated median wealth of about $66,000 while households in the top 20 percent had estimated median wealth of about $539,000, a difference of about $473,000 (or the top had about 8.2 times the median wealth of the bottom). Other researchers have found that that some households may not spend down their wealth as much during retirement due to factors including a generally higher propensity to save, a desire to leave bequests, and the desire to self-insure against medical costs. Households in the top 20 percent of mid-career earnings had greater participation in retirement accounts (see sidebar) and increased home equity relative to other households, which may have contributed to wealth disparities over the time period of our analysis. Retirement Accounts. Among households that had retirement accounts, the median value of retirement accounts increased for all of our income groups (see fig. 16); however, the continued wealth disparities between higher- and lower-earning households may be due to significant differences in the value of retirement accounts and in household participation. The value of retirement accounts for households in the top and bottom earnings groups increased substantially between 1992 and 2014 (a 93 percent and 138 percent increase, respectively). Some of the increase in retirement account balances over time may be due to contributions to DC plans and IRAs during years in which individuals worked, as well as waiting until age 70 ½, when many individuals are required to take minimum distributions from their IRAs. Despite this potential for gains in account balances across the distribution, disparities still exist. In 2014, among households that had retirement accounts, we estimated that households in the top 20 percent had about three times more in their retirement accounts compared to households in the bottom 20 percent (about $176,000 compared to about $54,000). Higher-earning households may not spend down their retirement account balances as much in retirement whereas lower-earning households may have spent down all or part of their account balances. In addition to having more in their retirement accounts, a greater percentage of households in the top earnings group had retirement accounts compared to households in the bottom earnings group. For example, in 2014, an estimated 69 percent of households in the top 20 percent had retirement accounts compared to an estimated 19 percent of households in the bottom 20 percent. Home equity. From 1992 to 2014, home equity increased across all mid-career earnings groups for households with home equity; however, households in the top two earnings groups saw greater increases in the value of their home equity compared to households in the bottom two earnings groups (see fig. 17). Over this time period, a greater percentage of households in the top 20 percent had home equity compared to households in the bottom 20 percent. More specifically, from 1992 to 2014, the percentage of households in the bottom 20 percent with home equity ranged from an estimated 61 percent to 70 percent. For the top 20 percent, the percentage of households with home equity ranged from 88 to 94 percent. Despite the recession from 2007 to 2009, which may have caused home values to depreciate, median home equity for households in the top 20 percent that had home equity increased by an estimated 30 percent from 1992 to 2014. At the same time, median home equity for the bottom 20 percent of households with home equity increased by an estimated 14 percent, though this change was not statistically significant. One expert we interviewed also noted recent real estate appreciation as benefiting wealthier retirees. Significant differences in income and wealth associated with race and ethnicity, as well as education levels, continued as households aged, according to our analysis of heads of households and their spouses or partners as they aged from roughly their 50s to their 70s or early 80s using 1992 through 2014 HRS data. Non-Hispanic, white households in the bottom 40 percent of mid-career earnings had higher estimated median incomes, and non-Hispanic, white households across the mid-career earnings distribution generally had greater wealth, than minority households. In terms of income, the gap between non-minority and minority households in the bottom 40 percent persisted even as median income decreased overall for households as they aged. For example, we estimated that, in 1992, non-Hispanic, white households in the bottom 20 percent had about $20,000 more in income than minority households. The income disparity was smaller (about $9,700) in 2014, but still remained. In terms of wealth, non-Hispanic, white households had persistently higher wealth compared to minority households across all levels of the mid-career earnings distribution. For example, among the bottom 20 percent of households, in 1992, non-Hispanic, white households had about $138,000 more in estimated median wealth than minority households. While this difference decreased to about $119,000 in 2014, the wealth difference remained. Similarly, for the top 20 percent of households, in 1992, non-Hispanic, white households had about $170,000 more in estimated median wealth than minority households, and, in 2014, the wealth disparity increased to about $294,000. Households headed by someone with at least some college education generally had higher median incomes and more wealth than households headed by someone who did not attend college. Income disparities existed across the mid-career earnings distribution from 1992 to 2014. For example, we estimated that, in 1992, households in the top 20 percent with heads who attended college had about $44,000 more in income compared to households in the top 20 percent with heads who did not attend college. We estimated that, in 2014, households with heads in the top 20 percent who had attended college still had greater income, though the difference was smaller (about $25,000). Similarly, heads of households in the bottom 20 percent who had attended some college had more income than heads of household who had not. For example, in 1992, households with heads who had attended some college had about $31,000 more in income than households with heads who had not, and that difference decreased to $9,700 in 2014. Wealth disparities generally existed across the mid-career earnings distribution over time. For example, in 1992, households in the top 20 percent with heads who had attended some college had about $166,000 more in estimated median wealth compared to households in the top 20 percent with heads who did not attend college. In 2014, the difference in estimated median wealth between these same groups was about $386,000. Similarly, households in the bottom 20 percent with heads who had attended some college had greater median wealth than households in the bottom 20 percent with heads who had not attended college. For example, we estimated that, in 1992, households in the bottom 20 percent with heads who attended college had about $176,000 more in wealth than heads who had not. In 2014, the difference in median wealth between these groups was about $120,000. Our findings are consistent with those of other researchers, who found that educational attainment was an important determinant of wealth at age 65, and that it was strongly correlated with wealth even after controlling for lifetime earnings. We provided a draft of this report to the Department of Labor, the Department of the Treasury, the Internal Revenue Service, and the Social Security Administration for review and comment. While none of the agencies provided official comments, the Department of Labor and Social Security Administration provided technical comments, which we incorporated as appropriate. As agreed with your office, unless you publicly announce the contents of this report earlier, we plan no further distribution until 30 days from the report date. At that time, we will send copies to the Secretary of Labor, the Secretary of the Treasury, the Commissioner of the Internal Revenue Service, and the Commissioner of the Social Security Administration. In addition, the report will be available at no charge on the GAO website at http://www.gao.gov. If you or your staff have any questions about this report, please contact me at (202) 512-7215 or jeszeckc@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made key contributions to this report are listed in appendix VIII. To determine how growing disparities in the distributions of income and wealth affect older Americans, we examined (1) the distributions of income and wealth among all older Americans over time; (2) the association between income, wealth, and longevity among older Americans; and (3) how the distributions of income and wealth have changed over time for a cohort of individuals as they aged. This appendix provides a detailed account of the data sources used to answer these questions and the analyses we conducted. The appendix is organized into three sections. Section I describes how we reviewed literature relevant to this report’s objectives and provides information on the interviews we conducted. Section II describes the information sources and methods we used to analyze the distributions of income and wealth among all older Americans over time. Section III describes the information sources and methods we used to analyze how income and wealth among older Americans are associated with longevity, and how the distributions of income and wealth changed as a cohort of individuals aged. For the purposes of our analysis, we defined wealth to be a household’s net worth—that is, total assets minus total debt. Net worth is a measure often used by researchers studying retirement security. Older Americans may have other future retirement resources, such as the present value of future income expected from defined benefit (DB) pension plans and Social Security. We supplemented our data analysis with a literature review and interviewed researchers to identify appropriate background information and context. We had two primary methods for identifying literature to include in our literature review: a snowball technique and a database search. To apply the snowball technique, we first identified possible relevant literature by examining the studies cited in our 2016 report examining the relationship between Social Security benefits and longevity. Then we reviewed the citations included in those studies. Finally, we reviewed relevant literature included in a weekly report called “Current Awareness in Aging Report,” produced by the Center for Demography of Health and Aging at the University of Wisconsin-Madison, which includes a comprehensive list of recently issued materials relating to aging, including retirement security. We compiled relevant citations across these sources and analyzed abstracts to identify working papers, journal articles, and reports that required further review. We identified reports for inclusion based on whether they provided insight into the following relationships: As older Americans age, the relationship between wealth and expenses, and income and wealth. For older Americans, how income and/or wealth inequality are (1) related to the topics below and (2) how, if at all, these relationships have changed over time or generations: Rural vs urban locations Role of inequality (income, wealth, longevity) in reliance on federal income security programs among older Americans To complement the snowball technique search, we also conducted a database search. We searched the Proquest database EconLit for scholarly journals and working papers for a 5-year span, from 2013 through 2018, that matched keywords related to our criteria for relevance. We took additional steps to enhance the robustness of our results. We solicited recommendations for literature from GAO stakeholders, agency officials, and contacts at the Congressional Research Service and Congressional Budget Office and added these recommendations to our list for consideration. During interviews with experts, we discussed contrary opinions and findings in the research and requested full citations as needed. We also attended retirement security events and reviewed news clippings for references to contrary opinions or findings in breaking research. Finally, an economist reviewed the methods and reliability of all studies. We included 26 out of 34 articles from the snowball technique search and expert recommendations and an additional 3 out of 160 articles from the database search (the database search identified some of the same articles as the snowball technique search). These 29 articles that best matched our criteria for inclusion were the articles we reviewed. We also identified and interviewed nine researchers whose work was relevant to our objectives and interviewed them in order to identify researchers’ explanations and theories about the relationships between inequality and longevity, health status, gender, education, and race and ethnicity. To select these researchers, we considered their areas of expertise; whether they worked for a federal agency, university, or other type of organization; and their ideological perspective, if known. This section describes the two main data sources we used to analyze trends in the distribution of income and wealth among all older Americans: the Survey of Consumer Finances (SCF) and the Financial Accounts of the United States (FA). To examine the distributions of income and wealth among all older Americans over time, we used 1989 through 2016 data from the SCF. The SCF is a triennial survey of household assets and income from the Board of Governors of the Federal Reserve System (Federal Reserve) and asks households detailed questions about their income—including pension benefits—and assets—including amounts in retirement accounts. The survey also asks about debt and demographic information, among other topics. A different sample of households was used for each year in our analysis. These data allow for comparison of the experiences of same-age households at different points in time. The SCF is conducted using a dual-frame sample design. One part of the design is a standard, multistage area-probability design, while the second part is a special over-sample of relatively wealthy households. This is done in order to accurately capture financial information about the population at large as well as characteristics specific to the relatively wealthy. The two parts of the sample are adjusted for sample nonresponse and combined using weights to make estimates from the survey data nationally representative of households overall. In addition, the SCF excludes people included in the Forbes magazine list of the 400 wealthiest people in the United States. Furthermore, the SCF omits observations that have net worth at least equal to the minimum level needed to qualify for the Forbes list. For example, the 2016 SCF surveyed 6,254 U.S. households and removed six households that had net worth equal to at least the minimum level needed to qualify for the 2016 Forbes list. Over time, the number of households interviewed has expanded (see table 2). We found the SCF to be reliable for the purposes of our report. While the SCF is a widely used federal data source, we conducted an assessment to ensure its reliability. Specifically, we reviewed related documentation and internal controls, spoke with agency officials, and conducted electronic testing. When we learned that particular estimates were not reliable for our purposes, or had sample sizes too small to produce reliable estimates, we did not use them. Nonetheless, the SCF and other surveys that are based on self-reported data are subject to nonsampling error, including the ability to get information about all sample cases; difficulties of definition; differences in the interpretation of questions; and errors made in collecting, recording, coding, and processing data. These nonsampling errors can influence the accuracy of information presented in the report, although the magnitude of their effect is not known. Estimates from the SCF are also subject to some sampling error since, for any given year, the sample is one of a large number of random samples that might have been drawn. Since each possible sample could have provided different estimates, we express our confidence in the precision of the sample results as 95 percent confidence intervals. These intervals would contain the actual population values for 95 percent of the samples that could have been drawn. In this report, we present 95 percent confidence intervals alongside the numerical estimates that were produced using SCF data. All financial figures using the SCF data are in 2016 dollars. We supplemented the SCF data with data from the Financial Accounts of the United States (FA). The FA include data on transactions and levels of financial assets, and liabilities, by sector and financial instrument; balance sheets, including changes in net worth, for households and nonprofit organizations, nonfinancial corporate businesses, and nonfinancial noncorporate businesses; Integrated Macroeconomic Accounts; and additional supplemental detail. These data provide an aggregate estimate of DB pension entitlements (or liabilities, as the FA refer to them), which can be apportioned across SCF respondents (see detailed explanation below). This section describes the analysis that we conducted using the SCF and FA to analyze trends in income and wealth over time for all older Americans. We chose to look at household-level resources because couples may pool their economic resources and the SCF asks some of its questions about resources for households. The Federal Reserve provides the underlying programming code for creating the variables presented in its publications. Where possible, we relied on variable definitions used for Federal Reserve publications using the SCF. For example, we used the race or ethnicity of the household head, defined as either 1) white, non- Hispanic or 2) non-white or Hispanic (which we renamed “minority” for ease of reporting). We also relied on the Federal Reserve’s definitions for net worth, which we refer to as “wealth” in this report; retirement account balances (DC plans and IRAs); income from withdrawals from retirement accounts; and income from Social Security, pension, or disability benefits or annuities. In other cases, we developed our own variables, based on the raw variables described in the SCF codebooks. For example: Older households: households in which the survey respondent or any spouse or partner were aged 55 or older. Household income: estimated total income by adding up all of the individual income components created by the Federal Reserve. Other assets: any other assets that are not retirement accounts, the present value of future income from Social Security or DB pensions, or the value of the household’s primary residence (if one is owned) or vehicles. Other income: any other income coming from a source besides wages; withdrawals from retirement accounts; and Social Security, pension, or disability benefits or annuities. The SCF is a cross-sectional survey, meaning it presents a nationally representative “snapshot” for each survey wave rather than following the same households over time. To create an income distribution, we rank ordered older households by household income and then broke them into five even groups, or quintiles. The “top” refers to the top 20 percent of households in this ranking while the “bottom” refers to the bottom 20 percent of households. We repeated this exercise for each year of the data. While the households included in the SCF are different every survey year, we were able to examine how the distribution of income and wealth across older households changed over time. We used the same method to create wealth distributions, except we rank ordered households by net worth, one measure of wealth, instead of income. To better understand increases in the top quintile, we also estimated the amount of income and wealth held among the top 10 percent, 5 percent, and 1 percent of households, when possible, for each survey year. We also created distributions of income and wealth for other subcategories of older households. As with the analysis for all older households, we broke the subcategory population into quintiles. We estimated distributions of income and wealth for the following subcategories for each survey year: Households in which the head was white and non-Hispanic Households in which the head was a minority Households in which the head attended at least some college Households in which the head did not attend college For all older households, we also estimated the percentage of households in each survey year that had 1) wage income, 2) income from retirement account withdrawals or 3) income from Social Security, pension, or disability benefits or annuities, as well as the amount of income provided by each source. Similarly, we estimated the percentage of older households that had a retirement account (DC or IRA), owned their home, or owned a vehicle, as well as the value of each of these assets. To better understand the importance of these asset types across the wealth distribution, we also estimated the percentage of households that had a retirement account (DC or IRA) with a balance of at least a $100; owned a vehicle worth at least $100; or had home equity of at least $100. We also analyzed the percentage of households with retirement account balances by bands of $50,000. Additional sensitivity analysis included comparing a household’s location in the income distribution to its location in the wealth distribution for each survey year. We found that the vast majority of households were in the same quintile of the income and wealth distributions or were only one quintile apart. Very few households were in the bottom quintile for income and top quintile for wealth or vice-versa. From 1989 through 2016, the percentage of households who fit these two scenarios was always under 1 percent. The literature on retirement adequacy emphasizes the importance of including measures of the value of future DB and Social Security benefits in measures of the wealth distribution. However, the SCF does not provide estimates of the present value of expected future DB and Social Security benefits. As a result, we did a separate analysis to estimate the present value of future income from DB and Social Security benefits using the SCF and FA data from the Federal Reserve, as well as life expectancy data from the Social Security Administration (SSA). In general, our analysis was done for respondents and spouses/partners separately at the individual level, and estimates were combined to create household totals. We generally followed methods presented in an 2016 paper entitled “Is the U.S. Retirement System Contributing to Rising Wealth Inequality?” by Devlin-Foltz, Henriques, and Sabelhaus (see bibliography for the full citation), but made some changes in the assumptions given our specific focus on older Americans. In order to estimate the present value of income expected from DB plans at the household-level, we started with the aggregate value of accrued DB benefits by survey year from the FA. Following Devlin-Foltz et al. (2016), we calculated aggregate DB pension entitlements as the portion of total pension entitlements not found in DC assets and annuities held in IRAs at life insurance companies. Then, we allocated aggregate DB entitlements across households in a series of steps, ultimately splitting the aggregate DB entitlements between SCF respondents who were already receiving benefits and those who were covered by DB plans but were not yet receiving benefits. In the first step of the allocation, we estimated the present value of promised DB benefits for current DB beneficiaries. The present value of promised DB benefits for those already receiving benefits was based on the reported values for DB benefits in the SCF, life tables from SSA, and an assumed 3 percent real discount rate. After solving for the present value of promised DB benefits for those currently receiving benefits, we subtracted the total amount of DB benefits promised to current DB beneficiaries from the aggregate DB assets to solve for the share to be distributed to future DB beneficiaries. By doing this, we effectively assumed that current DB beneficiaries had first claim to DB pension assets. We allocated the remaining DB assets to future DB recipients by assigning each future DB beneficiary a share of the amount of the residual of aggregate DB entitlements (left over after current beneficiaries claimed their share) based on their earnings, the number of years they participated in a DB plan, their expected retirement age as stated in the SCF, and a 3 percent real discount rate. We also estimated the present value of expected future Social Security benefits for current and future Social Security beneficiaries, using information from the SCF on Social Security benefits for current Social Security beneficiaries and earnings information for future Social Security beneficiaries. With respect to current Social Security beneficiaries, we solved for the present value of Social Security benefits using annual Social Security benefits as reported in the SCF, life tables from SSA, and an assumed 3 percent real discount rate, consistent with our DB analysis. For future Social Security beneficiaries, we used current earnings or earnings from the longest job held as reported in the SCF as the basis for the Social Security benefit. Given that our analysis focused on older Americans, we assumed that future Social Security beneficiaries were close enough to retirement that the earnings information in the SCF provided a reasonable proxy for lifetime earnings. We created a monthly average of these earnings, which we used as a simplified version of the average indexed monthly earnings (AIME). We used these thresholds to compute something similar to the primary insurance amount (PIA) by assigning 90 percent of earnings up to the first bend point, 32 percent of earnings between the first and second bend points, and 15 percent of earnings between the second bend point and the monthly taxable maximum. We assumed everyone who was not yet receiving benefits but would in the future started collecting benefits at 62 or at their current age if older than 62. We applied benefit rules associated with each individual’s birth year to the PIA as set by the Social Security Administration and made adjustments for spousal benefits. We estimated the present value of Social Security benefits for future beneficiaries using the estimated PIA, a retirement age of 62 or their current age if older than 62 and not yet receiving benefits, life tables from SSA, and a 3 percent real discount rate. While adding these present value estimates to wealth better captures the totality of resources available to older Americans, our estimates of the present value of income from future DB and Social Security benefits are subject to uncertainty and should be interpreted with caution. For example, our estimates of the present value of DB benefits for future beneficiaries are not based on SCF respondent-reported expected DB benefits. Instead, we used the aggregate DB entitlements in the FA data and allocated that amount across households with DB plans. We followed this method, in part, because it appears that workers do not have a good understanding of their pension plan parameters and confuse DB benefits with other types of payouts in the SCF data, according to Devlin-Foltz et al. (2016). Moreover, our estimates of the present value of Social Security benefits for future beneficiaries are not based on lifetime earnings since the SCF does not collect all of the inputs needed to project Social Security benefits for respondent-families. However, it is possible to get a sense of the distributional impact of Social Security by focusing on those near retirement in certain points in time. A general limitation of our analysis of the present value of future income from DB pensions and Social Security is that our estimates rely on assumptions about life expectancy, real discount rates, and retirement ages, which are unlikely to hold for all households. As a result, we conducted some sensitivity analyses, particularly with respect to real discount rates and retirement ages. For both the DB and Social Security sensitivity analyses, we varied the real discount rate given the uncertainty about future interest rates. In general, higher discount rates result in lower estimated present values, so our estimates of the present value of future DB and Social Security benefits are sensitive to the assumptions about the discount rate. This is especially important in the DB analysis, as changing the assumed discount rate affects the allocation of aggregate DB assets between current and future DB beneficiaries. For example, using a 2 percent real discount rate, as opposed to a 3 percent real discount rate, yielded a higher allocation of aggregate DB assets for current beneficiaries compared to our baseline estimates. Using a 4 percent real discount rate, as opposed to 3 percent, generated a higher allocation of aggregate DB assets for future DB beneficiaries relative to our baseline estimates. For future beneficiaries, we had to make assumptions regarding the respondent and spouse/partner’s retirement age. For the DB analysis, we used the SCF-reported expected retirement age, given that our focus is older Americans, and older people not yet claiming benefits are relatively close to retirement. Given these assumptions, we also did the analysis assuming that all future DB beneficiaries retired at 62 and 65. Assuming different retirement ages can change the amount of the share of aggregate DB assets allocated to individual future DB beneficiaries in the SCF. For the Social Security analysis, we generally assumed that future Social Security beneficiaries retired at 62, in part because a sizeable proportion of people claim Social Security at 62, despite increases in the full retirement age. In addition, according to Devlin-Foltz et al. (2016), assuming a low retirement age decreases the present value of benefits directly if the reductions for early retirement are not actuarially fair, and indirectly if the individual were to keep working at a high enough income to increase their average indexed monthly earnings. Agency officials raised technical concerns about choosing age 62. It is possible that setting the retirement age at 62 may overstate the present value of future Social Security benefits, depending on various factors including interest rates and mortality. We considered using alternative retirement ages and do not believe that choosing a different retirement age for those not yet retired would substantively change our findings. Alternative methods to using present value estimates of future income expected from Social Security and DB pensions for analyzing distributional disparities in retirement security exist. For example, one option would be to evaluate how future monthly income from Social Security and DB pensions would be expected to affect retirement security, perhaps by assessing how the standard of living for workers would be expected to change. Additionally, disparities in health in adulthood could contribute to subsequent disparities in income and wealth at older ages. However, for our analysis, it was useful to estimate the present value of Social Security and DB pensions so we could compare the value of these sources to retirement account balances. In addition, the SCF does not include sufficient data on health to consider its role in income and wealth disparities for this part of our analysis. This section describes the analysis we conducted to determine how the income and wealth of a specific cohort of older Americans were associated with longevity, and how the distributions of income and wealth changed as this cohort aged. For these analyses, we used data from the Health and Retirement Study (HRS), described below. We analyzed data collected through the HRS, a nationally representative survey of older Americans. The HRS is a longitudinal survey, meaning that it follows the same individuals and households over the course of the study, allowing us determine how households’ income and wealth changed over time. HRS is a project of the University of Michigan’s Institute for Social Research that is funded through a cooperative agreement with the National Institute on Aging (U01AG009740). It collects information on individuals over age 50 and, among other things, contains detailed data on their education, marital status, work history, health, assets, and income. When the HRS began in 1992, it consisted of a representative sample of Americans then aged 51-61, which is called the original or core HRS cohort. Since then, several additional cohorts of individuals have been added to the data to maintain representation of the older population, beginning in 1993 with the Asset and Health Dynamics Among the Oldest Old (AHEAD) cohort. Currently, a new cohort of participants aged 51-56 is added to the study every 6 years (see table 3). Respondents are surveyed every 2 years. We analyzed the HRS original cohort for our examinations of the association between longevity, income, wealth, and other factors; and our analysis of how income and assets change as the original HRS cohort aged. We also analyzed how income and assets changed for the War Babies cohort, which includes individuals born from 1942 through 1947. Figures from this analysis are presented in Appendix VI. We used three forms of HRS data: Public-Use HRS data: Most HRS datasets are available for download from the HRS website. For each wave, HRS makes an early release version of the data available prior to the final version. As of June 2019, final release files are available for each wave of the survey from 1992 through 2014, and the 2016 early release file is available. RAND HRS data: Researchers at RAND have created a more user- friendly version of the public-use HRS data (see below for more details). As of June 2019, RAND files are available through the 2014 final release data. Restricted-use HRS data: Some data resources in the HRS are restricted, meaning they are available only under special agreement because they contain sensitive and/or confidential information. For this report, we used restricted data containing earnings records from SSA. We conducted our analysis of the restricted-use files via a virtual desktop environment data enclave made available by the University of Michigan’s Center on the Demography of Aging (MiCDA). RAND, a research organization, cleans and processes the HRS data to create a user-friendly longitudinal dataset that has consistent and intuitive naming conventions and model-based imputations for missing wealth and income data. In most cases, we used the RAND version of the HRS variables due to the greater ease of use and the additional data cleaning already performed. RAND income and wealth variables were given in nominal dollars. We adjusted these variables to real 2016 dollars using the Consumer Price Index for All Urban Consumers. To calculate mortality, we supplemented the RAND files with information from the early release 2016 public use file to the extent that it provided additional information on mortality through 2014. See the data reliability section below for further discussion of the mortality data. We found the HRS variables presented in this report to be sufficiently reliable. We conducted a data reliability assessment of selected variables by conducting electronic data tests, reviewing documentation on the dataset, and reviewing related internal controls. When we learned that particular variables were not sufficiently reliable, we did not use them in our analysis. We selected our analyses to ensure there was sufficient sample size to produce reliable estimates. We produced variance estimates using a statistical technique chosen to account for the sample design of the HRS and adjusted the sample weights to account for potential bias due to the linkage to SSA administrative data, as described below. We identified additional limitations due to the survey responses being self-reported. As such, they are subject to the respondent’s possible errors in reporting specific financial amounts. We measured mortality from 1992 through 2014. Mortality data in the HRS, including an indicator for a respondent’s death in a given survey year and month and year of death, come from matches with the National Death Index or follow-up interviews with surviving family members. There is complete date of death (specifically month and year of death) information for nearly everyone who died prior to 2012. However, for deaths since 2012, the HRS data linked to the National Death Index was not available, which likely lead to more deaths without information on month and year of death. Since the 2012 and 2014 survey years, there has been time to gather death date information from follow up interviews with families, and less than 10 percent of those who died between the 2012 and 2014 survey years had incomplete data on month and year of death. However, in the 2016 survey year early release public use file, we found that a higher proportion of those who died did not have death dates, likely due to the lack of linkage with the National Death Index and a lack of time to follow up with families since the 2016 survey year to find out when survey participants died. As a result, we determined that we had reliable data on mortality through 2014. HRS contains restricted data drawn from SSA administrative sources for participants who have provided explicit consent to link their responses to administrative data and subsequently were successfully linked with the administrative data. It is possible that respondents who were linked may differ in systematic ways from respondents who were not linked, which would affect the generalizability of estimates derived solely from the subset of participants who were linked. The survey weights provided with HRS data account for the complexity of the survey design (e.g., oversamples of minorities and Floridians), nonresponse, and post- stratification adjustments for demographic distributions, but do not adjust for the administrative linkage. There is evidence that in at least some waves of the survey, there are modest but statistically significant differences in linkage rates on characteristics including race, income, and wealth. One technique to address this potential source of bias is to adjust the sample weights used in variance estimation for observed differences between those with and without linked administrative data. Kapteyn et al. suggest a technique for computing inverse probability weights to account for these differences. Following this technique, HRS has computed a set of weights that account for consent to SSA administrative linkage, but only for the 1992, 1998, and 2004 survey waves. However, this report needed adjusted household weights for all 12 waves and adjusted respondent weights for wave 1. We opted to address the potential non- linkage bias using a logistic model-based propensity score adjustment, rather than a weighting class adjustment for several reasons. First, we had the benefit of many variables with which to model the propensity of non-linkage. Second, weighting class adjustments, which involve creating mutually exclusive classes based on the variables associated with non- linkage, were not feasible because of the large number of variables we included in the adjustment. The number of respondents per cell would be too small. Third, the propensity score adjustment allows us to consider many variables at the same time. Finally, the propensity score adjustment allows us to rank respondents, rather than assume that the characteristics used in a weighting class adjustment would perfectly predict non-linkage. We compared estimates and standard errors obtained using the original weights to the non-linkage adjusted weights. The adjusted weights changed estimates and their standard errors in generally small amounts, but did not affect observed trends in this report. For instance, the median absolute value of the change was less than 1 percent for estimates of median household income for individuals by mid-career earnings quintiles from 1992 to 2014. The median absolute value of the change was 5.7 percent for the standard errors of those estimates. We used the balanced repeated replication method to estimate standard errors for the income and wealth statistics we reported using HRS because the income and wealth statistics were quantiles (i.e., medians). The standard Taylor series (Woodruff) variance estimation method assumes that quantiles can be expressed as a smooth function in the sample and population. However, quantile functions are not considered smooth. After ruling out Taylor series method, we explored replication methods such as jackknife, bootstrap, and balanced repeated replication. Of those, the balanced repeated replication is most suited for the two primary sampling units per stratum design of the HRS. The Fay adjustment stabilizes the estimates across strata when using the normal balanced repeated replication method. This adjustment is particularly relevant for smaller samples. The literature we reviewed suggested that the jackknife produces a poor estimate of the variance of quantiles (Lohr 2009 and Judkins 1990) and that the bootstrap requires more computations than balanced repeated replication. For our analyses, we wanted to classify HRS respondents into income groupings based on a relatively stable measure of income that uses multiple years of administrative data, to reduce measurement error in self- reported survey data and to reduce the chance of basing the income grouping on a single year of unusually low or high income. Several limitations prevent us from classifying households based on their full lifetime income from all sources. HRS does not contain administrative data on income sources besides earnings and Social Security benefits. Moreover, for years before 1978, the administrative earnings records are only available for earnings covered by Social Security and below the taxable maximum. Finally, not all sources of earnings are covered by Social Security. While around 96 percent of employment is currently covered by Social Security, this has not always been the case. In particular, successive expansions of coverage in the 1950s and 1960s greatly increased the proportion of the workforce covered by Social Security, such that relying on SSA earnings records going back to 1951 would underestimate the earnings of large numbers of older HRS participants. Thus, for our analysis, we constructed earnings groupings based on a measure of “mid-career” earnings, based on a household’s average annual reported earnings when the household head was age 41 to age 50. Earnings tend to peak (and remain relatively stable) for workers in their mid-40s through their early 50s. We begin measuring earnings at age 41 to avoid using data prior to expansions of Social Security coverage and to minimize our reliance on imputed earnings above the taxable maximum. In the early years of the study, HRS sought retrospective consent for administrative data linkages. As a result, some participants who only provided consent for the administrative linkage during their initial interview and did not provide consent in subsequent interviews did not have earnings records after age 50. Therefore, we set age 50 as the upper bound for our measure of mid-career earnings. Our goal was to determine how income, wealth, and other demographic and health-related factors are associated with the longevity of older Americans over age 50 in the original HRS cohort. We measured the proportion of original HRS participants still alive at the end of the survey to examine how longevity varied across the income and wealth distributions, as well as across different demographic and health-related variables, including race, educational attainment, gender, and self- reported health status at the beginning of the survey. In order to examine these relationships, we used data from the original HRS cohort to measure deaths over a maximum of 22 years (1992 through 2014). Every 2 years, the HRS attempted to measure whether the original respondents were still alive, but these longevity data were incomplete because some of the original respondents declined to participate in later waves of the survey. Once these respondents left the survey, their actual longevity could not be followed. This incomplete measurement of longevity is generally known as “censored data” in statistics. Special methods of “survival analysis” are required to avoid making inaccurate conclusions about actual longevity from this type of data, when the analyst can only measure longevity up to a certain time before death. Survival analysis accounts for survey respondents with complete or incomplete longevity data. Without making this distinction, ordinary statistical methods, such as linear regression models of the observed longevities, would not include the correct sample of respondents when estimating the chance that a respondent would die at any time within the observation period. In addition, ordinary methods would incorrectly treat the longevities observed in the observation period as actual longevities, when some of them are the shorter, censored longevities observed before the respondents dropped out of the study. Survival analysis methods correct for this problem, in order to reliably estimate the chance of death by any given time in the observation period. Most importantly, our analysis assumed actual longevity during the observation period did not have a systematic relationship with whether the original HRS respondents continued to participate in the study except that leaving the study implied a later death (“noninformative censoring”). In other words, participants with censored and actual longevities did not systematically differ in ways that affected longevity or the variables associated with it. We believe this assumption to be reasonable for the purpose of our analysis for two reasons. First, a small percentage (8 percent) of the original respondents dropped out of the survey, so that the impact of any longevity differences among the population who dropped out would likely have been small. Second, while some baseline characteristics of respondents do appear correlated with non-response over time, the population that dropped out of the study does not appear to vary significantly from those completing each wave, except for race and ethnicity. In our survival analysis, the dependent variable was composed of two parts, including the time in months to death and whether death was observed during the survey period. In general, we used continuous time survival models, including Kaplan-Meier and Cox proportional hazards regression models to estimate survival functions, which estimate the probability of surviving (or dying) up to the end of the survey period, and hazard functions, which estimate the probability of death, per time unit, given that an individual has survived up to that point in time. We used the Kaplan-Meier method to estimate survival probabilities as a function of time and to obtain univariate statistics on survival for different groups. For example, we estimated the percentage of survivors during the survey period across income and wealth quintiles. We also estimated survivorship across the demographic and health-related variables. Moreover, using the Cox proportional hazards regression models, we analyzed the relationship between income and longevity and wealth and longevity, controlling for related demographic and health-related variables, as well as age at the beginning of the survey. These regressions allow the relationships between various characteristics and death to be described as hazard ratios. For example, hazard ratios that are statistically significant and greater than 1.00 indicate that individuals with those characteristics are more likely to die during the survey period compared to a reference group. Hazard ratios that are statistically significant and less than 1.00 indicate that individuals with those characteristics are less likely to die in the study period compared to a reference group. We estimated survivorship among individuals with the following characteristics in combination: bottom income (earnings) quintile and no college; middle of the income (earnings) distribution (third quintile) and high school diploma or some college (excluding GED); and top of the income (earnings) distribution and college diploma. We then ran a subset of these scenarios using different combinations of self-reported health status for each of the three main scenarios. For example, we estimated survivorship among individuals in the bottom income (earnings) quintile, who had not attended college, and reported being in fair or poor health in 1992. Our results have limitations and should be interpreted with caution. Results from the survival analysis present correlations, not causal estimates. Moreover, while our main analysis includes self-reported health status at the beginning of the study period, we also excluded this variable as a sensitivity check given the interconnectedness of income, wealth, and health and the conclusions were similar. Furthermore, due to limitations with respect to the mortality data in later years of the HRS, we did not have specific months and years of death for 60 respondents we know died during the observation period due to death indicators in the interview status variables from HRS. As a result, we imputed their death dates based on the survey year they were indicated to have died in from the HRS interview status questions. While death is continuous in the sense that it can happen to any person at any time, we only observe death within a given month for those with death dates in the data, and only within a year for those whose death information we gathered for the interview status variables. As a sensitivity check, we redid the analysis using survival information at the person-year level and discrete survival analysis techniques and found similar results. This section describes how we used the HRS to determine how the distributions of income and wealth change as older Americans in the original HRS cohort aged. We focused this analysis on the original HRS cohort (born 1931-1941). This cohort entered the study in 1992 at ages 51-61 and had reached their 70s or early 80s by 2014, allowing us to analyze how income and assets changed as these households progressed through retirement. We conducted our analysis and reported results at the household level because couples may pool financial resources or co-own assets. Also, RAND HRS variables on income and wealth are presented at the household level. When necessary, we combined respondent and spouse or partner level variables we used from the public-use file in order to obtain household-level variables. We restricted this analysis to survey respondents (“household heads”), or any spouses or partners, who were still alive in 2014 to ensure we followed the same group of people throughout our analysis. We grouped households into five earnings groups based on their mid-career earnings, as described above. Our primary goal was to examine how the distribution of income and wealth changed over time for households in the original HRS cohort, based on their mid-career earnings groups. We also examined how specific sources of income and wealth changed over time. We also wanted to determine how these trends varied based on household demographic characteristics, including race and ethnicity and education level, without attempting to ascribe causality. Our analysis included survey respondents (heads of households) or their spouses or partners who responded to the survey in 1992 and were still alive and responded in 2014, which is the most recent year for which the data are complete. The heads of households we analyzed were from the original HRS cohort and were born in 1931 to 1941. If neither the head of household or the spouse or partner interviewed in 1992 was still alive in 2014, their household was not included in our sample. In order to do so, we estimated median levels of household wealth and income every 2 years for each earnings group, as well as median levels for specific sources of income and wealth. We estimated the percentage changes and absolute changes in median wealth and income for each earnings group from 1992 through 2014 in order to determine whether income or wealth levels increased or decreased over time. For specific sources of income and wealth, we estimated medians for all households in each earnings group as well as for only those households which reported having the specific source of income or wealth. For example, we determined the median home equity for all households in each earnings group as well as the median home equity for only those households with home equity for each earnings group. Finally, we calculated the percent of our sample having each type of wealth and income (e.g. home equity, Social Security benefits) for each year in the data. As a sensitivity check, we also analyzed how total assets and income changed for the HRS’s “War Babies” cohort (born 1942-1947). For this analysis, we report 99 percent confidence intervals alongside the percentage or other numerical estimates. We chose to use this level of confidence to account for the use of imputation in the RAND HRS data in addition to the sampling error that using survey data introduces. All financial figures using the HRS data are in 2016 dollars. This appendix compares the top 1 percent of the wealth distribution of older households to several other groups in this distribution: (1) the next 19 percent, (2) the top 20 percent, (3) the bottom 80 percent, and (4) the bottom 20 percent. These comparisons provide context for the financial security of the top 1 percent relative to other households at the top of the wealth distribution, the remainder of the wealth distribution, and households at the bottom of the distribution, respectively. To draw these comparisons, we used 2016 data from the Survey of Consumer Finances, a triennial, cross-sectional survey produced by the Board of Governors of the Federal Reserve System. A different sample of households was used for each year in our analysis. These data allow for comparison of the experiences of same-age households at different points in time. We chose to look at household-level resources because couples may pool their economic resources, and the SCF asks some of its questions about resources for households. We conducted our analysis for older households, which were defined as those in which the household head or any spouse or partner were ages 55 or older. We defined wealth as net worth, or assets minus debt. Because the sample size for the top 1 percent is small, we presented dollar values rounded to thousands of 2016 dollars. dollars) Estimated median value (2016 dollars) Vehicle(s) All other assets Debt n/a Not available. There were insufficient data to produce a reliable estimate of median debt. Financial resource Retirement account(s) Home Vehicle(s) loans, lines of credit, and credit card balances after the last payment. Financial resource Retirement account(s) Estimated median value (2016 dollars) Home Vehicle(s) All other assets n/a Not available. There were insufficient data to produce a reliable estimate of median debt. loans, lines of credit, and credit card balances after the last payment. Financial resource Retirement account(s) Home Vehicle(s) loans, lines of credit, and credit card balances after the last payment. Financial resource Retirement account(s) Home Vehicle(s) Estimated median value (2016 dollars) Financial resource Retirement account(s) Home Vehicle(s) 95 percent confidence interval lower bound 434,000 95 percent confidence interval upper bound 556,000 n/a Not available. There were insufficient data to produce a reliable estimate of median debt. loans, lines of credit, and credit card balances after the last payment. Financial resource Retirement account(s) Home Vehicle(s) mortgages, loans, lines of credit, and credit card balances after the last payment. Financial resource Retirement account(s) Home Vehicle(s) mortgages, loans, lines of credit, and credit card balances after the last payment. Estimated median value (2016 dollars) Financial resource Retirement account(s) Home Vehicle(s) Financial resource Retirement account(s) Home Vehicle(s) mortgages, loans, lines of credit, and credit card balances after the last payment. Financial resource Retirement account(s) Home Vehicle(s) mortgages, loans, lines of credit, and credit card balances after the last payment. Estimated median value (2016 dollars) Financial resource Retirement account(s) Home Vehicle(s) 95 percent confidence interval lower bound 0 0 3,000 1,000 95 percent confidence interval upper bound 0 0 4,000 1,000 n/a Not available. There were insufficient data to produce a reliable estimate of median debt. mortgages, loans, lines of credit, and credit card balances after the last payment. This appendix contains additional results from our survival analysis, as shown in the tables below. Appendix V: 2014 Population in the Health and Retirement Study (HRS) This appendix compares the demographic characteristics, as of 2014, of the HRS sample we used in our analysis. This appendix contains estimates of income and wealth for households, where the heads of households were born from 1942 through 1947. The Health and Retirement Study (HRS) refers to this cohort as the “War Babies” cohort. In addition to the contact named above, Michael Collins (Assistant Director), Jennifer Gregory (Analyst-in-Charge), Garrick Donnelly, Kathleen McQueeney, Chris Wickham, and Christopher Zbrozek made key contributions to this report. Also contributing to this report were Susan Aschoff, James Bennett, Deborah Bland, Melinda Bowman, Nisha Hazra, Kirsten Lauber, Jeffrey Miller, Oliver Richard, Amrita Sen, Joseph Silvestri, Jeff Tessin, Frank Todisco, Adam Wendel, and Sirin Yaemsiri. Auerbach, Alan J., Laurence J. Kotlikoff, and Darryl R. 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