the growing longevity gap between rich and poor …...-2- the growing longevity gap between rich and...
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The Growing Longevity Gap between Rich and Poor and Its Impact on Redistribution through Social Security
Barry Bosworth and Gary Burtless
THE BROOKINGS INSTITUTION
and
Kan Zhang Gianattasio *
GEORGE WASHINGTON UNIVERSITY
October 18, 2016
________________________
* The authors are, respectively, senior fellows in the Economic Studies program at the
Brookings Institution, Washington, D.C., and a graduate student in health policy at the George
Washington University. We are indebted to Mattan Alalouf and Eric Koepcke for excellent
research assistance. The authors gratefully acknowledge funding for this research from the
Alfred P. Sloan Foundation through its “Working Longer” program and from the Social Security
Administration as part of the Retirement Research Consortium. The views are solely our own
and do not represent those of Brookings, the Sloan Foundation, or the Social Security
Administration.
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The Growing Longevity Gap between Rich and Poor and Its Impact on Redistribution through Social Security
by
Barry P. Bosworth, Gary Burtless, and Kan Zhang Gianattasio1
THE BROOKINGS INSTITUTION
Washington, DC
October 18, 2016
Abstract
This paper uses interview data from the Survey of Income and Program Participation (SIPP)
combined with Social Security Administration records on worker earnings, pension benefits, and
mortality to determine the extent of widening differences in U.S. life expectancy by
socioeconomic status (SES). We construct alternative measures of SES using respondents’
educational attainment and average Social-Security-covered earnings in their prime working
ages, 41-50. We find large mortality rate differences among Americans past age 50 when they
are ranked by their SES. More importantly, the differences have grown significantly in recent
years, a finding confirmed using both measures of SES and alternative procedures to sort
respondents into high and low SES groups. Comparing men born in 1920 and 1940, for example,
the estimated gain in life expectancy at age 50 for men in the bottom one-tenth of the mid-career
income distribution was only 1.7 years versus a gain of 8.7 years among men in the top tenth of
the income distribution. Among women in the same two birth cohorts, the expected longevity
gains in bottom and top income deciles were 0.0 years and 6.4 years, respectively. It matters little
whether we use education or mid-career earnings to measure SES. The conclusions are also
robust across samples that include or exclude workers who claim disability benefits. We find
secular changes in differential mortality to be very large, but the influence of widening mortality
differences on the length of time Americans collect Social Security benefits is damped by the
fact that low SES workers and spouses tend to claim pensions at younger ages while high SES
workers are more likely to postpone retirement and benefit claiming. The changes in relative
mortality rates across the income distribution has offset a growing percentage of the
progressivity built into the Social Security benefit formula. We find, however, that the resulting
pattern of lifetime benefits nonetheless remains progressive, even taking account of the widening
mortality differentials in recent years.
1 Bosworth and Burtless are Senior Fellows in the Economic Studies program at the
Brookings Institution in Washington, DC. Gianattasio is a graduate student in health policy at
George Washington University, Washington, DC.
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The Growing Longevity Gap between Rich and Poor and Its Impact on Redistribution through Social Security
by
Barry P. Bosworth, Gary Burtless, and Kan Zhang Gianattasio 1
October 18, 2016
U.S. LIFE SPANS are unequal, and part of the inequality is linked to Americans’ social and
economic position. A long-standing literature shows that there are significant differences in life
expectancy between people with high and low socioeconomic status as measured by income and
educational attainment. New studies show that the longevity differential has widened in the
United States over the past three decades, reversing an earlier trend toward narrower differentials
(Waldron 2007, 2013; NAS 2015; Bosworth, Burtless and Zhang 2016; Chetty et al. 2016). A
few studies suggest that much of the recent increase in expected life spans is concentrated among
those with above-average incomes, and that life expectancy may be roughly constant or even
declining for Americans with lower status.
Recent trends in differential mortality raise profound questions about the equity of old-
age pension formulas. The Social Security retirement-worker pension provides a basic benefit at
the normal retirement age, known as the Primary Insurance Amount or PIA. The formula for this
pension is based on a worker’s average lifetime earnings and is highly redistributive. It provides
a more generous replacement rate for pensioners with low earnings compared with workers who
have high career earnings. This kind of redistribution helps compensate low-wage workers for
their shorter expected life spans. Retired workers’ actual monthly benefits are determined by
their PIA and the actuarial factors used to adjust the monthly pension to reflect early or late
benefit claiming. Workers who claim benefits at the earliest entitlement age, 62, receive reduced
benefits; workers who delay claiming benefits until the latest claiming age, 70, receive a monthly
payment that is about three-quarters greater than a benefit claimed at age 62. Since the early
1990s the average U.S. retirement age has trended upward, and there has also been a trend
toward later claiming of Social Security benefits (Bosworth and Burtless 2010; Bosworth,
1 We gratefully acknowledge the vital research contributions of Mattan Alalouf and Eric Koepcke.
This paper summarizes some of the research findings reported in Bosworth, Burtless and Zhang, Later
Retirement, Inequality in Old Age, and the Growing Gap in Longevity between Rich and Poor
(Washington: Brookings, 2016). We also acknowledge the generous research support provided by the
Alfred P. Sloan Foundation and the Social Security Administration.
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Burtless and Zhang 2016). Workers who delay benefit claiming receive bigger monthly pensions
as a result. If these workers earned better-than-average wages during their careers, the delay in
benefit claiming increases the gap between their monthly Social Security benefits and the
monthly benefits received by lower wage workers, who tend to retire at younger ages and claim
benefits sooner after age 62.
Differences in mortality mean that high-wage workers can expect to collect benefits
longer than low-wage workers who claim benefits at the same age. Because gains in expected
life spans are increasingly concentrated among high-wage workers, the proportional gap in
lifetime benefits between high- and low-wage pensioners has grown in recent years. A common
suggestion to deal with funding shortfalls in Social Security and Medicare is to lift the age of
eligibility for benefits. This policy makes sense if gains in expected life spans are enjoyed
equally by rich and poor. It seems less equitable to ask low-wage workers to wait longer for
retirement benefits when a disproportionate share of gains in life expectancy is enjoyed by the
affluent. In view of the changing relationship between workers’ average lifetime earnings and
their chances of surviving into late old age, how can we recalibrate the PIA formula and the
actuarial adjustment for delayed benefit claiming to protect the interests of low-wage workers?
This paper reports on research that expands the base of knowledge with which to answer this
question.
The remainder of the paper is divided into two parts. The first section presents our results
on the growing mortality differential between Americans based on their average Social-Security-
covered earnings. We organized a large data file containing information from the Census
Bureau’s Survey of Income and Program Participation (SIPP) matched to Social Security
lifetime earnings records and Social Security mortality records. These data permit us to analyze
determinants of mortality within a large sample of SIPP respondents born between 1910 and
1950 over the period from 1984 through 2012. Besides obtaining access to these confidential
data, the most challenging part of the research is devising measures of socioeconomic status that
permit us to make evenhanded comparisons between generations born over a 40-year time span.
This is a challenge because the measures of annual earnings contained in the Social Security
Administration (SSA) files were subject to different reporting limits during the ages we use to
estimate average earnings for successive birth cohorts. In our analyses of these data, we used two
methods for dealing with the limitations of the earnings data.
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The second section of the paper uses our estimates of the widening mortality rate
differential between higher and lower income workers to estimate the impact of growing lifespan
inequality on the lifetime redistribution that takes place through Social Security.
The rising mortality gap between the affluent and poor
Researchers have found evidence of widening in mortality differentials by social and
economic status in a number of recent studies. Singh and Siahpush (2006) offer evidence on
changes in differential mortality using county-level information from the decennial censuses.
They construct county-level indexes of SES linked to death records by location. Meara, Richards
and Cutler (2008) and Olshansky and others (2012) analyze death certificate data, using
educational attainment as a measure of status, and find a sharp rise in inequality. Waldron (2007)
uses administrative records containing information on career earnings and age at death to
establish a similar pattern for men covered by Social Security. Bosworth, Burtless, and Zhang
(2015) and NAS (2015) use longitudinal data from the Health and Retirement Study (HRS)
combined with Social Security earnings and death records to estimate the relationship between
mortality and lifetime earnings and other indicators of social and economic status. Both studies
find significantly faster gains in life expectancy among top Social Security earners compared
with low earners in recent decades. Chetty et al. (2016) combine information from income tax
records and Social Security death records covering the period from 1999 to 2014 to determine
trends in life expectancy by percentile of the national income distribution and, within
geographical regions, by quartile of the income distribution. In the time period they examine, life
expectancy increased 2.3 years among men and 2.9 years among women in the top 5 percent of
the U.S. income distribution. In contrast, among those in the bottom 5 percent of the distribution
life expectancy increased just 0.3 years among men and 0.04 years among women.
The SIPP sample. Our recent research extends our earlier analysis of the matched HRS
files by estimating trends in mortality using SIPP longitudinal survey files that were matched to
earnings, benefits, and mortality records drawn from the Social Security administrative files.
Our dataset contains records for SIPP respondents born after 1910 who were interviewed in the
1984, 1993, 1996, 2001, and 2004 panels.2 Our principal analysis focuses on men and women
2 For a description of the SIPP samples, sampling methodology, and interview methods see URL =
http://www.census.gov/programs-surveys/sipp/methodology/organizing-principles.html.
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born before 1951. We were able to successfully match about 80 percent of the SIPP respondents
to their corresponding Social Security earnings and death records, yielding a total sample of
41,000 men and 45,000 women (Table 1). We could also match about 95 percent of respondents
who were “married, with spouse present” at the time of the SIPP interview to their spouse’s
Social Security record. Note, however, that the SIPP interviews covered only about two and a
half years of each respondent’s career. We do not have post-interview information about
respondents’ later marriage partners.
The mortality rate in our analysis sample was 37 percent in the case of men and 30
percent for women. To perform our detailed analyses of mortality rates by age we created a
person-year dataset. Each respondent enters the sample in the year of attaining age 50 or the year
corresponding to his or her initial SIPP interview, whichever occurs later. Respondents remain in
the sample until the year they die or until 2012, the last year for which we have reliable
information about mortality. Our final dataset contains 487,000 person-year observations for
men and 573,000 person-year observations for women.
Indicators of socio-economic status. Analysts have used four main markers of socio-
economic status to indicate individuals’ position within the social hierarchy: educational
attainment; income or earnings; occupation; and wealth. In our recent analysis, we focused on
two of these indicators, educational attainment and mid-career Social-Security-covered earnings.
This summary focuses mainly on our findings based on mid-career earnings. One reason is that
findings based on these two indicators produced broadly similar results. This is reassuring.
Educational attainment and Social-Security-covered earnings each have some shortcomings as
indicators of socio-economic status for respondents born over a 40-year time span, as we argue
below. The fact that our analyses using the two indicators produce the same finding with respect
to widening mortality differentials tends to confirm the inference that the differential is in fact
growing.
Many studies of the link between socio-economic status and mortality have used
education as the principal indicator of status. Its measurement is straightforward and reasonably
accurate in most household surveys. Education is ordinarily determined by early adulthood, well
before we begin measuring mortality rates in middle age and late adulthood. Educational
attainment does have some limitations, however. Many studies of the effect of SES on mortality
use an absolute rather than a relative measure of attainment. This can be problematic when
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schooling attainment has risen strongly across successive generations. Such is the case for
Americans born during the four decades after 1910.
In the 1962 Current Population Survey, 58 percent of the men who were between 48 and
52 years old (and born between 1910 and 1914) reported they had not completed high school;
just 9 percent reported they had completed college. In the 1998 Current Population Survey, only
14 percent of 48-52 year-old men (born between 1946 and 1950) reported they had failed to
complete high school; 33 percent reported they had obtained a college degree. Clearly, lack of a
high school diploma was an indicator of much deeper social disadvantage for 48-52 year-old
men in 1998 than it was in 1962. Completion of college was a more marked indicator of social
and economic advantage in 1962 than it was in 1998. If we find that failure to complete high
school is associated with a much bigger increase in mortality among men born in 1946-1950
compared with those born in 1910-1914, we can hardly be surprised. Men who failed to
complete high school represented a much smaller and more disadvantaged population in 1998
compared with 1962.
In our analysis we deal with this problem by converting SIPP respondents’ educational
attainment reports into number of years of schooling and then normalizing each person’s years of
schooling relative to the average of the educational attainment of their immediately surrounding
birth cohorts (people born within two years before or after the person’s birth year). In essence we
are measuring education as the deviation of the person’s own attainment compared with that of
the average attainment of people born within two years of the person’s birth. The calculations
were done separately for men and women, effectively eliminating any trend in our measure of
“relative education” across successive birth cohorts.3
Mid-career earnings as an indicator of socio-economic status. Several early studies of
the effect of SES on mortality used current income as an indicator of status because it was the
only available measure of income in the household survey used by the analyst. Current income
has some problems as an indicator of SES because of its sensitivity to adverse health shocks or
other transitory, income-reducing events. The availability of Social Security earnings records
makes it possible to construct an average of workers’ past earnings, a measure we shall refer to
as “mid-career earnings.” This measure of SES avoids many of the problems caused by using a
3 Under this adjustment procedure, the normalized level of schooling has the same mean number of
years for each 5-year birth cohort.
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single year’s income. A 10-year average of mid-career earnings dilutes the role of transitory
influences and may come close to the concept of permanent income. Our use of average
earnings in mid-career also reduces, though it does not eliminate, the potential for reverse
causation flowing from health to income.
The quality and limitations of the Social Security earnings data have varied over the
years. Until 1978, the Social Security Administration maintained its own earnings records based
on quarterly reports of employers. In 1978 SSA switched to reliance on annual earnings
information collected by the IRS. Between 1951 and 1977 the earnings data were limited to
covered earnings up to the annual taxable wage ceiling. Unfortunately, the ceiling wage was not
regularly adjusted to reflect changes in the distribution of earnings. The ceiling wage was only 3
percent above the economy-wide average earnings level in 1965 but 69 percent above average
earnings in 1977.
There are two broad approaches to dealing with the limitations of the Social Security
earnings records before 1978. The first is to use information in the Social Security earnings files
to predict annual earnings for workers whose annual earnings are above the taxable earnings
ceiling. The second approach is to use only information on workers’ reported annual earnings
below a maximum percentile level. The maximum percentile is selected to correspond with the
capped earnings amount in the calendar year with the lowest taxable earnings ceiling relative to
the earnings distribution. In our longer research paper, we estimated the relation between
mortality rates and workers’ mid-career earnings using both of these procedures. Both yielded
broadly similar results, described below. This summary of our research findings focuses on
results obtained when we rely on actual reported earnings in those years when a worker’s
earnings are below the taxable wage ceiling and on imputed earnings in those calendar years
when the worker’s reported Social-Security-covered earnings are equal to the maximum taxable
amount.
To make our predictions we impute workers’ earnings above the taxable wage ceiling
using information on the quarter in which the worker’s earnings reached the maximum taxed
amount. For workers who reached the ceiling with 4 quarters of reported earnings, the imputed
annual total wage was set to 1.14 times the taxable maximum. For those with 3 quarters, we
assigned an imputed amount equal to 1.53 times the taxable maximum. For those with two
quarters, the imputed ratio was 2.36. For those who reached the ceiling in the 1st quarter, the
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imputed ratio was set at 5 times the taxable maximum.4 The annual earnings data available since
the early 1980s has the major advantage of providing measures of earnings in excess of the
taxable wage ceiling. In addition, it includes earnings from both Social Security covered and
uncovered jobs. We cap the annual earnings distribution at the 98th percentile to reduce the
impact on our results of a few very large values in the post-1977 data.5
We created a measure of mid-career average earnings by first deflating each worker’s
nominal annual earnings using the SSA average wage index with a base year of 2005. This
procedure largely eliminates the influence of secular economy-wide wage growth on our
measure of workers’ annual earnings. For each individual worker, we calculated mid-career
average earnings as the mean real nonzero earnings amount when the worker was between ages
41 and 50.6
The resulting mean values of workers’ mid-career average earnings are shown separately
for men and women by birth year in Figure 1. These earnings estimates raise some of the same
issues already mentioned in our discussion of educational indicators of SES. Because women
have been increasingly likely to be employed and to earn higher relative wages in recent birth
cohorts, their career earnings have increased compared with those of men. Meanwhile, the
average (indexed) wage of men has declined for the youngest birth cohorts. Note that the
economy-wide earnings index includes the annual wages of all workers in a given calendar year,
rather than only those of workers between 41 and 50. It follows that our indexed estimates of
4 The adjustment ratios were originally derived for a report to SSA (Toder et al. 1999). Class
intervals were set under an assumption of steady earnings throughout the year, and the class means were
derived from the distribution of wages in various reports of the Current Population Survey. Less than one
percent of the workers in the sample reached the taxable maximum in the first quarter. A similar
methodology was also used more recently in Cristia (2009) and Kopczuk, Saez, and Song (2010).
Additional problems with the changeover to W-2 records in 1978-80 led us to use an interpolation of
individuals’ earnings above the taxable ceiling between 1977 and 1981. No adjustment could be made for
the self-employed who were above the taxable wage ceiling as they file on an annual basis.
5 Even after our adjustments, the pre-1977 data are not fully compatible with the later years because
of bunching of imputed earnings after adjusting for the quarter in which individuals reach the taxable
wage ceiling.
6 In other words, calendar years in which a worker had no reported earnings were excluded in
calculating the worker’s average earnings. The computation of mid-career average earnings is adapted
from Waldron (2007). As she noted, the reliance solely on years with of nonzero earnings excludes some
low-wage workers who have very poor health in middle age. However, by excluding zero earnings years,
our measure probably gives a more reliable indicator of workers’ potential earnings in years not affected
by unemployment or severe health problems.
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mid-career earnings will be affected by changes in the age distribution of the overall work force
as well as the average wage of 41-50 year-olds relative to other earners.
To eliminate any secular drift in our estimates of average earnings across birth cohorts we
employed an adjustment similar to the one we used to convert individuals’ educational
attainment into a normalized measure of education. In particular, we calculated the mean indexed
earnings of each overlapping 5-year birth cohort, and we then normalized individual earnings
levels within a 5-year birth cohort so that the mean normalized value was equal to that of the
1938-1942 birth cohort. Thus, an individual’s mid-career earnings is measured relative to the
average mid-career earnings of people the same sex born within two years before or two years
after the worker’s own birth year.7 We calculated these estimates of workers’ normalized
earnings separately for men and women.
Majorities of men and women in our sample were married at the time of the SIPP
interview. For married women, the woman’s normalized mid-career earnings are often a poor
indicator of socioeconomic status. Married women in older cohorts frequently did not work
outside the home or earned very low annual wages, especially during years they were rearing
children. For this reason, it makes sense to use a household-based, rather than an individual-
based, measure of earnings to classify the socio-economic status of members of married couples.
Bearing this in mind, we combined normalized husband and wife earnings as our main income-
based measure of household-level SES. We defined “equivalized” household earnings for
individuals with a spouse as the sum of the two mid-career normalized earnings amounts divided
by the square root of two.8 For respondents who did not have a spouse, we used the worker’s
own mid-career earnings. Our measurement procedure requires us to exclude from the analysis
all SIPP respondents who were single and did not have positive Social-Security-covered earnings
between ages 41 and 50. It also requires exclusion of married couples where neither spouse had
positive covered earnings.
7 Under this adjustment procedure, the normalized level of mid-career earnings has the same mean
value for each 5-year birth cohort and is equal to the mean for the birth cohorts born from 1938 to 1942.
8 This is a common procedure for converting the total income of a two-person family into the
“equivalent income” of a one-person household. Economists estimate equivalent incomes by determining
the change in expenditure that is required to hold living standards constant when a household gets larger
or smaller. One popular adjustment, which we use here, assumes that a household’s spending
requirements increase in proportion to the square root of the number of household members.
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As noted, we also used an alternative procedure to minimize the impact reporting changes
in the historical records of workers’ Social-Security-covered earnings. Our alternative procedure
uses only information on workers’ reported SSA earnings up to a maximum percentile level. In
the case of men the maximum percentile is less than the median male earnings level in each
year.9 We selected the maximum percentile so that the earnings we counted in each calendar year
would be measured in a consistent way, regardless of whether the maximum taxable amount in
the year was high or low in relation to the earnings distribution in that year. Obviously, the
alternative method does not permit us to distinguish between the earnings of male workers who
earned average and well-above-average earnings, but it does give a consistent method for
distinguishing low-earnings men from men with average- or above-average incomes. In the
remainder of this paper, we will use information about the full range of earnings reported in the
Social Security records, including plausible imputations of earnings above the maximum taxable
earnings amount.
Other variables. In addition to the SES indicators just described, a number of other
personal characteristics are known to affect mortality rates. Two of these are race or ethnicity
and marital status. The SIPP interview also includes a self-reported measure of respondents’
health status. This indicator identifies one of the channels through which variations in SES may
influence mortality. The SIPP contains a health indicator that can range from a value of 1 for
respondents in excellent health to 5 for those in poor health. We also have an indicator showing
whether the respondent was ever disabled. For SIPP sample members, this information can be
derived from the individual’s Social Security benefit record, which shows whether the person
ever claimed Disability Insurance.
Estimation of mortality risks. Our statistical analysis is based on a logit regression of
mortality risk that takes the form:
9 In the mid-1960s, the maximum Social-Security-taxable earnings amount was attained at the 31st
percentile of the earnings distribution of 41-to-50 year-old men. Therefore, this was the maximum level
of annual earnings we used in order to classify male workers as low wage earners. Women’s reported
earnings were much less affected by the maximum taxable earnings amount. In years when the taxable
cap was low relative to economy-wide average wages, in 1951 through the early-1970s, working
women’s earnings were also comparatively low. As a result, annual wages up through the 80th earnings
percentile of 41-to-50 year-old women were observed, even in the calendar year with the lowest earnings
cap relative to the female earnings distribution.
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(1) (ℎ𝑖𝑡
1−ℎ𝑖𝑡) = 𝑒𝑥𝑝(𝛽𝑖𝑗 ∙ 𝑋𝑖𝑗𝑡) , where
hit = Pr(Yit = 1 / Yit-1 = 0) is the hazard that person i will die in year t; and
Xijt = Vector of determinants of mortality risk.
The determinants we include are the person’s SES, age, birth year, and categorical variables for
race/ethnicity, marital status, and disability.10 Ages range from 50 to 100. Our indicator of birth
year is the person’s year of birth minus 1900. The birth year is our basic indicator of cohort
effects. We employ two alternative indicators of SES (mid-career earnings and educational
attainment), social and economic indicators that are potentially linked to differential mortality.
For each of these measures we also include the interaction of SES with the birth year in order to
estimate the rise or decline in differential mortality across successive birth cohorts.
The fact that that the age-specific mortality rate is constrained to increase by a fixed
proportional amount at every age and across successive cohorts may impose an excessively
severe restriction on the mortality function. We experimented with alternative nonlinear
measures of age, but they were never statistically significant. In our longer paper we also show
the effect of estimating widening mortality differentials within narrower age groups than the age
50-to-100 age group that is examined here.
The logistic regression results for the SIPP sample are displayed in Table 2. Mortality
risks are estimated separately by gender. We show three sets of regression results for each sex.
The first column reports results from a regression that includes age, birth year, and the two
alternative measures of SES—(a) our equivalized and normalized indicator of household mid-
career earnings; and (b) our normalization of respondents’ educational attainment. The
coefficient on Age indicates a rising probability of death as respondents grow older. The
coefficient on Birthyear is negative, indicating that age-specific mortality risks are declining for
successive age cohorts. The specification in the first column includes both SES indicators, and
their estimated coefficients are negative and highly statistically significant as expected.11 Thus,
10 In addition, we included a categorical variable for the first calendar year of a respondent’s
enrollment in the sample, recognizing the fact that respondents were exposed to the risk of dying for less
than a full 12 months in that calendar year.
11 We estimated versions of the mortality equations that limited the measure of SES to either career
earnings or educational attainment, but the combination of both variables yielded a superior overall
statistical relationship without altering the coefficients on the non-SES indicators.
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consistent with earlier research we find strong statistical evidence of differential mortality by
social and economic status. People with high equivalized earnings or greater educational
attainment have lower rates of mortality than those who have less earned income or less
schooling. Marital status and past disability are also highly significant predictors of mortality
risk. The role of race is more marginal, however, once we include measures of mid-career
earnings and educational attainment.
Our tests for increasing differential mortality are displayed in columns 2 and 3 (for men)
and columns 5 and 6 (for women). The columns show results when we add an interaction
between birth year and one of the two measures of SES. While the interaction complicates the
interpretation of the other coefficients, a negative coefficient on the interaction term implies that
the size of the mortality difference across levels of mid-career earnings or educational attainment
is increasing in later birth cohorts. Perhaps surprisingly, there is very little to choose between the
two indicators of SES. They both yield very significant negative coefficients on the interaction of
the SES indicator with the birth year, implying strongly increasing differential mortality. The
measures of overall explanatory power are also virtually identical. The interaction of education
with birth year has a larger negative coefficient than the interaction of mid-career earnings with
birth year, which may seem to suggest a more pronounced pattern of increasing differential
mortality. However, the larger coefficient is due to the much more limited range of variation in
the educational attainment variable compared with mid-career earnings.
The coefficient estimates for men and women show very similar influences for many of
the determinants of mortality. For both sexes there is powerful evidence of increasing differential
mortality using either mid-career earnings or educational attainment as an indicator of SES. It is
noticeable, however, that marital status has a consistently smaller impact on female mortality.
Women’s mortality is apparently less adversely affected by living in an unmarried state.
The implications of our logit estimates for mortality rates at ages 65 and 75 are displayed
in Figures 2 and 3. The results in Figure 2 show predicted mortality rates of men born in
successive cohorts between 1920 and 1945, while the results in Figure 3 show comparable
predictions for women in the same birth cohorts. The predictions for men are based on the
parameter estimates displayed in column 2 of Table 2, and those for women are based on the
parameter estimates in column 5 of the same table. In both cases, our estimate of the changing
mortality gradient linked to SES is based on the interaction of respondents’ equivalized mid-
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career earnings and their birth year. We derive all the predictions in Figures 2 and 3 for men and
women who, with the exception of their mid-career earnings, have average characteristics for
respondents in the middle one-fifth of the equivalized mid-career earnings distribution. Each
chart contains three lines, and each line displays the trend of age-specific mortality rates for
successive birth cohorts at a given income level. The top line in each chart shows the trend in
mortality rates for individuals in the bottom one-tenth of the equivalized mid-career earnings
distribution. The middle line shows the trend in mortality rates for those in the middle fifth of
the mid-career earnings distribution, and the bottom line shows the mortality trend for men or
women in the top one-tenth of the mid-career earnings distribution.
The charts suggest that, holding other determinants of mortality constant, respondents
born in 1920 had similar mortality rates regardless of whether their households’ mid-career
earnings placed them at the top or the bottom of the income distribution. In later cohorts,
however, the mortality rate difference that is linked to respondents’ mid-career earnings widens.
For a male 65-year-old born in 1940, for example, a respondent in the top one-tenth of the
household mid-career earnings distribution faces a mortality risk that is just 40 percent of the risk
faced by a person with the same characteristics who is in the bottom one-tenth of the distribution.
In the case of 65-year-old women born in the same year, the mortality risk facing a woman in the
top tenth of the income distribution is just 38 percent of that facing a woman in the bottom
income decile. The mortality rate declined in later birth cohorts for men at all income levels, but
it fell much faster among men with above-average compared with below-average incomes.
Among women there is little evidence that women at the bottom of the mid-career earnings
distribution saw any mortality improvement at all. Women with average and above-average
equivalized household mid-career earnings, however, saw meaningful declines in their mortality
rates.
Note that our predictions are derived from a sample in which we do not observe all of the
age and birth year combinations displayed in the charts. For example, because our mortality data
end in 2012, our sample does not contain any 75-year-old men or women who were born in
1940. Consequently, some of our mortality rate calculations are based on out-of-sample
predictions. The out-of-sample predications are indicated in the charts by broken rather than
solid lines.
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Implications for life expectancy. Using the mortality equations in Table 2, we calculated
the probability of death at each age between 50 and 100 and cumulated the results for each
sample member in order to obtain the probability of survival to each age:
(2) 𝑆𝑥 = 𝑆𝑥−1 ∗ (1 − 𝐷𝑥),
where Dx is the expected conditional death rate at age x.
To see how life expectancy varies by respondents’ SES, we ranked all sample members by their
normalized mid-career earnings and divided the samples into ten equal-size groups. Within each
decile we calculated the mean life expectancy for the men or women in the earnings decile. The
distribution was calculated for both individual and equivalized household mid-career earnings,
but here we report only the results based on the equivalized household income measure. For
women in particular we believe this variable represents the better measure of a person’s relative
SES. Most of the following analyses focus on simulated expected life spans for the 1920 and
1940 birth cohorts. These two years represent the extremes of the birth years for which we have
reasonably complete earnings records and a sizeable number of actual deaths. We performed
simulations in which alternative estimates of life expectancy were computed using equations 1
and 2 and the coefficients reported in Table 2 for all people in the SIPP sample described above.
To simulate the life expectancy of a population born in 1920, we replaced each sample member’s
actual birth year with 1920 and calculated their expected remaining life assuming they survived
to age 50. To simulate the life expectancy of a population born in 1940 we followed the same
procedure, replacing each sample member’s actual birth year with 1940.12 The resulting
estimates of life expectancy for the simulated1920 and 1940 birth cohorts are shown in Table 3.13
The top panel of Table 3 shows simulated life expectancies for men. The results
displayed in three columns on the left are based on our specification that uses equivalized mid-
career earnings interacted with Birthyear to capture the changing effect of SES on mortality
12 The analysis is based on a simple exercise in which the estimated mortality equation is used to
generate a predicted mortality rate for each age from 50 to 100, using the birth years of 1920 and 1940 in
turn.
13 Note that the characteristics of the populations represented in Table 3 are those of the entire SIPP
sample, not just the members of that sample born in 1920 or 1940. We are attempting to measure the
change in life expectancy between 1920 and 1940, and we are estimating that change in a population with
the fixed characteristics of our entire estimation sample.
-15-
rates. Those in the three columns on the right are based on the specification that uses relative
educational attainment interacted with Birthyear to capture the changing effect of SES. The
implied differences in life expectancy between the top and bottom SES groups are very large.
The simulation results for the 1920 cohort suggest that men in the top decile of mid-career
earnings could expect to live 5.0 years longer than men in the bottom decile–79.3 versus 74.3
years. For men born twenty years later in 1940, the simulated improvements in life expectancy
added an average of 4.8 years to male life spans. However, the gain in life expectancy was only
1.7 years for men in the lowest earnings decile compared with a gain of 8.7 years for men in the
top decile. The gains are thus heavily skewed towards men at the top of the income distribution.
When we use education interacted with Birthyear to capture the changing effect of SES
(columns 4 – 6 in Table 3), the increase in average life expectancy is nearly the same, but the
differential gains in life expectancy between the top and bottom of the mid-career earnings
distribution appear smaller. For men the simulated increase in life expectancy is 3.8 years in the
bottom income decile compared with a gain of 5.5 years in the top decile. The gap in longevity
gains between the top and bottom deciles in columns 4 – 6 would appear wider if we ranked men
by their educational attainment rather than by their equivalized household mid-career earnings.
When we constructed decile rankings based on educational attainment instead of equivalized
earnings, the simulated increase in male life expectancy between the 1920 and 1940 birth cohorts
was 2.2 years in the bottom one-tenth of men and 6.5 years in the top tenth.
We observe a smaller increase in simulated average life expectancy among women
between the 1920 and 1940 birth cohorts. The average gain implied by the SIPP results is only
2.7 years. The gains are highly correlated with women’s SES, however. When we use
equivalized mid-career earnings interacted with Birthyear to capture the changing effect of SES,
there is in fact no apparent increase in life expectancy in the lowest income decile. This
compares to a gain of 6.4 years in life expectancy for women in the top earnings decile. Because
life expectancy gains among women were on average slower than they were among men, there is
a noticeable narrowing in the life expectancy gap between women and men. When we use
education interacted with Birthyear to capture the changing effect of SES, there is somewhat
weaker evidence that the increase in life expectancy is correlated with higher levels of household
-16-
earnings. The simulated gain in life expectancy is 2.0 years in the lowest decile and 3.0 years at
the top.14
Figure 4 summarizes the changes in predicted life expectancy for men and women. The
top panel of the chart shows changes in male life expectancy by fifths of the equivalized
household earnings distribution. The lower panel shows predictions for women. Both charts
show a strong upward tilt in favor of higher income people, for men and women born in both
1920 and 1940. As we have already seen, however, the upward tilt is considerably more
favorable for those born in 1940 compared with those born in 1920. The column of figures on the
right of the chart shows the years of gain in life expectancy between 1920 and 1940 by fifth of
the income distribution. The changes in life expectancy between the two birth cohorts within
each tenth of the mid-career earnings distribution are graphically displayed in Figure 5. The top
line shows longevity improvements among men, while the bottom line shows gains among
women. Although life span gains were faster among men than among women, for both sexes the
improvements in life expectancy past age 50 were considerably faster at the top of the income
distribution than at the bottom.
Robustness checks. In our longer paper we present results from an identical specification
using data from the HRS interviews matched to Social Security earnings and mortality records.
The HRS matched files have some disadvantages compared with the SIPP files. First, the
enrolled HRS sample is smaller than the SIPP sample. Second, there was a lower match rate of
HRS and SSA earnings records, further reducing the HRS sample size. Finally, the HRS sample
was enrolled later than the earliest SIPP samples, giving us evidence on the SES-mortality
gradient over a shorter range of years. Although the HRS results were not identical to the ones
displayed in Table 2, they strongly confirmed the basic finding reported here: Differential
mortality has increased significantly and markedly across successive birth cohorts. The estimated
coefficient on the SES interaction with birth year was negative and highly significant. However,
in the case of men the size of the coefficient on the interaction between education and birth year
was smaller and less significant than it is in the SIPP sample.
14 Again, the gap in life expectancy gains between the top and bottom deciles in columns 4 – 6 would
appear wider if we ranked women by their educational attainment rather than by their equivalized
household mid-career earnings.
-17-
In another robustness check, we re-estimated the relationship between SIPP respondents’
mid-career earnings and their age-specific mortality rates using very basic information about
mid-career earnings patterns reported in the Social Security Administration files. In particular,
we classified men as “low earners” under a range of definitions that relied solely on reported
earnings below the 31st percentile of the annual male earnings distribution.15 This information is
available (without any imputation) for all men in our SIPP sample, regardless of the calendar
years in which the men were between 41 and 50 years old. Similarly, we classified women as
“low earners” based on their mid-career earnings relative to those of other women born in the
same year. Further, we classified women as members of “low earnings households” based on the
combination of their own Social-Security-covered earnings and the “low earnings” status of their
spouse. In all cases, the classifications were based solely on observed (rather than imputed) mid-
career Social-Security-covered earnings of men and women in the SIPP sample. We then
estimated discrete-time logistic models of mortality risk using our binary classification of “low
earners” or “low earnings households” as indicators of respondents’ SES. To determine whether
the mortality differential between “low earners” and average or above-average earners increased
over time, we included an interaction term between the low earnings indicator and the
respondent’s year of birth.
The results obtained using this alternative methodology strongly confirmed the results
displayed in Table 2. For both men and women born between 1910 and 1956 the estimated
coefficients showed a statistically significant increase in the mortality rate difference between
low earnings workers and workers with average or above-average earnings. Respondents
classified as “low earners” saw noticeably slower reductions in mortality over successive birth
cohorts compared with respondents with average or above-average earnings. We found the same
pattern among both male and female SIPP respondents, regardless of our classification scheme
for identifying “low earners” or, in the case of women, “low earnings households.” Furthermore,
when we subdivided our overall sample into smaller subsamples restricted to observations in
narrower 7- or 9-year age groups, we found statistically significant and meaningfully large
increases in the mortality differentials in most of the age groups we analyzed.
15 For mid-career male earners in the mid-1960s, the maximum taxed earnings amount was attained
at the 31st percentile of the earnings distribution. See note 9.
-18-
Explanations. The reasons for the increase in differential mortality across SES groups
remain uncertain. In particular, it is unclear whether differential access to health care is the main
channel through which SES affects mortality, as opposed, for example, to socio-economic
differences in behaviors, such as smoking, drinking, and lack of regular exercise, that are linked
to early mortality. Using a large sample of adults age 25-64 covering the period of 1970 to 2000,
Cutler and others (2010) concluded that behavioral factors, such as smoking and obesity, have
strong effects on mortality risk, but they contributed little to explaining the growing disparity in
mortality by levels of educational attainment.
The HRS includes information on self-reported heath status and some behavioral risks,
including alcohol use, smoking, and levels of physical activity. We re-estimated our HRS
mortality regressions to include heath status and the three behavioral risk factors.16 In all cases,
health status and the behavioral variables had high statistical significance in predicting mortality,
but they had a relatively small effect in reducing the size or statistical significance of the
coefficient on the interaction term between the SES indicator and birth year–our measure of
increasing differential mortality. Including these variables does not serve as a substitute for the
SES-birth year interaction. Overall, we interpret these results as showing that a consistent
pattern of increasing differential mortality is operating through channels in addition to health
status and the behavioral measures. In all of our analyses, disability has a very large and
significant positive impact on mortality risk. Yet inclusion or exclusion of disability status has
very little impact on the estimated size of the coefficient on the SES-birth year interaction. When
we exclude from the estimation sample respondents who received a DI benefit, there is very little
effect on the coefficients of the SES indicators or their interaction with respondents’ year of
birth. Thus, our estimates of widening mortality differentials linked to SES are quite robust to
alternative methods for dealing with disability.
One possibility is that rising income inequality has increased the mortality rate
differences between Americans with a high and a low rank in the income distribution. Whatever
the longevity advantage conferred by higher income, the fact that the proportional income gap
between high and low income Americans has widened over the past three decades may have had
16 Including the number of alcoholic drinks consumed in a typical day averaged across all survey
waves, whether individuals ever smoked or were smoking at the time of the last interview, and whether
they engaged in vigorous physical activity at least three times per week.
-19-
spillover effects that tended to widen the mortality differences between high and low income
groups.
The growing mortality gap and redistribution through Social Security
Informed observers have long recognized that differences in life expectancy linked to
workers’ earnings offset some portion of the progressivity of the Old Age Survivors and
Disability Insurance (OASDI) system when benefits are measured on a lifetime rather than an
annual basis. The goal of this part of our analysis is to measure the distribution of retirement
benefits relative to that of mid-career earnings and then to use the results from our mortality
analysis to calculate expected lifetime benefits and their distribution.
In the SIPP sample we have tabulated benefits reported on respondents’ matched OASDI
benefit records. As we did when estimating the mortality rate model, we restrict the sample to
respondents born between 1910 and 1950. For men, 35,000, or 86 percent of the sample of those
over age 50, received a benefit at some time before 2012 when the data end. Among women
there are 40,000 beneficiaries, which implies that 88 percent of our estimation sample received a
benefit. Benefits are initially defined at the individual level and include retirement, disability,
and survivor benefits minus a deduction of any Medicare Part B premiums. We converted all
benefit amounts into 2005 dollars using the CPI-U-RS price index. In the absence of any change
in benefit classification (e.g., from disabled worker to retired worker or from spouse to survivor
beneficiary), we expect the real benefit level to be relatively constant after a pension begins. The
real values of benefit payments are averaged across the years for which they were reported
beginning at age 50 and up through 2012.
Table 4 shows the implications of the simulated differences in life expectancy for the
distribution of lifetime Social Security benefits. Our results for men are displayed in the top
panel, and the results for women are shown at the bottom. The decile measures of equivalized
mid-career earnings are the same as those used to calculate life expectancy in Table 3. The
distribution of mid-career earnings across income deciles is shown in column 1. The mean
equivalized earnings in a decile is shown as a ratio of the decile mean to the overall average of
earnings across all deciles. For the SIPP sample, average male earnings range from a low of 18
percent of overall mean earnings in the lowest decile up to 213 percent of the mean in the top
decile. A similar distributional measure of annual (point-in-time) Social Security benefits is
shown in column 2. Estimated lifetime benefits are displayed in column 3. Annual benefits are an
-20-
average of the values reported on the SSA benefit record, converted to 2005 dollar values.17 We
calculate lifetime benefits as the product of average benefits and remaining life expectancy at the
age of first receipt of benefits (column 5). As noted, equivalized mid-career earnings for men
(column 1) rise from 18 percent of the overall mean earnings in the lowest decile up to 213
percent of mean earnings in the top decile. The range of earnings is even wider for women (12
percent up to 234 percent, shown in the bottom panel, column 1). A comparison of the
distribution of annual benefits (column 2) and earnings (column 1) highlights the strongly
progressive structure of the Social Security benefit formula. The formula provides a benefit for
bottom-decile men that is more than three times more generous than would be provided by a
formula in which benefits were strictly proportionate to earnings. In contrast, the average benefit
in the top male decile is only 60 percent of the amount these men would obtain under a
proportionate benefit formula. The results for women show an even larger compression of the
distribution of point-in-time benefits relative to earnings.
Some of the progressivity of the benefit formula is offset on a lifetime basis because
workers at the top of the earnings distribution are expected to live longer than workers at the
bottom. Thus, lifetime benefits for men in the lowest decile of mid-career earnings are reduced
by about 15 percent with a comparable gain for men in the top (compare columns 2 and 3).
However, the effect on lifetime benefits is less than we might expect given the large differences
in life expectancy across the income distribution. That is because individuals in the lowest
deciles of the earnings distribution begin receiving benefits at a younger age than workers at the
top, counterbalancing part of the difference in life expectancy. While remaining life expectancy
for men at age 50 varies from 25.6 years up to 36.1 years (column 4), a range of 10.5 years, the
range in expected benefit years is only 6.2 years (column 6).18 We see a similar pattern of
variation across the earnings distribution for women, but compared with men, women have a less
unequal distribution of benefits on both an annual and lifetime basis.
17 For most beneficiaries, the real value of the benefit (adjusted for inflation) will remain constant
over their retirement. We also equivalized the benefit for couples, which reduces the benefit change
associated with a shift among the categories of retiree, spouse, and survivor.
18 This conclusion is particularly influenced by our decision to include the disabled throughout the
analysis. An alternative version of the analysis that excludes the disabled yields a range of benefit years
across the earnings distribution more similar to that for life expectancy.
-21-
Table 5 provides evidence on the pattern of change in OASDI benefit distribution over
time. We show changes in the distribution based on our simulated life expectancies for the 1920
and 1940 birth cohorts. Average remaining life expectancy at age 50 for men is projected to rise
from 26.8 to 31.6 years, and the number of benefit years is projected to increase from 17.5 to
21.5 years between the 1920 and 1940 birth cohorts. However, the distribution of gains is
skewed across the income distribution. The increase in life expectancy between the 1920 and the
1940 birth cohorts is 8.7 years for the top earnings decile compared to just 1.7 years for those in
the bottom decile, and the change in expected years of benefit receipt is roughly proportionate to
the change in life expectancy.19
Because the years of benefit receipt rise throughout the distribution, lifetime benefits also
increase, but the magnitude of the benefit increase varies substantially. Expected lifetime
benefits increase about 9 percent for men in the lowest decile and by about 39 percent in the top
decile (Figure 6). Thus, the increase in differential mortality leads to a substantial widening of
the distribution of lifetime benefits. For the 1940 birth cohort, lifetime benefits of men in the top
decile of earners are 3.3 times those of men in the bottom decile, compared to a multiple of just
2.6 for the 1920 birth cohort. The distributional change is similar for women, but because there
is no increase in life expectancy for women in the bottom decile, expected lifetime benefits in the
bottom decile remain unchanged. In contrast, there is a 25 percent gain in lifetime benefits for
women in the top income decile. Nonetheless, the range of lifetime benefits is more compressed
for women than for men, and this is the case regardless of whether we use survival probabilities
for the 1920 or 1940 birth cohorts.
A recent report of the National Academies of Sciences, Engineering, and Medicine
(2015) also found evidence of a growing gap in life expectancy across income groups that is
similar to that reported in this study. However, the National Academy report includes a wider
range of benefit programs than our focus on OASDI. Americans with lower SES rank (income
and education) receive larger benefit payments from Medicaid and Supplemental Social
Insurance, and, compared with Social Security, Medicare payments are relatively equally
distributed across income groups. Thus, OASI payments are a smaller share of total benefits in
the lower portions of the income distribution, and program changes have a smaller effect on their
19 The simulation of the change in benefits allows only for increased life expectancy. It does not
incorporate any change in the age of benefit claiming or change in the benefit formula.
-22-
total benefits. The NAS report also calculates future lifetime benefits on a discounted basis,
which reduces the importance of longer retirement lives.
Conclusions
Three principal conclusions emerge from this analysis. First, like earlier researchers we
find large mortality rate differences among aged Americans when they are ranked by their
socioeconomic status. Second, those differences have grown significantly in recent years. It
matters little whether we use education or mid-career earnings to measure SES, though earnings
are more closely related to the income concept used to determine Social Security benefits. We
find the statistical evidence of increasing differential mortality to be compelling in a data sample
constructed by combining Social Security records with the demographic, social, health, and
economic information available in the SIPP interviews. The conclusions are also robust across
samples that include or exclude workers who claim disability benefits.
The basic results in this paper are strongly confirmed when we use an alternative
procedure for translating earnings reported in the Social Security Administration files into
indicators of workers’ and spouses’ socioeconomic status. They are also strongly confirmed
when we estimate changes in mortality differentials within narrower age groups in the population
older than 50.
We find the secular changes in differential mortality to be very large, but the influence of
widening mortality differences on the length of time Americans collect Social Security benefits
is damped by the fact that low SES workers and spouses tend to claim pensions at younger ages
while high SES workers are more likely to postpone retirement and benefit claiming. Differences
in mortality across the earnings distribution offset some of the progressivity built into the Social
Security benefit formula, but the resulting pattern of lifetime benefits nonetheless remains
progressive, even taking account of the widening mortality differentials in recent years.
In research not described in detail here, we explored potential causes of the growing gap
in life expectancy between those in the top and bottom ranks of SES. The HRS provides
information on respondents’ self-assessment of their health status and measures of several
behaviors associated with early mortality, including smoking, drinking, and lack of regular
exercise. Each of these variables has significant effects on mortality when they are added to the
basic mortality equations, but each has only a minor influence on the size of the coefficient on
the SES indicator and its interaction with birth year. This makes it hard to account for the
-23-
fundamental causes of the growing gap in life expectancy. The results reported here only confirm
that it exists.
These findings on the pervasive pattern of increasing differential mortality have
important implications for policy. They suggest that the current emphasis on increasing the
retirement age in line with increases in average life expectancy may have unintended
distributional consequences. Recent mortality gains have been concentrated among the well-
educated and those at the top of the income distribution. The life expectancy trends reported
here thus mirror the pattern of rising inequality in Americans’ incomes.
-24-
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-25-
Table 1. Respondents in the Matched Social Security Record and SIPP Sample by Respondents' Decade of Birth
Note: Sample sizes available to analyze mortality in the matched Social Security Record
and SIPP files.
Birthyear
cohort Total
With nonzero
household
earnings
Deaths up to
2012 (w/
nonzero hhold
earnings)
Death
rate
Social
Security
Benefits
Receipients
1910-1919 4,560 3,931 3,632 92% 4,071
1920-1929 10,767 8,193 5,388 66% 8,455
1930-1939 14,010 10,566 3,650 35% 10,790
1940-1950 23,740 17,839 2,270 13% 14,829
Total men 53,077 40,529 14,940 37% 38,145
1910-1919 6,829 4,640 4,037 87% 5,833
1920-1929 13,869 9,508 4,986 52% 10,662
1930-1939 16,199 11,761 2,831 24% 12,263
1940-1950 25,972 19,568 1,732 9% 17,441
Total women 62,869 45,477 13,586 30% 46,199
Men
Women
Table 2. Discrete-Time Logistic Results for Predicting Mortality using Alternative SES Indicators with Birthyear Interaction
Note: * indicates p < 0.1;
** indicates p < 0.01;
*** indicates p < 0.001.
Source: Authors’ calculations using Survey of Income and Program Participation (SIPP) interview data matched to Social Security
earnings and mortality records as explained in text. Respondents were born between 1910 and 1950 and interviewed in the 1984,
1993, 1996, 2001, and 2004 SIPP panels.
No SES
Interaction
No SES
Interaction
Intercept -8.3905 *** -8.8043 *** -8.9317 *** -9.8098 *** -10.1610 *** -10.4118 ***
Age 0.0824 *** 0.0819 *** 0.0820 *** 0.093 *** 0.0928 *** 0.0930 ***
Birthyear (- 1900) -0.0222 *** -0.0054 * 0.0004 -0.0126 *** 0.0022 0.0125 **
SES
Household Earnings -0.00408 *** 0.0054 *** -0.0038 *** -0.00425 *** 0.0060 *** -0.0040 ***
Earnings x Birthyear Interaction -0.0004 *** -0.0004 ***
Relative Education -0.2661 *** -0.2729 *** 0.3150 *** -0.3252 *** -0.3288 *** 0.2915 **
Education x Birthyear Interaction -0.0236 *** -0.0259 ***
Race / ethnicity
Black (yes=1) 0.1002 ** 0.1083 *** 0.1068 *** 0.0366 0.0438 0.0473White, Hispanic, Other
Marital Status
Never (yes=1) 0.3071 *** 0.2789 *** 0.3081 *** 0.2253 *** 0.1811 *** 0.2210 ***
Separated / Divorced (yes=1) 0.3341 *** 0.3128 *** 0.3360 *** 0.1159 *** 0.0745 * 0.1204 ***Married / Widowed (yes=1)
Ever Disabled 0.8643 *** 0.8388 *** 0.8489 *** 0.9186 *** 0.8933 *** 0.9078 ***
First-year in Survey (yes = 1) -0.7202 *** -0.7220 *** -0.7202 *** -0.9014 *** -0.9010 *** -0.9014 ***
No. of observations
Psuedo R-square
Women
(1) (4)
573,188 573,188 573,188
0.028 0.028 0.028 0.024 0.024 0.024
Reference Group
Reference Group
487,061 487,061 487,061
Relative
Education
(2) (3) (5) (6)
Men
Equivalized
Midcareer
Earnings
($1000's)
Relative
Education
Equivalized
Midcareer
Earnings
($1000's)
-26-
Table 3. Life Expectancy by Income Decile, SIPP Estimation Sample that Includes Disabled Workers
Expected age at death assuming survival to age 50
Source: Authors' calculations using the matched SIPP-Social Security records file described
in text. Mortality predictions derived from coefficient estimates reported in Table 2.
Deciles 1920 1940 Difference 1920 1940 Difference
Bottom 74.3 76.0 1.7 73.5 77.3 3.8
2nd 75.0 77.7 2.7 74.5 78.5 4.0
3rd 75.7 79.0 3.3 75.3 79.5 4.2
4th 76.3 80.2 3.9 76.1 80.5 4.3
5th 76.7 81.0 4.4 76.6 81.1 4.5
6th 77.0 81.9 4.9 77.1 81.7 4.6
7th 77.5 82.8 5.4 77.7 82.5 4.8
8th 77.8 83.9 6.0 78.3 83.2 4.9
9th 78.4 85.3 6.9 79.1 84.2 5.1
Top 79.3 88.0 8.7 80.6 86.1 5.5
Bottom 80.4 80.4 0.0 79.8 81.9 2.0
2nd 80.4 81.0 0.6 80.0 82.1 2.1
3rd 80.9 82.1 1.2 80.7 82.9 2.2
4th 81.2 83.0 1.8 81.2 83.4 2.3
5th 81.7 84.0 2.3 81.8 84.1 2.4
6th 82.1 85.0 2.9 82.4 84.8 2.5
7th 82.4 85.8 3.4 82.8 85.3 2.5
8th 82.7 86.7 4.0 83.3 85.9 2.7
9th 83.2 88.0 4.8 84.0 86.8 2.8
Top 84.1 90.5 6.4 85.4 88.4 3.0
Career Earnings x
Birth Year interaction
Education x
Birth Year interaction
Males
Females
-27-
Table 4. Distribution of Annual and Lifetime OASDI Benefits by Equivalized Earnings Deciles
Dollar amounts in 2005 $
Household earnings
decile
Equivalized earnings (ratio to mean)
Annual equivalized
benefits (ratio to mean)
Lifetime equivalized
benefits (ratio to mean)
Est. life expectancy at age 50 (in years)
Est. years of benefits received
(1) (2) (3) (4) (5)
Men
1 0.18 0.56 0.47 25.6 17.5
2 0.43 0.73 0.65 27.0 18.4
3 0.60 0.87 0.79 28.2 18.9
4 0.74 0.98 0.92 29.3 19.5
5 0.87 1.03 1.00 30.0 20.0
6 1.00 1.08 1.07 30.8 20.5
7 1.14 1.10 1.12 31.7 21.1
8 1.31 1.15 1.21 32.6 21.7
9 1.56 1.21 1.31 33.9 22.5
10 2.13 1.28 1.47 36.1 23.7
Mean $55,839 $11,961 $246,795 30.5 20.4
Women
1 0.12 0.80 0.76 30.4 22.2
2 0.31 0.86 0.80 31.0 22.0
3 0.48 0.92 0.85 31.9 22.1
4 0.66 0.95 0.90 32.7 22.5
5 0.83 1.00 0.96 33.6 23.0
6 1.00 1.01 1.00 34.4 23.6
7 1.18 1.04 1.05 35.1 24.1
8 1.39 1.08 1.12 35.9 24.8
9 1.68 1.14 1.22 37.1 25.5
10 2.34 1.20 1.36 39.1 26.9
Mean $45,922 $8,394 $198,805 34.1 23.6
Source: Authors' calculations using the matched SIPP - Social Security records file as
described in text. Equivalized earnings and benefits use the combined total for couples
divided by the square root of 2.
-28-
Table 5. Estimated Life Expectancies and Expected Years of Benefits by Equivalized Household Earnings Deciles, 1920 and 1940 Birth Years Dollar amounts in 2005 $
Equivalized household earnings
decile
Remaining life expectancy at
age 50 (in years)
Expected years of benefits
Lifetime equivalized
benefits (ratio to 1920 mean)
1920 1940 1920 1940 1920 1940
(1) (2) (3) (4) (5) (6)
Men
1 24.3 26.0 16.4 17.8 0.52 0.57
2 25.0 27.7 16.7 19.0 0.70 0.79
3 25.7 29.0 16.9 19.7 0.83 0.98
4 26.3 30.2 17.2 20.5 0.96 1.14
5 26.7 31.0 17.4 21.1 1.02 1.24
6 27.0 31.9 17.6 21.7 1.08 1.33
7 27.5 32.8 17.8 22.4 1.11 1.40
8 27.8 33.9 18.0 23.1 1.18 1.51
9 28.4 35.3 18.3 24.1 1.25 1.65
10 29.3 38.0 18.8 26.0 1.36 1.89
Mean 26.8 31.6 17.5 21.5 $209,382
Women
1 30.4 30.4 22.2 22.2 0.83 0.83
2 30.4 31.0 21.5 22.0 0.85 0.87
3 30.9 32.1 21.3 22.3 0.89 0.93
4 31.2 33.0 21.2 22.8 0.92 0.99
5 31.7 34.0 21.4 23.4 0.97 1.06
6 32.1 35.0 21.7 24.2 1.00 1.12
7 32.4 35.8 21.8 24.8 1.04 1.18
8 32.7 36.7 22.1 25.6 1.08 1.26
9 33.2 38.0 22.3 26.6 1.16 1.38
10 34.1 40.5 22.9 28.5 1.25 1.57
Mean 31.9 34.7 21.8 24.2 $182,307
Source: Authors' calculations using the matched SIPP - Social Security records file as
described in text. Equivalized benefits use the combined total for couples divided by the
square root of 2.
-29-
Figure 1. Male and Female Average Mid-Career Earnings by Birth Year, 1910-1950
Source: Authors' calculations with matched SIPP - Social Security earnings records as
explained in text.
-30-
Figure 2. Mortality Probabilities of Men at Ages 65 and 75, by Birth Cohort and Position in Income Distribution
Source: Authors' tabulations of the matched SIPP - Social Security records file as described in the text.
Except for the individuals' assumed incomes, calculations are based on average characteristics of men in
the middle income quintile.
0%
1%
2%
3%
4%
5%
1920 1925 1930 1935 1940 1945Year of birth
Male mortality probability at age 65(Percent)
Bottom income decile
Middle income quintile
Top income decile
0%
1%
2%
3%
4%
5%
1920 1925 1930 1935 1940 1945Year of birth
Male Mortality Probability at Age 75(Percent)
-31-
Figure 3. Mortality Probabilities of Women at Ages 65 and 75, by Birth Cohort and Position in Income Distribution
Source: Authors' tabulations of the matched SIPP - Social Security records file as described in the text.
Except for the individuals' assumed incomes, calculations are based on average characteristics of women
in the middle income quintile.
0%
1%
2%
3%
1920 1925 1930 1935 1940 1945Year of birth
Female mortality probability at age 65 (Percent)
Bottom income decile
Middle income quintile
Top income decile
0%
1%
2%
3%
1920 1925 1930 1935 1940 1945Year of birth
Female Mortality Probability at Age 75(Percent)
-32-
Figure 4. Predicted Change in Life Expectancy between 1920 and 1940 Birth Cohorts by Position in Normalized Household Earnings Distribution
Source: Authors' tabulations of the matched SIPP - Social Security records file as described in
the text. Results based on Career Earnings X Birth Year interaction (see Table 3).
74.6
76.0
76.8
77.7
78.8
76.8
79.6
81.5
83.3
86.6
65 70 75 80 85 90
Bottom
2nd
Middle
4th
Top
Life expectancy at age 50
Equ
ival
ize
d h
ou
seh
old
ear
nin
gs
Men: Predicted life expectancy by fifths of income distribution
Born in 1940
Born in 1920
Change
+7.8
+5.7
+4.6
+3.6
+2.2
80.4
81.1
81.9
82.5
83.6
80.7
82.5
84.5
86.3
89.3
75 80 85 90
Bottom
2nd
Middle
4th
Top
Life expectancy at age 50
Equ
ival
ize
d h
ou
seh
old
ear
nin
gs
Women: Predicted life expectancy by fifths of income distribution
Born in 1940
Born in 1920
Change
+5.6
+3.7
+2.6
+1.5
+0.3
-33-
Figure 5. Predicted Change in Life Expectancy between 1920 and 1940 Birth Cohorts by Position in Normalized Household Earnings Distribution
Source: Authors' tabulations of the matched SIPP - Social Security records file as described in
the text. Results based on Career Earnings X Birth Year interaction (see Table 3).
-34-
Figure 6. Lifetime Equivalized Social Security Benefits Calculated with Mortality Rates of 1920 and 1940 Birth Cohorts
Source: Authors' calculations using the matched SIPP - Social Security records file as described in text.
Equivalized benefits use the combined total for couples divided by the square root of 2.
0.5
1.0
1.5
2.0
Bottom 2 3 4 5 6 7 8 9 Top
Decile of mid-career equivalized earnings distribution
Men Ratio of lifetime equivalized benefits to 1920 mean lifetime benefits
Birth year 1940
Birth year 1920
0.5
1.0
1.5
2.0
Bottom 2 3 4 5 6 7 8 9 Top
Decile of mid-career equivalized earnings distribution
Women Ratio of lifetime equivalized benefits to 1920 mean lifetime benefits
Birth year 1940
Birth year 1920