Research Paper No. 2007-01 

Mortality and Lifetime Income Evidence from Social Security Records* 

by 

James E. Duggan 

U. S. Department of Treasury

Robert Gillingham

International Monetary Fund

John S. Greenlees 

U. S. Bureau of Labor Statistics

December 2006 

*The views expressed in this paper are the authors alone, and do not necessarily reflect the views

of the Department of Treasury, the International Monetary Fund, or the Bureau of Labor Statistics.

 
Mortality and Lifetime Income: 

Evidence from Social Security Records 

JEL No. I12, H55, I18 

Abstract 

Studies of the empirical relationship between income and mortality often rely on data aggregated

by geographic areas and broad population groups and do not distinguish disabled and 

nondisabled persons. We investigate the relationship between individual mortality and lifetime

income with a large micro data base of current and former retired workers. Logit models by 

gender and race confirm a negative relationship. Differences in age of death between low and 

high lifetime income are on the order of two to three years. Income-related mortality differences 

between blacks and whites are largest at low-income levels while gender differences appear to be 

large and persistent across income levels. 

 
2

I. INTRODUCTION

The calculation of initial benefits in the U. S. Social Security system makes no attempt to

reflect systematic differences across the population in mortality risk. Individuals of the same age

with identical lifetime earnings profiles, and therefore with identical contribution amounts, 

receive the same initial benefit at retirement irrespective of differences in expected length of life. 

One consequence is that unintended redistribution can occur that depends on differences in life 

expectancy related to such factors as income, gender, and race. Milton Friedman pointed this out 

over thirty years ago, noting that higher income people have higher life expectancy and therefore 

receive benefits for a longer period of time (Friedman, 1972). Existing proposals to reform the 

Social Security system also generally ignore mortality risk. Even proposals for individual 

accounts often include annuitization provisions that could cause significant transfers from groups 

with higher mortality risk to those with lower risk (Brown, 2002). 

Economists, sociologists, demographers, and health professionals have all studied the 

relationship between life expectancy and socioeconomic status, and the literature has grown 

dramatically since the oft-cited study of Kitagawa and Hauser (1973). A common finding is that 

life expectancy is higher for higher income persons (Brown, 2002) and some research reports that the gap

in mortality risk has been growing (Schalick, et al, 2000). Nevertheless, the literature in this area is 

relatively young and appropriate data are scarce. Most early research and some recent work rely

on data aggregated by geographic areas and broad population groups, often categorizing people 

into income classes based on a single year of income. Such an approach cannot distinguish 

between populations that are temporarily and permanently in a certain income class, obscuring

the distinct effects of permanent and transitory income. Moreover, most available databases do 

not distinguish disabled and nondisabled persons. Because disabled persons have far higher

mortality than the general population (Zayatz, 1999) and their lifetime income is likely to be 

affected by their health status, combining the two populations may result in seriously misleading 

implications of the income-mortality relationship for Social Security calculations. 
3

In this paper we analyze empirically the seldom-investigated relationship between 

mortality and individual-specific socioeconomic status with data that largely avoid the 

aforementioned problems. Our data are administrative records from the Social Security 

Administration’s (SSA) 2002 one-percent Continuous Work History Sample (CWHS) that has 

actual covered earnings information spanning the period 1937 to 2002. The file contains month 

and year of death information for deceased persons covered by the Social Security program,

along with race and gender indicators. Our data set also includes all of the benefit records from

SSA’s Master Beneficiary Record (MBR) file that are associated with the CWHS records. The

benefit records contain additional death information and allow us to separate the mortality 

experience of retired from disabled workers. 

Our results give strong empirical support to a negative relationship between individual 

lifetime income and mortality. For black and white males and females the difference in age of

death between low and high lifetime income is on the order of two to three years. Workers with 

positively-trended earnings over their work life may live an additional six to eighteen months 

relative to those with declining earnings. Income-related mortality differences between blacks 

and whites are largest at low-income levels, particularly for males, and narrow substantially at 

higher income levels. On the other hand, gender differences in mortality appear to be large and 

persistent across income levels. 

In section II we briefly review previous research on income and mortality. In section III 

we describe our data in detail, define variables of interest and illustrate mortality patterns that

arise in our sample. In section IV we develop logit models of the earnings-mortality relationship

for retired-worker beneficiaries. In section V we use estimated models to simulate survivor rates 

in order to further highlight the importance of lifetime income for mortality of retired workers.

Section VI concludes with a brief discussion of the implications of our results for Social Security

analyses. 
4

II. Previous Research on Income and Mortality 

The effect of income (or socioeconomic status generally) on mortality cannot be

determined a priori. Higher income may be associated with higher mortality if the consumption 

of some goods and services that adversely affect health (red meats or skydiving, for example)

increases with income. On the other hand, higher income may lead to increased consumption of 

goods and services that support health and prolonged life, such as shelter, protective clothing and

medical care. The relationship between income and mortality is complex, but we would expect 

larger improvements in life expectancy to result from income increases at the lower end of the 

income distribution than at the upper end. 

Research on mortality has typically grouped people into socioeconomic strata in order to

identify differences in mortality rates.1

 Socioeconomic status has been proxied by education 

(e.g., Kitawaga and Hauser, 1973; Brown, 2002), family income (e. g., Kitawaga and Hauser,

1973; Hadley and Osei, 1982; Duleep, 1986; Rogers, 1992), and wealth (Menchik, 1993; 

Attanasio and Hoynes, 2000). Generally, these report a negative relationship between mortality 

and socioeconomic status. As pointed out by Brown (2002), however, socioeconomic status is 

captured imperfectly by education, family income, and wealth. Education may serve only as a 

rough proxy for lifetime earnings, while current family income is a poor measure of lifetime

resources (low current income may simply reflect current poor health). Wealth is superior to 

education and current income in that respect though it may be correlated with health. In this 

paper we use a direct measure of lifetime resources (lifetime earnings) that avoids the drawbacks 

associated with other measures of socioeconomic status.

Obviously, other factors also affect mortality. Mortality may vary by occupation,

although most of the occupational differences in mortality are explained by income or education 

(Wilmoth and Dennis, 2006). Several researchers have assessed the comparative roles of race 

and other socioeconomic variables in determining death hazards, but agreement is lacking. 

Some authors find that black-white differences in mortality are fully explained by socioeconomic 

status while others find that socioeconomic status reduces but does not eliminate black-white 
5

mortality differences (Wilmoth and Dennis, 2006). This paper provides new evidence on the

income-race-mortality nexus. 

III. Data and Variable Definitions 

Our primary data sources are the Social Security Administration’s 2002 one-percent 

Continuous Work History Sample (CWHS) and Master Beneficiary Record (MBR). The 

“active” portion of the CWHS (i.e., individuals with some covered earnings) is a historical 

record of Social Security earnings for over three million current or former workers in covered

employment spanning the period 1937 to 2002. Each record has date of birth and race and sex 

indicators. When an individual files a claim for benefits based on her or his previous earnings 

history, SSA opens a claim account in the MBR file, the principal official record of historical 

benefit payments. If a worker attains beneficiary status and subsequently dies, the date of death 

is recorded in the MBR. Death information in the MBR is considered to be of very high quality 

(Aziz and Buckler, 1992). 

The administrative records have several advantages over data sets used in previous 

analyses of the determinants of mortality. The coverage is unparalleled, with the number of 

records many times larger than in any of the surveys with information on date of death. The 

CWHS has individual longitudinal information on lifetime earned income, as well as 

demographic information (age, race, sex), that is matched to mortality data. These data obviate 

aggregation or one-time measurement of the income variable that is of primary interest.2

The CWHS also has limitations for estimating mortality models. Most important for our 

purpose is that it contains information only on income earned in covered employment. Also, 

family background information that may impact on mortality rates is not included. Finally, race 

information in the administrative data permits only a three-way classification (blacks, whites,

other).3

For empirical analysis we selected all retired-worker beneficiary records with birth years 

1900 to 1942 who claimed benefits at age 62 or later. The birth year range allows us to observe 
6

a complete life for early cohorts and a nearly complete work life for later cohorts. Because our 

benefit/death information extends through 2004, the oldest observed age in our sample is 104. 

Accounting for edits and deletions for anomalous cases, our sample has nearly 550,000 

observations.4

 Some characteristics of our sample are shown in Table 1. 

Nearly half of retired-worker beneficiaries in our sample have died. The percentages are 

higher for males than females and higher for blacks than whites. For retired-worker beneficiaries

who have already died, the median female lived about four years longer than the median male

and the median white lived about one to two years longer than the median black. 

Lifetime earnings are measured for each individual as the sum of real earnings (2005 

dollars) over ages 35 to 60 (ages 37 to 60 and 36 to 60 for birth years 1900 and 1901, 

respectively).

5

 As seen in Table 1, males earned more than females and whites earned more than 

blacks. The relationship between age of death and lifetime earnings for deceased male retired

workers in our sample is illustrated in Figure 1 (a similar but less steeply-sloped pattern arises 

for females). The figure shows three five-year birth cohorts, selected to represent patterns across 

the birth cohorts in our sample, with each five-year birth cohort sorted into deciles of lifetime

earnings. The figure plots the differences between the average age of death in each earnings 

class and the average age of death across the earnings classes, thereby adjusting for scale 

differences across birth cohorts. For the 1910-14 birth cohorts, difference in age of death 

between highest and lowest earnings deciles is about twenty months; for the 1920-24 cohorts, the 

difference is about a fifteen months and about eight months for the most recent cohorts. The 

figure clearly depicts a positive relationship between lifetime earnings and length of life. Later 

we will examine in some detail the importance of this measure of “permanent income” for 

mortality outcomes. 

We are also interested in the relationship between the pattern of earnings over the work

cycle and life expectancy. Many workers exhibit low adherence to the workforce due to limited 

skills or poor health while others, for similar reasons, may have steady workforce attachment but 

achieve little progress in real earnings. On the other hand, workers who continue to improve their 
7

earnings positions are likely to be more skilled, in better health, and generally more productive. 

The latter group may also have longer life expectancies. We attempt to capture these effects by 

classifying workers according to whether the trend in their real earnings over prime working ages 

has risen, remained flat, or fallen.6

 Specifically, consider an individual worker’s average real 

earnings during the three equal age periods 37 to 44, 45 to 52, and 53 to 60 and label the periods 

AE1, AE2, and AE3, respectively. The trend in a worker’s real earnings can be measured as: 

Trend = (AE3 – AE1)/(AE1 + AE3). 

We classify workers according to earnings trend as follows: 

Declining if Trend < -1/9, 

Flat if -1/9 ≤ Trend ≤ 1/9, 

Rising if Trend > 1/9. 

Figure 2 shows a steady decline in the percentage of male retired workers with a rising Trend 

and a steady rise in the percentage with a declining Trend. A similar but more modest change in 

Trend has occurred for females. Figure 3 illustrates the relationship between cumulative age of

death and the trend in earnings for deceased male retired workers. The age-of-death distribution

for male workers with a declining earnings trend lies above the distribution for workers with 

rising or flat trends. The difference in median age of death between those with rising trends and 

those with declining trends is about four years. 

. 

IV. Empirical Mortality Models 

In this section we estimate mortality models for white and black male and female retired 

workers. The unit of observation is an individual year of a worker’s life and the dependent 

variable is the binary realization of the “hazard rate,” indicating whether or not death took place 
8

in that year, given that the individual was alive at the end of the previous year. We estimate the

hazard directly using the logit probability model so that variables with positive coefficients are

associated with higher death rates and shorter expected lifetimes. Explanatory variables in our 

models include (log of) real lifetime earnings, age, year of birth, and indicators of rising and flat 

earnings trends (declining trend is omitted). In this specification, real earnings will vary both

across and within cohorts.7

 The birth year variable is designed to capture trends in mortality 

unrelated to the growth in real income over time. The earnings variable is specified as a cubic 

polynomial in order to allow flexibility in the way earnings affect mortality. 

Retired Male Workers 

Table 2 displays our parameter estimates for male retired workers. Most coefficients are 

highly significant, reflecting in part the large sample sizes.8

 The probability of death rises with 

age and falls with cohort birth year. An additional year of age increases the odds of death by 

nine to ten percent for retired male workers.9

 The probability of death falls for more recent birth

cohorts, presumably reflecting trends in health care and nutrition, although the estimated 

coefficients are not statistically significant for black retirees. For white and black males born in 

1930, for example, the odds of death are sixteen and five percent lower, respectively, than for 

retirees born in 1920, ceteris paribus. The trend in earnings also has a negative effect on 

mortality. Relative to a declining trend, a rising trend reduces the odds of mortality by about 

twenty percent and a flat trend reduces the odds by ten to thirteen percent. In each case,

mortality is significantly negatively related to the log of lifetime earnings, a result that we 

explore further below. 

Retired Female Workers

For many female workers, the primary source of earned income may not be their own 

earnings but that of their spouses. In these cases, spousal earnings, in addition to a worker’s own 

earnings, may better capture the effects of lifetime income on mortality. Unfortunately, Social 

Security records do not identify marital status per se so that pairing all married workers is not 
9

possible. Yet, the records do identify beneficiary couples when an individual receives a benefit 

based on their own earnings history and an auxiliary benefit based on the earnings of their 

current or former spouse. These dual beneficiary cases are common among female retirees – 

about half of female retired workers are dual beneficiaries.10 For these cases, we are able to

match the actual earnings history of the (current or former) spouse with the beneficiary’s record 

and thereby include a second known source of income in the female mortality model.11

Female dual beneficiaries are current or former retired-worker beneficiaries who also 

received either a spousal or widow’s benefit based on the earnings of a current or former spouse. 

Female nonduals are current or former retired-worker beneficiaries with no auxiliary benefit.12 

Duals and nonduals differ considerably in their income and work experience, as shown in Table

3. The median earner nondual white female had nearly three times the earnings of the median 

dual white female; for blacks, the ratio was nearly two and a half to one. Duals had six to eight 

years fewer years of market work than nonduals due at least in part to child-rearing. Finally, the 

median-earner spouse of white female duals earned nearly seven times that of the dual whereas 

the median-earner spouse of black females had nearly four times the earnings of the dual (about 

five percent of black female retired workers are duals). 

Table 4 presents parameter estimates for mortality models for the nondual and dual 

retired female workers. Own lifetime earnings has a significantly negative effect on mortality

for both dual and nondual females. For white female duals, lifetime earnings of their spouses 

also has a significantly negative effect on mortality; the effect is insignificant for black female 

duals. The variables measuring the trend in earnings have a significantly negative effect on 

mortality for all four groups represented in Table 4. In the dual equations, birth year has a 

positive coefficient, in contrast to the birth-year effect in the other mortality equations. This

trend could reflect intertemporal changes in the relationship between dual-beneficiary status and 

health status.13
10

V. How Large Are Income-Related Differences in Life Expectancy?

In this section we use the estimated mortality models to compare survivor rates and life

expectancies at different income levels for retired workers. As backdrop for this analysis, figure

4 has the cumulative distributions of lifetime income for black and white males and females. 

The black and white female distributions are quite similar whereas the distribution for black

males lies well below that for white males.14 Almost 80 percent of black males fall below the

median value for white males and only 26 percent of white males fall below the median value for

black males. The shapes of the male distributions are also important when comparing changes in 

earnings between blacks and whites. 

Black and White Males 

Figure 5 shows survivor rates, the predicted proportions of age-62 workers still alive at 

subsequent ages, for hypothetical white male retired workers at the 10th, 50th, and 90th percentiles

of the white male earnings distribution, computed with the coefficient estimates in Table 2. The

hypothetical workers were born in 1920 and have a flat real earnings profile. The median age of

death for the white male median earner is 80.3 years. For the low and higher earners, the median 

ages of death are 78.8 and 81.6, respectively. For these white male retired workers, there is an 

approximately thirty-three month difference in age of death between the lowest and highest

deciles (Table 5). For hypothetical workers with a positive earnings profile the median ages of 

death are about a year higher at each percentile. 

Figure 6 has survivor rates for similarly defined hypothetical black male retired workers. 

The median age of death for the black median-earner retired worker is 77.8, two and a half years 

lower than that of the white median earner. For low and high earners, the median ages of death 

are 76.8 and 80.3, respectively. The age-of-death difference between the lowest and highest

earners is over three years (forty-two months). For black workers a positive trend results in 

about a half year greater life expectancy. 
11

Differences in the black-white earnings distributions are large enough to make mortality

comparisons at the top of the white male distribution infeasible. For example, the black male 

survivor function doesn’t generate a median age of death of 80.3, the median age of death for 

median-earner white males, until about the 96th percentile of the black male earnings distribution. 

On the other hand, points along the black earnings distribution are feasible for whites, even if 

incomplete. At the 10th and 90th percentile values of the black male distribution, the median ages

of death for white males are 79.0 and 81.3, respectively, compared to 76.8 and 80.3 for black 

males. This indicates that the largest differences in mortality between black and white males 

occur at low earnings levels with the differences narrowing as earnings levels rise. 

Improvements in mortality at the high end of the white male distribution are outside the range of

black male earnings. 

An alternative illustration of the black-white income-mortality gap is provided in Figure

7. The figure shows mortality rates by earnings levels for 65-year-old black and white males. 

The rates decline as earnings levels increase. Differences in the rates decline steadily,

particularly at lower earnings levels. The mortality rate difference begins at about 0.5 percentage 

points at the lowest income level and falls by a third at about $1.4 million in lifetime earnings, 

which occurs at the 95th percentile of the black male earnings distribution. 

Black and White Females 

We define hypothetical female workers in a similar way to that used for males. For 

nonduals, survivor profiles are shown in Figures 8 and 9 for white and black females, 

respectively. For white nonduals, the median age of death ranges from 82.1 to 84.3 between the 

10th and 90th percentiles of their lifetime earnings distribution, a difference of 26 months (Table

5). For black nonduals, the difference in the median age of death is larger – 45 months. For 

black and white nonduals, a positive earnings trend adds over a year to life expectancy. 

For duals there are two sources of lifetime income variation – own and spouses – and we 

construct survivor profiles by allowing one or the other to vary while holding one to its median 
12

value.15 The results are summarized in Table 5. Higher own lifetime earnings has a significantly

negative effect on the mortality of female duals, comparable to that for female nonduals. Higher 

spousal lifetime earnings has a significantly negative effect for white female duals but an

insignificant effect for black duals. For both white and black duals, a positive earnings profile

adds about another year of life.

For all groups shown in Table 5, lifetime income has a relatively large positive effect on 

life expectancy and the effects are remarkably consistent across the groups when evaluating the 

variation in group-specific income – i. e., reading results across each row of the table. In order to

make direct comparisons across major demographic groups we computed ages of death for white 

and black males and white and black nondual females at two points from the earnings 

distributions represented in Figure 10: the fifth percentile from the white male distribution and 

the 95th percentile from the nondual black female distribution.16 The results are shown in Table 

6. 

The difference in age of death between white and black males is about twenty-six months 

at the low-income level but this difference narrows substantially to fifteen months at the higherincome level. The age-of-death difference between white males and females is about three years 

at the low-income level and this difference widens somewhat at the higher-income level. The 

differences between black males and black females is substantial at both income levels while the 

age-of-death differences are smaller and narrow between the two income levels for white and 

black females. Apparently, age-of-death differences between whites and blacks are largest at 

low-income levels while the differences between males and females are large and persistent 

across income levels. 

VI. Conclusion

This paper provides strong evidence, based on a large file of current and former workers, 

that mortality is negatively related to lifetime income. For black and white males and females 
13

the difference in age of death between low and high lifetime income is on the order of two to 

three years. Workers with positively-trended earnings over their work life may live an additional 

six to eighteen months. Income-related mortality differences between blacks and whites are 

largest at low-income levels, particularly for males, and narrow substantially at higher income

levels. On the other hand, gender differences in mortality appear to be large and persistent 

across income levels. 

The income-related differences in life expectancy are substantial enough to require 

consideration when evaluating the distributional consequences of proposals to modify various 

features of the Social Security program or when evaluating the existing program. Income-related 

differences in life expectancy curtail, but do not reverse, the progressivity of the basic Social 

Security benefit structure (Liebman, 2002) and have significant implications for individualaccount proposals (Brown, 2002; Liebman, 2002).17 In fact, the income-mortality relationship 

has implications for a wide range of Social Security program rules. For example, many reform 

proposals include a provision to raise the normal retirement age or to increase the earlyretirement age. Analyses of such proposals that do not account for the effect of income on life 

expectancy will misrepresent the differential effects of these proposals on those persons who

currently retire early compared to those who retire at later ages. 

From a somewhat different perspective, the feedback of life expectancy on retirement

decisions complicates any attempt to adjust benefits by age of retirement. The “actuarially fair” 

adjustment—defined as the adjustment that leaves the internal rate of return on contributions

unchanged—will vary across income groups (as well as other groups with systematic differences 

in life expectancy. We can expect that for any given adjustment, retirees are likely to choose a 

retirement age that benefits them. Unless the adjustment is either extremely high or extremely 

low—leading to corner solutions—low-income workers will retire at an earlier age than highincome workers, and both will gain from their choices. A single adjustment factor will cause

workers to select “against” the system.

18
14

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16

Endnotes 

1

Our references are not intended to be exhaustive of the literature on this topic. Kitawaga and 

Hauser (1973) thoroughly review early studies. Feinstein (1993) and Wilmoth and Dennis 

(2006) review more recent literature. Brown (2002) summarizes major findings from the 

literature on the socioeconomic status - mortality relationship. 

2

Hoyert, Singh, and Rosenberg (1995) summarize the principal data sets available to evaluate 

mortality and socioeconomic status. 

3

Since 1980, race information collected in the administrative records has expanded to include 

five race/ethnic responses: White (not Hispanic), Black (not Hispanic), Hispanic, Asian or

Pacific Islander, and American Indian or Alaskan Native. Prior to 1980, only the three-way 

classification was allowed. The vast majority of records analyzed in this paper would not, of 

course, benefit from the expanded classification. 

4

One anomaly in our sample occurs with records that show no recent work activity, no benefits, 

insufficient quarters of coverage for benefit eligibility, and no death information. These records 

accounted for less than nine percent of the sample before edits. The vast majority of these 

records are missing death information in our file due to state agreements with SSA on the use of 

death reports. We deleted these records on the grounds that their exclusion would be less 

distorting than their inclusion. The exclusion has no effect on our later econometric estimates 

that focus on beneficiaries. 

5

Earned income in the CWHS consists of lump-sum taxable earnings for 1937 to 1950, annual 

taxable earnings from 1951 to 1977, and total (uncapped) earnings from 1978 to 2002. We use 

these earnings data to develop a summary measure of real lifetime earnings following three 

adjustments to the data: (1) we prorated the 1937 to 1950 lump-sum under the assumption that, 

between the reported first and last years of employment prior to 1951, earnings grew at one 

percent per year of age beyond the economy-wide growth in wages for males and at one-half 
17

percent for females, using the average annual earnings between 1937 and 1950 from Myers 

(1993); (2) for individuals who earned the taxable maximum during years 1937-1980, we 

imputed earnings above the maximum using a Tobit model of earnings under the assumption that 

earnings are lognormally distributed; (3) earnings (actual or imputed) in each year were indexed

to 2005 using the Consumer Price Index. 

6

The earnings classification scheme described below is based on work performed on the 1972 

CWHS and reported in the Committee on Finance (1976), pp. 75-81. 

7

As noted above, the Consumer Price Index is used to deflate nominal earnings. Duggan and 

Gillingham (1999) examine potential errors in that index series and the potential impact of those 

errors on Social Security finances. 

8

The basic sample consists of 277,571 while males and 24,160 black males. In person-year 

format, there are over 3.8 million white-male observations and over 317 thousand black-male 

observations. 

9

The logit parameters measure the marginal impact of independent variables on the log odds of 

death; i.e., on ln[p/(1-p)] where p is the probability of death. In percentage terms, the effect of 

an additional year of age on the odds of death for retired white male workers, for example, is 

given by 100(e

.0952 – 1). 

10Some unknown proportion of female retired worker beneficiaries may be married but not 

receive an auxiliary benefit because their own benefit exceeds the auxiliary benefit amount. For 

those individuals, benefit records do not reveal marital status. Male retirees may also be dual 

beneficiaries but the number is very small. 

11The lifetime income of spouses were computed in the same manner as for the primary worker, 

described in section III. That is, we first impute above-cap earnings for those earning the 

maximum and then sum their real earnings over ages 35-60. 
18

12For the empirical analysis, we have 123,979 nonduals and 94,945 dual beneficiaries (about 95 

percent of the white and black females represented in Table 1. We omitted about 10 percent of 

duals who were mainly divorced wives and widows who were not the primary claimants on a 

dual account. 

13This could be investigated using a latent-variable sample-selection model but we chose not to 

as it isn't central to our objective in this paper. 

14For females, the distributions of dual earnings for blacks and whites are quite similar while the 

distribution of nondual earnings for whites lies somewhat to the right of that for blacks. Figure 4 

combines duals and nonduals. 

15Allowing both to vary at the same time would provide the largest income-related differences in 

life expectancy so our simulations are conservative in this respect. Because we found little 

empirical relationship between own and spouse’s lifetime earnings we allow the income 

variables to vary independently. 

16The two lifetime income values are $77,642 and $1,163,370 and correspond to the following 

percentiles from the respective earnings distributions: white males – 5th and 65th; black males – 

12th and 88th; white females – 9th and 95th; black females – 17th and 95th. The two points are not 

feasible for own income of duals. 

17 See also Duggan, Gillingham, and Greenlees (1993, 1996) on the distributional impacts of 

Social Security. 

18The implications of constant actuarial adjustments across cohorts and income classes are 

analyzed in Duggan and Soares (2002). 
19

Table 1

Characteristics of Retired-Worker Beneficiaries

(Birth Years 1900-1942, N=548,681)

Females Males

Category Sample White Black Other White Black Other

Sample (%) 100 38.5 3.7 1.1 50.6 4.4 1.7

Dead (%) 49.0 43.6 45.4 24.4 54.2 59.0 37.5

Median Age of

Death 77 80 78 79 76 75 77

Median Lifetime 

Earnings (sum) $517,158 $269,574 $236,917 $233,227 $921,999 $476,051 $413,088 

Source: authors' calculations based on CWHS and MBR. Earnings are in 2005 dollars.
20

Table 2

Estimated Mortality Hazard Models

Male Retired Workers Age 62+

White Black

Variable Males Males

Intercept -9.9906 -9.3995

(0.0459) (0.1421)

Age 0.0952 0.0876

(0.0004) (0.0012)

Birth Year -0.0173 -0.0056

(0.0004) (0.0011)

Lifetime Earnings (LE)

 LE -0.3405 -0.4056

(0.0232) (0.0637)

 LE 2 0.0666 0.0794

(0.0035) (0.0099)

 LE 3 -0.0030 -0.0036

(0.0001) (0.0004)

Trend in Earnings

 Rising -0.2144 -0.1950

(0.0072) (0.0218)

 Flat -0.1011 -0.1292

(0.0088) (0.0294)

Note: Estimated standard errors are shown in parentheses. All estimated coefficients are

statistically significant.
21

Table 3

Income and Work Years of

Female Dual and Nondual Beneficiaries

Duals Nonduals

(n=94,945) (n=123,979)

White Black

own spouse own spouse White Black

Lifetime Income* $150,749 $1,013,750 $130,029 $501,740 $420,491 $310,227

Work Years** 20 23 28 29

*Lifetime income is the median of the sum of real earnings (2005 dollars) over ages 35-60.

**Work Years is the number of years over the period 1951-2002 with positive earnings.
22

Table 4

Estimated Mortality Hazard Models

Female Retired Workers

White Black

Variable Duals Nonduals Duals Nonduals

Intercept -12.8834 -10.3237 -12.3689 -9.3110

(0.0868) (0.0561) (0.3960) (0.1573)

Age 0.1286 0.0998 0.1193 0.0850

(0.0006) (0.0005) (0.0027) (0.0014)

Birth Year 0.0030 -0.0152 0.0141 -0.0118

(0.0007) (0.0005) (0.0031) (0.0015)

Lifetime Earnings (LE)

 LE -0.1804 -0.2052 -0.4511 -0.1792

(0.0280) (0.0295) (0.1435) (0.0723)

 LE 2 0.0393 0.0463 0.0863 0.0482

(0.0048) (0.0048) (0.0238) (0.0119)

 LE 3 -0.0021 -0.0022 -0.0043 -0.0025

(0.0002) (0.0002) (0.0011) (0.0005)

Trend in Earnings

 Rising -0.2627 -0.2727 -0.1879 -0.2888

(0.0097) (0.0106) (0.0421) (0.0298)

 Flat -0.1245 -0.1161 -0.1632 -0.1702

(0.0171) (0.0152) (0.0759) (0.0456)

Lifetime Earnings of Current/Former Spouse (LESP)

 LESP -0.3419 -0.0855*

(0.0492) (0.1546)

 LESP 2 0.0567 0.0239*

(0.0074) (0.0238)

 LESP 3 -0.0023 -0.0011

(0.0003) (0.0010)

Note: Estimated standard errors are shown in parentheses.

*Indicates that the estimated coefficient is less than twice its standard error.
23

Table 5

Median Ages of Death for Retired Workers by Percentiles and Trends

in Group-Specific Lifetime Earnings

(Year of Birth=1920)

Flat Trend Positive Trend

90th-10th 90th-10th

Retirees 10th Median 90th (months) 10th Median 90th (months)

Males

 white 78.8 80.3 81.6 33 79.8 81.3 82.6 33

 black 76.8 77.8 80.3 42 77.3 78.3 80.8 42

Female Nonduals

 white 82.1 83.1 84.3 26 83.5 84.5 85.8 27

 black 81.1 82.5 84.8 45 82.3 83.8 86.1 45

Female Duals

 white

 own earnings 84.1 85.0 86.4 28 85.1 86.1 87.5 29

 husband's earnings 84.6 85.0 85.4 10 85.7 86.1 86.4 9

 black

 own earnings 82.1 83.3 85.9 46 83.0 84.3 86.9 47

 husband's earnings 83.2 83.3 83.5 4 84.2 84.3 84.5 4
24

Table 6

Age-of-Death Differences at Constant Lifetime Incomes*

Low Income High Income

age of differences age of differences

Retirees death in months death in months

white males 78.9 80.8

 less:

 black male 26 15

 white female -38 -47

black males 76.8 79.5

 less:

 black females -54 -73

white females** 82.1 84.7

 less:

 black females 10 -11

black females** 81.3 85.6

*Low income is $77,642; high income is $1,163,370 (see text).

**Female nonduals.
25

Figure 1: Male Retired Workers

Deviations from Average Age of Death

By Lifetime Earnings and Birth Years

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

12345678 9 10

Lifetime Earnings Class

 1910-14

 1920-24

 1930-34

Average Age of Death by Birth Year:

1910-14: 77.9

1920-24: 74.8

1930-34: 68.1
26

Figure 2: Percentage of Male Retired-Worker Beneficiares with

Rising, Level, Declining Earnings by Birth Cohort

0%

10%

20%

30%

40%

50%

60%

70%

80%

1910-14 1915-19 1920-24 1925-29 1930-34 1935-39

Birth Cohort

Rising Level Declining
27

Figure 3: Cumulative Distributions of Age of Death by Trend in Lifetime Earnings

Male Retired Workers

0

10

20

30

40

50

60

70

80

90

100

62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94

Age of Death

Percent

Declining Trend

Rising Trend

Flat Trend
28

Figure 4

Cumulative Distribution of Lifetime Earnings

Retired Workers Born 1900-1942

(thousands of 2005 dollars)

0

10

20

30

40

50

60

70

80

90

100

40 160 280 400 520 640 760 880 1000 1120 1240 1360 1480 1600 1720 1840 1960 2080

Thousands of Dollars

Percent

White Males

Black Males

White Females

Black Females
29

Figure 5

Survivor Rates for White Male Retired Workers at Three Earnings Levels

Birth Year=1920, Flat Earnings Profile

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

62 65 68 71 74 77 80 83 86 89 92 95 98

Age of Death

Percent

90th Percentile

Median Earnings

10th Percentile
30

Figure 6

Survivor Rates for Black Male Retired Workers at Three Earnings Levels

Birth Year=1920, Flat Earnings Profile

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

62 65 68 71 74 77 80 83 86 89 92 95 98

Age of Death

Percent

90th Percentile

Median Earnings

10th Percentile
31

Figure 7

Mortality Rates by Earnings Levels

65-Year Old Black and White Males

1.3%

1.5%

1.7%

1.9%

2.1%

2.3%

2.5%

2.7%

2.9%

80 160 240 320 400 480 560 640 720 800 880 960 1040 1120 1200 1280 1360 1440

Thousands of Dollars

Percent

Blacks

Whites
32

Figure 8

Survivor Rates for White Female NonDuals

Birth Year=1920, Flat Earnings Profile

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

62 65 68 71 74 77 80 83 86 89 92 95 98

Age of Death

Percent

Median Earnings

10th Percentile

90th Percentile
33

Figure 9

Survivor Rates for Black Female NonDuals

Birth Year=1920, Flat Earnings Profile

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

62 65 68 71 74 77 80 83 86 89 92 95 98

Age of Death

Percent

90th Percentile

Median Earnings

10th Percentile