Retirement Security: Income and Wealth Disparities Continue through Old Age

Income and wealth inequality in the United States have increased over the last several decades. At the same time, life expectancy has been rising, although not uniformly across the U.S. population. Taken together, these trends may have significant effects on Americans' financial security in retirement.

GAO was asked to examine the distribution of income and wealth among older Americans, as well as its association with longevity, and identify the implication that these trends may have on retirement security. This report examines (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 changed over time for a cohort of individuals as they aged. To conduct this work, GAO analyzed data from two nationally representative surveys: the SCF, using data from 1989 through 2016, and the HRS. GAO used 1992 through 2014 HRS data linked to earnings records from the Social Security Administration. While preliminary 2016 HRS data are available, GAO used 2014 data, which contain more complete information for GAO's analysis. GAO also reviewed studies and interviewed researchers to further analyze the relationships between income, wealth, longevity, and retirement security.

Disparities in income and wealth among older households have become greater over the past 3 decades, according to GAO's analysis of Survey of Consumer Finances (SCF) data. GAO divided older households into five groups (quintiles) based on their income and wealth. Each year of data in the analysis, and, thus, each quintile, included different sets of households over time. Average income and wealth was generally higher over time (see fig. 1 for average income), disproportionately so for the top quintile (top 20 percent). For example, in 2016, households in the top quintile had estimated average income of $398,000, compared to about $53,000 for the middle quintile and about $14,000 for the bottom quintile. GAO also found that for quintiles with lower wealth, future income from Social Security and defined benefit pensions provide a relatively significant portion of resources in retirement for those who expect such income.

A substantial number of older Americans born from 1931 through 1941 lived at least into their 70s or early 80s, according to GAO's analysis of data from the Health and Retirement Study (HRS), a nationally representive survey which follows the same individuals over time. GAO divided individuals born from 1931 through 1941 into quintiles based on their mid-career household earnings using records from the Social Security Administration. GAO's analysis, as well as that of other researchers, shows that differences in income, wealth, and demographic characteristics were associated with disparities in longevity. However, even with these disparities, we found a substantial number of people in the sample were alive in 2014, including those with characteristics associated with reduced average longevity, such as low earnings (see fig. 2) and low educational attainment. Taken all together, individuals may live a long time, even individuals with factors associated with lower longevity, such as low income or education. Those with fewer resources in retirement who live a long time may have to rely primarily on Social Security or safety net programs.

GAO's analysis of HRS data also found that disparities in household income decreased while disparities in wealth persisted as a cohort of older Americans aged from approximately their 50s into their 70s or early 80s. Income disparities decreased between higher- and lower-earning households because higher-earning households saw larger drops in income over time, indicating the possible transition from working to retirement. For example, we estimated median income for the top mid-career earnings group decreased by 53 percent while estimated median income for the bottom earnings group decreased by 36 percent over the same period. Wealth remained relatively steady for households in the bottom three earnings groups over the time period GAO examined, while households in the top two earnings groups experienced larger fluctuations in wealth. GAO estimated that median retirement account balances and median home equity increased across earnings groups for households that had these assets. However, the continued wealth disparities may be due to significant differences in the median value of retirement accounts and home equity between higher- and lower-earning households. GAO also found that white households in the bottom two earnings groups had higher estimated median incomes, and white households in all earnings groups generally had greater estimated median wealth, than racial minority households in those earnings groups. In addition, within each earnings group, households headed by someone with at least some college education generally had higher median incomes and wealth than households headed by someone who did not attend college.

Income and wealth inequality in the United States have increased over  several decades. While income inequality in the United States was  relatively stable from the 1940s to the 1970s, since then wage growth at  the top of the income distribution has outpaced the rest of the distribution,  and inequality has risen. Wealth has become increasingly concentrated  as well. By 2013, those families in the top 10 percent of the wealth  distribution held 76 percent of the wealth held by all families in the United  States.  Inequality among older Americans, specifically, is an area of  concern for some policy makers and researchers, particularly given  trends related to the U.S. retirement system over this same time period.  For example, average life expectancy has increased. This is a positive  development, but it also requires more planning and saving to support  more years in retirement. Further, income, wealth, and longevity are each  interconnected with one another. For example, life expectancy has not  increased uniformly across all income groups, and people who have  lower incomes tend to have shorter lives than those with higher incomes.  There is concern among some researchers and policy makers that  disparities in income, wealth, and life expectancy may be indicative of  potential problems for many Americans’ financial security in retirement.

You asked us to examine the distribution of income and wealth among  older Americans and identify the implication of these trends, along with  associations with longevity, on retirement security. This report examines  (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.

To examine the distribution of income and wealth among all older  Americans over time, we used 1989 through 2016 data from the Survey  of Consumer Finances, a triennial, cross-sectional survey produced by  the Board of Governors of the Federal Reserve System (Federal  Reserve). 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. For each survey year, we examined the distribution of  income and wealth for older households as a whole and by household  heads’ race and ethnicity, marital status and gender, and education level.  We defined older households as those in which the household head or  any spouses or partners were aged 55 or older. We also analyzed the  percentage of households that held various sources of income and wealth  and the amounts of such sources across the income and wealth  distributions.

Lastly, we used these data, supplemented by data from the Financial  Accounts of the United States–another data source published by the  Federal Reserve–to estimate the present value of future income expected  from defined benefit (DB) pension plans and Social Security. To do so,  we followed methods developed by economists at the Federal Reserve,  with some modifications to the Social Security methods, in particular, to  meet the purposes of our analysis.  Alternative methods of 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 of how income and wealth  are distributed across older Americans over time, 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.

To examine the association between income, wealth, and longevity  among a cohort of older Americans, we used 1992 through 2014 data  from the Health and Retirement Study (HRS), a nationally representative,  longitudinal survey that follows the same set of Americans from their 50s  through the remainder of their lives. Use of a longitudinal survey allows us  to follow changes for specific individuals as they age. We analyzed data  for the cohort of individuals born from 1931 through 1941.  We identified  the distribution of income across these individuals by constructing a  measure of mid-career earnings. This measure was constructed at the  household level and was based on the household’s average annual  reported earnings when the household head was aged 41 to 50.  Household earnings data came from administrative records from the  Social Security Administration linked to survey responses.

We then analyzed how the longevity of these individuals varied across  mid-career household earnings and demographic characteristics, such as  race and education level, using a technique called survival analysis. 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. 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.

We also used the HRS data and the mid-career household earnings  measure to compare trends in the distributions of income and wealth, at  the household level, as the cohort aged. We restricted this analysis to  survey respondents (“household heads”), or any spouses or partners as  of 1992, who were still alive in 2014 to ensure we followed the same  group of people throughout our analysis. This analysis included an  examination of trends by demographic characteristics and by specific  sources of income and wealth.

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. As mentioned  above in our summary of how we examined the distribution of income and  wealth over time, older Americans may also have other future retirement  resources, not included in net worth, such as the present value of future  income expected from defined benefit (DB) pension plans and Social  Security. For all three questions, we supplemented analyses with expert  interviews and a literature review to provide greater insight. We  specifically identified researchers’ explanations and theories about the  relationships between inequality and longevity, health status, gender,  race and ethnicity, or education.

For all of the datasets used in our study, we reviewed documentation,  interviewed or obtained information from officials responsible for the data,  and tested the data for anomalies. We determined that these data are  sufficiently reliable for the purposes of this report. To provide additional  context on the relationships among income, wealth, longevity, and  retirement security, we reviewed 29 studies. The bibliography at the end  of this report lists these studies, as well as other recent studies, that  informed this report. We also reviewed relevant federal laws and  regulations. See appendix I for more detailed information about our scope  and methodology.

We conducted this performance audit from August 2017 to August 2019  in accordance with generally accepted government auditing standards.

Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.

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.

Increases in the Number of Older Americans

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 Increased Among Older Households Even As More Households Had Retirement Accounts

Disparities Increased from 1989 to 2016, with Households in the Top 20 Percent Generally Having Disproportionately Higher Income and Wealth in 2016

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 Disparities by Demographic Characteristics

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.

Percentage of Older Households with Retirement Accounts Has Increased Since 1989, Although Non-Retirement Assets Remain Important

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 Are Living Into Their Seventies or Early Eighties, Which May Have Implications for Retirement Security

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.

While Income Disparities Declined As a Cohort of Older Americans Aged and Worked Less, Disparities in Wealth Persisted

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.

Income Disparities Decreased Overall as Higher-Earning Households in Our Cohort saw Drops in Income

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.

Wealth Remained Steady for Most Households in Our Cohort, and Disparities Persisted

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.

Race and Ethnicity and Education Were Factors in Persistent Income and Wealth Disparities As Households in Our Cohort Aged

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.

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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.

Section I: Literature Review and Interviews

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:

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.

Section II: Analyzing Trends over Time in the Distribution of Income and Wealth among All Older Americans

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.

Estimating the Present Value of Social Security and Defined Benefit Pension Benefits

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.

Section III: Analyzing Income and Wealth: How it Changes as Older Americans Age and Associations with Longevity

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.

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.

Mid-Career Household Earnings Measure Construction

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.

Analyzing the Association Among Income, Wealth, Longevity, and Other Variables

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.

Analyzing How Income and Wealth Change as Older Americans Aged

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.

Appendix II: Financial and Demographic Characteristics across the Wealth Distribution

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.

By race and ethnicity of household head:

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)

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)

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.

mortgages, loans, lines of credit, and credit card balances after the last payment.

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.

Appendix IV: Additional Survival Analysis Results

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)

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.

Appendix VI: Estimated Income and Wealth for War Babies Cohort

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.

Appendix VII: GAO Contact and Staff Acknowledgments

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.

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Gustman, Alan, Thomas Steinmeier, and Nahid Tabatabai. “Distributional  Effects of Means Testing Social Security: Income Versus Wealth.” NBER  Working Paper Series, no. 22424 (2016).

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Olshansky, S. Jay, Toni Antonucci, Lisa Berkman, Robert H. Binstock,  Axel Boersch-Supan, John T. Cacioppo, Bruce A. Carnes, Laura L.  Carstensen, Linda P. Fried, Dana P. Goldman, James Jackson, Martin  Kohli, John Rother, Yuhui Zheng, and John Rowe. “Differences in Life  Expectancy Due to Race and Educational Differences Are Widening, and  Many May Not Catch Up.” Health Affairs, vol. 31, no. 8 (2012).

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Saez, Emmanuel and Gabriel Zucman. “Wealth Inequality in the United  States Since 1913: Evidence from Capitalized Income Tax Data.” The  Quarterly Journal of Economics, vol. 131, no. 2 (2016).

Waldron, Hilary. “Mortality Differentials by Lifetime Earnings Decile:  Implications for Evaluations of Proposed Social Security Law Changes.”  Social Security Bulletin, vol. 73, no. 1 (2013).

Waldron, Hilary. “Trends in Mortality Differentials and Life Expectancy for  Male Social Security-Covered Workers, by Socioeconomic Status.” Social  Security Bulletin, vol. 67, no. 3 (2007).

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Retirement Security: Most Households Approaching Retirement Have  Low Savings, an Update. GAO-19-442R. Washington, D.C.: Mar. 26,  2019.

The Nation’s Retirement System: A Comprehensive Re-evaluation  Needed to Better Promote Future Retirement Security. GAO-19-342T.  Washington, D.C.: Feb. 6, 2019.

The Nation’s Retirement System: A Comprehensive Re-evaluation Is  Needed to Better Promote Future Retirement Security. GAO-18-111SP.  Washington, D.C.: Oct.18, 2017.

Older Workers: Phased Retirement Programs, Although Uncommon,  Provide Flexibility for Workers and Employers. GAO-17-536. Washington,  D.C.: June 20, 2017.

Retirement Security: Low Defined Contribution Savings May Pose  Challenges. GAO-16-408. Washington, D.C.: May 5, 2016.

Retirement Security: Shorter Life Expectancy Reduces Projected Lifetime  Benefits for Lower Earners. GAO-16-354. Washington, D.C.: Mar. 25,  2016.

Retirement Security: Better Information on Income Replacement Rates  Needed to Help Workers Plan for Retirement. GAO-16-242. Washington,  D.C.: Mar. 1, 2016.

Retirement Security: Most Households Approaching Retirement Have  Low Savings. GAO-15-419. Washington, D.C.: May 12, 2015.

Retirement Security: Trends in Marriage and Work Patterns May Increase  Economic Vulnerability for Some Retirees. GAO-14-33. Washington,  D.C.: Jan. 15, 2014.

Retirement Security: Women Still Face Challenges. GAO-12-699.  Washington, D.C.: July 19, 2012.

Unemployed Older Workers: Many Experience Challenges Regaining  Employment and Face Reduced Retirement Security. GAO-12-445.  Washington, D.C.: Apr. 25, 2012.

Income and wealth inequality in the United States have increased over the last several decades. We looked at whether these trends continue for older Americans as they age.

We compared income and wealth for all older households from 1989 through 2016 and found households in the top 20% saw disproportionately greater gains than other households.

We also looked at income and wealth for a group of older Americans as they aged. We found disparities in income decreased, possibly due to the transition from working to retirement. Disparities in wealth increased, perhaps because of significant differences in the size of some assets, such as home equity.