The Growing Gap in Life Expectancy by Income: Recent Evidence and Implications for the Social Security Retirement Age

May 12, 2017 – July 6, 2021 R44846

The Growing Gap in Life Expectancy
by Income: Recent Evidence and Implications
for the Social Security Retirement Age
Updated July 6, 2021
Congressional Research Service
https://crsreports.congress.gov
R44846

The Growing Gap in Life Expectancy by Income

Summary
Life expectancy is a population-level measure that refers to the average number of years an
individual wil live. Although life expectancy has general y been increasing over time in the
United States, with a notable exception for the period of the COVID-19 pandemic, researchers
have long documented that it is lower for individuals with lower socioeconomic status (SES)
compared with individuals with higher SES. Recent studies provide evidence that this gap has
widened in recent decades. For example, a 2015 study by the National Academy of Sciences
(NAS) found that for men born in 1930, individuals in the highest income quintile (top 20%)
could expect to live 5.1 years longer at age 50 than men in the lowest income quintile. This gap
has increased significantly over time. Among men born in 1960, those in the top income quintile
could expect to live 12.7 years longer at age 50 than men in the bottom income quintile. This
NAS study finds similar patterns for women: the life expectancy gap at age 50 between the
bottom and top income quintiles of women expanded from 3.9 years for the 1930 birth cohort to
13.6 years for the 1960 birth cohort.
Gains in life expectancy are general y heralded as good news by lawmakers and others, signifying
improved wel -being in the population. Yet widening differentials in life expectancy are more
troubling. Congress may be interested in recent research on this topic for many reasons, including
the implications for Social Security benefits as wel as Social Security reform proposals.
Social Security provides monthly benefits to retired and disabled workers and their dependents,
and to dependents of deceased workers. A key goal of the Social Security program is
redistribution of income from the high earner to the low earner by way of a progressive benefit
formula. Widening gaps in life expectancies by SES pose a chal enge to meeting this goal. When
Social Security benefits are measured on a lifetime basis, low earners, who show little to no gains
in life expectancy over time, are projected to receive increasingly lower benefits than those with
high earnings. For instance, in the 2015 NAS study, men in the lowest earnings quintile saw little
or no improvement in the value of their lifetime Social Security retirement benefits between the
1930 and 1960 birth cohorts (roughly $125,000 in 2009 dollars in lifetime benefits for both birth
cohorts). Due to gains in life expectancy for higher earners, however, men in the highest earnings
quintile born in 1930 had lifetime Social Security benefits of $229,000, and men in the highest
earnings quintile born in 1960 had estimated lifetime benefits of $295,000. Thus, according to
this 2015 NAS analysis, differential gains in life expectancy increased the disparity in the lifetime
value of Social Security retirement benefits between the top and bottom earnings quintiles by
about $70,000 (in 2009 dollars) for the later birth cohort.
In response to rising life expectancy, some commonly discussed Social Security reform proposals
involve increasing the retirement age. These proposals would affect low earners
disproportionately (i.e., reductions in their lifetime Social Security benefits would be
considerably larger than for high earners). Congress may be interested in policy proposals that
mitigate the uneven effects of increasing the retirement age and protect the interests of lower-
earning, shorter-lived workers.
This report provides a brief overview of the concept of life expectancy, how it is measured, and
how it has changed over time in the United States. While life expectancy may be studied in a
variety of contexts, this report focuses on the link between life expectancy and SES, as measured
by lifetime income. In particular, this report synthesizes recent research on (1) the life expectancy
gap by income and (2) the relationship between this gap and Social Security benefits. Final y, this
report discusses the implications of this research for one type of Social Security reform proposal:
increasing the Social Security retirement age.
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The Growing Gap in Life Expectancy by Income

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The Growing Gap in Life Expectancy by Income

Introduction
Demographers have established that the rich live longer, on average, than the poor. In recent
years, a substantial body of research has also demonstrated that the gap in average life expectancy
between the rich and the poor is growing significantly. For example, a 2015 study by the National
Academy of Sciences (NAS)1 finds that among male workers born in 1930, those in the bottom
lifetime earnings quintile can expect to live to age 77, on average, while male workers in the top
quintile can expect to live to 82. For the later 1960 cohort, this same study estimates that male
workers in the bottom quintile show no gains in life expectancy as compared with those born
three decades earlier, while men at the top quintile of lifetime earnings can expect to live more
than seven years longer, to age 89.2
Current interest in the growing gap in life expectancy by income has been fueled by several high-
profile studies on this issue. In addition to the NAS work (The Growing Gap in Life Expectancy
by Income: Implications for Federal Programs and Policy Responses, 2015), in 2016 the
Government Accountability Office (GAO; Retirement Security: Shorter Life Expectancy Reduces
Projected Lifetime Benefits for Lower Earners), the Brookings Institution (Later Retirement,
Inequality in Old Age, and the Growing Gap in Longevity Between Rich and Poor), and Stanford
economist Raj Chetty and colleagues (The Association Between Income and Life Expectancy in
the United States, 2001-2014) al published new evidence on the growing gap in life expectancy
by income. These studies also discuss the policy implications of these findings.
While policymakers and others may view increases in life expectancy as a positive outcome, they
may be concerned with widening differentials in longevity. For instance, Congress may be
interested in the connection between the growing gap in life expectancy between the rich and the
poor and federal expenditures on programs like Social Security. Social Security provides monthly
benefits to retired and disabled workers and their dependents, and to dependents of deceased
workers. The goals of Social Security, a redistributive program, may be compromised by
widening gaps in life expectancies. The program is designed to be progressive by redistributing
income from those with high lifetime earnings to those with low lifetime earnings. When Social
Security retirement benefits are measured on a lifetime basis, low earners, who show little to no
gains in life expectancy over recent decades, are projected to receive relatively smal er benefits
when compared with high earners. A commonly discussed Social Security reform proposal in the
United States involves increasing the retirement age, which would affect low earners’ lifetime
benefits disproportionately.3 Congress may wish to reevaluate this type of reform proposal in light
of the growing gap in life expectancy by income and may be interested in policy proposals that
protect the interests of lower-earning, shorter-lived workers, for example.

1 According to its website, the National Academy of Sciences (NAS) is “ a private, nonprofit organization of the
country’s leading researchers. T he NAS recognizes and promotes outstanding science through election to me mbership;
publication in its journal, PNAS; and its awards, programs, and special activities.” For more background, see
http://nationalacademyofsciences.org.
2 National Academies of Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by Income:
Im plications for Federal Program s and Policy Responses (Washington, DC: National Academies Press, 2015), Figure
S-1.
3 Other options exist to address the financing challenges posed by increasing longevity. For example, a number of other
countries have adopted automatic adjustments of life expectancy indexing in their public pension programs to address
an aging population. J. A. T urner, Longevity Policy: Facing Up to Longevity Issues Affecting Social Security, Pensions,
and Older Workers (Kalamazoo, MI: Upjohn Institute Press, 2011).
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This report provides a brief overview of the concept of life expectancy, how it is measured, and
how it has changed over time in the United States.4 While life expectancy may be studied in a
variety of contexts, this report focuses on the link between life expectancy and socioeconomic
status (SES), as measured by lifetime income. In particular, this report synthesizes recent research
on (1) the life expectancy gap by income and (2) the relationship between this gap and Social
Security retirement benefits. Final y, this report discusses the implications of this research for one
type of Social Security reform proposal: raising the Social Security retirement age.
Life Expectancy in the United States
Life expectancy is a measure of population longevity that refers to the average number of years
an individual wil live, given survival to a particular age and subject to age-specific mortality
rates. Life expectancy has an inverse relationship with mortality rates (also referred to as death
rates):5 as mortality rates decline, life expectancy increases.6 These measures can be studied in the
aggregate (i.e., full population) or separately across demographic subgroups. Differential
mortality rates across groups—for example, by age, sex, or race—result in differential life
expectancy estimates.
Life expectancy is commonly presented as life expectancy at birth as wel as at age 65. It can,
however, be calculated at any age. When calculated at birth, life expectancy represents the
average life span. Alternatively, life expectancy may refer to additional years of life when it is
calculated for ages after birth (e.g., a life expectancy of 10 years at age 75, which indicates an
expected age at death of 85).7 According to data from the Centers for Disease Control and
Prevention (CDC) National Center for Health Statistics (NCHS), in 2018 (the most recent
published data, which do not reflect consequences of the COVID-19 pandemic discussed in Box
2), life expectancy at age 65 in the United States was estimated to be 19.5 years (meaning
individuals would be expected to live to age 84.5), whereas life expectancy at birth was 78.7
years.8
Life expectancy is often broken out by sex and race due to observed differences in sex-specific
and race-specific death patterns. For example, in 2018, life expectancy at birth in the United
States was estimated to be 76.2 years for men and 81.2 years for women. The comparable figures
for life expectancy at birth by race were 74.7 years for Blacks and 78.6 years for Whites. In 2018,

4 T his report focuses on life expectancy in the United States. It does not discuss international trends in life expectancy.
T here is, however, a large, comparative lit erature on international life expectancy, including differences in life
expectancy in the United States versus other affluent countries. See, for instance, National Research Council, Panel on
Understanding Divergent Trends in Longevity in High -Incom e Countries, ed. Eileen M. Crimmins, Samuel H. Preston,
and Barney Cohen (Washington, DC: National Academies Press, 2011).
5 Mortality rates are calculated by dividing the number of deaths that occur in a given time period by the number of
person-years lived in that same time period. Mortality rates are age-specific when they refer to deaths occurring among
a particular age group.
6 At the same time, differential patterns in mortality decline across age groups are also reflected in life expectancy
estimates.
7 Life expectancy estimates generally indicate greater longevity when estimated at older ages (e.g., at age 65 versus at
birth). For instance, life expectancy at age 65 presents a higher expected age at death than life expectancy calculated at
birth because someone who lives to 65 has already survived to a later age (i.e., having experienced lower mortality risk)
and has a higher chance of living to 90, for example, than someone at a younger age.
8 Based on final mortality data for 2018—the mostly recently available data from NCHS; they do not reflect any
mortality consequences related to the COVID-19 pandemic. See Elizabeth Arias and Jiaquan Xu, United States Life
Tables, 2018, NCHS, National Vital Statistics Reports, vol. 69, no. 12 (November 17, 2020), https://www.cdc.gov/
nchs/data/nvsr/nvsr69/nvsr69-12-508.pdf.
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life expectancy at age 65 in the United States was estimated to be 18.1 years for men (so an
expected age of death of 83.1 years [65+18.1=83.1]) and 20.7 years for women (so an expected
age of death at 85.7 years [65+20.7=85.7]). Life expectancy at age 65 in 2018 was 18.0 years for
Blacks (so an expected age of death at 83.0 [65+18.0=83.0]) and 19.4 years for Whites (so an
expected age of death at 84.4 years [65+19.4=84.4]).9
For more background on the data and methods used to estimate life expectancy, see Box 1. The
research discussed throughout this report uses life expectancy estimates calculated at various ages
(e.g., at age 50, age 65, or at the various ages of sample participants). For ease of comparison,
these study estimates wil be referred to as “life expectancy,” although in some cases they may
represent expected age of death (or total years of life expected to be lived).

9 Other types of race/ethnic differences in life expectancy exist as well (e.g., Hispanic/non -Hispanic). T hey are not
discussed in this report.
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Box 1. Estimating Life Expectancy: Data and Methods
To estimate life expectancy, researchers first require raw data on the number and timing of deaths in a population.
Sources for this information on the U.S. population include the CDC National Vital Statistics (NVS) and the Social
Security Administration (SSA) Death Master File. Next, researchers use these raw mortality data to calculate age-
specific mortality rates and then apply wel -established mathematical techniques to produce a life table that
includes estimates of life expectancy.10
Life expectancy may be calculated using a “period” or “cohort” approach. Period life expectancy estimates are
based on mortality observed in a given year (i.e., time period); therefore, period life expectancy is derived by
assuming that a population experiences the most recent, annual, age-specific mortality rates throughout their lives.
For example, period estimates assume that individuals who are 65 years old wil today face the same mortality
rates in 10 years (when they are 75 years old) as those who are 75 years old today. Life tables produced by the
CDC NCHS provide estimates of period life expectancy.11
In the cohort approach, however, either observed mortality rates or projected estimates for a particular birth
cohort are used.12 For example, cohort estimates assume that individuals who are 65 years old today wil face
different mortality rates in 10 years (when they are 75 years old) than mortality rates for 75 -year-olds today (i.e.,
mortality rates that are observed or estimated—and are likely to be lower than mortality rates for 75-year-olds
today). SSA’s Office of the Chief Actuary (OACT) produces estimates of cohort life expectancy.13
Life expectancy estimates are typical y constructed using period life tables. At least in part, this preference is du e
to convenience: period mortality rates for a current year are readily available (e.g., via CDC’s NVS), whereas
cohort mortality rates require either observing a cohort from birth until death—which involves a considerable
time lag—or producing estimated (rather than observed) cohort mortality rates based on assumptions and
modeling techniques.
Period life expectancy estimates tend to be lower than cohort life expectancy estimates due to the overal trend
of decreasing mortality rates over time. For instance, based on cohort life tables, SSA estimated that, in 2018, life
expectancy at birth was 86.4 for women and 82.3 for men. SSA’s estimates for cohort life expectancy at age 65 in
2018 result in an expected age of death at 86.4 for women (life expectancy at age 65 of 21.4 years) and 83.8 for
men (life expectancy at age 65 of 18.8 years).14 In comparison, NCHS estimated that, in 2018, period life
expectancy at birth was 81.2 for women and 76.2 for men, and estimates based on period life expectancy at age
65 result in an expected age of death at 85.7 for women (life expectancy at age 65 of 20.7 years) and 83.1 for men
(life expectancy at age 65 of 18.1 years).15

Figure 1 and Figure 2 provide CDC’s recent estimates of period life expectancy in the United
States over 1950-2018 (see Box 1 for a discussion of period life expectancy).16 Figure 1 presents
life expectancy trends over this period for men and women at birth as wel as expected ages of
death based on life expectancy at age 65. Figure 2 graphs the same data broken out for Whites
and Blacks. Two key observations may be drawn from these figures. First, life expectancy has
increased over time for al groups. These increases in life expectancy have been driven by
decreases in mortality rates. In particular, during the second half of the 20th century,
improvements in the prevention and control of chronic disease (e.g., heart disease and
cerebrovascular diseases) have contributed to reduced adult mortality rates. Additional y,
advances and innovations in medical technology (such as vaccines and antibiotics) as wel as

10 Samuel H. Preston, Patrick Heuvenline, and Michael Guillot, Demography: Measuring and Modeling Population
Processes (Malden, MA: Blackwell Publishing, 2001).
11 See http://www.cdc.gov/nchs/products/life_tables.htm#life.
12 A cohort is a group of individuals who experience the same event at the same time. A birth cohort is a group of
individuals born in the same year (or during the same years).
13 See https://www.ssa.gov/oact/tr/2020/V_A_demo.html#wwfootnote_inline_26 (SSA’s OACT also provides period
life expectancy estimates. See https://www.ssa.gov/oact/tr/2020/V_A_demo.html#wwfootnote_inline_21 .)
14 See T able V.A5 (Intermediate), https://www.ssa.gov/oact/tr/2020/V_A_demo.html#228705.
15 See NCHS, National Vital Statistics, http://www.cdc.gov/nchs/products/life_tables.htm#life/.
16 Available at http://www.cdc.gov/nchs/products/life_tables.htm#life.
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public health measures (for instance, clean water and sanitation initiatives and antismoking
campaigns) have also contributed to mortality declines.17 (See Box 2 for a discussion of recent
research, including recent work by Case and Deaton18 that complicates this finding.)
Second, there are gaps in life expectancy across sex and race that—despite evidence of narrowing
over recent periods—have persisted over time. Worldwide, life expectancy is general y higher for
women than for men. Researchers have proposed biological as wel as behavioral and social
factors to explain this differential in life expectancy by sex.19 In terms of race, in the United
States, Whites tend to live longer, on average, than Blacks—although the longevity gap, as
calculated at birth, has decreased over time. To explain this racial differential, researchers point to
higher mortality for Blacks due to health disparities as wel as interactions among factors such as
inequalities in socioeconomic status, behavioral factors, access to health care, and environmental
surroundings.20
Box 2. The Impact of COVID-19 on U.S. Life Expectancy
The excess mortality associated with COVID-19 is expected to have a significant impact on U.S. life expectancy.
Moreover, given its disproportionate impact on certain racial and ethnic minority groups,21 COVID-19 is expected
to exacerbate existing disparities in mortality and life expectancy. While final data on mortality and life expectancy
for a given year are typical y not available until several years after the data year, the Centers of Disease Control
and Prevention (CDC), as wel as several researchers and academics, have used provisional mortality data and/or
projections of future cumulative COVID-19 deaths to inform estimates about the associated decline in U.S. life
expectancy.
NCHS released provisional life expectancy at birth estimates for the first half of 2020 (i.e., the period from January
2020 through June 2020) in order to assess the impact of the observed excess mortality during 2020. This is the
first time NCHS has published life expectancy estimates based on provisional vital statistics data. According to the
NCHS, in the first half of 2020, U.S. life expectancy at birth was 77.8 years, a 1.0 -year reduction from 2019 (78.8
years) and the lowest level of U.S. life expectancy since 2006. Larger reductions in life expectancy were observed
for males as compared to females and for the non-Hispanic Black and Hispanic populations as compared to the
non-Hispanic White population. Life expectancy at birth for males, in the first half of 2020, was 75.1 years, a 1.2 -
year reduction from 2019 (76.3 years), while life expectancy at birth for females was 80.5 years, a 0.9-year
reduction from 2019 (81.4 years). The non-Hispanic Black population experienced a 2.7-year reduction in life
expectancy in the first half of 2020 (74.7 years to 72.0 years) and the Hispanic population experienced a 1.9-year
reduction in life expectancy (81.8 years to 79.9 years). The non-Hispanic White population experienced a 0.8-year
reduction in life expectancy (78.8 years to 78.0 years). Provisional vital statistics data were not made available for
other racial and ethnic categories.22

17 For a longer discussion of improvements in life expectancy, see CRS Report RL32792, Life Expectancy in the United
States.
18 Anne Case and Angus Deaton, “Rising Morbidity and Mortality in Midlife among White Non-Hispanic Americans in
the 21st Century,” Proceedings of the National Academ y of Sciences of the United States of Am erica , vol. 112, no. 49
(2015), pp. 15078-83; Anne Case and Angus Deaton, Mortality and Morbidity in the 21st Century, Brookings
Institution, Brookings Paper on Economic Activity; Prepared for the Brookings Panel on Economic Activity, March 17,
2017.
19 T hese trends of decreasing mortality/increasing life expectancy as well as the sex differential in life expectancy are
not unique to the United States; they have been observed internationally as well. In general, life expectancy in the
United States is higher than the global average, which includes less-developed countries, but only slightly higher th an
in comparable, developed countries. See international data on life expectancy from the Organisation for Economic Co-
operation and Development (OECD), which are available at https://data.oecd.org/healthstat/life-expectancy-at-
birth.htm and https://data.oecd.org/healthstat/life-expectancy-at-65.htm#indicator-chart.
20 For more information on differentials in life expectancy by race and sex, including a discussion of causal
mechanisms, see CRS Report RL32792, Life Expectancy in the United States.
21 CDC, “COVID-19 Racial and Ethnic Health Disparities,” updated December 10, 2020, https://www.cdc.gov/
coronavirus/2019-ncov/community/health-equity/racial-ethnic-disparities/disparities-deaths.html.
22 NCHS, Vital Statistics Rapid Release, Report No. 010, February 2021, https://www.cdc.gov/nchs/data/vsrr/VSRR10-
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Other researchers and academics have estimated similar reductions in U.S. life expectancy. Theresa Andrasfay and
Noreen Goldman, in a February 2021 paper published in the Proceedings of the National Academy of Sciences
(PNAS), used COVID-19 mortality projections of varying severity (a lower, medium, and higher mortality
scenario) from the Institute for Health Metrics and Evaluation to estimate U.S. life expectancy, at birth and at age
65, in 2020. The institute’s “higher” mortality scenario (a projection of approximately 348,000 U.S. COVID-19
deaths through December 31, 2020) was the closest of the three severity projections to the actual U.S. COVID-
19 death count, which, according to the December 31, 2020, update to the CDC COVID Data Tracker, was
341,199. Using this “higher” mortality scenario, Andrasfay and Goldman estimated a 1.22 -year reduction in U.S.
life expectancy at birth and a 0.94-year reduction in life expectancy at age 65. They estimated a 2.26-year
reduction in life expectancy at birth (-1.86 at age 65) for non-Hispanic Blacks, a 3.28-year reduction in life
expectancy at birth (-2.41 at age 65) for Hispanics, and a 0.73-year reduction in life expectancy at birth (-0.86 at
age 65) for non-Hispanic Whites.23
Figure 1. Life Expectancy by Sex at Birth and Age 65, 1950-2018

Source: Centers for Disease Control and Prevention (CDC), National Vital Statistics.
Notes: Period life expectancy estimates based on period mortality rates. Life expectancy at age 65 refers to
expected age of death.

508.pdf
23 T heresa Andrasfay and Noreen Goldman, “Reductions in 2020 US Life Expectancy Due to COVID-19 and the
Disproportionate Impact on the Black and Latino Populations,” Proceedings of the National Academy of Sciences of the
United States of Am erica, vol. 118, no. 5 (February 2021).
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Figure 2. Life Expectancy by Race at Birth and Age 65, 1950-2018

Source: Centers for Disease Control and Prevention (CDC), National Vital Statistics.
Notes: Period life expectancy estimates based on period mortality rates. Life expectancy at age 65 refers to
expected age of death. Beginning with the 2018 data year, NCHS reported life expectancy by race and Hispanic
origin based on the 1997 Office of Management and Budget (OMB) revised standards for the classification of
federal data on race and ethnicity. These revised standards introduced the new categories of non -Hispanic single-
race White and non-Hispanic single-race Black, which differ from the bridged-race categories (based on the 1977
OMB standards) used in past reports. NCHS noted that “single-race categories based on the 1997 standards are
not completely comparable with those based on the 1977 standards.”24 For the 2007-2017 life tables, NCHS
reports life expectancy using both bridged-race and single-race categories in order to document the impact of
the revised OMB standards. In order to align these data more closely with the 2018 life tables (which only use
single-race categories), CRS used data from single-race categories for 2007-2017.

24 Elizabeth Arias and Jiaquan Xu, United States Life T ables, 2018, NCHS, National Vital Statistics Reports, vol. 69,
no. 12 (November 17, 2020), https://www.cdc.gov/nchs/data/nvsr/nvsr69/nvsr69-12-508.pdf.
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Box 3. Recent Increases in Mortality
Among U.S. Middle-Aged, Non-Hispanic Whites
Pre-pandemic, researchers documented increasing life expectancy and decreasing mortality rates over time for
the entire U.S. population. A 2015 study by Anne Case and Angus Deaton,25 however, disaggregates mortality
rates by age and race. Their findings contradict this overal trend of declining mortality rates for a specific
subpopulation: middle-aged, non-Hispanic Whites. In particular, Case and Deaton conclude that for White,
non-Hispanic Americans aged 45-54, mortality rates have increased over 1999-2013, while mortality rates have
fal en over this same period for al other age and race groups. Additional y, the mortality increases for this
group result largely from increased death rates among individuals with a high school degree or less. According
to Case and Deaton, the increased mortality for middle-aged, non-Hispanic Whites in recent years is due to
deaths from drug and alcohol poisoning, suicide, and chronic liver diseases and cirrhosis (what the authors
refer to as “deaths of despair”).
Other researchers have pointed out that Case and Deaton do not adjust for the changing age composition of
the 45-54 age group over the time period in question. For example, Andrew Gelman points out that with the
aging of the population—particularly that of the large baby boom cohort (born 1946-1964)—the mean age
within the 45-54 age bracket increased between 1989 (49.1 years) and 2013 (49.6 years). And higher age is
associated with higher mortality, which could affect the results. For example, Gelman finds that using age-
adjusted mortality rates in the analysis reduces by half the observed increase in mortality found by Case and
Deaton, and confines this observed mortality increase to the 1999-2005 period.26
Additional y, Laudan Aron et al. are critical of Case and Deaton for not analyzing mortality trends separately
for men and women. In their analysis of the same data, Aron et al. find that the average increase in age-specific
mortality over this recent period is more than three times higher for women than men, with important
implications: “By lumping women and men together, the study ... missed the important point that the increases
in mortality are affecting women of reproductive and childrearing ages, a finding that has huge implications for
children, families, and communities.”27
In 2017, Case and Deaton confirmed their 2015 findings in a conference paper that builds on their earlier work
and examines mortality in the United States through 2015.28 In this paper, Case and Deaton propose that the
recent increases in mortality for middle-aged, non-Hispanic Whites with a high school education or less may be
due to cumulative disadvantages for these individuals over time and across a number of social and economic
dimensions, including the labor market, health, and family structure.
Measuring Gaps in Life Expectancy
In addition to documenting differences in life expectancy across sex and race, researchers have
also focused on disparities in life expectancy by socioeconomic status. SES serves as a measure
of class, or an individual’s economic and social position in society relative to others. It is a
common indicator of social stratification and inequality.29 Although gaps in life expectancy by
sex and race have narrowed over time, there is evidence that the gap in life expectancy by SES
has been growing over time, particularly across recent decades, as this report wil discuss in detail
below.
SES is commonly measured by income, education, occupation, or some interaction of these
concepts. In the field of life expectancy research, there are a variety of ways to operationalize

25 Anne Case and Angus Deaton, “Rising Morbidity and Mortality in Midlife Among White Non -Hispanic Americans
in the 21st Century,” Proceedings of the National Academ y of Sciences of the United States of Am erica , vol. 112, no. 49
(2015), pp. 15078-83.
26 Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia
University. See Andrew Gelman, “ Correcting statistical biases in ‘Rising morbidity and mortality in midlife among
White non-Hispanic Americans in the 21st century’: We need to adjust for the increase in average age of people in the
45-54 category,” November 6, 2015, http://andrewgelman.com/2015/11/06/correcting-rising-morbidity-and-mortality-
in-midlife-among-white-non-hispanic-americans-in-the-21st-century-to-account-for-bias-in/; “ Age adjustment
mortality update,” November 6, 2015, http://andrewgelman.com/2015/11/06/age-adjustment -mortality-update/; “ What
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SES. The two most common approaches are to measure SES with education or with income. The
positive relationship between education and life expectancy—or the negative relationship
between education and mortality—has been wel -documented.30 Additional y, studies have found
a growing gap in life expectancy by education over time, although this trend may not be uniform
across sex and race.31
While education is a useful measure of SES because it is often stable by middle age, there are
several drawbacks to this measure. First, education is subject to reporting error (e.g., misreported
data), particularly in death record files.32 Second, it is a comparatively gross measure of SES;
there are large components of the population that have attained each level of education (e.g., high
school and college).33 Using such broad educational categories could gloss over disparities in life
expectancy between population subgroups. Final y, there have been significant shifts in
educational attainment in the United States over the last century (e.g., increasing rates of high
school completion). Such shifts in educational attainment over this period make drawing
conclusions about time trends in life expectancy chal enging. That is, high-school-educated
individuals today are a different, more disadvantaged group than high-school-educated
individuals born at the beginning of the 20th century, when high school completion rates were
significantly lower.34

happened to mortality among 45-54-year-old white non-Hispanics? It declined from 1989 to 1999, increased from 1999
to 2005, and held steady after that,” November 6, 2015, http://andrewgelman.com/2015/11/06/what-happened-to-
mortality-among-45-54-year-old-white-non-hispanic-men-it-declined-from-1989-to-1999-increased-from-1999-to-
2005-and-held-steady-after-that/; and “ Death rates have been increasing for middle-aged white women, decreasing for
men,” November 10, 2015, http://andrewgelman.com/2015/11/10/death-rates-have-been-increasing-for-middle-aged-
white-women-decreasing-for-men.
27 Laudan Aron et al., “ T o Understand Climbing Death Rates Among Whites, Look T o Women Of Childbearing Age,”
Health Affairs Blog, November 10, 2015, http://healthaffairs.org/blog/2015/11/10/to-understand-climbing-death-rates-
among-whites-look-to-women-of-childbearing-age/.
28 Anne Case and Angus Deaton, Mortality and Morbidity in the 21st Century, Brookings Institution, Brookings Paper
on Economic Activity, prepared for the Brookings Panel on Economic Activity, March 17, 2017.
29 David B. Grusky, ed., Social Stratification: Class, Race, and Gender in Sociological Perspective, 4th ed. (Boulder,
CO: Westview Press, 2014).
30 Evelyn M. Kitigawa and Philip M. Hauser, Differential Mortality in the United States: A Study in Socioeconomic
Epidem iology (Cambridge, MA: Harvard University Press, 1973); Gregory Pappas et al., “ T he Increasing Disparity in
Mortality Between Socioeconomic Groups in the United States, 1960 and 1986,” New England Journal of Medicine,
vol. 329 (1993), pp. 103-109; Samuel H. Preston and Irma T . Elo, “ Are Education Differentials in Adult Mortality
Increasing in the United States?” Journal of Health and Aging, vol. 7, no. 4 (1995), pp. 476-496; Robert A. Hummer
and Elaine M. Hernandez, The Effect of Educational Attainm ent on Adult Mortality in the United States, Population
Reference Bureau, Population Bulletin No. 68 (1), Washington, DC, 2013.
31 Ellen R. Meara, Seth Richards, and David M. Cutler, “T he Gap Gets Bigger: Changes in Mortality and Life
Expectancy, by Education,” Health Affairs, vol. 27, no. 2 (2008), pp. 350-360; S. Jay Olshansky et al., “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), pp. 1803 -1813; Jennifer K. Montez and Anna Zajacova, “ Explaining the Widening
Education Gap in Mortality Among U.S. White Women,” Journal of Health and Social Behavior, vol. 54, no. 2 (2013),
pp. 165-181.
32 See Brian Reston et al., “Education Reporting and Classification on Death Certificates in the United States,” Vital
and Health Statistics, series 2, no. 151 (2010), pp. 1-16.
33 Additionally, the categories of “some college” or “college attainment” may mask differences in the quality of
education. And this dimension of quality could also have implications for SES.
34 Jennifer B. Dowd and Amar Hamoudi, “Is Life Expectancy Really Falling for Groups of Low Socio -Economic
Status? Lagged Selection Bias and Artefactual T rends in Mortality,” International Journal of Epidemiology, vol. 43,
no. 4 (2014), pp. 983-988.
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This report presents recent evidence on life expectancy gaps by income, another measure of SES.
Income is chosen for several reasons. First, unlike education, income may be measured in more
detail. It can also be expressed as a relative measure, which al ows researchers to compare where
an individual’s income fal s in a population distribution. While income may suffer from reverse
causality in the sense that poor health—and, therefore, exposure to higher mortality risk—may
lead to lower earnings, this problem can be at least partial y addressed by measuring income over
a period of time (e.g., during prime working years). In other words, using a measure of average
lifetime income attempts to capture adult SES. Final y, income—particularly lifetime earnings,
which is a component of lifetime income and, therefore, correlated with total income—is chosen
because it is a measure of SES that is directly linked to the calculation of Social Security
benefits.35
The Growing Gap in Life Expectancy by Income:
Recent Evidence
There is a rich literature on differences in life expectancy by socioeconomic groups in the United
States. This section highlights a selection of significant studies on the relationship between life
expectancy and SES, as measured by income.36 Using high-quality datasets and various
quantitative methods, the authors of these studies find consistent evidence of a growing gap in life
expectancy by income. Table A-1 provides summary information for each of the studies
discussed in the section.
In her 2007 research, Hilary Waldron37 makes a significant contribution to understanding trends
in life expectancy by earnings (i.e., labor income). A major strength of the study is its use of a
rich and large longitudinal data set. Waldron uses Social Security administrative data on taxable
wages matched with benefits records and official death records.38 She analyzes earnings for males
aged 60 or older for 30 successive birth year cohorts (1912-1941), and the available official data
al ow her to observe deaths at ages 60-89 (1972-2001).39 For her measure of SES, Waldron uses
positive earnings from ages 45 through 55 for each individual in her sample relative to the
national average wage in a given year (i.e., percentile). An individual’s annual relative earning
values are then averaged over years of nonzero earnings to create a measure of peak lifetime
earnings, which she describes as a “rough proxy for socioeconomic status.”40 Men with zero
earnings during that time are dropped because Social Security is not able to distinguish between
periods of unemployment and earnings not covered by Social Security.

35 Additionally, measuring earnings over prime working years captures individuals who are likely to have survived long
enough to qualify and/or receive Social Security benefits.
36 Most of the studies discussed in this report measure income using earnings, often Social Security–covered earnings.
Earnings, or labor income, is only one component of an individual’s or household’s income.
37 Hilary Waldron, “T rends in Mortality Differentials and Life Expectancy for Male Social Security -Covered Workers,
by Socioeconomic Status,” Social Security Bulletin, vol. 67, no. 3 (2007), pp. 1-28.
38 SSA’s Continuous Work History Sample (CWHS) is a longitudinal 1% sample of issued Social Security numbers
that contains Social Security taxable wages from 19 51 to the most recent year. Waldron matches the 2001 CWHS with
a 1% sample of SSA’s Master Beneficiary Record file and a 1 % sample of the Numident (death) file.
39 Waldron focuses on male earnings and excludes female earnings because women’s increasing participation in the
labor market during the time period of her data would likely lead to improper classification of women’s relative
earnings groupings.
40 Hilary Waldron, “T rends in Mortality Differentials and Life Expectancy for Male Social Security -Covered Workers,
by Socioeconomic Status,” Social Security Bulletin, vol. 67, no. 3 (2007), p. 1.
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Figure 3 provides Waldron’s estimates of cohort life expectancy at age 65 for male Social
Security–covered workers for the 1912-1941 birth cohorts over two segments of the earnings
distribution: the bottom half and the top half. The research does not address significant observed
changes in the income distribution itself, which implies that the top half of the income
distribution may be quite different in composition now than it was before. That is, those who are
in the bottom half now may be poorer compared with those in the top half of the earnings
distribution. The box-and-whisker plot depicts the 95% confidence interval surrounding the
estimates of life expectancy; the widening whiskers show the increasing variance (uncertainty) in
later birth cohorts. The life expectancy gap by relative earnings over time is growing. For men
born in 1912, those in the top half of the income distribution could expect to live about a year
longer than those in the bottom half. For men born in 1941, those in the top half could expect to
live 5.3 years longer than those in the bottom half. The bottom half of the income distribution
from the 1912 birth year cohort to the 1941 birth year cohort wil see little improvement in life
expectancy (1.3 years), while the top half wil see a considerably larger improvement (6.0 years).
Waldron also shows life expectancies at ages 60-90 by year of birth and earnings group.41 Those
results reinforce the findings in Figure 3 of stronger gains in life expectancy at al ages made by
those in the top half of the income distribution.
Figure 3. Life Expectancy at Age 65 for Male Workers, by Birth Year and Earnings

Source: Hilary Waldron, “Trends in Mortality Differentials and Life Expectancy for Male Social Security-
Covered Workers, by Socioeconomic Status,” Social Security Bul etin, vol. 67, no. 3 (2007), Chart 3.
Note: This box-and-whisker plot depicts the 95% confidence interval surrounding the estimates of life
expectancy.
Waldron asserts that her contribution lies in being able to show that a wide swath of the earnings
distribution (the bottom half) is experiencing very smal gains in life expectancy, a phenomenon
not relegated to an extreme low end of the earnings distribution. This finding is consistent with
other research that shows that it is not just those at the lowest end of the income distribution who
experience smal gains in life expectancy.42 This disaggregation of the earnings distribution into

41 T able 4 of Waldron’s 2007 study provides detailed results by ages 60, 65, 70, 75, 80, 85, and 90, for the top and
bottom half of the income distribution.
42 Waldron’s 2007 study cites other research, which finds the link between SES and health to be a gradient (see p. 49).
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two broad groups is insightful; it shows the existence of a gap, with life expectancy increasing
continuously with lifetime earnings.
Among the limitations of this study, Waldron acknowledges that her final results may not be
representative of the population for various reasons. For instance, her sample of men must be
healthy for them to have positive, Social Security–covered earnings from ages 45 to 55.43
Additional y, the gross income comparison groups used by Waldron—the top half compared with
the bottom half of the earnings distribution—do not al ow for nuanced conclusions about life
expectancy trends (i.e., they may conceal heterogeneity within these groups). Waldron also
employs assumptions in her life expectancy projections that more recent birth cohorts follow
recent mortality patterns, which may or may not be accurate.
Expanding on the work of Waldron, Julian Cristia’s 2009 study44 analyzes the lifetime earnings of
both men and women during the 1983-2003 period when the sample is aged 35-75.45 This study
defines lifetime earnings in a given year as the average of each individual’s earnings over a
period of time, using a time lag.46 This work also finds increases in life expectancy differentials
across lifetime earnings groups during the period of study. Cristia uses data from the U.S. Census
Bureau’s 1984, 1993, 1996, and 2001 Survey of Income and Program Participation (SIPP)47
panels matched to earnings, benefit, and mortality data from SSA and earnings data from the
Internal Revenue Service (IRS). He estimates life expectancy at various ages using a period
approach that is based on sample mortality rates as wel as estimates of mortality after age 75.
Cristia finds a substantial increase in the differentials in life expectancy between top and bottom
lifetime earnings quintiles. For men, this top-to-bottom life expectancy differential increased over
this period by about 30%, from 2.7 years to 3.6 years. For women, it doubled, from 0.7 years to
1.5 years. This study is subject to some of the same limitations as Waldron (2007), including the
use of assumptions regarding future mortality patterns as wel as just two income comparison
groups. Additional y, Cristia particularly worries that the exclusion of unhealthy individuals—
who are out of the labor force and, thus, not included in his sample due to lack of earnings—
might bias the relationship between lifetime earnings and life expectancy. For example, Cristia
notes that “[g]iven the post-1964 expansion of transfer programs, a reasonable supposition is that
such programs siphoned off from the labor force chronical y il persons with a higher than
average probability of death.”48

43 Although most workers are covered by Social Security, not all workers are. Certain state and local government
workers, who have coverage under their employers’ retirement systems, comprise the largest group of noncovered
workers.
44 Julian Cristia, Rising Mortality and Life Expectancy Differentials by Lifetime Earnings in the United States, Inter-
American Development Bank, Working Paper 665, Washington, DC, January 2009, http://www.iadb.org/res/
publications/pubfiles/pubWP-665.pdf.
45 Cristia’s sample contains 130,000 individuals, aged 35 to 75, observed annually over a 26 -year period (1978-2003).
46 As Cristia notes, the average earnings calculation varies depending on age: “ For individuals older than 53, earnings
from age 41 to 50 are used to capture years when the person was most closely attached to the labor market. For younger
individuals, averages ranging from 5 to 10 years were computed without including the immediately preceding three
years (e.g., for individuals aged 43, earnings from age 31 to 40 are used)” (see p. 11).
47 For information on the SIPP, see http://www.census.gov/sipp/.
48 Julian Cristia, Rising Mortality and Life Expectancy Differentials by Lifetime Earnings in the United States, Inter-
American Development Bank, Working Paper 665, Washington, DC, January 2009, http://www.iadb.org/res/
publications/pubfiles/pubWP-665.pdf, p.21.
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The Congressional Budget Office (CBO) has also studied the life expectancy income gap. For
example, a 2014 CBO study49 uses a detailed model, the Congressional Budget Office Long-Term
(CBOLT) microsimulation model, with data on a representative sample of individuals that
simulates demographic and economic outcomes for that population over time. This model also
incorporates SSA administrative data, with additional demographic and economic data matched
using the SIPP, the Health and Retirement Study (HRS),50 and the Current Population Survey
(CPS).51 Like the other research discussed here, results of the CBOLT microsimulation model
depend on the accuracy of its underlying assumptions; for example, assumptions about future
mortality patterns.
Based on this CBOLT modeling and analysis, CBO estimates that period life expectancy at age 65
wil continue to increase, but at a higher rate for those individuals with higher lifetime earnings.
CBO compares today’s life expectancy and lifetime earnings to life expectancy and earnings in
the year 2039, and determines that, in 2014, a 65-year-old man in the upper lifetime earnings
quintile is expected to live more than three years longer than someone with the same observable
characteristics in the lowest lifetime earnings quintile. A similar trend exists for women: in 2014,
a 65-year-old woman in the upper lifetime earnings quintile would be expected to live more than
one year longer than this same woman in the lowest lifetime earnings quintile. In the year 2039,
CBO projects that a 65-year-old man with higher lifetime earnings wil live around six years
longer than a 65-year-old man in the lower income quintiles, while a 65-year-old, high-earning
woman wil live around three years longer than a 65-year-old, low-earning woman.
Another study of life expectancy in the United States, conducted by the NAS, draws the same
general conclusions about a growing gap in life expectancy for both men and women. This 2015
NAS study52 uses biennial waves of HRS data over 1992-2008, matched to SSA records to
compare life expectancy between the cohort born in 1930 and the cohort born in 1960. This study
defines lifetime earnings as average, nonzero, Social Security–reported household earnings for
individuals aged 41-50. This NAS study estimates cohort life expectancy at age 50 for the two
birth cohorts studied. Projections are used to calculate life expectancy when mortality cannot be
observed for younger individuals in the sample (i.e., after 2008), which means that mortality is
estimated for the 1930 birth cohort after age 78 and for the entire 1960 birth cohort. As with
Waldron’s 2007 study, because individuals must be healthy in order to have positive earnings
from ages 41 to 50, the results of this analysis may not be generalizable to the entire population.
According to the NAS, for both the 1930 and 1960 birth cohorts, life expectancy, when calculated
at age 50, for men increased as income rose—and the gap between the bottom and top income
quintiles more than doubled between the two cohorts. (See Figure 4.)53 The study finds that men
in the bottom income quintile born in 1930 could expect to live an average of 26.6 additional
years at age 50 (an expected age of death at 76.6), yet there has been no gain in life expectancy
for men in the bottom quintile born in the 1960 cohort (life expectancy at age 50 is 26.1 years, so

49 Congressional Budget Office, The 2014 Long-Term Budget Outlook, July 2014, https://www.cbo.gov/publication/
45471.
50 For information on the HRS, see http://hrsonline.isr.umich.edu/.
51 For information on the CPS, see http://www.census.gov/programs-surveys/cps.html.
52 National Academies of Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by Income:
Im plications for Federal Program s and Policy Responses (Washington, DC: T he National Academies Press, 2015).
53 T he study’s authors acknowledge the increasing dispersion in income in the United States in recent decades, but not e
that they do not discuss what bearing this may have on the widening gap in life expectancy. See CRS Report R44705,
The U.S. Incom e Distribution: Trends and Issues, for a description and analysis of the changes in the income
distribution.
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an expected age of death at 76.1). The top income quintile of men, however, has experienced
increases in life expectancy: for the 1930 cohort, life expectancy at age 50 was 31.7 additional
years, while for the 1960 cohort, it rises to 38.8 additional years. Thus, the gap in life expectancy
at age 50 between men in the lowest and highest income quintiles has risen from 5.1 years for the
1930 cohort to 12.7 years for the 1960 cohort. Growth in the life expectancy gap over this time
period is driven primarily by longevity gains among men in the top income quintile, but a smal
decline in longevity among men in the bottom quintile is also a contributing factor.54
Figure 4. Life Expectancy at Age 50 for Males and Females Born in 1930 and 1960,
by Income Quintile

Source: National Academy of Sciences, The Growing Gap in Life Expectancy by Income: Implications for Federal
Programs and Policy Responses (Washington, DC: The National Academies Press, 2015), Figure 3-2.
Notes: Cohort life expectancy estimates calculated using observed sample mortality where possible and
projected mortality for younger sample individuals (i.e., older than age 78 for the 1930 birth cohort and for the
entire 1960 birth cohort).
For women, the pattern is general y similar: across the two cohorts, the life expectancy gap
between the bottom and top income quintiles also increased, and there was evidence of a decline
in life expectancy for the lowest two income quintiles. (See Figure 4.) The NAS authors estimate
that the life expectancy gap between the bottom and top income quintiles of women expanded
from 3.9 years in the 1930 birth cohort to 13.6 years in the 1960 birth cohort. The authors note
that, although the findings for women are more pronounced than those for men, these results are
less reliable (i.e., because significant changes in women’s labor force participation over this
period affected the composition of the female sample).55

54 T he NAS authors propose several possible explanations for this trend, including (1) greater relative deprivation for
individuals in the bottom quintile over time due to increases in income inequality over time; (2) ine quality itself
negatively impacting health and leading to higher mortality for lower quintiles; and (3) education as a factor driving
both disparities in income as well as disparities in health. See discussion on pp. 53 -55 of National Academies of
Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by Incom e: Im plications for Federal
Program s and Policy Responses (Washington, DC: T he National Academies Press, 2015).
55 Ibid., p. 52. For the birth cohorts used in this analysis, in comparison with men, women’s lower levels of labor force
participation, as well as variations in labor force participation across subgroups of women (e.g., marital status, income,
and race), complicate interpretation of results. For a general discussion of trends in women’s labor force participation,
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A 2016 study by the Brookings Institution56 also analyzes income inequality among the 50-and-
older population and the growing longevity gap between income groups. This study analyzes
demographic data from two large public use surveys, the HRS and the SIPP, that have been
matched to Social Security administrative data on earnings, benefits, and dates of death.57 The
authors calculate study cohort life expectancy given survival to age 50 based on mortality risk
projections for the sample. Like other research, this study employs simplifying assumptions about
future mortality risks, which may or may not be accurate. Additional y, like the previously
discussed Waldron study, the sample includes only years of positive, Social Security–covered
earnings, which has the potential to exclude workers with poor health and higher mortality risk.
Figure 5 shows life expectancy by income decile for men and women as observed in the SIPP
dataset. According to Figure 5, men in the lowest income decile born in 1920 could expect to live
to be about 74.3 years old, compared with about 79.3 years for men in the top income decile. The
life expectancy gap by income grows with time. For men born in 1940, those in the lowest
income decile could expect to live to be about 76, compared with 88 for those in the topmost
income decile. Thus, among men the gap in life expectancy between the bottom and top income
deciles grew from five years for those born in 1920 to 12 years for those born in 1940.58
For women, the results from the Brookings study show no rise at al in life expectancy for those
in the lowest income decile. For example, women in the lowest income decile born in 1920 could
expect to live to 80.4, whereas those in the highest income decile could expect to live to 84.1
(Figure 5). For the 1940 birth cohort, women in the lowest income decile show no gains in life
expectancy relative to the 1920 cohort, whereas those in the top income decile could expect to
live to 90.5, gaining 6.4 years.59 In tables (not shown here) in the Brookings study, one can
examine other deciles in the income distribution, not just the two ends. Results for both men and
women confirm that the gains in life expectancy are skewed toward those with higher incomes.

see CRS Report R44055, An Overview of the Em ploym ent-Population Ratio.
56 Barry Bosworth, Gary Burtless, and Kan Zhang, Later Retirement, Inequality in Old Age, and the Growing Gap in
Longevity Between Rich and Poor, Brookings Institution, Washington, DC, 2016.
57 T he sample used for this study includes individuals born between 1910 and 1950 (SIPP) or 1957 (HRS). T he authors
construct an average measure of past earnings based on each worker’s real nonzero earnings in the age range of 41-50.
T hey then impute workers’ earnings above the Social Security taxable wage base and construct a relative earnings
measure by measuring individual earnings relative to the average midcareer earnings o f adjacent birth year cohorts.
T hey combine the earnings of husbands and wives to produce a household earnings measure; for individuals without a
spouse, they use individual earnings.
58 Similar results are shown with the HRS, although the changes are a bit smaller in magnitude than the SIPP results. In
the HRS sample, although men’s overall life expectancy, given survival to age 50, was slightly lower for both birth
cohorts than in the SIPP sample, the life expectancy gap grows to be quite large. T he gap in life expectancy between
men, given survival to age 50, at the bottom and top income deciles grew from six years for the 1920 cohort to 11 years
for the 1940 cohort.
59 T he HRS sample shows a slight drop in life expectancy, given survival to age 50, for women in the lowest income
decile and a gain of four years for those in the top decile.
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Figure 5. Life Expectancy for Males and Females Born in 1920 and 1940,
by Income Decile

Source: Barry Bosworth, Gary Burtless, and Kan Zhang, Later Retirement, Inequality in Old Age, and the Growing
Gap in Longevity Between Rich and Poor, Brookings Institution, Washington, DC, 2016, Table IV-4.
Note: Cohort life expectancy estimates calculated assuming survival to age 50 and using projected mortality
risks.
In a 2016 study, Raj Chetty et al.60 also examine life expectancy across time in the United States.
The authors examine federal income tax data matched with SSA records for individuals aged 40-
76 during the 1999-2014 period. They calculate life expectancy using a period approach with
mortality rates for ages 40-76 estimated based on sample mortality as wel as mortality rates for
older ages that are projected using modeling techniques. They document that higher income (as
measured by pretax household earnings at age 61 for individuals aged 63 and older) is associated
with higher period life expectancy throughout the income distribution. For the 2001-2014 period,
they find a life expectancy gap between the bottom 1% and top 1% of 14.6 years for men and
10.1 years for women. They categorize this life expectancy gap as increasing in size above the
lowest two percentiles, but with smal er gains in life expectancy at higher income levels.61
In addition, Chetty et al. conclude that this life expectancy gap increased over the 2001-2014 time
period: life expectancy for the top 5% of men increased by 2.34 years (2.91 years for the top 5%
of women), but for the bottom 5% of men life expectancy increased only by 0.32 years (0.04
years for the bottom 5% of women).
Among the limitations of this study, the authors rely on assumptions about future mortality
patterns. The authors also recognize that the relationship between income and life expectancy is

60 Raj Chetty, Michael Stepner, and Sarah Abraham, et al., “T he Association Between Income and Life Expectancy in
the United States, 2001-2014,” Journal of the Am erican Medical Association, vol. 315, no. 16 (2016), pp. 1750 -1766.
61 For instance, Chetty et al. provide the following illustration of this concept: “For example, increases in income from
$14,000 to $20,000 (the 10th vs the 14th income percentiles), $161,000 to $224,000 (the 90 th vs the 95th income
percentiles), and $224,0000 to $1.95 million (the 95 th vs the 100th income percentiles) were all associated with
approximately the same difference in life expectancy (i.e., an increase of 0.7-0.9 years, averaging men and women)” (p.
1753).
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likely confounded by other unmeasured factors that affect health; this relationship may thus be
overstated.
Implications for Social Security Benefits
The Social Security program provides monthly benefits to retired workers and their dependents,
disabled workers and their dependents, and survivors of deceased workers. Benefits available to
dependents of retired, disabled, or deceased workers are known as “auxiliary benefits.” Al
benefits are inflation-adjusted for life. Approximately 94% of workers are covered,62 and their
earnings (up to a taxable maximum) are subject to the Social Security payroll tax. Workers and
their employers pay these taxes over their working years. After meeting eligibility requirements,
Social Security beneficiaries may claim benefits.63
In February 2021, roughly 65 mil ion beneficiaries received a total of $92 bil ion in Social
Security benefit payments, and the average monthly benefit was $1,425.64 Approximately 46
mil ion were retired workers receiving an average monthly benefit of $1,548. Life expectancy—
which can vary by year of birth and by income, as just shown—is a key factor in both the number
of years a worker paid taxes into the system and the number of years of benefit receipt. This
section discusses the impact of gaps in life expectancy by income on lifetime receipt of Social
Security retirement benefits, which includes retired worker and dependent benefits available at
retirement.65
Full monthly benefits are payable at the full retirement age (FRA), which is age 66 for those born
between 1943 and 1954 and wil rise to 67 for those born in 1960 and later.66 Individuals are
eligible for retired worker benefits if they have 10 years of covered earnings. Retired worker
benefits may be claimed as early as age 62, known as the earliest eligibility age (EEA). Benefits
are permanently reduced if claiming before the FRA. Benefits are increased (i.e., delayed
retirement credits [DRC] apply) if claiming past the FRA, up to age 70. There is no additional
increase in benefits if claiming past age 70. For example, for a person with a FRA of 67, claiming
at age 62 brings a 30% reduction of the unreduced monthly benefit. Conversely, for those who
claim at age 70, benefits increase by 24% of the unreduced monthly benefit. The adjustments to
claiming before and after the FRA are designed to be actuarial y equivalent for those with average
life expectancy. These are calculated to provide approximately the same total value of lifetime
benefits for those with average life expectancy regardless of the age when one claims benefits.
Social adequacy is a key goal of the Social Security program. It entails providing basic income
support to al covered workers and their dependents, helping to mitigate the financial impacts of
retirement, disability, and death. A long-standing objective of the program in this context is to be
progressive by awarding higher replacement rates of lifetime earnings for low-earning workers
than for high-earning workers. The underlying rationale for this progressivity is that higher
earners typical y have greater access to other forms of retirement savings by way of employer
pensions and private savings.

62 OACT , Social Security Program Fact Sheet, January 29, 2021, https://www.ssa.gov/oact/FACT S/index.html.
63 See CRS Report R42035, Social Security Primer, for a description of the Social Security program.
64 SSA, Monthly Statistical Snapshot, February 2021, T able 2, at https://www.ssa.gov/policy/docs/quickfacts/
stat_snapshot/2021-02.html.
65 Some studies include retired worker benefits only and exclude dependent spouse and survivor benefits. See the 2011
study by Shah, Shoven, and Slavov discussed later in the section.
66 See CRS Report R44670, The Social Security Retirement Age.
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The other key goal of the Social Security program is individual equity. The program serves this
goal by tying benefits to a worker’s earnings history and being available to beneficiaries without
evidence of need. Also, the benefit formula provides higher benefits to higher earners. Whether
the program maintains a fine balance in meeting the twin goals of social adequacy and individual
equity has been a matter of debate.67
Progressivity is built into the Social Security benefit formula—that is, monthly Social Security
benefits replace less of lifetime career average monthly earnings for higher earners than for lower
earners. In order to calculate the full monthly benefit amount, known as the Primary Insurance
Amount (PIA), past earnings are indexed to wages, and then converted to a monthly average
known as the Average Indexed Monthly Earnings (AIME). A progressive formula converts the
AIME into the PIA. In 2021,
PIA = 0.90 × (the first bend point of $996) + 0.32 × (over $996 and through the second
bend point of $6,002) + 0.15 × (AIME greater than $6,002).
The formula provides individuals with lower career-average earnings monthly benefits that
replace a higher percentage of their career-average earnings than for those with higher career-
average earnings.68 The dollar values are cal ed “bend points” because when the PIA formula in
shown in a graph, there are three line segments, and the dollar values represent the bends. These
bend points are adjusted annual y to the average wage index. A worker with AIME of $996 wil
have her monthly retired worker benefits replace 90% of her monthly earnings. As AIME rises,
the replacement rate declines, as seen in the formula, which has a replacement rate of 32% of
monthly earnings that fal between $996 and $6,002. For earnings above $6,002, benefits replace
15% of monthly earnings. Redistribution occurs because the rate of return on lifetime
contributions to Social Security declines, the higher the earnings. There is a cap on earnings
subject to Social Security tax, limiting the amount of benefits received. For a worker retiring at
FRA in 2021, the maximum retired worker benefit is $3,148.69
One measure of adequacy of the program is this replacement rate, or the percentage of career-
average earnings that Social Security benefits wil replace. SSA’s Office of the Chief Actuary
calculates replacement rates for five hypothetical worker profiles: those with very low earnings,
low earnings, medium earnings, high earnings, and maximum earnings.70 The replacement rates
for workers born in 1954 who retire at the FRA, for example, are 77%, 56%, 42%, 35%, and 27%
for the lowest- to highest-earning workers, respectively. These reflect the progressivity in the
design of the benefit formula.
However, the Social Security program includes both contributions and benefits. Therefore, when
researchers measure whether the program is progressive, they typical y compare lifetime benefits
to lifetime payroll taxes. Common measures include the ratio of lifetime benefits to lifetime

67 American Academy of Actuaries, Social Adequacy and Individual Equity in Social Security, January 2004.
68 T he unreduced benefit, called the Primary Insurance Amount (PIA), for a worker with average indexed monthly
earnings of $3,000, for example, is calculated as follows: [0.9 × (996) + 0.32 × (3,000 -996) + 0.15 × (0)]=$$1,537.68.
T he PIA would be rounded down to $1,537.60. See CRS Report R43542, How Social Security Benefits Are Com puted:
In Brief. T he maximum monthly benefit amount for a worker who claims benefits at FRA and who had steady earnings
at the taxable maximum for his full work history is $3,148 in 2021.
69 See SSA, Social Security Fact Sheet, 2021, https://www.ssa.gov/news/press/factsheets/colafacts2021.pdf.
70 OACT , Replacement Rates for Hypothetical Retired Workers, Actuarial Note Number 2020.9, April 2020, T able C.
T he levels of earnings are a percentage of the Average Wage Index (AWI; $54,100 for 2019). T he medium earner is at
the AWI, whereas the very low, low, and high earners are at 25%, 45%, and 160% of the AWI, respectively . T he
maximum earner has earnings at or above the contribution base (the taxable maximum was $142,800 in 2021) for her
earnings history.
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taxes; net benefits or the net present value (NPV),71 which is the difference between the present
value of lifetime benefits and lifetime payroll taxes; and the internal rate of return (IRR), which
can be described as the rate of return on lifetime payroll taxes. If any of these measures—the
lifetime benefit to lifetime tax ratio, NPV, or IRR—is higher for lower earners than for higher
earners, then the program is considered to be progressive.
Variation in life expectancy can weaken the basic progressivity built into the Social Security
benefit formula. Life expectancy determines the number of years a person receives benefits, and
this varies significantly by worker. Consider, for example, workers of the same age with identical
lifetime earnings profiles who claim benefits at the same age. They wil receive identical initial
monthly Social Security benefits. However, the value of their lifetime gross Social Security
benefits can be quite different depending on their individual life expectancies. Individual life
expectancy also affects net Social Security benefits, which account for taxes paid into the
program. For example, the rate of return on one’s contributions that compares the present value of
a stream of benefits received to the present value of taxes paid during one’s working years wil be
affected by life expectancy, which determines the number of years in which benefits are claimed.
Social Security currently makes no adjustments in its initial benefit formula for any variation in
individual life expectancy. If life expectancy varies by income, then lifetime Social Security
benefits wil vary as wel . Progressivity that is based on the value of lifetime Social Security
benefits and contributions may be undermined. A life expectancy gap by income that is growing
wil further undermine the redistributive nature of the current Social Security benefit formula.
Research from a few decades ago looked at the question of whether gaps in life expectancy have
a significant impact on progressivity of Social Security lifetime benefits. Although it was
common knowledge that life expectancy tends to be higher for higher-income groups, lack of data
limited research on the size of the impact of differential mortality. In an early study, researchers
using Social Security earnings and benefit records found that higher life expectancy for higher-
income individuals does reduce progressivity but does not reverse the broad conclusion that
Social Security retirement benefits continue to be strongly progressive. For example, without any
adjustments for mortality, men in the low-income group earned a rate of return on their lifetime
contributions to Social Security that was 1.24 percentage points higher than that of men in the
highest-income group. With adjustments for mortality, this difference shrank to 1.13 percentage
points.72
Recent Evidence
Using newer evidence on mortality differentials that have continued to widen, researchers in
recent years have been able to provide estimates of how these differentials affect receipt of Social
Security benefits. Each of the studies discussed below makes specific assumptions about how
earnings, Social Security benefits, life expectancy, and other related variables are measured and
projected. Their results are contingent upon their assumptions and methods. Despite the
limitations and drawbacks that these assumptions and methods pose, there is general consensus
among these studies that higher earners, with their greater gains in life expectancy, can expect to

71 Present value is defined as the current worth of a future sum of money or stream of cash flows given a specified rate
of return.
72 For a discussion of earlier research and estimates of impact of differential mortality on an early cohort of Social
Security beneficiaries born between 1917 and 1922, see James E. Duggan, Robert Gillingham, and John S. Greenlees,
Progressive Returns to Social Security: An Answer from S ocial Security Records, U.S. T reasury, Research Paper No.
9501, November 1995, https://www.treasury.gov/resource-center/economic-policy/Documents/rp9501.pdf.
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collect higher monthly benefits over an increasingly longer period of time, compared with the
lower-earning, shorter-lived segments of the population.
The 2015 NAS study73 provides estimates of the distribution of lifetime Social Security benefits.
These estimates consider only gross lifetime benefits and not contributions to Social Security.
Individuals are assigned to lifetime income quintiles based on an average measure of midcareer
earnings. The earnings measure is the average of nonzero earnings over ages 41 to 50.74 Earnings
above the taxable maximum are estimated based on the month in which the cap is reached.
Lifetime income is adjusted for married couples. For individuals who are part of a couple, their
lifetime income is adjusted for a two-person household by dividing the sum of their earnings by
an equivalence scale.75 The study compares outcomes for the 1930 birth cohort with those of the
1960 cohort. Social Security benefits are simulated for each cohort—assuming benefits are
claimed at the EEA and taking into account their estimated life expectancies—and their present
values are estimated at age 50 for each individual. (Present value, or present discounted value, is
the current worth of a future stream of benefits.) As described earlier, in estimated life
expectancy, mortality projections are used for younger individuals in the sample (i.e., for the 1930
birth cohort after age 78 and for the entire 1960 birth cohort).
Figure 6. Average Lifetime Social Security Benefits for Males and Females
Born in 1930 and 1960, by Income Quintile
(in thousands of real 2009 dol ars)

Source: National Academies of Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by
Income: Implications for Federal Programs and Policy Responses (Washington, DC: The National Academies Press,
2015), Figures 4-5 and 4-6.
Note: Underlying cohort life expectancy estimates calculated using observed sample mortality where possible
and projected mortality for younger sample individuals (i.e., older than age 78 for the 1930 birth cohort and for
the entire 1960 birth cohort).

73 National Academies of Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by Income:
Im plications for Federal Program s and Policy Responses (Washington, DC: T he National Academies Press, 2015).
74 T he authors note that their choice of earnings is reasonable given that their goal is not to estimate rates of return , but
to assess how changing life expectancy gaps affect receipt of lifetime benefits.
75 For married persons, household earnings are summed and adjusted for household equivalence by dividing by the
square root of 2. T he needs of a household grow with its size but not in direct proportion. Equivalence scales allow for
assigning the needs of a household to its size. One scale used by the Organisation for Economic Co -operation and
Development divides household income by the square root of household size.
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Figure 6 shows the present value of Social Security benefits at age 50, adjusted for life
expectancy for males and females, by earnings quintiles. For both the 1930 and 1960 cohorts,
benefits increase with earnings. Although the Social Security benefit computation formula al ows
for lower earners to earn a higher replacement rate than higher earners, their dollar value of
benefits received is lower because benefits increase with earnings. From Figure 6, males born in
1930 who are in the bottom earnings quintile receive $126,000 (in 2009 dollars) in lifetime
benefits, compared with $229,000 for males in the highest earnings quintile, a difference of
$103,000. The lowest earnings groups in the NAS study have seen little or no improvements in
life expectancy. As a result, males born in 1960 who are in the bottom earnings quintile see their
benefits stay flat at about $122,000, roughly the same as the 1930 cohort. Males in the highest
earnings quintile—who can expect to live about seven years longer than those in the 1930
cohort—can expect to receive $295,000 in benefits, almost $173,000 more than those in the
lowest earnings quintile. In short, gains in life expectancy increase the difference between the top
and bottom earnings quintiles from $103,000 for the 1930 cohort to $173,000 for the 1960 cohort.
The increase in benefits is not confined to the highest earnings quintile. For men, the third and
fourth earnings quintiles also see significant gains in benefits compared to those in the two lowest
earnings quintiles.
Figure 6 shows the same general pattern in lifetime benefits for females.76 These benefits include
dependent spouse and survivor benefits. Like males, the difference in lifetime Social Security
benefits for females between the lowest and highest income quintiles is larger for the 1960 cohort
than for the 1930 cohort. The gains in benefits are most pronounced at the highest earnings
quintile—from the 1930 cohort to the 1960 cohort, females in the top quintile see their benefits
increase from $208,000 to $235,000.
These NAS figures show that Social Security retirement benefit differentials by earnings have
grown between the 1930 and 1960 cohorts for both men and women, driven by gains in life
expectancy skewed to those with higher earnings. The increased differential is not just
concentrated at the very top earnings quintile. Rather, it is observed at al but the bottom two-
fifths of the earnings distribution. These two bottom quintiles have experienced either a tiny
decline or very smal life expectancy gains.
The 2016 Brookings report77 looks at the impact of estimated differences in life expectancy for
the 1920 and 1940 birth cohorts on the distribution of lifetime Social Security retirement benefits.
The results reported here are based on Brookings’s tabulations of data from the Census Bureau’s
SIPP, which are matched to Social Security earnings and benefit records.78 Similar to the 2015
NAS study, individuals are assigned to household earnings deciles based on midcareer earnings,
and adjustments to earnings are made for married couples. As described earlier, in this study
males in the top earnings decile in 1940 gain about 8.7 years in life expectancy, given survival to
age 50, compared with the 1920 cohort. Those in the lowest earnings decile gain about 1.7 years.
The steep lines in Figure 7 show the strong positive relationship between life expectancy gains
and increases in expected lifetime Social Security retirement benefits. Comparing males born in
1940 with those born in 1920, expected Social Security lifetime benefits increase by 10% for the

76 A. J. Auerbach, et al., How the Growing Gap in Life Expectancy May Affect Retirement Benefits and Reforms,
National Bureau of Economic Research, W23329, April 2017, summarizes many of the NAS study findings. T he study
states that it does not discuss the results on females because estimates of mortality differences by income for females
are often seen as less reliable.
77 Barry Bosworth, Gary Burtless, and Kan Zhang, Later Retirement, Inequality in Old Age, and the Growing Gap in
Longevity Between Rich and Poor, Brookings Institution, Washington, DC, 2016.
78 T he authors also report results from the Health and Retirement Study (HRS), which are similar to those based on the
SIPP. HRS-based results are not discussed here.
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bottom earnings decile, about 24% for the sixth earnings decile, and 40% for the topmost
earnings decile. Females in the top earnings decile in 1940 gain about six years compared with
the 1920 cohort. There is no increase in life expectancy for women in the lowest earnings decile.
Thus, there are no expected Social Security lifetime benefit increases for this decile. Benefits for
females increase by 12% for the sixth earnings decile and 26% for the topmost earnings decile.
The underlying data in the study show that the highest earnings decile of the 1940 male cohort
receives 3.3 times the Social Security benefits of the lowest earnings decile, compared with the
highest earnings decile in the 1920 cohort receiving 2.6 times the Social Security benefits of the
lowest earnings decile. (Al benefit amounts are in 2005 dollars.) For females, the gains in
benefits are compressed. The highest earnings decile in the 1940 cohort receives 1.9 times the
benefits of the lowest earnings decile, compared with a disparity of 1.5 times for the 1920 cohort.
Like the NAS findings, Figure 7 shows longevity gains resulting in a widening of differentials in
Social Security lifetime retirement benefits by earnings.
Figure 7. Change in Life Expectancy and Percentage Change in Lifetime Social
Security Benefits for the 1920 and 1940 Birth Cohorts, by Earnings Deciles

Source: Barry Bosworth, Gary Burtless, and Kan Zhang, Later Retirement, Inequality in Old Age, and the Growing
Gap in Longevity Between Rich and Poor, Brookings Institution, Washington, DC, 2016, Table IV-6.
Notes: Numbers based on Brookings authors’ tabulations using data from the Survey of Income and Program
Participation panels (1984, 1993, 1996, 2001, and 2004) matched to SSA earnings and benefit data. Earning s
deciles are based on distribution of midcareer household earnings. Cohort life expectancy estimates calculated
assuming survival to age 50 and using projected mortality risks. Lifetime Social Security benefits are the product
of remaining life expectancy at age of first benefit receipt and average of the benefit values reported in the SSA
benefit records. Benefits are measured in 2005 dol ars.
A 2016 GAO study79 reports similar results of widening differentials in Social Security retirement
benefits by earnings. The authors use the SSA “Quick Calculator” to estimate lifetime retirement
benefits (excluding survivor benefits) for hypothetical individuals using SSA’s average life
expectancy estimates and compare them with benefits based on life expectancies for males
estimated by Waldron’s 2007 study.80 Income percentiles are based on the 2015 CPS, and Social

79 Government Accountability Office, Shorter Life Expectancy Reduces Projected Lifetime Benefits for Lower Earners,
GAO-16-354, March 2016.
80 Hilary Waldron, “T rends in Mortality Differentials and Life Expectancy for Male Social Security -Covered Workers,
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Security benefits are initial y unadjusted for present value. GAO finds that lower-income (25th-
percentile) males see a projected reduction in lifetime Social Security benefits of about 11% to
14% compared to what they would have received if they had average life expectancy. Higher-
income (75th-percentile) males see an increase in lifetime Social Security benefits of about 16% to
18% compared to those with average life expectancy. Differential life expectancy results in
smal er (bigger) estimated lifetime Social Security benefits for lower- (higher-) income groups
relative to those with average life expectancy with present value adjustments as wel .
In a 2011 study, Gopi Shah Goda, John Shoven, and Sita Slavov81 examine the progressivity of
Social Security explicitly for retired worker benefits when differential mortality patterns are taken
into account. They use two metrics: (1) the net present value (NPV), which is the difference
between the present discounted value of expected Social Security cash inflow and outflow,
assuming a “safe” rate of return (e.g., 2%); and (2) the IRR, which can be interpreted as the rate
of return earned in the aggregate by individuals within a cohort.82 Higher NPVs and IRRs
represent more favorable outcomes. They use SSA’s Benefits and Earnings Public-Use File,
2004, which has earnings histories and other data on a 1% sample of December 2004
beneficiaries.83
The authors find that (1) women general y have higher IRRs and NPVs than men because of their
longer life expectancies and lower earnings and (2) later cohorts (e.g., 1931 to 1939) have higher
IRRs and NPVs than earlier cohorts (e.g., 1915 and 1923). Additional y, although differential
mortality makes a relatively smal difference to the IRRs/NPVs among older cohorts, it produces
a significantly larger effect for younger cohorts due to greater inequality in mortality, even
reversing progressivity, as measured in this study.84 For instance, the authors find that men in the
75th percentile in the 1931 and 1938 cohorts attain higher IRRs than men in the 25th percentile,
when differential gains in life expectancy are taken into account. According to the study, “At least
in terms of rates of return, an apparently progressive system becomes regressive.”85 The authors
demonstrate that Social Security is no longer progressive for later birth cohorts (i.e., 1931 and
1939) of men due to increases in mortality inequality, while Social Security remains progressive
for women.86
In 2020, Sanchez-Romero et al.87 studied how the differential mortality by income quintile would
affect the progressivity of six different pension systems, including the U.S. Social Security

by Socioeconomic Status,” Social Security Bulletin, vol. 67, no. 3 (2007), pp. 1-28. See report section on “ The Growing
Gap in Life Expectancy by Income: Recent Evidence” for a discussion of Waldron’s study.
81 Gopi Shah Goda, John Shoven, and Sita Slavov, “Differential Mortality by Income and Social Security
Progressivity,” in Explorations in the Econom ics of Aging , ed. David A. Wise (Chicago: University of Chicago Press,
2011).
82 Ibid., p. 197. T his is the interest rate at which the NPV of Social Security benefits equals zero .
83 T hey construct stylized earnings profiles of individuals with earnings at the 25 th, 50th, and 75th percentiles, and also
study the actual earnings histories of individuals born in 1931-1939. Stylized earnings need to be created because of
lack of Social Security annual earnings data from 1937 to 1950.
84 T he authors’ findings using SSA data on actual workers are consistent with the results from stylized workers.
85 Gopi Shah Goda, John Shoven, and Sita Slavov, “Differential Mortality by Income and Social Security
Progressivity,” in Explorations in the Econom ics of Aging , ed. David A. Wise (Chicago: University of Chicago Press,
2011), p. 199.
86 Among the limitations of this study are the broad categories used for income (e.g., top half versus bottom half of
income distribution), as well as the lack of data on more recent birth cohorts (e.g., baby boomers).
87 Miguel Sanchez-Romer, Ronald D. Lee, and Alexia Prskawetz, “Redistributive effects of Different Pension Systems
When Longevity Varies by Socioeconomic Status,” Journal of the Econom ics of Ageing, vol. 17 (October 2020).
Similar results are also available at Ronald D. Lee and Miguel Sanchez-Romero, “ Overview of the Relationship of
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system and a hypothetical Social Security system with benefit adjustments to mortality
differences. Similar to the NAS study discussed earlier, the authors compare outcomes for U.S.
males in the 1930 birth cohort with those in the 1960 cohort, using an overlapping generation
model in which individuals make optimizing choices for education, labor effort, age at retirement,
and consumption trajectories.
The result in this study implies that the differential mortality between low earners and high
earners reduce the progressivity of Social Security. Estimates show that, under current Social
Security, the IRR (i.e., the rate of return of lifetime payroll taxes) for the 1930 cohort in the
bottom income quintile is 1.67% compared with 2.28% for those in the top income quintile. This
difference in IRR is widened for the 1960 cohort due to larger mortality differences—0.6% for
the bottom income quintile compared with 2.46% for the top income quintile. The study also
analyzes a hypothetical Social Security system with corrections for mortality differences by
adjusting retirement benefits upward for people with shorter life expectancy and downward for
those with longer life expectancy where the life expectancy may depend on various factors, such
as gender, race, education levels, and income.88 This correction could general y reduce the
negative effect of mortality differences on the program progressivity. The estimates show that, for
the 1930 cohort, the IRR for those in the bottom income quintile would be 1.94% compared with
1.98% for those in the top income quintile and 1.55% and 2.06% for the 1960 cohort in the
bottom and top quintiles, respectively. Adjustments for differential mortality in the pension
system may provide an option to improve the progressivity in the program. The methods of
estimating life expectancy for different individuals and the approaches of incorporating the
mortality differences into the existing pension system would need further analysis.
Research discussed here provides evidence that uneven gains in life expectancy have a significant
impact on lifetime Social Security retirement benefits. If the life expectancy gap by income
continues to grow, the gap in lifetime benefits between low earners and high earners would
continue to widen. The total value of lifetime retirement benefits would continue to increase for
high earners, as would their returns to contributions made to the Social Security program, in
contrast to the lifetime benefits and rates of return for low earners. Thus, progressivity that
considers both lifetime benefits and contributions would likely erode.
The discussion here has focused only on retirement benefits (benefits available to workers and
their dependents at retirement). The previously discussed 2006 CBO study examined three
components of Social Security benefits separately—retired worker, disability, and auxiliary (for
dependents of retired, disabled, or deceased workers)—and found that disability and survivor
benefits continue to be strongly progressive.89

Heterogeneity in Life Expectancy to Pension Outcomes and Lifetime Income,” in Non-Financial Defined Contribution
Schem es (NDC): Facing the Challenges of Marginalization and Polarization in Econom y and Society , ed., R.
Holzmann et al. (Washington, DC: World Bank, 2019).
88 For more discussion about the adjustment to longevity heterogeneity in pension systems, see Mercedes Ayuso, Jorge
Miguel Bravo, and Robert Holzmann, “ Addressing Longevity Heterogeneity in Pension Scheme Design,” Journal of
Finance and Econom ics, vol. 6, no. 1 (2017), pp. 1-21.
89 Noah Meyerson and John Sabelhaus, “Is Social Security Progressive?,” CBO, Economic and Budget Issue Brief,
2006.
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Policy Considerations for Proposals That Increase
the Retirement Age
Increasing longevity is an unmistakable sign of progress in a population. Nevertheless, as people
live longer, the policy chal enge of how to pay for these additional years must be confronted.90
Increased time spent in retirement exerts immense upward pressure on costs for various public
programs. According to the Social Security Chief Actuary,91 the permanent shift in the age
distribution between 2010 and 2030 (primarily because of lower birth rates as wel as increased
life expectancy) remains the dominant factor accounting for the increased Social Security
program cost for the next several decades. Several options exist that would address Social
Security’s financing chal enges. Prominent among these are proposals that slow the growth in
benefit expenditures. These include changing the benefit formula, changing how initial benefits
are indexed for price changes, and/or changing the retirement age. Other options aim to increase
Social Security program revenue by changing the taxation of earnings; these include increasing
the payroll tax rate or the taxable maximum. CBO92 lists various other options commonly
proposed by policymakers and analysts to address Social Security’s financial imbalance.93 This
CRS report focuses on one kind of proposal; specifical y, it discusses options to increase the early
or full retirement age.
In the area of retirement policy, a common public policy response to the fiscal pressures of a
population living longer and healthier is to propose increases in the retirement ages. A suggested
option is to increase the Full Retirement Age beyond 67 given that the population may be able to
work to older ages in the future. For those who claim benefits at the FRA, an increase in the FRA
results in fewer months of benefits and a reduction in the total amount of lifetime Social Security
benefits received. An increase in the FRA reduces benefits.94 The 1983 Social Security
amendments, for example, increased the FRA but not the earliest eligibility age.95 They increased
the penalty for claiming benefits at the EEA. Increases to the FRA were phased in to al ow
individuals to adjust by making behavioral responses, such as delaying claiming benefits.
CBO routinely considers a set of changes in the FRA and EEA in its study of Social Security
policy options. Various entities have proposals on how to implement a change in the retirement

90 A common measure of the overall impact of aging populations on societies is the dependency ratio, which measures
the total number of dependents (aged 0-14 and over 65) to the working-age population (aged 16-64). See CRS Report
RL32981, Age Dependency Ratios and Social Security Solvency.
91 Stephen C. Goss, “ T he Future Financial Status of the Social Security Program,” Social Security Bulletin, vol. 70, no.
3 (2010). T he permanent upward shift in Social Security’s program cost rate is due mostly to a permanent drop in the
birth rate that followed the birth of the baby boomers, and therefore increasing the FRA is not seen as the principal
solution to address Social Security funding issues.
92 CBO, Social Security Policy Options, 2015.
93 Although Social Security is a self-financing program, the Social Security T rust Funds (from which benefits are paid)
are expected to be exhausted in 2034, as per the 2016 Annual Report of the Social Security Board of Trustees
(https://www. ssa.gov/OACT /T R/2016/tr2016.pdf.) If no action is taken to address this impending exhaustion, the
Social Security Actuaries project that only approximately three-fourths of full benefits under current law can be paid
with incoming payroll tax revenue and income from taxation of Social Security benefits. See CRS Report R42035,
Social Security Prim er.
94 Distributional Effects of Accelerating and Extending the Increase in the Full Retirement Age , Social Security Policy
Brief 2011-01, January 2011.
95 See CRS Report R44670, The Social Security Retirement Age.
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ages, which include increasing the FRA and/or the EEA.96 For example, in its 2016 report, the
Bipartisan Policy Center Commission on Retirement Security and Personal Savings
recommended gradual y raising the FRA (to 69) and the maximum benefit age (to 72), both by
one month every two years.97 In the 116th Congress, the Social Security Solvency and
Sustainability Act (S. 3234) proposed increasing the FRA to 69 and the EEA to 64. In the 114th
Congress, the Social Security Reform Act of 2016 (H.R. 6489) proposed increasing the FRA to 69
as one in a set of proposals to reform the program, Alex’s Law (H.R. 1366) proposed increasing
the FRA to 70 and EEA to 65, and the S.O.S. Act (H.R. 5747) proposed increasing the FRA by
two months per year until it reached age 69 and then by one month per year thereafter. The 2010
National Commission on Fiscal Responsibility and Reform98 (commonly referred to as the
Simpson-Bowles Commission, after its co-chairs) also proposed increasing both the EEA and
FRA, adjusting future increases to changes in longevity, and al owing for a hardship exemption,
thereby protecting workers from the effects of an increase in retirement ages. By about 2070,
under their proposal, the EEA would reach 64 and the FRA would reach 69 for most workers.
Estimated Impacts of Policy to Increase Earliest Eligibility Age
and Full Retirement Age
This section describes recent research that estimates the impact that the growing gap in life
expectancy can have on policy proposals that would increase retirement ages. It briefly discusses
the limitations of a hardship threshold that is often suggested in these proposals to mitigate the
effects of the growing gap on the low earner who has made little to no gains in life expectancy.
The 2015 NAS study,99 for example, simulates the policy impact of increasing both Social
Security’s EEA and the FRA. It considers two mechanisms by which a policy change can affect
benefits. One is the pure mechanical effect that is a direct outcome of the proposed change (a
worker wil see a reduction in full benefits if benefits are claimed before the new full retirement
age), and the other is a behavioral effect that measures changes in behavior in response to a new
policy. For example, a higher retirement age may induce a person to work longer, claim later, or,
if eligible, claim disability benefits earlier.
Effect of Proposals to Increase the Earliest Eligibility Age
The first policy simulation discussed in the NAS study (a summary of the data is provided in
Table A-1) increases the EEA from age 62 to 64. Note that claiming at the EEA reduces one’s
monthly benefit; however, the longer stream of benefits that begins at the EEA is actuarial y fair,

96 Automatic indexing—increasing the FRA automatically with increases in average life expectancy —is another
proposed option to confront rising longevity. T hese proposals can automatically adjust both benefits and taxes paid;
however, implementation can be difficult. See Peter Diamond and Peter Orszag, Saving Social Security: A balanced
approach (Washington, DC: Brookings Press, 2004), for a description of their automatic indexing proposal. In 1998,
Sweden implemented life expectancy indexing with automatic adjustments. It incorporated improvements in life
expectancy at age 65 into the benefit formula. Germany had life expectancy adjustments indirectly built into its benefit
formula. J. A. T urner, Longevity Policy: Facing Up to Longevity Issues affecting Social Security, Pensions, and Older
Workers (Kalamazoo, MI: Upjohn Institute Press, 2011), provides examples of international experiences with indexing.
97 Bipartisan Policy Center: Securing our Financial Future: Report of the Commission on Retirement Security and
Personal Savings, June 2016, http://bipartisanpolicy.org/library/retirement-security/.
98 National Commission on Fiscal Responsibility and Reform, The Moment of Truth: Report of the National
Com m ission on Fiscal Responsibility and Reform , December 1, 2010, http://www.washingtonpost.com/wp-srv/politics/
documents/T heMomentofTruth.pdf.
99 National Academies of Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by Income:
Im plications for Federal Program s and Policy Responses (Washington, DC: T he National Academies Press, 2015).
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on average, when compared to the shorter stream of benefits calculated at the FRA. Under current
law, an individual claiming at the EEA of 62 would see a 30% reduction in her monthly benefit
compared to what she could have received had she claimed at a FRA of 67. The monthly benefit
reduction is smal er the closer one claims to the FRA. Assuming no change in behavior, raising
the EEA would have no significant effect on lifetime benefits for most people. Individuals who
would otherwise claim at ages 62 or 63 before the policy change would now claim at age 64, the
higher EEA, but receive higher monthly benefits (i.e., less reduced benefit) for fewer years when
compared with the earlier EEA. Note that this policy change wil not affect those who claim at the
FRA or higher. However, when life expectancy varies by income, the outcomes of an EEA policy
change wil not be neutral. When the EEA is increased from the current age of 62 to 64, NAS
finds that for both the 1930 and 1960 cohorts, after the policy change, the average lifetime Social
Security benefit for the lowest income quintile rises by a modest amount and is close to being
actuarial y fair. That is, there is no significant change to lifetime benefits for the lowest earnings
quintile. Although a majority of those who claim at the EEA tend to have low education (high
school or less) and are low earners, a much smal er but sizable group is made up of high earners
who also claim at the EEA.100 For these high earners, the change in expected lifetime benefits is
somewhat larger. They receive higher lifetime benefits due to their higher life expectancy. It is
plausible to assume that higher earners are better able to accommodate delaying claiming than the
lower earners in the population.101 Thus, for higher earners, changes in the EEA may al ow for
higher monthly benefits due both to delayed claiming and increased years of benefit receipt due
to high gains in life expectancy.
Increasing the EEA from age 62 to 64 increases the difference in Social Security benefits between
the lowest and highest earnings quintiles across the two cohorts. For example, in 1960 for males
(females), the gap between the high and low earners was 142% (158%). After simulating the EEA
policy change, the gap stood at 145% (162%). This policy simulation shows that an increase in
the EEA would skew the distribution of Social Security benefits in favor of high earners.
In her 2013 study, Waldron102 examines the effects of increasing the EEA using 2008 SSA data
files on earnings for individuals born between 1937 and 1945. She discusses policy proposals (for
example, the hardship exemption in the Simpson-Bowles Commission’s plan) that score an
income threshold of hardship. These thresholds are constructed so that workers who fal below
this income threshold are expected to be adversely affected by an increase in the EEA and are
thus exempted from any change in their EEA. This threshold general y fal s at the bottom 20% of
the income distribution, with the implicit assumption that only workers below the threshold are
not expected to experience gains in life expectancy. Waldron rejects the idea of an income
threshold model. She finds that income and mortality are strongly linked even above these
hardship thresholds.103 She estimates mortality differentials at ages 63-71 by lifetime earnings
decile and reports that for at least the bottom 80% of the male earnings distribution, the higher the
earnings, the lower the mortality risk. Only in the top 20% of the income distribution does the
link between mortality and earnings weaken. If an income threshold is to effectively protect
individuals who experience relatively modest to no gains in life expectancy from adverse effects

100 Melissa Knoll and Anya Olsen, “Incentivizing Delayed Claiming of Social Security Retirement Benefits Before
Reaching the Full Retirement Age,” Social Security Bulletin, vol. 74, no. 15 (2014).
101 For a discussion of how today’s older workers are relatively better educated than earlier generations, see Gary
Burtless, The Im pact of Population Aging and Delayed Retirem ent on Workforce Productivity , Center for Retirement
Research, Boston College, May 2013.
102 Hilary Waldron, “Mortality Differentials by Lifetime Earnings Decile: Implications for Evaluations of Proposed
Social Security Law Changes,” Social Security Bulletin, vol. 73, no. 1 (2013), pp. 1-37.
103 T he 2016 Chetty et al. study discussed earlier in the report makes the same claim.
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The Growing Gap in Life Expectancy by Income

of EEA increases, mortality risk must be roughly constant above the hardship income threshold. A
simple cutoff of a low-income hardship threshold wil protect only those who experience almost
no gains in life expectancy from EEA policy changes. A graduated income threshold that phases
in changes in the EEA may be one potential remedy.
Effect of Proposals to Increase the Full Retirement Age
The rationale for another popular policy proposal, an increase in the FRA, is that with increasing
life expectancy, not al the additional years of life should be spent in retirement. Absent any
change in claiming behavior, an increase in the FRA would result in a reduction in lifetime
benefits for al retirees. NAS, in a policy experiment in its 2015 study, simulated an increase in
the FRA to age 70.104 The study found that for the 1930 cohort of males, for the lowest income
quintile, the increase in the FRA reduces benefits by 25% of baseline benefits, and for the highest
income quintile, benefits are reduced by 22%. The ratio of Social Security benefits for the
topmost to the lowest quintile rises from 1.82 to 1.88. For the 1960 cohort of males, benefits too
fal by 25% for the lowest income quintile and 20% for the topmost quintile, and the ratio of
benefits of the topmost quintile to the lowest increases from 1.42 to 1.57. This simulation is able
to capture behavioral responses to an increase in the FRA, and the authors find that higher earners
are able to delay claiming retirement benefits longer than lower earners, and their longer life
expectancy in post-benefit years results in a smal er drop in lifetime benefits. Thus, an increase in
the FRA would increase the gap in lifetime benefits by income quintiles.
In their 2016 analysis, Chetty et al.105 examine IRS income data on individuals aged 40-76 years
for the period 2001-2014 to study the association between income and life expectancy. As
discussed previously, this study finds that men in the top 1% of the income distribution lived 14.6
years longer than men in the bottom 1% (averaged across years and ages), and life expectancy
gaps increased over time. Their most relevant finding for Social Security reform is that life
expectancy increased continuously with income and that, according to them, “[t]here was no
dividing line above or below which higher income was not associated with higher life
expectancy.”106 At increasingly higher levels of income, they report that an increase in income of
a given dollar amount produced positive but smal er gains in life expectancy.
Policy proposals that increase the retirement age wil tend to skew Social Security benefits toward
higher earners. Even if a threshold were adopted that protects very low earners who have
experienced little to no longevity gains, research discussed here finds that the positive association
between life expectancy and income weakens only around the top fifth of the income distribution.
Women, who on average tend to live longer than men, typical y have lower lifetime earnings than
men. If a low earnings hardship threshold were adopted to protect low earners from a change in
the FRA, this could have the perverse effect of protecting women with a life expectancy
advantage while failing to protect many men with somewhat higher earnings but lower life
expectancy. Thus, a simple hardship threshold based on low earnings in policy proposals that
increase the retirement age wil likely not adequately protect al affected by the uneven gains in
life expectancy. One potential solution is for proposals to focus on a graduated income threshold
that phases out at higher levels of earnings.

104 T he results for females, not reported here, are not as dramatic.
105 Raj Chetty et al., “T he Association Between Income and Life Expectancy in the United States, 2001 -2014,” Journal
of the Am erican Medical Association, vol. 315, no. 16 (2016), pp. 1750 -1766.
106 Ibid., p. 1762.
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The Growing Gap in Life Expectancy by Income

The Bipartisan Policy Center, in its 2016 report on retirement security,107 provides an array of
Social Security reform proposals. One of these increases the FRA. The report acknowledges that
longevity increases have not been evenly shared across the income distribution, and states that
their other policy recommendations of changes to the benefit formula and minimum benefits
would more than offset the disproportionately negative impact of raising the FRA on those with
lower lifetime earnings.
In 2019, Reznik et al.108 examined how options for raising the retirement age affect Social
Security beneficiaries (excluding disabled beneficiaries) in 2030, using the Modeling Income in
the Near Term (MINT) microsimulation model.109 For a list of policy options, the study compares
simulated Social Security monthly benefits, lifetime benefits, household income, and poverty
rates with current law for different individuals across the shared lifetime earnings distribution.110
Those options mainly include (1) increasing the FRA from age 67 to 69, (2) increasing the FRA
with differential mortality adjustment factors, (3) increasing the FRA and raising the EEA from
age 62 to 64, and (4) increasing the FRA and EEA with differential mortality adjustment factors.
These factors would adjust the basic Social Security benefits upward for those with relatively
lower career-average earnings/shorter life expectancy and downward for those with relatively
higher career-average earnings/longer life expectancy.111
As mentioned earlier, without other adjustments or changes in the claiming behavior, increasing
the FRA to age 69 would result in reduction in Social Security monthly benefits, lifetime benefits,
and household income but increases in poverty rates. The authors find that, due to the gap in life
expectancy, the simulated median household monthly income would decrease by 4% for
beneficiaries in the bottom three quintiles of the shared lifetime earnings distribution, compared
with 2% in the top quintile. After the mortality adjustment, the simulated median household
monthly income would decrease by 0% in the lowest quintile of the shared lifetime earnings
distribution, compared with 4% in the top quintile. The poverty rate for those in the lowest shared
lifetime earnings quintile would increase by 4.9 percentage points under the FRA increase
without the mortality adjustment, compared with decreasing by 0.2 percentage point with the
mortality adjustment. The effect of mortality adjustments on median household monthly income
and poverty would be similar for options that would increase both the FRA to age 69 and the EEA
to age 64, as wel as the options that assume a delay in benefit claiming by two years due to the
increases in FRA and EEA.
Adjustments in Social Security benefits for differential mortality provide an option to reduce the
gap in lifetime benefits by income quintiles (or shared lifetime earnings quintiles in Reznik et al.)
under an increase in the retirement age. It is noticeable that such mortality adjustments could

107 Bipartisan Policy Center, Securing Our Financial Future: Report of the Commission on Retirement Security and
Personal Savings, June 2016, http://cdn.bipartisanpolicy.org/wp-content/uploads/2016/06/BPC-Retirement-Security-
Report.pdf.
108 Gayle L. Reznik et al., “Longevity-Related Options for Social Security: A Microsimulation Approach to Retirement
Age and Mortality Adjustments,” Journal of Policy Analysis and Management, vol. 38, no. 1 (2019), pp. 210-238.
109 SSA, Office of Research, Evaluation, and Statistics, “MINT Overview,” https://www.ssa.gov/policy/about/
mint.html. T he MINT microsimulation model is built primarily on the Survey of Income and Program Participation and
linked at the individual level with SSA benefits and earnings records.
110 T he study defines shared lifetime earnings as lifetime earnings from the individual and earnings of his or her prior,
current, or expected spouse during years of marriage. T he paper claims that shared lifetime e arnings provide a better
comparison of the economic status of married and single individuals over their lives, particularly for women.
111 T he mortality adjustment alters the PIA of beneficiaries in 2030 based on their AIME or the lifetime earnings of the
retired workers if those persons are auxiliary beneficiaries (such as spouses or survivors). In effect, the adjustment
mitigates the growing gap in average life expectancy by amplifying the PIA for those in lower AIME quartiles and
reducing the PIA for those in higher AIME quartiles for male and female, respectively. See T able 2 in Reznik et al.
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The Growing Gap in Life Expectancy by Income

improve the progressivity of Social Security even under current law. The question of what
administratively effective methods could be used to produce fair and accurate estimates of
mortality adjustment factors for different types of individuals remains.
Conclusion
Recent research documents a substantial and growing gap in life expectancy by income. In
comparison with individuals born earlier in the 20th century, cohorts of Americans born more
recently are experiencing wider such gaps in life expectancy. That is, individuals with lower
lifetime earnings are living shorter lives, on average, than their counterparts with higher lifetime
earnings—and this gap has continued to widen over recent decades.
The studies discussed in this report use specific assumptions and methods to measure earnings,
Social Security benefits, life expectancy, and other related variables, and necessarily have
limitations. Stil , the evidence clearly indicates that this growing gap in life expectancy has
important implications for Social Security. Specifical y, recent evidence shows that higher
earners, with their higher-than-average gains in life expectancy, can expect to collect Social
Security benefits over increasingly longer periods of time than the lowest-earning groups, who
have experienced little to no gains in additional years lived. Public policy proposals that increase
the retirement age in response to rising life expectancy and also improve Social Security’s
financing are quite common. However, these policies would further erode the progressivity of
retirement benefits, a long-standing goal of the program, which aims for low lifetime earners to
receive a higher return on their lifetime contributions than high lifetime earners. Additional y, life
expectancy is found to increase continuously with income, with the link weakening only at the
very top of the income distribution. A hardship income threshold, often recommended in
retirement age increase proposals to protect the low earner with limited gains in life expectancy,
may need to be constructed carefully. There appears to be no simple income cutoff point above
which life expectancy gains do not increase with income. Mortality adjustments to Social
Security benefits might improve the progressivity of the program and reduce the gap in lifetime
benefits by income under an increase in the retirement age. However, additional research is stil
needed in analyzing the proper method of constructing mortality adjustment factors and
incorporating those factors into the benefit formula. Alternatively, other policies may have to be
considered simultaneously to mitigate the effects of a higher FRA on low lifetime earners.
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Appendix. Summary of Selected Studies on the Life Expectancy Gap
by Income
Table A-1. Selected Studies on the Life Expectancy Gap by Income
Study
Data and Key Measures Used
Main Results
Waldron (2007)
Data: SSA data that include taxable wages matched with

The life expectancy gap by earnings over time is substantial and growing.
benefits records and official death records for men born 1912-

For men born in 1912, those in the top half of the distribution could expect to
1941
live about a year longer than those in the bottom half.
Measure of income: Average of men’s positive earnings from

ages 45-55

For men born in 1941, those in the top half could expect to live 5.3 years
longer than those in the bottom half.
Income comparison groups: Earnings relative to the
national average wage (i.e., bottom or top half of earnings
distribution)
Type of life expectancy measure: Cohort life expectancy
(mortality projections used for more recent birth cohorts;
other results not discussed in this report use period life
expectancy)
Cristia (2009)
Data: 1984, 1993, 1996, and 2001 SIPP panels matched to

For both men and women, differentials in life expectancy between top and
earnings, benefit, and mortality data from SSA, and earnings
bottom lifetime earnings quintiles increased substantial y during the period of
data from IRS
study (1983-2003).
Measure of income: Average earnings lagged by three years
(e.g., if older than 52, then average of earnings from age 41-50;
if 52 or younger, then average of 5 to 10 years of earnings)
Income comparison groups: Quintiles of lifetime earnings
distribution
Type of life expectancy measure: Period life expectancy
(mortality projections used for individuals older than 75)
CRS-31

Study
Data and Key Measures Used
Main Results
Congressional
Data: SSA data, with additional demographic and economic

In 2014, a 65-year-old man in the upper lifetime earnings quintile would be
Budget Office
data matched using SIPP, HRS, and CPS
expected to live more than three years longer than that same man in the
(2014)
Measure of income: Lifetime earnings
lowest lifetime earnings quintile.
Income comparison groups: Income quintiles

In 2014, a 65-year-old woman in the upper lifetime earnings quintile would be
Type of life expectancy measure: Period life expectancy
expected to live more than one year longer than this same woman in the
(mortality projections used for future years)
lowest lifetime earnings quintile.

By 2039, 65-year-old men with higher lifetime earnings are expected to live
around six years longer than 65-year-old men in the lower income quintiles,
while high-earning, 65-year-old women wil live around three years longer than
low-earning, 65-year-old women.
National
Data: Biennial waves of HRS data, 1992-2008, matched to SSA

For the 1930 and 1960 birth cohorts, life expectancy at age 50 for men rose as
Academy of
records and employer pension plans
income increased. The life expectancy gap between the bottom and top
Sciences (2015)
Measure of income: Average nonzero Social Security–
income quintiles increased across these cohorts from 5.1 years for men born
reported household earnings for ages 41-50
in 1930 to 12.7 years for men born in 1960.
Income comparison groups: Quintiles of lifetime earnings

For women, the pattern is general y similar: for the two cohorts, higher
distribution
income quintiles experienced higher life expectancy at age 50, except for the
Type of life expectancy measure: Cohort life expectancy
second quintile born in 1930. The life expectancy gap between the bottom and
at age 50 (mortality projections used for younger sample
top income quintiles for women also increased—from 3.9 years for the 1930
individuals [i.e., for the 1930 birth cohort after age 78 and for
cohort to 13.6 years for the 1960 cohort—and there was evidence of a decline
the entire 1960 birth cohort])
in life expectancy for the lowest two income quintiles for women across birth
cohorts.
Brookings
Data: SIPP data on individuals born 1910-1950 and HRS data

At age 50, men in the lowest income decile born in 1920 can expect to live to
(2016)
on individuals born in 1957 matched to SSA data on earnings,
be about 74 years old, compared with about 79 years for men in the top
benefits, and dates of death
income decile.
Measure of income: Average of nonzero earnings for ages

The life expectancy gap by income grew with time. For men born in 1940, at
41-50 (household earnings used for married individuals;
age 50 those in the lowest income decile can expect to live to be about 76,
individual earnings for single individuals)
compared with 88 for those in the topmost income decile.
Income comparison groups: Income decile

For women, the results show no rise at al in life expectancy for those in the
Type of life expectancy measure: Cohort life expectancy
lowest income decile.
(mortality projections used for more recent birth cohorts)
CRS-32

Study
Data and Key Measures Used
Main Results
Chetty et al.
Data: IRS tax data matched with SSA records for individuals

Over 2001-2014, the average longevity gap between the bottom 1% and top
(2016)
for the years 1999-2014; mortality data from NLMS; U.S.
1% was 14.6 years for men and 10.1 years for women.
Census data to weight racial/ethnic composition of income

percentiles

For the 2001-2014 period, those with higher incomes have longer life
expectancy; the life expectancy gap by income increases across time.
Measure of income: Pretax household earnings; income for

individuals aged 63 and older measured at age 61

Among low-income individuals, life expectancy varies by geographic area.
Income comparison groups: Percentile ranks (1-100) based
on age- and sex-specific household earnings for each year
Type of life expectancy measure: Period life expectancy
(mortality projections used for ages older than 76)
Source: Analysis by Congressional Research Service of Hilary Waldron, “Trends in Mortality Differentials and Life Expectancy for Male Social Security-Covered Workers, by
Socioeconomic Status,” Social Security Bul etin, vol. 67, no. 3 (2007), pp. 1-28; Julian Cristia, Rising Mortality and Life Expectancy Differentials by Lifetime Earnings in the United States,
Inter-American Development Bank, Working Paper 665, Washington, DC, January 2009; Congressional Budget Office, The 2014 Long-Term Budget Outlook, July 2014; National
Academy of Sciences, Engineering, and Medicine, The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses (Washington, DC: The
National Academies Press, 2015); Barry Bosworth, Gary Burtless, and Kan Zhang, Later Retirement, Inequality in Old Age, and the Growing Gap in Longevity Between Rich and Poor,
Brookings Institution, Washington, DC, 2016; and Raj Chetty, Michael Stepner, and Sarah Abraham, et al., “The Association between Income and Life Expectancy in the
United States, 2001-2014,” Journal of the American Medical Association, vol. 315, no. 16 (2016), pp. 1750-1766.
Notes: CPS = Current Population Survey. HRS = Health and Retirement Study. IRS = Internal Revenue Service. NLMS = National Longitudinal Mortality Study. SIPP =
Survey of Income and Program Participation. SSA = Social Security Administration.
CRS-33

The Growing Gap in Life Expectancy by Income

Author Information

Katelin P. Isaacs
Zhe Li
Specialist in Income Security
Analyst in Social Policy

Sharmila Choudhury
Isaac A. Nicchitta
Deputy Assistant Director and Specialist in
Research Assistant
Domestic Social Policy

Acknowledgments
Abigail Overbay, Senior Research Librarian at CRS, made significant contributions to the research for this
report.

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