#9910
September 1999
The Impact of Pay Inequality, Occupational
Segregation, and Lifetime Work Experience on
the Retirement Income of Women and Minorities
by
Olivia S. Mitchell
University of Pennsylvania-Wharton School
Phillip B. Levine
Wellesley College-Department of Economics
John W. Phillips
University of Pennsylvania-Population Studies Center
#9910
September 1999
The Impact of Pay Inequality, Occupational
Segregation, and Lifetime Work Experience on
the Retirement Income of Women and Minorities
by
Olivia S. Mitchell
University of Pennsylvania-Wharton School
Phillip B. Levine
Wellesley College-Department of Economics
John W. Phillips
University of Pennsylvania-Population Studies Center
The Public Policy Institute, formed in 1985, is part of the
Research Group of the AARP. One of the missions of the
Institute is to foster research and analysis on public policy issues
of interest to older Americans. This paper represents part of
that effort.
The views expressed herein are for information, debate, and
discussion, and do not necessarily represent formal policies of
the Association.
 1999, AARP.
Reprinting with permission only.
AARP, 601 E Street, N.W., Washington, DC 20049
Acknowledgments
We acknowledge research support from the AARP and the Pension Research Council at the
University of Pennsylvania, and computer support from the University of Pennsylvania’s Aging
Research Center. The views expressed herein are those of the authors and do not necessarily
represent the views of the institutions with which they are affiliated.
Olivia S. Mitchell
Phillip B. Levine
John W. Phillips
i
Foreword
In response to the persistent problem of poverty among older women and minorities,
analysts and policymakers have sought to determine why these vulnerable groups have such a high
likelihood of being poor in old age. Women and minorities bring different labor market
experiences to retirement than do white men. For women, lower earnings and fewer years in the
work force mean less retirement income because pensions and Social Security typically reward
those with higher pay and more years of paid work. Likewise, for blacks and Hispanics, lower
levels of education and lower earnings have a negative impact on their anticipated retirement
income.
The causes of the wage gap that affects women and minorities have been the subject of
considerable research. Some of this work has examined the impact of occupational segregation
and other labor market experience on these wage differentials. How wage inequality,
occupational segregation, and lifetime work experience influence the future retirement incomes of
women and minorities, however, has not been as thoroughly studied. Even less information is
available about the impact of these factors on the three major components of retirement income—
Social Security benefits, pension income, and savings.
This vital information—the impact of pay inequality, occupational segregation, and
lifetime work experience on the retirement income of women and minorities—is the focus of this
report by researchers Olivia Mitchell and John Phillips of the University of Pennsylvania and
Phillip Levine of Wellesley College. By merging the first wave of the Health and Retirement
Study with two additional files—the Pension Provider File and the Earnings and Benefits File
(Social Security data)—these researchers examine the anticipated wealth available to groups, by
sex and minority status, from Social Security, employer-provided pensions, and other financial
resources, including housing.
The authors find that occupational segregation and pay differences account for most of the
difference in the retirement income of women and men. Even controlling for factors such as
number of years worked and level of education, two-thirds of the annual difference in the
retirement income of nonmarried women and men reflects two key measures: the proportion of
women in an occupation and the pay gap during prime earning years.
The losses women incur as a result of these pay differences show up primarily in their
pension and other income, not in their Social Security benefits. This is because Social Security’s
progressive calculation formula and the availability of spousal benefits play an important role in
helping women counteract the retirement impact of working in female-dominated jobs and having
lower earnings. The anticipated retirement incomes of nonwhite men and women do not differ
markedly; both have low levels of wealth to draw on in retirement.
These findings raise important policy questions about how to improve the retirement
income prospects of women and minorities, particularly how to enhance their opportunities to
obtain employer-provided pensions and to increase their ability to save. In addition, women and
ii
minorities could benefit from better information about the long-term consequences of leaving
school early, taking low-paying jobs with no career ladders, and working in female-dominated
occupations. The findings also raise questions about Social Security reform proposals that might
reduce the program’s ability to provide a foundation of retirement income security for women and
minorities.
Jules H. Lichtenstein, Ph.D.
Senior Policy Advisor
AARP Public Policy Institute
iii
Table of Contents
Acknowledgments........................................................................................................................ i
Foreword .................................................................................................................................... ii
List of Tables .............................................................................................................................. v
Executive Summary.................................................................................................................... vi
Introduction ................................................................................................................................ 1
Prior Studies ............................................................................................................................... 2
Data and Statistical Analysis........................................................................................................ 4
Explanatory Factors .............................................................................................................................5
Subsample Issues .................................................................................................................................6
Household Resources............................................................................................................................7
Methodological Approach.....................................................................................................................7
Empirical Findings....................................................................................................................... 8
Regression Results.............................................................................................................................. 10
Decomposition Results ....................................................................................................................... 12
The Role of Marital History................................................................................................................ 13
Discussion and Conclusions....................................................................................................... 14
Data Appendix .......................................................................................................................... 22
References ................................................................................................................................ 38
iv
List of Tables
Table 1. Median Projected Retirement Wealth by Race/Ethnicity, Sex, and Current Marital
Status………………………………………………………………………………………… 17
Table 2. Median Projected Annual Retirement Income by Race/Ethnicity, Sex, and Current
Marital Status...………………………………………………………………………………
18
Table 3. Predicted Changes in Total Projected Annual Retirement Income Associated with
Key Explanatory Variables…………………………………………………………………..
19
Table 4. Decomposing Differences in Projected Annual Retirement Income by Type:
Dollar Gap Attributable to Differences in Respondent Characteristics……………………...
21
Appendix Table 1. Unweighted Sample Sizes by Race/Ethnicity, Sex, and Current Marital
Status………………………………………………………………………………………… 25
Appendix Table 2. Mean Projected Retirement Wealth by Race/Ethnicity, Sex, and Current
Marital Status………………………………………………………………………………...
26
Appendix Table 3. Mean Projected Annual Retirement Income by Race/Ethnicity, Sex, and
Current Marital Status……………………………………………………………………….. 27
Appendix Table 4. Regression Results for Projected Annual Retirement Income by Type…
28
Appendix Table 5. Mean Values of Explanatory Variables by Sex and Current Marital
Status………………………………………………………………………………………… 32
Appendix Table 6. Unweighted Sample Sizes by Sex, Race, and Marital History………….
33
Appendix Table 7. Median Projected Annual Retirement Income by Source and Marital
History……………………………………………………………………………………….
34
Appendix Table 8. Regression Results for Total Projected Annual Retirement Income by
Marital History……………………………………………………………………………….
35
Appendix Table 9. Percentage of Men and Women in HRS Occupation Codes……………
37
v
Executive Summary
Introduction
Women and minorities are especially vulnerable in retirement: both groups face a
disproportionately high risk of poverty in their old age. Women over the age of 65, for example,
are about twice as likely to live in poverty as their male counterparts. Although more women are
working and their pension coverage is rising, the labor market experience of women still differs
substantially from that of men. By the time women are in their fifties, they have spent fewer years
working, have earned less over their lifetimes, and have held lower quality jobs than similar-aged
men. Lower earnings and fewer years in the work force translate into less retirement income
because pensions and Social Security typically reward those with higher pay and more years of
paid work. Minorities, who tend to have lower levels of education and lower earnings, are at a
similar disadvantage in retirement.
Purpose
This report reviews and synthesizes existing research in the area of pay inequality—
differences in earnings between working men and women and between minority and white
workers. It then uses the Health and Retirement Study (HRS) to evaluate the relative importance
of personal characteristics and labor market experiences in determining the retirement income
prospects of older women and minorities. The report examines the anticipated wealth available,
by sex and minority status, from three sources: Social Security, employer-provided pensions, and
other financial assets, including housing. This research helps evaluate how the next generation of
retirees may fare compared with prior cohorts. Few studies have followed workers into
retirement to determine whether labor market differences continue to have an impact at older
ages.
Methodology
The first section of the study reviews the labor economics literature on pay inequality. It
discusses the difference between the concept of pay inequality and pay inequity. It examines the
sources of the wage gap between men and women, including the effects of occupational
segregation, lifetime labor market attachment, education, and experience.
The second section of the study estimates anticipated retirement income using the first
(1992) wave of the HRS, a nationally representative sample of U.S. households on the verge of
retirement. The HRS focuses on respondents aged 51 to 61 in 1992, along with their spouses,
and contains information on earnings and labor market work patterns as well as the pension plans
of people anticipating or in the early stages of retirement. Two additional information sources
were merged with the HRS data to provide information about Social Security benefits and
pension income. The Pension Provider File (PPF) contains data from pension plan descriptions
and provides estimates of anticipated pension benefits of respondents; the Earnings and Benefits
File (EBF) contains information about expected retirement income from the Social Security
vi
Administration’s administrative records. Together, the HRS, PPF and EBF represent one of the
richest data resources available to analyze retirement income: no other current data source has
detailed, linked administrative records for this group of households. Using these three sources,
the anticipated retirement wealth of each household in the HRS was computed from the
annuitization of wealth from three sources: Social Security, employer-provided pensions, and
other financial assets, including housing.
The analysis has two phases. The first explores how much of the difference in projected
retirement income between groups is represented by pay inequality, occupational segregation, and
differences in labor market attachment. The second phase assesses how women might be
expected to fare in retirement if their labor market experiences were similar to those of men, with
race/ethnicity and other important factors held constant.
Principal Findings
Occupational segregation and pay differences account for most of the difference in the
retirement income of women and men. Even controlling for factors such as the number of years
worked and the level of education, the proportion of women in an occupation and the pay gap
during prime earning years together account for two-thirds of the more than $8,000 per year
difference, on average, between the retirement incomes of nonmarried women and men.
In both cases, the losses women incur as a result of pay differences show up primarily in
their pension and other income, not in their Social Security benefits. This is because Social
Security’s progressive calculation formula and availability of spousal benefits play an important
role in helping women counteract the retirement impact of working in female-dominated jobs, and
having lower earnings. For instance, for each 10 percent rise in the proportion of women in a job,
nonmarried women lose about $157/year in pension income and about $389/year in other income,
but only $20/year in Social Security income. Married women lose $375/year in pension income,
$173/year in other income, and just $9/year in Social Security income.
The study estimates projected retirement income based on anticipated retirement wealth
for each household. Married couples have three times the wealth and, when wealth is translated
into annual retirement income flows, much more retirement income than their nonmarried
counterparts. The median wealth of a married couple on the verge of retirement exceeds half a
million dollars. By contrast, more than half of nonmarried people have less than $200,000 in all
forms of wealth. The projected annual retirement incomes of nonmarried people are only onethird the level for married men and women. Moreover, nonmarried women can anticipate less
retirement income than nonmarried men. The retirement income gap among nonmarried men and
women is greatest for whites primarily because minorities have little retirement wealth. The
anticipated retirement income of nonwhite men and women do not differ markedly from each
other. Minorities are particularly vulnerable at older ages because they have very low levels of
wealth to draw on in retirement.
Hypothetically, it would be possible to close 75 percent of the overall gap between men
and women’s anticipated retirement incomes. This would entail raising women’s educational level
vii
to that of men and closing the gap in years of work, average pay, and occupational attainment.
Closing these gaps would also help equalize retirement incomes of whites and minorities—the
largest gains would be for blacks and Hispanics, who have the lowest levels of wealth.
Conclusions
There has been considerable research on the factors affecting the wage gap facing women
and minorities. Some has involved examining the impact of occupational segregation and other
labor market experience on these wage differentials. Less understood is the impact of wage
inequality, occupational segregation, and lifetime work experience on future retirement incomes
of women and minorities. Even less information is available about how these impact three major
components of retirement income—Social Security benefits, pension income, and savings.
The results of this study suggest several lessons and policy implications. First, there are
close links between earnings during a lifetime and income during retirement, especially income
from pension and other financial accumulations. These links imply that women and minorities
could benefit from better information about the long-term consequences of leaving school early,
taking lower-paying jobs, and working in female-dominated occupations. They could also profit
from improved information about the implications of dropping out of the labor force for extended
periods of time, although the effects of this behavior have fewer negative consequences for them.
Expanding the information available about the benefits of obtaining higher education on their
economic status while working and during old age (in retirement) could also be beneficial to them.
There are trends which point to improved retirement income prospects. If women’s pay
levels continue to climb and women are less segregated occupationally than they have been in the
last decade or so, women approaching retirement will have earned more over their lifetimes,
enhancing their well-being absolutely and relative to men. As women’s labor market histories
become more congruent with men’s over time, the gap in retirement income between men and
women should diminish.
viii
Introduction
Persons age 65 and over were once among the poorest segment of the population in the
United States, but today the rate of poverty for this group is at least as low as that of younger
segments of the population. Nevertheless, pockets of poverty remain within the older population.
Specifically, women over the age of 65 are about twice as likely to live in poverty in the U.S. as
are similarly aged men.1 One factor believed to contribute to women’s disadvantage in old age is
their different labor market experiences during their prime working years. For instance, women
who hold lower-wage jobs, work for fewer years, or work in more female-dominated jobs than
men may end up with less retirement income. This is because pensions and Social Security
retirement benefits typically reward those with higher-paying jobs and more years of paid work.
In addition, other important differences between men and women in terms of education, health,
and other factors may also translate into differences in retirement wealth. Similarly, minorities
face a disproportionate risk of old-age poverty relative to whites.
In this paper we use the Health and Retirement Study (HRS) to evaluate how labor market
experiences (including differences in years worked, levels of pay, and types of occupations) and
related factors help explain why older women and minorities face relatively poor retirement
income prospects. This research is important to young workers entering the labor market who
could profitably be alerted to the lifetime implications of their job-market patterns. Policymakers
may find this research of interest to the extent that poverty in old age is influenced by job-market
conditions and experiences earlier in life. And because women workers today spend more of their
lives in paid employment and earn more relative to men than they have in the past, our research
helps evaluate how the next generation of retirees may fare compared to prior cohorts.
To preview our conclusions, we find that the median wealth of a married couple on the
verge of retirement today exceeds half a million dollars, with substantial accumulations in Social
Security, pensions, housing, and other holdings. By contrast, nonmarried people approaching
retirement are in far worse shape. More than half of the nonmarried population has less than
$200,000, counting all forms of retirement wealth. Few can look forward to future pension
benefits, and even Social Security wealth is not large. Within the nonmarried group, women are
particularly disadvantaged, having a level of wealth about one-quarter lower than that of
nonmarried men. This difference is concentrated in the white population, since blacks and
Hispanics have very low and relatively similar levels of retirement wealth regardless of sex or
marital status. The anticipated retirement wealth values captured in this study translate into
median annual retirement incomes of about $28,000 per year (in 1992 dollars) for married men
and women, but only $13,000 for single men and $9,000 for nonmarried women.
Our statistical analysis of the determinants of these differences in anticipated retirement
income points to several factors that explain why women and minorities face such poor prospects
in retirement. One reason is that women currently on the verge of retirement have lower lifetime
average earnings, which translate into lower pensions and Social Security benefits. Another
1
For an extensive list of references see Levine, Mitchell, and Moore (forthcoming).
1
explanation is that women have spent fewer years in the paid labor market than men, a difference
that also penalizes them somewhat in retirement. Further, we find that working in occupations
heavily dominated by females reduces women’s eventual retirement income; by contrast, this effect is
not evident for their male counterparts. We also examine the extent to which other differences
between men and women contribute to the retirement income gap. For instance, among older
people on the verge of retirement today, married women have somewhat lower educational
attainment than do men, particularly at the post-college level. This accounts for part of the
measured retirement income differential by sex. We also find that blacks and Hispanics, who rely
on Social Security for the bulk of their retirement income, typically can expect far lower levels of
retirement income than can whites. But the anticipated retirement incomes of nonwhite men and
women do not differ markedly.
Before we proceed with our analysis, it is useful to define the economic terminology used
in this study. By pay inequality, we mean differences in earnings between working men and
women. This stands in contrast to pay inequity, a term often used interchangeably with
“comparable worth,” which refers to a policy that seeks to redress wage differentials caused by
occupational segregation. Our analysis evaluates the determinants of anticipated retirement
income differences, from which policy implications can be drawn, rather than assuming specific
policy implications. We include indicators of labor market occupational segregation because it
may be a potential contributor to the differences in well-being among older persons. As will be
explained below, we focus on retirement income that could potentially be derived from the
annuitization of wealth from three sources: Social Security, employer pensions, and other financial
assets.
Prior Studies
Labor economics research to date has explored differences in labor market wages
(earnings for paid market work) between men and women as well as between minorities and
whites, generally focusing on “prime age” persons.2 Two distinct research strands may be
discerned in this literature.
One approach considers whether observed wage gaps can be “explained” or accounted for
in a statistical sense by differences in characteristics likely to be related to worker productivity,
such as age, education, and labor market experience. To the extent that a wage differential exists
after controlling for these factors, it is typically attributed to labor market discrimination. This
strand of the literature consistently finds evidence of discrimination by this definition.3
A second approach used in explaining sources of the male/female wage gap evaluates
occupational segregation and its impact on wage differentials. Studies have shown that more than
half of all women would have had to move from female-dominated to male-dominated jobs in
2
There is no unique definition of “prime age” in the labor economics literature. In what follows we use this term
to refer to the period between age 20 and 50 since the dataset we use provides summary labor market measures
over this age range.
3
See Blau and Kahn (1997); Gunderson (1989); and Blau and Ferber (1987).
2
1990 if the occupational distribution of men and women were to be equalized.4 This estimate is
lower than the 70 percent figure that applied in 1970. Yet despite the diminution of occupational
segregation by sex over time, some have contended that it continues to play a role in the wage
gap between men and women because traditional “women’s jobs” may pay less than traditional
“men’s jobs.” These findings have led to calls by some to implement a comparable worth, or “pay
equity,” pay policy that would adjust the wages of female-dominated jobs to bring them in line
with their “value” or “worth.” The extent of the adjustment required would depend upon the
wage penalty experienced by occupants of female-dominated jobs. Therefore, studies in this area
have sought to determine the impact of occupational segregation on the wage gap between men
and women.
However, many economists have noted that observed differences in pay between maleand female-dominated jobs do not necessarily amount to discrimination because factors related to
productivity, such as education and experience, may differ between those holding femaledominated jobs and those in other jobs.5 Therefore, the statistical methodology typically
employed in this literature includes a control for the percentage of women in a worker’s
occupation in a wage regression that also controls for other productivity-related characteristics of
workers. The estimated coefficient associated with this sex composition variable then is
interpreted as an estimate of the penalty faced by occupants of female-dominated jobs, holding
constant these other factors. Results from earnings studies tend to find that people employed in
an occupation dominated by women still earn somewhat lower wages, even after controlling for
measurable productivity differences.6
While the literature on younger workers is lengthy, few researchers have followed workers
into retirement to determine whether labor market differences continue to have an impact at older
ages. Our previous work (Levine, Mitchell, and Moore, forthcoming) is an exception to this
generalization, since in that study we sought to determine whether differences in lifetime labor
market attachment accounted for differences by sex in projected retirement income. We
concluded in that study that health and family responsibilities had only tiny measured impacts on
projected retirement incomes. One drawback of the previous study was that only self-reported
labor market data could be used, rather than more precise data taken from administrative records
on labor market experience. In addition, that analysis focused on total retirement income and did
not consider its components separately.
Our more ambitious goal in the present study is therefore to derive and use better quality
data than heretofore available on pension and Social Security wealth, and to explore how labor
market and other factors influence anticipated retirement income by source. We begin by
describing our data and analytic approach and then document sources of differences in expected
retirement income flows by sex and race/ethnic status, both in aggregate terms and using
multivariate statistical analysis.
4
See Blau, Ferber, and Winkler (1998) and Blau, Simpson, and Anderson (1998).
5
See Macpherson and Hirsh (1995).
6
For evidence on this point see Sorenson (1989 and 1990); Killingsworth (1990); Filer (1989) and Johnson and
Solon (1986).
3
Data and Statistical Analysis
Our statistical analysis takes place in two phases. First, we explore how pay inequality,
occupational segregation, and differences in lifetime labor market attachment account for
observed variability in projected retirement income across different population groups. Second,
we use the first-stage results to assess how women might be expected to fare in retirement if their
labor market experiences were to be more similar to men’s, controlling for race/ethnicity and
other important factors. This analysis uses the HRS, which is a nationally representative sample
of U.S. households drawn from a cohort on the verge of retirement (age 51 to 61 in 1992).7 The
HRS provides extensive and detailed demographic, health, wealth, income, and family structure
data for respondents and their spouses. We also use information from two additional files
containing invaluable information on respondents’ pension and Social Security benefits. One file,
known as the Earnings and Benefits File (EBF), provides measures of expected retirement income
derived from Social Security benefits as well as labor market history data. A second file, the
Pension Provider File (PPF), contains estimates of anticipated pension benefits. These merged
files have been obtained for a majority of HRS respondents who gave permission to link their
survey data with administrative records supplied by the Social Security Administration, and also
with pension plan descriptions provided by respondents’ employers.8 Together, the HRS, EBF,
and PPF data represent one of the richest data resources available to analyze retirement. There is
no other current data source with equivalently detailed linked administrative records for this
cohort.9
7
For more detail on the HRS dataset, see www.umich.edu/~hrswww; the Data Appendix describes variable
creation for the present study.
8
Because of the confidential nature of these data, researchers may access them only under restricted conditions; see
www.umich.edu/~hrswww for details.
9
The availability of the Social Security and pension matched data makes the HRS uniquely valuable among all
datasets covering retiring Americans. Though Social Security benefits were calculated for most of the age-eligible
HRS respondents in the sample, in a few cases this information could not be computed, and the respondent had to
be omitted from the sample analyzed in this paper (more detail on sample sizes is given below). One reason for
missing Social Security benefits was that respondents gave permission for the University of Michigan to request
their Social Security records, but no match was obtained because their records did not match SSA identification
information. Another reason is that the Social Security Administration excluded from the match file any
respondents receiving Social Security Disability Insurance benefits. Also, some age-eligible respondents declined to
sign the release form permitting their Social Security data to be matched with the HRS. In this study we rely on
Social Security wealth estimates as well as earnings histories provided in the EBF, so respondents lacking these
data are excluded from our analysis. This selection might bias results if those with an EBF file differ from those
without a match; we have no evidence that results are biased, and, indeed, respondents lacking consents for a
Social Security match are quite diverse. Thus, some of the very wealthy (having high levels of financial assets) did
not sign the special release, while some blacks and Hispanics also did not provide consent. Inasmuch as people at
both ends of the wealth distribution are missing EBF matched records, we believe the direction of potential bias is
ambiguous. More formally, an econometric solution to this sample issue would require finding an instrumental
variable correlated with the probability of having an EBF match but uncorrelated with Social Security wealth.
Such a variable does not exist in our sample.
4
Using these three files, we compute anticipated retirement wealth for each household.10
This wealth value is allocated or spread over the household’s retirement period using conventional
annuity factors.11 In other words, we take each household’s assets and divide them up to reflect
the annual payments that a given level of wealth would yield if it were drawn down to zero over
the household’s remaining life expectancy. The annuity factors used to convert wealth to annual
income flows reflect the different life expectancies of men and women at different ages. Thus, annuity
factors for older respondents and men are smaller than those for younger respondents and women,
since older respondents and men have shorter life expectancies than younger respondents and women.
For example, the annuity factor for a nonmarried 56-year-old male in our sample is 14.9474 at age 62
and 13.5577 at age 65. The values for nonmarried women of these same ages are 17.4563 and
15.5938, respectively. Turning a stock of wealth into an annual income flow makes it easier to
interpret and understand exactly what older Americans command by way of retirement
resources.12
Explanatory Factors
We develop and use two indicators to capture respondents’ employment and earnings over
their working lives. One is average annual earnings between the respondent’s 20th and 50th
birthday using data on annual pay up to the Social Security earnings ceiling.13 We call these
“prime age” earnings since they begin at age 20 when most of the respondents would have
completed their schooling, and the average ends at age 50 because the level of labor market
activity beyond that age may begin to be influenced by early retirement. In general, we anticipate
that people with higher prime age earnings will anticipate higher retirement wealth and hence
more annual income in retirement. This is a reflection of the way pension formulas work, and also
of the way earnings are translated into Social Security benefits. Empirically, of course, it is of
interest to estimate the specific way in which higher earnings result in higher retirement income.
The second indicator of labor market attachment used here is a count of the number of
years of Social Security-covered employment up to the respondent’s 50th birthday.14 This factor
is invaluable in assessing how another year of work is converted into additional retirement
income, via pension, Social Security, and saving mechanisms.
To examine the impact of occupational segregation, we determined each worker’s longest
job along with a summary measure indicating the occupation of that job.15 Following the
10
Dollar figures throughout this paper are in constant 1992 dollars.
11
Levine, Mitchell, and Moore (forthcoming) discuss several ways to model well-being; here we simply focus on
levels of retirement income, since these are more readily understood. Burkhauser et al. (1985), Moon (1977), and
Hurd (1989) employ similar measures.
12
The regression analysis uses the natural log of all wealth variables because of the skewness of the distribution of
these variables.
13
The natural log of prime-age earnings is used because of the skewness of its distribution; the derivation of the
prime-age earnings measure is described in Mitchell, Olson, and Steinmeier (forthcoming).
14
Variable creation is described in Mitchell, Olson, and Steinmeier (forthcoming).
15
The Data Appendix describes creation of this occupation variable.
5
literature cited above on job segregation, we sought to assess whether having a higher
concentration of women in a given occupation also has long-term negative consequences for their
retirement income. This was tested in our multivariate model by evaluating whether a higher
proportion of women in the worker’s occupation on her/his longest job is associated with higher,
or lower, retirement income. For purposes of the study, the sex composition of a worker’s
occupation was assigned on the basis of the occupation in which the respondent was employed
the longest.16
The remaining information we obtained on respondent characteristics was available directly
from the HRS. Thus, for instance, survey respondents supplied extensive information on the
economic, social, demographic, and other attributes of household members. The survey delved
into household members’ incomes, assets, debt, and health for respondents aged 51 to 61 in 1992
and their spouses (of any age). In addition, we were able to determine each worker’s longest job
along with a summary measure indicating the occupation of that job.
Subsample Issues
Starting with the full HRS sample of 12,652 persons interviewed in 1992, we imposed
several criteria to generate the sample used for empirical analysis. A few households (95) lacked
a “financial respondent” responsible for providing financial data to the interviewers, and these had
to be dropped. We also restricted the respondent sample to include only “age-eligibles”, namely
the 9,714 respondents who were age 51 to 61 in 1992. It should be noted that people in this age
bracket were interviewed along with their spouses (irrespective of the spouse’s age).17 Next,
since we needed anticipated Social Security benefits to conduct our analysis, the sample included
only those respondents and spouses who furnished a consent form; for whom the Social Security
Administration could locate a matched file; and who were not receiving disability benefits at the
time of the 1992 interview. These conditions generated an interim sample of some 5,800
respondents. Finally, we omitted from the analysis persons whose race/ethnic status was not
recorded as white, black, or Hispanic, in order to focus attention on the groups of most interest
here. The analysis sample then consisted of 5,684 individuals.
One important advantage of using the HRS over other possible surveys is that the survey
intentionally oversampled blacks and Hispanics. This was done because these groups are
relatively rare in the population at large, and their retirement income status is of special interest to
those concerned with the well-being of relatively disadvantaged segments of the population. In
the descriptive tables below, we therefore draw distinctions by race/ethnicity when comparing
retirement incomes by sex. Nevertheless, even with the HRS oversample of minorities, there
prove to be too few respondents from these groups to permit the estimation of separate
16
One limitation of the HRS data used here is that we can only identify each worker’s occupation at the 2-digit
level; this restriction was required in order to obtain the earnings and Social Security benefits data key for
retirement well- being computations. We are able, therefore, to identify professional and technical occupations
separately from craft occupations but cannot distinguish further within these categories. Future researchers with
access to narrower occupational definitions might assess the impact of such occupational aggregation.
17
In any event, a spouse’s wealth is included in the analysis irrespective of the spouse’s age.
6
regressions by sex, marital status, and race/ethnicity (see Appendix Table 1 for sample sizes).
Therefore, the multivariate approach taken below includes indicators of respondents’
race/ethnicity rather than estimating models for each race/ethnicity group separately.
Household Resources
In order to evaluate access to anticipated retirement income for HRS respondents, we must
distinguish between an individual's own resources and those available to his or her household.18 In the
present analysis we assess projected retiree wealth available to a household without seeking to allocate
assets within the household to individual members of a married couple. In other words, the models
assume that retirement income generated by different assets is equally available to a husband and a wife
in a married couple. Hence, the analysis assumes that household resources are consumed jointly as
long as both spouses are living.19 As a result, sex differences in retirement well-being, in theory, can
result only from measured differences in the well being of nonmarried men and women.20
Methodological Approach
The analysis that follows first describes HRS respondents’ wealth levels along with the
anticipated annual income flows they represent. Next, we estimate multivariate models of annual
retirement income for men and women.21 In this analysis we focus on the influence of labor
market variables in driving retirement income differences by sex: that is, years in the labor market,
average prime-age pay, and a measure of how sex-segregated the respondent’s longest job was. We
also include socioeconomic factors (e.g., education, marital history, and number of children) along
with race/ethnic indicators and measures of respondents’ health and preferences (including indicators of
respondents’ planning horizon and risk aversion). In the case of married respondents, we also include
the same measures for the respondent’s spouse, since his/her characteristics may also contribute to
differences in family resources available in retirement. These regression models are estimated
separately by sex and marital status so that results can be compared across groups. Identical model
specifications are estimated for the three dependent variables of key interest, namely, income flows
from Social Security, pensions, and other financial wealth including housing. Our methodological
18
All values are computed assuming retirement will occur at age 62. See the Data Appendix for more discussion
of this point.
19
After one party dies, the surviving spouse is assumed to keep half the pension in a joint-and-survivor arrangement, and
Social Security benefits continue for eligible widows (widowers). Housing and other wealth is bequeathable to the
surviving spouse in its entirety.
20
Nonmarried persons in the HRS are those who are not currently married; this population includes the never married,
the divorced, and the widowed, based on self-reported marital status. Married persons are likewise self reported.
Practically speaking, there are slight differences in married men’s and women’s measured resources in the HRS because
the age-eligible women in HRS couples are slightly younger than the women in couples with HRS age-eligible men.
21
The particular empirical models examined are analogous to those employed by Levine, Mitchell, and Moore
(forthcoming). There, as here, we recognize that there may be dual causality in the regression models between
retirement income, on the one hand, and earnings as well as work years on the other. That is, more work at higher
pay would be anticipated to raise retirement income, but conversely, having higher retirement assets might
discourage people from working more years or seeking out higher pay. In order to reduce the possibility of
endogeneity of these variables, the labor market variables we adopt are strictly retrospective measures. That is, a
worker’s years of labor market experience are measured up to age 50 but not thereafter; average pay is likewise
computed based on the worker’s Social Security earnings reported between ages 20 and 50; and the occupation to
which the fraction female refers is the respondent’s longest job.
7
approach is informed by the approaches followed in prior studies that have sought to explain
differences in pay for active workers.
Having in hand estimates of the effects of each factor on projected retirement income, we
next evaluate how much of anticipated retirement income differences by sex could be attributed to
differences in the workers’ characteristics.22 To conduct this exercise with regard to differences
in labor market characteristics, we ask the hypothetical question: how much would the gap in
projected retirement income decrease if lifetime labor market characteristics of men and women
were identical? In other words, based on the estimated returns to these characteristics, we predict
what women’s retirement income would be if they had characteristics that were equal in value to
those of men, on average. Since men tend to have had stronger labor force attachment during
their working years, one would expect the gap between men’s and women’s projected retirement
income to be smaller, or potentially even zero, when it is based upon this prediction. Finally, we
estimate and report the dollar reduction in the sex gap in projected retirement income between the
predicted and observed levels. A similar analysis can be conducted regarding other factors
included in the model, such as socioeconomic factors. This permits us to evaluate how retirement
income gaps might narrow as workers’ traits grow more congruent.
Empirical Findings
Median retirement wealth expected by HRS respondents appears in Table 1 by sex, marital
status, and race/ethnic group. We focus on medians since differences in averages (Appendix
Table 2) may be driven by a relatively small number of individuals with very large levels of wealth.
A first point to note regarding Table 1 is that married men and women anticipate similar levels of
22
Oaxaca (1973) devised the statistical technique used here to show how differences in outcomes might be
allocated to different sources. In the present context, we have adapted this approach to decompose the difference in
projected log annual retirement income between older women and men into two parts: the portion due to differences in
characteristics that differ by sex, and the portion due to differences in returns to those characteristics between men and
women. These analyses are conducted separately by marital status groups. We use women’s returns to characteristics to
determine how much of the gap in log retirement income would be closed if women's characteristics became like those of
men. Specifically, we compute:
RY m − RY f =
∑
k
i =1
βi f ⋅ ( X im − X i f ) +
∑
k
i =1
X im ⋅ (βim − βi f ) ,where RY represents a particular measure of
economic well-being; b represents the vector of regression coefficients estimated using the multivariate model described
above; the X values represent a vector of mean characteristics, f and m represent women and men, respectively; and k
indexes characteristics. The first expression on the right hand side of this equation is said to represent the "explained"
part of the differential in retirement income because it is attributed to the different characteristics of men and women. The
second expression is said to represent the "unexplained" part of the differential because it would result in differences in
income even if men and women had the same characteristics. Our simulation computes the percentage reduction in the
retirement income gap between men and women that would occur if both had identical characteristics. Formally, this
involves estimating:
∑
% Gap =
k
i
βi f ⋅ ( X im − X i f )
RY m − RY
f
⋅ 100
This expression represents the gap in log retirement income that can be "explained" by differences in characteristics as a
percentage of the size of the gap. Below we also compute the dollar contribution to the gap in retirement income by
applying the percentages to the dollar gap in projected annual retirement income.
8
retirement wealth, a reasonable expectation in view of the model’s assumption that retirement
wealth is pooled at the household level.23 Retirement wealth levels are quite substantial for
married couples, exceeding half a million dollars if pensions, Social Security, and other financial
assets are counted. By contrast, projected retirement wealth for nonmarried people appears much
lower, totaling only about one-third as much as for married couples (between $157,000 and
$192,000). There are also striking sex differences disfavoring women; that is, nonmarried men
have almost 20 percent more retirement wealth than nonmarried women.
These overall differences become even sharper when we examine the subcomponents of
wealth. For example, married couples’ Social Security wealth totals about $180,000, a figure not
too different from their $160,000 in housing and net financial assets. Their pension wealth
amounts to approximately $98,000-$121,000. By contrast, Social Security wealth represents a
dominant portion of total wealth for nonmarrieds, housing is less important, and–particularly
striking–pension wealth is very tiny indeed. The median nonmarried woman, for instance, has no
pension wealth at all, compared to her nonmarried male counterpart who has $26,000, and her
married female counterpart with close to $100,000 in household pension assets.
Patterns of retirement wealth by race/ethnic status in Table 1 indicate that the relative
disadvantage faced by nonmarried women versus men is most concentrated among the white
population. This is because the wealth gap for black and Hispanic nonmarried men versus women
is very small or even nonexistent. Thus, nonmarried black women actually have higher levels of
total wealth ($106,000) than their nonmarried black male counterparts ($75,000); for Hispanics
total wealth is $91,000 for nonmarried men and $72,000 for nonmarried women. Pension wealth
is effectively nil for black and Hispanic nonmarried people, and other wealth is similarly
minuscule. In sum, differences in retirement wealth between whites and minorities are
considerably larger than those between men and women.
How these wealth figures translate into annual retirement income flows by sex, marital
status, and race/ethnicity is evident from Table 2.24 The retirement assets shown previously are
estimated to produce annual income equivalents for married men and women that are similar to
each other, on the order of about $28,000-$29,000 per year. About one-third of the anticipated
retirement income is attributable to Social Security benefits totaling about $10,000 per year for
the median married household, exceeding the annuitized value of housing and financial wealth that
totals about $8,000-$9,000 annually. Median pension income for married couples might seem
low, at only about $5,000-$6,000 per year, but it must be recalled that many in the HRS sample
anticipate receiving no pension income (zeros are included in Tables 1 and 2).
Projected annual retirement income for nonmarried people is expected to be only about
one-third the size of married couples’ income, at $13,000 for nonmarried men and $9,000 for
nonmarried women. The relative disadvantage of nonmarried women stems partly from the fact
23
For respondents and spouses who are both age-eligible, wealth levels would be expected to be identical for men
and women. Small differences emerge in the HRS dataset, because some age-eligible women respondents have
spouses older than 61, while age-eligible men are more likely to have spouses younger than age 51.
24
An analogous table of means appears in Appendix Table 3.
9
that they are anticipated to live longer than men on average, which makes the gap in annual
retirement income flows larger than the wealth gap. Furthermore, nonmarried people probably
require more than half a married couple’s income to maintain a comparable living standard.25
Hence, the finding that nonmarried respondents expect so much less income in retirement than do
married couples does not bode well for their prospective retirement income status.
Looking further at the components of retirement income flows, it appears that the
redistributive nature of Social Security benefits somewhat narrows the retirement gap between
nonmarried men and women. However, median expected annual benefit levels are low, on the
order of $5,500 for men and $3,600 for women. A problem confronting the median nonmarried
woman approaching retirement is that she has no pension wealth at all, whereas nonmarried men
have small accumulations, and the median married couple can expect $5,500-$6,200 of pension
income annually. Nonmarried men and women have similar levels of net financial and housing
wealth, but it is worth pointing out that more nonmarried men have very high levels of other
wealth, since the medians are similar but the means are higher for the men.
Finally, focusing on the differences in anticipated annual retirement income by
race/ethnicity, we find that the median married black couple would anticipate $19,000 annually,
and the married Hispanic couple $12,000-$15,000 annually. This compares to much lower levels
expected by nonmarried persons, with black and Hispanic women expecting $4,000-$6,000 per
year in total income, and black as well as Hispanic men anticipating income in the same range.
Table 2 clearly shows minority groups’ heavy reliance on Social Security, since they can expect
relatively little income from sources other than Social Security. These very low income levels do
not differ much by sex for minorities. The exception is for married black women, who have
pension income almost comparable to that of married white women.
Regression Results
Moving beyond simple tabulations of the data, we next evaluate how changes in
respondent characteristics might improve retirement well-being. Specifically, we are interested in
the “returns” that people anticipate receiving in the form of higher retirement income for a given
increase in earnings and work experience; we are also interested in examining how the sex
composition of jobs affects anticipated retirement well-being. Results from multivariate statistical
analysis controlling for these factors as well as other socioeconomic factors appear in Table 3.26
Specifically, the table shows how a change in one of the explanatory variables of the model might
be expected to influence the average person’s annual retirement income.27 These should be
interpreted as “marginal” relationships, that is, the likely effects of a specified change in the
explanatory variable on the outcome of interest.
25
For a discussion of equivalence scales see Levine, Mitchell, and Moore (forthcoming).
26
Appendix Table 4 reports regression results also controlling for respondents’ health status and risk preferences;
the latter coefficient estimates are not reported in detail here.
27
Values are computed at the sample mean unless otherwise noted. Mean values of independent variables appear
in Appendix Table 5.
10
Simulations of this type are carried out for total retirement income and also for the three
component elements of wealth. For example, the first panel of Table 3 shows how working an
extra year between the ages of 20 and 50 influences overall retirement income as well as the three
components of wealth. For nonmarried women, an additional year of work is found to have a tiny
positive effect ($79) holding other things constant. However, the confidence interval around this
estimate includes zero (see Appendix Table 4), suggesting that additional work might conceivably
have no positive statistically significant effect on its own. Although an extra year of work is
estimated to actually reduce total retirement income for nonmarried men, that estimate is also not
statistically significantly different from zero.
By contrast, an additional year worked apparently reduces total retirement income for
both married men and women, and now the estimates are statistically significant. This effect is the
result of offsetting effects on the three main components of retirement income. That is, additional
years of work translate into higher Social Security benefits for both groups (which was also true
for nonmarried women). But an additional work year is associated with lower lifetime pension
payments and with lower income from other assets. One explanation for this finding is that levels
of education, highly correlated with these forms of income, are reported in the dataset by an
indicator for the highest degree received rather than by incremental years of schooling (to
preserve respondent confidentiality). Within degree categories, it is likely that fewer years of
education would translate into additional years worked and therefore lower levels of pension and
other income.
Having higher lifetime earnings generally translates into higher anticipated retirement
income levels for all groups, holding other factors constant. Thus, an additional $1,000 in
average annual pay earned during the prime age period (age 20 to 50) is associated with an
additional $220-$250 per year in retirement income for women, and $350-$500 per year for men.
This positive effect is robust across all retirement income components: that is, higher average
prime-age earnings are consistently associated with higher Social Security income, pension
income, and other income (although some of the estimated effects are not significantly different
from zero; see Appendix Table 4).
It is also interesting that Social Security benefit formulas reward nonmarried persons more
at the margin for higher earnings than they do married persons. This is because married couple
benefits are heavily influenced by Social Security survivor payments that pay off in the event of
the death of one spouse, and this valuable benefit stream is influenced only modestly by additional
earnings during the prime-age period. By contrast, a nonmarried person’s Social Security benefit
is payable only as long as the retiree is alive; lacking the death benefit, retirement income streams
become more closely earnings-linked than is true for married persons. Also as a result, higher
earnings translate directly into higher pension income, with somewhat higher effects for
nonmarried men ($207) than for women ($131); this may be because men are covered by more
generous pension benefit formulas than women. The fact that other income rises more for
additional pay may suggest that personal saving is more feasible for those earning higher salaries.
Turning to the final labor market variable of interest, which is the proportion of females in
the respondent’s “main” occupation, we find that this is associated with a statistically significant
11
reduction in total retirement income for both married and nonmarried women, but not for either
group of men. The effect is reasonably large for the nonmarried women, in particular; a 10percentage point increase in the fraction of women in an occupation is associated with a decrease
in nonmarried women’s retirement benefits of $424 per year. It is interesting that this negative
effect is primarily effective through pension and other income, but not through Social Security.
This may be because jobs dominated by women are less likely to provide generous pensions (and
perhaps other benefits as well), so women’s financial emergencies may result in lower eventual
saving.
The next section of Table 3 shows estimated effects on retirement income attributable to
race/ethnicity. Confirming our earlier tabular results, the multivariate analysis shows that black
and Hispanic women are at a disadvantage relative to their white counterparts, having
accumulated much less retirement wealth even after holding other things constant. For black men,
this is primarily due to large differences in other income but not pensions, whereas black women
actually have an advantage over their white counterparts (the effect for Hispanic women is not
positive). The retirement income disadvantage created by deficits in the other income category is
particularly large for black and Hispanic men (ranging between $15,000 and $25,000). The large
standard errors on many of the marginal effects may reflect the relatively small number of
minorities in the sample.
The last section of Table 3 shows the effect of education on retirement income. It is clear
that education has the most prominent positive impact on projected retirement income across the
board for all marital groups. That is, if a nonmarried woman earned a college degree as compared
to having only completed high school, she would be expected to be rewarded with an additional
$5,700 per year in retirement income; the effect for married women is about $4,400. Clearly,
improvements in women’s educational attainment yield a payoff well beyond the end of the
working life, indeed into retirement as well. Effects for men are even larger, at $8,000-$13,000
more per year.
Decomposition Results
Having described how anticipated retirement income patterns vary across the population,
we next decompose projected retirement income gaps into their component parts. This exercise
asks the question: how would women fare in terms of retirement income if their labor market and
other characteristics were to become equivalent to those of men? Specifically, we evaluate the
dollar difference in annual retirement income by sex that can be attributed to differences in labor
market experience and other factors. When we estimate models for married persons, we also
control for spousal characteristics.
The results of this analysis appear in Table 4, where we first point to similarity in married
peoples’ anticipated average retirement income. The reason that the retirement income gap for
married women and men is negligible is that both members of a married couple are posited to
consume retirement wealth jointly. Of course, the older married women in the HRS do have labor
market and educational patterns different from their husbands, but these differences do not
depress their retirement income, since spouse characteristics have a dominant impact on wives’
12
anticipated retirement income. In other words, remaining married in retirement (to the extent that
people have control over this outcome) more than compensates wives in economic terms for their
lower market returns on their lesser labor force attachment.
By contrast, nonmarried men anticipate receiving about $8,000 more per year in
retirement on average than nonmarried women. Using the decomposition framework, this gap
favoring men is accounted for mainly by differences in men’s and women’s labor market histories.
Thus, $2,685 (33 percent) of the gap is due to differences in average prime-age earnings, $2,773
(34 percent) to differential occupational segregation, and $1,092 (14 percent) to different lengths
of labor market attachment. Other factors such as education, race/ethnicity, health and
preferences, and other socioeconomic factors play a relatively negligible role. The potent role of
the labor market variables for the nonmarried groups is reiterated for each of the three income
types, though by far the most powerful influence is for income other than pensions and Social
Security. Thus, labor market differences account for more than the entire gap in non-pension,
non-Social Security income, indicating that if women had men’s labor market experience, pay, and
occupational patterns, the retirement income gap would be expected to be more than fully closed.
Taken as a whole, the decomposition results confirm the central role of labor market
variables in accounting for projected retirement gaps by sex. Thus, a nonmarried woman with
lifetime labor force attachment, pay, and occupational attainment similar to her male counterpart could
expect retirement income quite similar to his.
The Role of Marital History
Thus far our analysis has concentrated on anticipated retirement income differences by
sex, holding constant peoples’ current marital status. Nevertheless, marital histories of HRS
respondents are also available, including whether a previous marriage occurred and how it ended
– i.e., through widowhood or divorce. This allows us to identify for the purpose of analysis three
separate nonmarried groups: the divorced, the widowed, and the never-married (though the
reader should be cautioned that sample sizes for this analysis are rather small; see Appendix Table
6). A similar multivariate approach offers evidence that anticipated retirement income does not
vary much across marital status for nonmarried men, but it does for women. That is, the median
never-married woman expects about 60 percent more retirement income than nonmarried,
divorced, or widowed women (Appendix Table 7).28 The main source of this difference is
pension income: the median never-married woman expects over $2,000 in annual pension benefits,
while the median widowed and divorced woman expects none.
Unfortunately, the small number of nonmarried persons in the HRS sample limits the
ability of statistical models to distinguish the separate contribution of many factors thought to
28
The values may in fact actually be closer than they appear in our data. This is because divorced and widowed
respondents have claims to the Social Security benefits of their former spouses if they had been married for at least
10 years prior to the marital breakup (the 10-year requirement does not pertain to a spouse who becomes widowed
while still married). However, the EBF file does not report Social Security earnings and benefits for previous
spouses because of confidentiality restrictions. Therefore, our estimate of Social Security wealth for divorced and
widowed women will be understated for those who had been married at least a decade.
13
affect retirement income (Appendix Table 8). For instance, although point estimates of the effect
of labor market history variables differ across groups, these differences are typically not
statistically significant.
Discussion and Conclusions
The continuing problem of poverty among older women and minorities prompts analysts and
policymakers to ask why these groups have a high likelihood of being poor in old age. Other studies
have investigated the role of marital status changes–widowhood in particular–in generating this result.
Here we took a different tack, asking instead how labor market events and exposure affect different
groups’ anticipated retirement incomes. Our specific focus has been to explore the role of differences
in earnings patterns, years of labor market experience, and occupation as influenced by the
concentration of women in these jobs. In addition, we controlled for differences in race/ethnicity and a
series of other socioeconomic factors. We analyzed these questions using the HRS, a nationally
representative data set on older Americans. We constructed measures of projected retirement income,
derived by converting retiree wealth into a simulated cash flow available for consumption needs, taking
full account of anticipated Social Security and pension benefits.
Our analysis proceeded in stages. First, we established the size of anticipated gaps in projected
retirement wealth. We found that the typical married couple looks forward to around half a million
dollars in retirement assets, while the median nonmarried man has about $190,000 and the median
nonmarried woman about $160,000. These asset levels were in turn converted into annual retirement
income flows, where women’s longer life expectancies in retirement exacerbate the sex gap. But we
concluded that the main reason older nonmarried women on the verge of retirement should anticipate
lower levels of retirement income than their male counterparts is that they have much lower retirement
assets than do other demographic groups. This gap was most prominent for whites; blacks and
Hispanics have fewer assets and consequently a smaller gap by sex. Nonmarried black and Hispanic
men and women expect relatively little income from sources other than Social Security. The median
nonmarried minority in the sample will not receive any employer pension income and only modest
income from other financial assets.
The second stage of the analysis explored the factors that were most closely associated with
differences in anticipated retirement income. The results suggested that an additional year of labor
market work between the ages of 20 and 50 has only a small positive effect on nonmarried
women’s retirement income and no positive effect for wives, holding other things constant.
Higher average earnings, however, have a positive effect on women’s retirement income (holding
other factors constant). Interestingly, women who worked in occupations with a higher
proportion of women face lower total retirement income than women who worked in occupations
with fewer women, but this occupational mix variable was not statistically significant for men.
This difference may be due to the fact that jobs dominated by women were less likely to offer
pensions and other employee benefits, implying that women’s financial emergencies during the
work years might translate into lower saving. Confirmation of this new hypothesis awaits further
analysis of nonmarried women’s saving patterns.
14
Black and Hispanic women were persistently found to be at a disadvantage in retirement
income relative to whites. The disadvantage was greatest in the case of nonpension income
(Social Security and saving): here, minority status reduced income among women from this
source by $1,600-$14,000 per year, on average. Our results also showed that education has a
substantial beneficial impact on women’s projected retirement income; thus, completing a college
degree boosts retirement income by $4,400-$5,700 per year, as compared to having only a high
school diploma. Education has little influence on the magnitude of anticipated Social Security
benefits, probably because of the Social Security system’s highly redistributive benefit formula.
By contrast, the effect of education was quite large for pension and other income. Attaining an
advanced degree may lead to jobs with better benefits such as pensions and health insurance,
which in turn could contribute to both larger pension and larger nonpension financial wealth.
Having more children also takes a toll on worker asset accumulations, leading to lower eventual
retirement income for women.
The final phase of the investigation asked to what extent differences in these factors could
explain why women and minorities arrive on the doorstep of retirement with fewer resources and lower
projected income. A major part of the explanation, at least in the HRS, is that women and minorities
earn less during their working lives, and women in particular have fewer years in the paid labor market
and are more likely to work in occupations dominated by women. We conclude that the nonmarried
retirement income gap by sex is mainly attributable to differences in earnings and occupations. By
contrast, differences in lifetime labor market attachment play a smaller role. We also find that minority
status is negatively correlated with retirement income in the multivariate analyses, but this does not
account for much of the retirement income gap between men and women.
Our results suggest certain lessons and policy implications. One lesson worth underlining
is the tight link between labor market earnings and retirement income. While this link seems
intuitive, we believe that the size of the link is important, and particularly so for pension and other
financial wealth accumulations. They imply that young women and minorities could benefit from
better information about the long-run consequences, versus the short-run returns, of leaving
school early, taking lower-paying jobs, and, to a lesser extent, dropping out of the paid labor
market for extended periods. The results also imply that women who take jobs in femaledominated occupations could profit from improved information about the negative retirement
income consequences of these jobs. And while not all can do so, women and minorities who do
obtain higher education are more likely to improve their economic status during their worklives
and also in old age.
Another conclusion from our work is that older blacks and Hispanics have very low levels
of wealth to draw on in retirement. This point has been made in previous studies, but the fact that
it is confirmed with the HRS–the most comprehensive source of information on older Americans’
wealth ever developed–makes it a more reliable conclusion and underscores the economic
vulnerability of many minorities at older ages.
Our results indicate that the overall gap between men’s and women’s anticipated
retirement incomes could be closed by raising women’s educational level to that of men’s. But
the finding is not specific to the nonmarried group, since nonmarried women’s current educational
15
attainment levels are quite similar to those of their nonmarried male counterparts. By contrast,
married women with less education than married men, would improve their retirement income
substantially if they completed additional education. The model also indicates that closing the sex
gap in years of work, average pay, and occupational attainment could help shrink quite
substantially the retirement income gap for nonmarried people. That is, three-quarters of the
overall retirement income gap would be eliminated if, over their lifetimes, women and men had
similar lifetime earnings, labor market attachment, and occupational attainment. This should be
true for whites and minorities alike, with potentially the biggest gain to those with the lowest
current wealth levels, namely blacks and Hispanics.
Looking ahead, what might be projected regarding the future? One optimistic note is that if
women’s pay levels continue to climb over time as they have in the last decade or so, future cohorts of
women approaching retirement will have earned more over their lifetimes, enhancing their well- being
both absolutely and relative to men. The same holds true regarding the slower, but steady, observed
fall in occupational segregation over time.
On the other hand, if these labor market differentials persist, one might alternatively propose
changes in the formulas through which Social Security benefits are calculated, to ensure minimum
benefit protection in old age. Such changes are a feature of several of the Social Security reform
proposals under current discussion in the policy arena (Mitchell, Myers, and Young, 1999). That
is, while current benefit formulas are redistributive, the lack of a minimum benefit leaves those
with low lifetime earnings and erratic labor market attachment at risk for poverty in old age.
In overview, our findings suggest that workers must become more alert to the potential
for economic hardship in old age; public policy can help them prepare for it by emphasizing the
retirement-age returns to lifelong earnings, occupation, and educational attainment. Additionally,
efforts undertaken sooner rather than later stand a better chance to remedy the problem of
inadequate retirement incomes for women and minorities, since people are more likely to respond
effectively to retirement system changes when they still have some flexibility in job choice, work
years, pay, and occupation during the years leading up to retirement.
16
Table 1. Median Projected Retirement Wealth by Race/Ethnicity, Sex, and Current Marital Status
Men
Total Projected
Household Wealth
Total Wealth
Social Security Wealth
Pension Wealth
Other Wealth*
17
Women
Total Projected
Household Wealth
Total Wealth
Social Security Wealth
Pension Wealth
Other Wealth*
White
Nonmarried Married
$236,154
$88,618
$34,556
$57,131
$567,835
$177,729
$130,187
$171,805
White
Nonmarried Married
$179,904
$66,548
$3,985
$56,026
$555,309
$185,786
$104,393
$179,477
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
Note: All data weighted by HRS sample weights.
* Housing and Net Financial Assets
Black
Nonmarried Married
$74,691
$53,863
$0
$840
$358,561
$143,196
$110,418
$70,309
Black
Nonmarried Married
$105,703
$60,685
$0
$18,114
$332,656
$158,330
$99,319
$60,534
Hispanic
Nonmarried Married
$91,345
$60,968
$0
$5,750
$249,132
$125,137
$1,792
$58,551
Hispanic
Nonmarried Married
$71,929
$47,544
$0
$1,531
$252,618
$135,912
$717
$52,020
All
Nonmarried Married
$191,836
$82,522
$25,518
$38,926
$535,341
$173,775
$120,549
$157,744
All
Nonmarried Married
$157,098
$63,642
$0
$45,711
$530,032
$183,018
$98,247
$161,969
Table 2. Median Projected Annual Retirement Income by Race/Ethnicity, Sex, and Current Marital Status
Men
Total Projected Annual
Household Income
Total Income
Social Security Income
Pension Income
Other Income*
18
Women
Total Projected Annual
Household Income
Total Income
Social Security Income
Pension Income
Other Income*
White
Nonmarried Married
$15,756
$5,968
$2,312
$3,832
$30,123
$9,316
$7,062
$9,005
White
Nonmarried Married
$10,246
$3,821
$230
$3,198
$30,790
$10,226
$5,881
$9,871
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
Note: All data weighted by HRS sample weights.
* Housing and Net Financial Assets
Black
Nonmarried Married
$5,040
$3,593
$0
$57
$18,628
$7,418
$5,425
$3,818
Black
Nonmarried Married
$6,042
$3,489
$0
$1,042
$18,942
$8,749
$5,425
$3,337
Hispanic
Nonmarried Married
$6,080
$4,079
$0
$385
$12,301
$6,576
$114
$3,116
Hispanic
Nonmarried Married
$4,115
$2,735
$0
$87
$14,675
$7,979
$41
$3,116
All
Nonmarried Married
$12,950
$5,500
$1,699
$2,618
$28,023
$9,095
$6,240
$8,309
All
Nonmarried Married
$9,016
$3,637
$0
$2,626
$29,479
$10,135
$5,474
$9,051
Table 3. Predicted Changes in Total Projected Annual Retirement Income Associated
with Key Explanatory Variables (standard errors in parentheses)
Change in
Explanatory Variable
+1 Year of Work
Total Retirement Income
Nonmarried
Women
$79
(102)
Social Security Income
$90
(19)
Pension Income
-$82
(98)
Other Income
-$52
(143)
+$1,000 Average Prime-age Earnings
Total Retirement Income
$223
(98)
Social Security Income
$58
(20)
Pension Income
$131
(77)
Other Income
$188
(112)
+10% Female Occupation
Total Retirement Income
-$424
(104)
Social Security Income
-$20
(14)
Pension Income
-$157
(119)
Other Income
-$389
(173)
(Continued)
19
Nonmarried
Men
Married
Women
Married
Men
-$321
(309)
$55
(44)
-$866
(396)
$7
(471)
-$209
(106)
$36
(10)
-$118
(83)
-$262
(113)
-$359
(166)
$157
(23)
-$466
(127)
-$460
(207)
$509
(186)
$76
(33)
$207
(148)
$320
(173)
$248
(93)
$24
(9)
$108
(71)
$169
(86)
$345
(59)
$44
(11)
$75
(48)
$390
(81)
-$140
(186)
$11
(14)
-$60
(24)
-$254
(229)
-$292
(196)
-$9
(18)
-$375
(151)
-$173
(248)
$72
(117)
$9
(10)
$200
(84)
-$315
(133)
Table 3. Continued.
Change in
Explanatory Variable
Black (vs. White)
Total Retirement Income
Social Security Income
Pension Income
Other Income
Hispanic (vs. White)
Total Retirement Income
Social Security Income
Pension Income
Other Income
College degree (vs. HS only)
Total Retirement Income
Social Security Income
Pension Income
Other Income
Nonmarried
Women
Nonmarried
Men
Married
Women
Married
Men
-$2,628
(1079)
-$460
(217)
$3,670
(809)
-$7,577
(1705)
-$10,965
(2604)
-$936
(289)
-$1,378
(3351)
-$25,168
(4541)
-$6,607
(2081)
-$650
(229)
$3,296
(1411)
-$13,883
(3184)
-$8,913
(1862)
-$553
(178)
-$678
(1415)
-$15,220
(2957)
-$2,146
(1462)
$567
(299)
-$4,067
(2195)
-$11,512
(3224)
-$912
(4663)
-$117
(432)
$267
(3270)
-$14,959
(10299)
-$12,280
(2519)
-$149
(281)
-$2,882
(3493)
-$1,424
(4543)
-$14,748
(2745)
-$150
(243)
-$5,214
(2281)
-$17,543
(4854)
$5,660
(1667)
-$130
(209)
$4,312
(1404)
$4,832
(2059)
$12,927
(4576)
$802
(334)
$5,288
(4270)
$7,626
(4637)
$4,394
(2627)
-$511
(215)
$900
(1527)
$4,277
(2146)
$8,003
(2333)
$517
(146)
$5,112
(1527)
$6,431
(1974)
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
20
Table 4. Decomposing Differences in Projected Annual Retirement Income by Type:
Dollar Gap Attributable to Differences in Respondent Characteristics
Nonmarried Men vs. Women
Total*
Social
Security*
Pension**
Other*
Av. Total Projected Annual Retirement
Income Gap
Dollar gap attributable to:
All Labor Market Differences
Labor Market Attachmenta
Average Prime-age Earnings
Occupational Segregationb
Education
Less than High School Degree
Greater than High School Degree
Race/Ethnicity
Black
Hispanic
Other
Age
Ever Divorced
Ever Widowed
Number of Children
Health & Preference
Residual***
$8,041
$1,396
$5,396
$3,217
$6,550
$1,092
$2,685
$2,773
$1,220
$639
$472
$109
$1,465
-$416
$1,137
$744
$12,795
-$396
$6,191
$7,000
$234
-$106
$13
-$9
-$22
-$170
$661
$124
$53
-$37
$6
$7
-$248
-$23
$276
-$573
-$190
-$125
-$477
$615
$306
$1,217
-$43
-$13
$61
$14
$16
$123
$150
-$52
$446
$504
$191
$3,155
-$780
-$432
-$4,349
$905
$43
-$5,455
-$222
-$1,055
$1,308
-$41
$502
-$291
$347
$446
$754
$317
$230
$208
$2,255
-$1,087
$1,003
$2,339
$29
-$89
$59
$59
-$16
$131
-$8
$42
-$13
$375
-$4
$25
-$9
-$20
-$6
-$2
$45
-$19
-$5
-$5
$0
-$41
$17
$23
$107
-$1,469
$551
$8
-$5
$10
$4
$14
-$3,341
$1,474
-$8
-$130
$36
$7
$158
$1,078
-$2,476
$2
-$7
$4
$9
$35
-$288
$164
Married Men vs. Women
Av. Total Projected Annual Retirement
Income Gap
Dollar gap attributable to:
All Labor Market Differences
Labor Market Attachmenta
Average Prime-age Earnings
Occupational Segregationb
Education
Less than High School Degree
Greater than High School Degree
Race/Ethnicity
Black
Hispanic
Other
Age
Ever Divorced
Ever Widowed
Number of Children
Health & Preference
Spousal Characteristics
Residual***
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992), weighted data.
* Nonnegative wealth-holders only. **Positive wealth-holders only.
a
Sum of the effects from years of work to age 50 and never worked variables. See Appendix Table 4.
b
Sum of the effects from percent female in occupation, military occupation, and missing occupation variables. See
Appendix Table 4.
***Fraction of gap not accounted for by differences in characteristics.
Note: Decompositions use coefficients reported in Appendix Table 4 and means reported in Appendix Table 5.
21
Data Appendix
In this study we use age 62 as the common age at which retirement assets are computed. This is
the modal age for Social Security benefit filing purposes and is the earliest age at which one can
currently file for Social Security benefits. While it is straightforward to specify an assumed
retirement age for a nonmarried individual, it is more complex for a married couple since the
retirement date for spouses of differing ages may differ. Here we follow HRS practice where the
survey interviewer designated as the “primary respondent” that household member having the
greatest knowledge of the household’s financial matters. Usually this respondent was age eligible
for the HRS survey, in which case we assumed the retirement commencement was triggered on
this person’s attainment of age 62. If the primary respondent was not HRS age eligible, this
guaranteed that the secondary respondent was age eligible. In this instance, we assumed that the
age-eligible household member keyed off retirement at the attainment of age 62.
Values for each of the main retirement asset classes are projected to retirement using a range of
projection technologies and assumptions (the approach is described in Moore and Mitchell,
forthcoming). In brief, net financial wealth is projected forward using averages of market returns
based on historical rates; housing wealth is projected forward using survey data on the purchase
price of the respondent’s house, year of purchase, outstanding debt owed on homes, and
mortgage payment amount and frequency. We assume that the market value of the house grows
in line with the general inflation rate so there is no real appreciation in housing values, though
mortgage payments decrease the remaining principal on the mortgage. Respondents’ pension and
Social Security wealth values are projected assuming workers remain employed to their retirement
age (see Gustman et al., forthcoming). Pension benefits are derived based on the plan provisions
of employer-provided pensions and respondents’ answers to salary and years of service (where
appropriate). Social Security projected amounts are computed as described in Mitchell, Olson,
and Steinmeier (forthcoming) for those respondents agreeing to supply a data link; for them we
also have available work history and average pay variables for each respondent. This includes
average lifetime salary and total labor market experience up to age 50. Present values of benefits
are calculated using mortality, interest rate, inflation, and wage growth assumptions as described
in Moore and Mitchell (forthcoming). All dollar values are given in 1992 dollars. After the death
of one spouse, we assume that remaining housing and net financial assets transfer to the survivor;
Social Security benefits are available to the widow(er) according to program rules; and pension
rules now require survivor benefits unless a spouse agrees to the contrary in writing. Other
research studies using some of these data include Dwyer and Mitchell (forthcoming), Gustman et
al. (forthcoming), Mitchell and Moore (forthcoming), and Mitchell et al. (1998).
The estimated multivariate model uses the natural log of retirement income RYig where i refers to
the individual; g refers to the respondent’s sex (f for female, m for male); WH is lifetime pay and work
experience; %FEM is the fraction of women in the respondent’s longest occupation; X is a vector of
age, ethnicity, previous marital status, number of children, education; H represents health and
preference controls; P refers to a measure of risk aversion and planning horizon; and u refers to a
disturbance term that captures otherwise unmeasured characteristics (see also Levine, Mitchell, and
Moore, forthcoming, and Blau and Graham, 1990):
22
(RY)ige = (WHig)b1g+ (%FEM)ig b2g + Higb3g + Xigb4g + Pigb5g + uig
In these equations, the b coefficients represent the effects of a change in work history, occupational
segregation, health status, other socioeconomic variables, and preferences on projected retiree income
within a given sex/marital status group. If bi is statistically significant and positive (negative), then we
would conclude that there is a positive (negative) association between that explanatory variable and
respondent retirement income. Since the dependent variable is expressed in natural log form, we also
compute the partial effects of each factor on retirement income in dollars, in what follows below.
We eliminate from the analysis any sample respondents with negative projected total wealth at age
62 (8 individuals), and to produce viable log values, we impute one dollar of wealth to
respondents reporting zero total wealth. Similar issues arise with the components of total wealth,
namely Social Security, employer pensions, and housing/financial wealth. The last category,
which we term “other wealth” here, is the aggregate of financial and housing wealth. It, too, can
take on negative and zero values. The empirical analysis of these other wealth values proceeds in
the same fashion as the total wealth analysis: persons reporting negative values are dropped from
the sample, and cases with zero wealth are assigned one dollar. There are no negative reports of
employer pension wealth in the sample, but 32 percent of the respondents reported they
anticipated no employer pension. Persons without pensions were excluded from the analysis of
employer pension wealth. We have separately estimated, but do not describe here, additional
Probit models to explore factors associated with having positive values of each type of wealth.
Controlling for sample selection does not change the qualitative conclusions reported here.
The explanatory variables in the multivariate analysis control for various socioeconomic
characteristics of survey households (descriptive statistics appear in Appendix Table 5). We also
include controls for spouse variables for married couples, which must be included because retirement
wealth measures relate to households rather than individuals. In all cases, missing values for
explanatory variables are assigned a flag variable that is then included in all estimates. While
some explanatory variables (age, race, and education) do not require description, others (like
share of women in longest occupation) require a brief description as follows:
Marital History Variables: We estimate separate equations for currently married and
nonmarried men and women, but each set of estimates controls for respondents’ marital history.
Qualitative variables identifying previous divorce and widowhood appear in each equation, where
the omitted category varies depending on the sample group. (For example, in the case of single
women, the omitted category is “never married”; for married women it is “married”). In separate
analyses we also focus on the never-married, divorced, and widowed among the nonmarried
population; however, sample sizes are small.
Percentage of Females in Longest Occupation: The HRS provides two-digit occupation codes
(OC) for three types of jobs: current, last, and a previous job of at least five years duration held
after 1972. If a respondent was working in 1992, the survey recorded a respondent’s OC and the
number of years the respondent had been working at that current job. The survey then asked
about any previous jobs that lasted at least five years after 1972. If the respondent had worked at
23
such a job, tenure and OC of that job is recorded. If the respondent was not working in 1992, the
survey then asked about the respondent’s last job. If the last job ended after 1972, the survey
collected OC and tenure for that job. If the respondent had held a job prior to his or her last job
that lasted longer than five years, the OC and tenure for that job were recorded. Therefore, given
a respondent’s current job status, a respondent could have at most two jobs with OC and tenure
data. For the present analysis we selected an occupation code for the respondent’s longest job
based on length of tenure for the two jobs reported, and assigned fraction of women in that twodigit occupation based on Levine and Zimmerman (1995), who devised these figures for the 1980
Census. That year is appropriate since it best reflects the sex composition of our sample workers’
occupations while the HRS respondents were in their prime working years. Respondents in the
military were assigned a military “flag” variable, since the fraction female variable is not available
from this source for that year. If the respondent never worked for pay (as defined above), a flag
variable (NO_WORK) was set to one. If the respondent worked but did not report an occupation
code, the flag variable MISSING was set equal to one. In all cases, where we could not match a
respondent’s occupation to the share of women in it, we substituted the mean percentage of
women. Occupation codes and the percentage of men and women in them (including those for
whom OC is missing) can be found in Appendix Table 9.
Health and Preferences: The empirical analysis also controls on several measures of health and
preferences, including indicators of respondents’ physical and mental functioning as well as
financial planning horizons. These results are not discussed in detail here; the interested reader
may consult Mitchell, Moore, and Phillips (forthcoming).
24
Appendix Table 1. Unweighted Sample Sizes by Race/Ethnicity, Sex, and
Current Marital Status
Total
White
Black
Hispanic
Nonmarried
Women
Nonmarried
Men
Married
Women
Married
Men
952
564
309
79
406
252
117
37
2117
1739
228
150
2209
1771
261
177
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
Note: Unweighted N's given; all other tables and charts use weighted N's.
25
Appendix Table 2. Mean Projected Retirement Wealth by Race/Ethnicity, Sex, and Current Marital Status
Men
Total Projected
Household Wealth
Total Wealth
Social Security Wealth
Pension Wealth
Other Wealth*
26
Women
Total Projected
Household Wealth
Total Wealth
Social Security Wealth
Pension Wealth
Other Wealth*
White
Nonmarried
Married
$383,697
$84,114
$129,866
$169,717
$795,845
$165,727
$238,971
$391,146
White
Nonmarried
Married
$258,248
$69,107
$66,833
$122,308
$749,119
$175,462
$203,251
$370,406
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
Note: All data weighted by HRS sample weights.
* Housing and Net Financial Assets
Black
Nonmarried
Married
$133,662
$58,971
$50,063
$24,629
$485,332
$140,513
$191,021
$153,798
Black
Nonmarried
Married
$210,617
$61,018
$73,924
$75,674
$489,737
$148,395
$184,509
$156,833
Hispanic
Nonmarried
Married
$170,499
$62,030
$49,847
$58,623
$344,507
$124,038
$83,618
$136,851
Hispanic
Nonmarried
Married
$117,359
$52,521
$20,690
$44,148
$367,776
$140,839
$93,702
$133,235
All
Nonmarried
Married
$326,074
$78,274
$110,641
$137,159
$751,791
$161,886
$227,682
$362,224
All
Nonmarried
Married
$241,242
$66,644
$65,477
$109,121
$719,538
$172,582
$197,844
$349,113
Appendix Table 3. Mean Projected Annual Retirement Income by Race/Ethnicity, Sex, and Current Marital Status
White
Men
Total Projected Annual
Household Income
Total Income
Social Security Income
Pension Income
Other Income*
Nonmarried
Black
Married
$25,641
$5,625
$8,670
$11,425
$42,266
$8,824
$12,638
$20,832
White
27
Women
Total Projected Annual
Household Income
Total Income
Social Security Income
Pension Income
Other Income*
Nonmarried
$14,801
$3,959
$3,825
$7,052
* Housing and Net Financial Assets
Married
$8,938
$3,947
$3,341
$1,681
$25,559
$7,364
$10,119
$8,097
Black
Married
$41,748
$9,816
$11,321
$20,635
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
Note: All data weighted by HRS sample weights.
Nonmarried
Hispanic
Nonmarried
$12,056
$3,495
$4,228
$4,370
Nonmarried
All
Married
$11,350
$4,139
$3,314
$3,901
$17,956
$6,539
$4,415
$7,019
Hispanic
Married
$27,441
$8,357
$10,268
$8,880
Nonmarried
$6,708
$3,004
$1,183
$2,540
Nonmarried
$21,788
$5,234
$7,385
$9,234
Married
$39,894
$8,608
$12,042
$19,272
All
Married
$20,708
$7,998
$5,205
$7,513
Nonmarried
$13,822
$3,817
$3,747
$6,292
Married
$40,116
$9,663
$11,018
$19,460
Appendix Table 4. Regression Results for Projected Annual Retirement Income by Type:
Coefficients (Standard Errors)
A. Nonmarried Women
Labor Market Variables
Years of Work to Age 50
Average Earnings
Percentage of Females in
Occupation
Military Occupation
Never Worked
Occupation Missing
Socioeconomic Factors
Age
Black
Hispanic
No High School
College
Graduate School
Ever Divorced
Ever Widowed
Number of Children
R-Squared
Sample Size
Total
0.01
(0.01)
0.16*
(0.07)
-0.55**
Social
Security
0.02**
(0.05)
0.15**
(0.05)
-0.09
Pension
-0.01
(0.01)
0.23
(0.13)
-0.37
Other
-0.01
(0.02)
0.28
(0.17)
-1.03*
(0.14)
0.00
(0.00)
-0.88**
(0.36)
-0.52**
(0.19)
(0.06)
0.00
(0.00)
-0.93**
(0.33)
-0.34*
(0.15)
(0.28)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
(0.46)
0.00
(0.00)
-1.74*
(0.81)
-1.01
(0.63)
0.03**
(0.01)
-0.19*
(0.08)
-0.15
(0.11)
-0.55**
(0.07)
0.41**
(0.12)
0.71**
(0.12)
-0.14*
(0.07)
0.10
(0.07)
-0.04**
(0.02)
0.03**
(0.01)
-0.12*
(0.06)
0.15
(0.08)
-0.17**
(0.04)
-0.03
(0.05)
0.08
(0.13)
-0.08
(0.05)
-0.07
(0.04)
-0.01
(0.02)
-0.04*
(0.02)
0.48**
(0.11)
-0.53
(0.29)
-0.28
(0.15)
0.56**
(0.18)
0.84**
(0.18)
-0.21
(0.15)
-0.23
(0.15)
-0.10**
(0.04)
0.06*
(0.03)
-1.10**
(0.25)
-1.67**
(0.47)
-1.44**
(0.24)
0.70*
(0.30)
0.83*
(0.35)
-0.41
(0.26)
0.61*
(0.30)
-0.05
(0.06)
0.84
946
0.63
946
0.29
443
0.29
863
Notes: See below
28
Appendix Table 4 (cont.).
B. Nonmarried Men
Labor Market Variables
Years of Work to Age 50
Average Earnings
Percentage of Females in
Occupation
Military Occupation
Never Worked
Occupation Missing
Socioeconomic Factors
Age
Black
Hispanic
No High School
College
Graduate School
Ever Divorced
Ever Widowed
Number of Children
R-Squared
Sample Size
Total
-0.01
(0.01)
0.43**
(0.16)
-0.20
Social
Security
0.01
(0.01)
0.27*
(0.12)
0.07
Pension
-0.07*
(0.03)
0.34
(0.24)
-0.13
Other
0.00
(0.05)
0.60
(0.32)
-0.78
(0.26)
0.06
(0.40)
-0.46
(0.28)
-0.67*
(0.28)
(0.08)
-0.05
(0.16)
-0.05
(0.18)
-0.27
(0.16)
(0.54)
-0.32
(0.47)
0.00
(0.00)
0.00
(0.00)
(0.71)
0.20
(1.17)
-0.93
(1.28)
-3.03
(1.64)
0.02
(0.02)
-0.50**
(0.12)
-0.04
(0.21)
-0.26*
(0.13)
0.59**
(0.21)
0.58**
(0.17)
0.09
(0.13)
0.04
(0.18)
-0.07*
(0.03)
0.03**
(0.01)
-0.18**
(0.06)
-0.02
(0.08)
-0.02
(0.07)
0.15*
(0.06)
0.13
(0.07)
0.09
(0.06)
-0.02
(0.07)
0.00
(0.01)
-0.06
(0.03)
-0.11
(0.26)
0.02
(0.25)
-0.14
(0.22)
0.40
(0.33)
0.41
(0.27)
0.60*
(0.25)
0.54
(0.33)
-0.09
(0.06)
0.09
(0.06)
-2.49**
(0.45)
-1.48
(1.02)
-0.63
(0.37)
0.75
(0.46)
1.05*
(0.47)
-0.09
(0.38)
-0.05
(0.59)
-0.03
(0.09)
0.42
404
0.51
404
0.18
212
0.35
373
Notes: See below
29
Appendix Table 4 (cont.).
C. Married Women
Labor Market Variables
Years of Work to Age 50
Average Earnings
Percentage of Females in
Occupation
Military Occupation
Never Worked
Occupation Missing
Socioeconomic Factors
Age
Black
Hispanic
No High School
College
Graduate School
Ever Divorced
Ever Widowed
Number of Children
R-Squared
Sample Size
Total
Social
Security
Pension
Other
-0.005*
(0.00)
0.05**
(0.02)
-0.13
0.004**
(0.00)
0.02**
(0.01)
-0.02
-0.01
(0.01)
0.07
(0.04)
-0.47**
-0.01*
(0.01)
0.07*
(0.04)
-0.16
(0.09)
1.47**
(0.46)
0.01
(0.11)
-0.03
(0.08)
(0.03)
0.62**
(0.19)
0.08
(0.05)
0.02
(0.04)
(0.19)
1.78**
(0.36)
0.21
(0.22)
-0.03
(0.19)
(0.23)
1.66
(0.91)
-0.07
(0.31)
0.04
(0.18)
0.00
(0.01)
-0.17**
(0.05)
-0.31**
(0.06)
-0.19**
(0.04)
0.11
(0.07)
0.40**
(0.08)
-0.14**
(0.05)
-0.16
(0.09)
-0.03**
(0.01)
0.01**
(0.00)
-0.07**
(0.02)
-0.02
(0.03)
-0.06**
(0.02)
0.05*
(0.02)
0.06
(0.03)
-0.01
(0.02)
-0.06
(0.03)
0.00
(0.00)
0.00
(0.01)
0.23*
(0.10)
-0.20
(0.24)
-0.21*
(0.09)
0.06
(0.11)
0.48**
(0.12)
-0.16
(0.11)
-0.21
(0.22)
0.00
(0.02)
0.02
(0.02)
-0.70**
(0.16)
-0.72**
(0.23)
-0.47**
(0.11)
0.22*
(0.11)
0.57**
(0.13)
-0.21
(0.13)
-0.3
(0.21)
-0.08**
(0.02)
0.64
2117
0.55
2117
0.17
1578
0.25
2075
Notes: See below
30
Appendix Table 4 (cont.).
D. Married Men
Labor Market Variables
Years of Work to Age 50
Average Earnings
Percentage of Females in
Occupation
Military Occupation
Never Worked
Occupation Missing
Socioeconomic Factors
Age
Black
Hispanic
No High School
College
Graduate School
Ever Divorced
Ever Widowed
Number of Children
R-Squared
Sample Size
Total
-0.01*
(0.00)
0.20**
(0.03)
0.06
Social
Security
0.02**
(0.00)
0.12**
(0.03)
0.03
Pension
-0.03**
(0.01)
0.11
(0.07)
0.41*
Other
-0.02*
(0.01)
0.45**
(0.09)
-0.52*
(0.10)
0.03
(0.09)
-0.49
(0.32)
-0.19
(0.29)
(0.04)
-0.10
(0.06)
-0.14
(0.08)
-0.14
(0.08)
(0.17)
0.42**
(0.15)
-0.24
0.48)
-3.42**
(0.81)
(0.22)
-0.26
(0.20)
-1.82
(1.77)
-1.20
(1.20)
0.00
(0.01)
-0.22**
(0.05)
-0.37**
(0.07)
-0.23**
(0.04)
0.20**
(0.06)
0.35**
(0.06)
-0.01
(0.05)
0.16
(0.11)
-0.04**
(0.01)
0.01**
(0.00)
-0.06**
(0.02)
-0.02
(0.03)
-0.04**
(0.01)
0.06**
(0.02)
0.06**
(0.02)
0.02
(0.02)
0.00
(0.03)
-0.01*
(0.00)
-0.03*
(0.01)
-0.04
(0.09)
-0.33*
(0.15)
-0.19*
(0.09)
0.32**
(0.10)
0.49**
(0.10)
-0.05
(0.09)
0.26
(0.21)
-0.05*
(0.02)
0.02
(0.01)
-0.77**
(0.15)
-0.89**
(0.25)
-0.42**
(0.09)
0.33**
(0.10)
0.54**
(0.10)
0.05
(0.11)
0.38
(0.20)
-0.10**
(0.02)
0.68
2209
0.23
2209
0.16
1660
0.28
2147
Notes to Appendix Table 4:
1) Retirement Income and Average Earnings expressed in natural logs.
2) Estimates exclude respondents with negative total wealth.
3) Pension Income estimates conditional on nonzero pension wealth.
4) Other Income estimates conditional on nonzero other wealth.
5) Our estimates also include variables that controls for spousal characteristics where appropriate.
6) Information regarding the treatment of missing values as well as health and preference control appears
in the data appendix.
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
** Coefficient statistically significant at the 1% level.
* Coefficient statistically significant at the 5% level.
31
Appendix Table 5. Mean Values of Explanatory Variables by Sex and Current Marital Status
Nonmarried Women
Mean
Standard
Deviation
32
Labor Market Variables
Years of Work to Age 50
Average Earnings
Occupation (% female)
Military Occupation
Never Worked
Missing Occupation Code
Socioeconomic Factors
Age
Black (%)
Hispanic (%)
No High School (%)
College (%)
Graduate School (%)
Ever Divorced (%)
Ever Widowed (%)
Number of Children
Nonmarried Men
Mean
Standard
Deviation
Married Women
Mean
Standard
Deviation
Married Men
Mean
Standard
Deviation
18.51
$10,017
0.55
0.00
0.03
0.04
8.55
$7,411
0.20
0.00
0.18
0.18
25.12
$18,678
0.33
0.01
0.01
0.01
6.75
$9,283
0.23
0.12
0.12
0.10
15.64
$8,196
0.55
0.00
0.04
0.08
8.44
$6,208
0.19
0.02
0.20
0.27
26.26
$22,587
0.31
0.04
0.00
0.01
6.21
$8,591
0.20
0.18
0.04
0.07
55.93
0.19
0.06
0.30
0.07
0.09
0.63
0.35
2.77
3.21
0.39
0.23
0.46
0.26
0.29
0.48
0.48
2.01
55.55
0.17
0.07
0.28
0.10
0.07
0.68
0.10
2.07
3.13
0.38
0.25
0.45
0.30
0.26
0.47
0.31
1.92
55.85
0.06
0.04
0.20
0.09
0.05
0.22
0.04
3.48
3.16
0.23
0.20
0.40
0.29
0.22
0.41
0.20
1.98
55.94
0.07
0.05
0.22
0.12
0.11
0.27
0.02
3.30
3.17
0.25
0.22
0.41
0.33
0.31
0.44
0.14
1.97
Note: Earnings reported in 1992 dollars (not in natural logs)
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992), weighted data.
Appendix Table 6. Unweighted Sample Sizes by Sex, Race, and Marital History
Never Married
White
Black
Hispanic
Total*
Men
61
29
8
98
Women
63
41
13
117
Divorced &
Nonmarried
Men
173
74
26
273
Women
356
188
52
596
Widowed &
Nonmarried
Men
23
18
3
44
Women
208
101
19
328
Currently Married
Men
1,771
261
177
2,209
Women
1,739
228
150
2,117
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992), weighted data.
* The sum of this row does not equal the sample size because 106 respondents have experienced both divorce and widowhood. When these observations
are accounted for, the total is equal to the sample of respondents with nonnegative total wealth at age 62 (5,676).
33
Appendix Table 7. Median Projected Annual Retirement Income by Source and Marital History
Median Projected Annual Retirement Income
Income
Source
Total
Social Security
Pensions
Other
Never Married
Men
Women
$13,102
$14,364
$4,866
$4,950
$1,126
$2,275
$3,035
$3,157
Divorced & Nonmarried
Men
Women
$12,412
$8,186
$5,682
$3,603
$1,798
$0
$2,574
$1,902
Widowed & Nonmarried
Men
Women
$12,613
$8,808
$4,436
$3,281
$2,738
$0
$2,562
$3,430
Currently Married
Men
Women
$28,023
$29,479
$9,095
$10,135
$6,240
$5,474
$8,309
$9,051
Divorced & Nonmarried
Men
Women
$21,678
$12,480
$5,427
$3,726
$7,692
$3,340
$8,583
$5,446
Widowed & Nonmarried
Men
Women
$18,497
$13,511
$4,948
$3,535
$6,232
$2,298
$7,334
$7,697
Currently Married
Men
Women
$39,894
$40,116
$8,608
$9,663
$12,042
$11,018
$19,272
$19,460
Median Projected Annual Retirement Income
Income
Source
34
Total
Social Security
Pensions
Other
Never Married
Men
Women
$23,523
$19,025
$4,723
$4,797
$7,073
$8,558
$11,728
$5,679
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992), weighted data.
Appendix Table 8. Regression Results for Total Projected Annual Retirement
Income by Marital History: Coefficients (Standard Errors)
A. Women
Labor Market Variables
Years of Work to Age 50
Average Earnings
Percentage of Females in
Occupation
Military Occupation
Never Worked
Occupation Missing
Socioeconomic Factors
Age
Black
Hispanic
No High School
College
Graduate School
Number of Children
R-Squared
Sample Size
Never
Married
0.01
(0.02)
0.22
(0.13)
-0.64
Divorced &
Nonmarried
-0.01
(0.01)
0.33**
(0.08)
-0.49**
Widowed &
Nonmarried
0.01
(0.01)
0.05
(0.09)
-0.50*
(0.43)
0.00
(0.00)
-1.30**
(0.33)
-1.75**
(0.45)
(0.16)
0.00
(0.00)
-0.84
(0.60)
-0.26
(0.32)
(0.23)
0.00
(0.00)
-0.70
(0.40)
-0.42*
(0.20)
0.00
(0.03)
-1.05**
(0.23)
-0.47
(0.30)
-0.65**
(0.24)
0.21
(0.36)
0.15
(0.29)
0.13*
(0.07)
0.02
(0.01)
0.00
(0.10)
-0.10
(0.13)
-0.44**
(0.08)
0.61**
(0.02)
0.90**
(0.16)
-0.06**
(0.02)
0.06**
(0.15)
-0.29*
(0.13)
-0.04
(0.24)
-0.69**
(0.12)
0.15
(0.17)
0.97**
(0.18)
-0.05
(0.03)
0.64
117
0.43
596
0.44
328
Notes: See below
35
Appendix Table 8 (cont.).
B. Men
Labor Market Variables
Years of Work to Age 50
Average Earnings
Percentage of Females in
Occupation
Never
Married
-0.02
(0.03)
0.06
(0.31)
-0.10
Divorced &
Nonmarried
-0.02
(0.02)
0.039*
(0.17)
-0.46
Widowed &
Nonmarried
0.03
(0.02)
0.81**
(0.27)
1.34**
(0.56)
1.14
(0.67)
0.28
(0.47)
0.00
(0.00)
(0.31)
0.84**
(0.17)
-1.01**
(0.14)
-0.72**
(0.03)
(0.42)
-1.00**
(0.27)
0.00
(0.00)
0.00
(0.00)
0.03
(0.04)
-0.76**
(0.25)
0.33
(0.49)
0.03
(0.33)
0.69*
(0.33)
0.71*
(0.34)
-0.07
(0.11)
0.01
(0.02)
-0.42**
(0.14)
-0.09
(0.25)
-0.33*
(0.14)
0.67*
(0.28)
0.63**
(0.21)
-0.06
(0.04)
0.09**
(0.03)
-0.34
(0.23)
0.21
(0.25)
-0.23
(0.20)
-0.20
(0.23)
0.15
(0.29)
-0.14**
(0.04)
0.57
98
0.39
273
0.86
44
Military Occupation
Never Worked
Occupation Missing
Socioeconomic Factors
Age
Black
Hispanic
No High School
College
Graduate School
Number of Children
R-Squared
Sample Size
Notes (see also Appendix Table 4):
1) Retirement Income and Average Prime-age Earnings expressed in natural logs.
2) Estimates exclude respondents with negative total wealth.
3) Information regarding health and preference controls as well as treatment of missing values appears in the data
appendix.
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992)
** Coefficient statistically significant at the 1% level.
* Coefficient statistically significant at the 5% level.
36
Appendix Table 9. Percentage of Men and Women in HRS Occupation Codes
Occupation
Managerial Specialty Operation
Professional Specialty Operation and Tech Support
Sales
Clerical, Administrative Support
Service: Private Household, cleaning and building
Service: Protection
Service: Food Preparation
Health Services
Personal Services
Farming, Forestry, Fishing
Mechanics and Repair
Construction Trade and Extractors
Precision Production
Operators: Machine
Operators: Transportation, etc.
Operators: Handlers, etc.
Armed Forces
Missing Occupation Code*
Men
19.9
13.9
8.0
5.6
0.0
2.8
0.9
0.1
2.3
3.9
7.3
7.2
6.5
7.1
8.0
3.4
3.2
1.0
100.0
Women
10.7
16.6
11.0
29.1
1.8
0.2
6.0
4.2
6.7
0.9
0.2
0.2
2.0
7.6
1.1
1.7
0.1
10.5
100.0
Total
15.3
15.2
9.5
17.5
0.9
1.5
3.5
2.2
4.5
2.4
3.7
3.7
4.2
7.4
4.5
2.5
1.6
6.0
100.0
Source: Authors' calculations, Health and Retirement Study Wave 1 ($1992), weighted data.
* Six percent of HRS respondents in our sample lack an occupation code. Of those, some never worked for pay (37 percent) or did not respond to
the occupation question in the survey (63 percent).
37
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