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~2
Key Research Issues
The economic security of retirees depends on their lifetime experiences of work,
savings, and family ties and their health care and other consumption needs. Such
experiences depend on individual choices; they also depend on decisions of em-
ployers that affect the provision of jobs, earnings, savings vehicles, and retire-
ment and health care benefits. Government policies and programs constrain and
shape all these individual and employer decisions. Consequently, projecting the
implications for retirement income security of proposed changes in government
policy requires basic research knowledge about the likely behavioral responses of
people and employers and a capability for using it to estimate future outcomes.
Knowledge to project behavioral effects is not needed for every retirement-
income-related policy question, but it is essential for many questions, particularly
when projections are needed for the medium and long term. Contrast short-term
versus long-term projections of the implications of reducing the annual cost-of-
living adjustment (COLA) for Social Security benefits. In the short term, say for
a 5-year period, the projection is straightforward: the Social Security benefits of
current retirees and those expected to retire during the next 5 years are increased
each year by a reduced COLA instead of by the full estimated amount of inflation
as is currently done. If the 5 years are expected to be a period of low inflation, the
effect of reduced benefits will not be great for many people, although some
people will be moved below the poverty line. Over a longer projection period
and particularly if the COLA is cut significantly, the real value of Social Security
benefits will decline significantly over people' s retirement years, with potentially
severe effects on the income security of many retirees. Given this prospect, one
would expect more and more people to change their behavior, for example, by
39
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40
ASSESSING POLICIES FOR RETIREMENT INCOME
delaying retirement or increasing savings. Employers may also change their
pension plan provisions to compensate for workers' expected lower Social Secu-
rity income. To the extent that such behavioral responses occur, projections need
to take them into account to be useful for policy making.
Some policy proposals represent major, systemic changes for which there are
no historical parallels and, hence, little possibility of obtaining appropriate be-
havioral parameters from available (or potentially available) data to use in projec-
tions. In these instances, it can still be useful to develop projections with a range
of assumptions, reflecting expert judgment. Such estimates may indicate the
extremes of possible outcomes, although they remain highly uncertain even then.
Many proposals, however, can be expected to influence behavior in ways
that research can illuminate. In addition, for some major changes with which the
United States has no experience, it may be possible to obtain useful data by
analyzing the experience of other countries (such as the experience in several
countries with various forms of privatization of social security systems; see World
Bank, 1994~. Although the cultural and social milieus are different, there may
still be knowledge from comparative cross-national research that can contribute
to U.S. projections.
In this chapter, we summarize what is known and not known about factors
that influence key retirement-income-related behaviors of individuals and em-
ployers. For employers, we look at research about decisions on pensions and
other benefits and demand for older workers. For individuals, we look at re-
search about savings, consumption, and labor supply. Our reviews in the chapter
text are quite brief, highlighting key knowledge gaps. They draw from extensive
reviews of the literature and research issues in a set of papers we commissioned
for our study (Hanushek and Maritato, 1996; see Appendix A for contents).]
For projecting the likely effects of retirement-income-related policy changes,
particularly over the long term, it is also important to understand likely demo-
graphic and health-related trends. We thus look at research about factors and
trends in basic demographic processes, particularly mortality, that will affect the
size and makeup of the population of workers and retirees at future times; the
health status of workers and retirees that will determine their needs for health
care services and affect their decisions about work and savings; and health care
costs and financing arrangements that will affect retirees' living standards. Our
reviews of these areas are also brief.2 Finally, we touch on factors and trends in
1 The papers by Lumsdaine, Parsons, and Poterba review the literature on labor supply, employer
behavior, and savings and consumption, respectively; the paper by Gustman and Juster looks at the
distribution and sources of income and wealth of households with retired workers and reviews
analytical models of labor supply, savings, and pension decisions that contribute to income and
wealth at retirement.
2For more extensive discussion and literature reviews, see the paper by Lee and Skinner (in
Hanushek and Maritato, 1996; see also Moon, 1995).
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KEY RESEARCH ISSUES
41
marriage and divorce, which can have important consequences for the economic
well-being of the elderly.
Our discussion covers a large number of areas, but it is not comprehensive.
For example, we do not consider factors that influence trends in worker produc-
tivity, even though such trends are key to growth in real earnings, which, in turn,
affects Social Security and pension entitlements. Also, we do not discuss factors
that influence the financial return to pension investments and personal savings,
although the distribution of returns is of growing importance for retirement in-
come security and will become even more important if such policy changes as
privatizing all or part of Social Security contributions are implemented. None-
theless, we cover most of the critical areas for which a strong research base is
needed for retirement-income-related policy analysis. We identify important
strengths and weaknesses in available knowledge and recommend improvements.
Our discussion of research topics is ordered by our assessment of which
areas are most deficient in terms of basic knowledge that is relevant for retire-
ment income security policy analysis. These deficiencies are largely due to
deficiencies in data, which in some instances have hampered the development of
theory and in other instances have impeded the development of robust estimates
of behavioral parameters. Hence, improvements in data (discussed in Chapter 4)
will be required to carry out much of the research agenda that we recommend.
However, basic research should not stand still. Although some research cannot
proceed very far without better data, other research can go forward with im-
proved data that are or will shortly become available or with the use of methods
(e.g., case studies) that require much less investment in new or better data.
Generally, research should proceed to refine and improve analytical models
with the best available data. Indeed, data development and basic research go
hand in hand: new findings from data suggest the need to rethink theories and
models, and, in turn, analytical developments suggest further improvements to
data. We stress in our report the need for investments in data and analytical
modeling and research in areas that most need an improved base of knowledge
with which to support retirement-income-related policy work. Given constrained
resources, however, we recommend against major investments in complex, new
projection models for policy purposes until investments in data and basic re-
search bear fruit.
We begin our review with research on employer behavior. Employers play
an important role in the provision of retirement income security through their
decisions about personnel and benefits. Moreover, their behavior can change
rapidly, as evidenced by the marked increase in recent years in the number of
employers offering managed care health insurance plans. Yet very little is known
about the strength of the factors that may influence employers' decisions. We
next consider research on individuals' choices of savings and consumption, an-
other area about which relatively little is known, and individuals' labor supply
and retirement decisions. We then consider trends in relevant demographic char
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ASSESSING POLICIES FOR RETIREMENT INCOME
acteristics. Lastly, we comment briefly on key questions related to health care
costs.
EMPLOYER BEHAVIOR
Employer pensions represent a significant source of retirement income for many
workers, and their availability and characteristics are important determinants of
retirement decisions and other relevant behaviors. The availability of other kinds
of employer benefits (e.g., disability insurance and retiree health insurance) also
plays a role in the retirement-related behavior of workers. More generally, em-
ployers' demand for older workers affects retirement income security, both di-
rectly in terms of the labor market opportunities for older workers and indirectly
in terms of the effects on employer decisions about benefits.
Employers' decisions to offer a pension plan or plans (or other benefits) and
of what typos) depend on several factors:
· expected benefits in terms of work force productivity, worker recruitment
and retention, and retirement of older workers;
· federal tax law provisions, such as tax deductions for qualified plans,
which are an incentive to provide benefits, and nondiscrimination rules for quali-
fied plans, which are a disincentive;
· other government policies, including those for Social Security and Medi-
care, pension insurance laws and regulations, and antidiscrimination laws with
respect to age and disability;
· employers' financial objectives and concerns, including tax liabilities,
administrative costs, and costs of compliance with regulations;
· trends in benefit policies by similar employers; and
· the demand for benefits by employees.
There are a large number of competing theories about why employers offer
pension plans, which differ in the importance accorded to the various factors
listed above. For example, some theories and models emphasize the importance
of tax deferral, others stress the use of pension plans as a worker selection device
or as a productivity enhancement mechanism, while others stress the importance
of pension plans as incentives for workers to retire. To date, there is no agree-
ment on which of these theories best explains employers' behavior or on the
extent to which different types of employers may have different mixes of mo-
tives.
In a literature review commissioned by the panel, Parsons (1996) emphasizes
the importance of transaction costs in explaining the well-documented phenom-
enon that large, unionized employers are much more likely to offer pensions than
are smaller employers: large employers can benefit from economies of scale in
administering pension plans that are not available to small employers. He at
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KEY RESEARCH ISSUES
43
tributes the fact that small employers that do offer pension plans almost invari-
ably offer a 401(k) or other type of defined contribution plan, rather than a
defined benefit plan, to the same reason, namely, lower administrative costs.
However, Parsons (1996:179) acknowledges that "alternative hypotheses . . . can
explain many of the same observations." Moreover, such analysis cannot explain
why some relatively large firms do not offer pensions and some relatively small
firms do, nor the variety of pension plan provisions that characterize firms in the
same size class. Nor does the research yet provide agreed-upon values of behav-
ioral parameters that could be used to estimate the strength of employer responses
to policy and other changes: for example, what degree of reduction in plan
administrative costs or in regulatory burden would induce a specified percentage
of small employers to set up pension plans of a particular type.
Employer demand for older workers is also affected by many factors, includ
~ng:
· desired work force characteristics, in terms of retention, skill levels, pro-
ductivity, and other attributes;
.
employer perceptions of relative productivity of workers of different ages;
· employer financial objectives and concerns (such as the costs of provid-
ing benefits, costs of providing job training);
.
· employer personnel practices, such as flexibility in reassigning workers;
government policies and regulations, including anti-age discrimination
laws and restrictions on mandatory retirement; and
· older worker supply.
Again, a pervasive finding is that the larger the employer, the lower the share
of older workers (age 55 and older) in an employer's work force. Moreover,
when mandatory retirement rules were legally permitted in the United States,
larger, unionized employers were more likely to have them. Considerable analy-
sis has been conducted of differences among employers in mandatory retirement
provisions, but there is no agreement on the underlying behavioral mechanisms.
Theories to explain this phenomenon include:
· the propensity of large employers to have more formal rules of all types,
reflecting their higher costs of making idiosyncratic decisions;
· the "representative worker" model, which posits that employers that en-
gage in long-term contracting with workers (predominantly larger employers)
pay workers more as they age than they are worth in terms of productivity and
hence that these employers cap total compensation by mandating retirement by a
specific age; and
· the desire of employers to limit the propensity of people who are hired
later in life to work into their less productive older years in order to accumulate
more generous pension rights.
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ASSESSING POLICIES FOR RETIREMENT INCOME
Alternatively, workers' decisions could play a large role in the age profile of
employers' work forces: that is, the smaller share of older workers at large
employers could be attributed to the propensity of large employers to offer pen-
sions, which, in turn, encourage retirement.
Untangling the mix of factors that influence personnel and benefit policies of
different kinds of employers is critical for reliable projections of the likely effects
of government policy changes, such as the effect of changes in Social Security
and Medicare provisions on employer hiring and compensation policies and the
consequences for workers' overall retirement income security. However, Par-
sons (1996:179-180) concludes that "we are not close to having the level of
understanding that would permit us to make quantitative estimates on employer
behavior."
There is yet another problem for estimating employer responses to policy
changes in projection models, that of projecting trends in the mix of employer
types. Given the association of pension offerings and fewer older workers with
larger employers, a continued shift of employment from the large, highly union-
ized sector of the economy to the service sector, with many more small employ-
ers, may mean that older workers in the future have greater access to jobs but less
access to pensions. Whether this trend will continue, and at what rate, and
whether other significant shifts in employer mix will occur are difficult but
important questions to answer, as is the question of whether observed relation-
ships between employer type and pension and employment practices will con-
tinue to hold.
Even more than in the case of personal consumption and savings behavior
(see below), data gaps and measurement problems greatly constrain researchers'
ability to develop reasonable analytical models of employer behavior with regard
to pension and other retirement-related benefits. Available cross-sectional data
sets suffer from several limitations. The Form 5500 database of the U.S. Depart-
ment of Labor, which characterizes employer benefit plans from annual filings to
the Internal Revenue Service, does not cover public employers; it also does not
provide information on the full range of private employer benefits, which include
nonqualified as well as qualified pension plans, retirement window opportunities,
retiree health insurance, disability insurance, and other relevant benefits. The
Form 5500 database also contains limited information for analyzing differences
in benefit packages as a function of employer or work force characteristics.
The Employee Benefits Survey (EBS) of the Bureau of Labor Statistics
provides extensive information on types of public and private employer benefits
for workers in broad occupational categories. However, the survey is based on
small samples, has at present no data on benefit costs, and has very little informa-
tion on employer characteristics. The National Employer Health Insurance Sur-
vey of the U.S. Department of Health and Human Services has large sample
sizes, but it is limited to health care benefits and costs. Private sector surveys of
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KEY RESEARCH ISSUES
45
pension and health care benefit offerings by consulting firms and others are
generally limited to larger employers and often to the clients of the survey spon-
sor. Case studies by researchers with data from one or a few companies have
supported innovative analysis,3 but the results of such studies are not readily
generalizable because of the heterogeneity of employers.
There are almost no panel data on employers that could be used to trace
changes in benefit packages over time or to determine the factors that influence
employer behavior in this regard. (The Form 5500 database can be used for only
limited kinds of longitudinal analysis.) Similarly, there are no panel (or cross-
sectional) data that could support analysis of interactions among employer and
employee characteristics that in turn affect benefit plan decisions and, ultimately,
retirement income security. Yet such analysis is critically important given the
prominent role of employer pensions and other benefits in retirement income
security and the evidence that employer behavior with regard to the extent and
type of such benefits is dynamic and sensitive to public policies as well as
broader economic factors.
Finally, there are almost no data with which to analyze employer demand for
older workers. Repeated cross-sectional data from the decennial census and the
Current Population Survey (CPS) have been used to study the employment of
older workers by industrial sector, and panel data from the Retirement History
Survey (RHS) have been used to study retirement paths of older workers (e.g.,
retiring from a career job versus moving from a career to a "bridge" job before
exiting the labor force completely). However, no nationally representative data
set exists that permits direct analysis of the factors that influence employers'
hiring and retention decisions. A longitudinal employer database that the Census
Bureau constructed from census and survey data is limited to manufacturing
companies, and it contains no information on work force age structure except for
a subsample of establishments with records matched to 1990 population census
records for their workers. Researchers have made innovative use of personnel
and other records of one or a few employers to study workers' compensation in
relation to measures of productivity and other characteristics (e.g., Kotlikoff and
Gokhale, 1992; Medoff and Abraham, 1981), but such studies are few and limited
in generalizability.
In short, there are glaring gaps and deficiencies in data about employers (and
their workers) with which to develop behavioral parameters for projecting em-
ployer responses to government policy changes and other factors that may affect
personnel and benefit decisions. In Chapter 4, we describe in more detail the
problems of existing data sets on employers and recommend improvements.
3An example is Mitchell and Luzadis (1988), who analyzed pension policies of 14 employers
before and after legislation curtailing mandatory retirement.
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ASSESSING POLICIES FOR RETIREMENT INCOME
CHOICES OF FAMILIES AND INDIVIDUALS
Savings and Consumption
People's decisions about allocating income between savings and consumption in
their pre-retirement years have important implications for the level of consump-
tion that they will be able to sustain after retirement. In this context, personal
savings includes after-tax investments as well as tax-sheltered investments, in-
cluding Individual Retirement Accounts (IRAs) and voluntary employee contri-
butions to 401(k) and other pension vehicles.
The theoretical basis for savings and consumption choices has long been
posited in terms of a life-cycle model, in which younger people, by borrowing,
and older people, by spending down assets, exhibit high consumption-to-income
ratios, while middle-aged people with the highest earnings potential exhibit rela-
tively low consumption-to-income ratios. However, a "pure" life-cycle model
does not explain observed macroeconomic and individual behavior, so the basic
model has been elaborated in various ways (see Poterba, 1996~. Three well-
worked-out modifications include:
· a model that attributes precautionary savings motives to individuals (e.g.,
workers with pensions may save additional amounts to guard against possible
future job loss);
· a model that attributes bequest motives (i.e., the desire to leave assets to
descendants); and
· a model that imposes liquidity constraints (e.g., young people may not be
able to obtain affordable loans).
All three types of analytical models fit reasonably well with cross-sectional data
on the distribution of wealth at retirement, and there is no basis as yet to choose
among them or to determine whether and what kind of mixed-motive model best
explains savings behavior. Moreover, none of the existing models explains well
the trends in personal savings rates over time.
Yet other types of models have been posited, in which behavioral or psycho-
logical elements play a significant role. One such model assumes that people
hold different assets in distinct "mental accounts," which implies that changes in
the level of one asset may have relatively small substitution effects on the hold-
ings of other assets, contrary to the life-cycle model assumption. Other models
posit that people use rules of thumb or other simple heuristics to make savings
decisions (e.g., deciding to save a fixed percentage of earnings each year, regard-
less of the expected return on saving). These models are intriguing but, to date,
have not been well specified or tested.
There are major unanswered questions in the area of consumption and sav-
ings behavior:
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47
· Why have personal savings rates declined in recent years? As just noted,
none of the existing models explains this trend. It may be that cohort factors for
example, the baby boom generation was not exposed to the Depression are
involved or that the development of a government safety net has had an effect.
· Why are so many middle-income people approaching retirement with
very low wealth levels, when Social Security is not a sufficient income source to
maintain their standard of living?
.
To what extent does saving in IRAs and other voluntary pension plans
offset other saving? There is continuing controversy about whether IRA-type
investments are made with dollars that people would have invested in any case.
A key policy question is whether making such accounts more substitutable with
other saving (e.g., by permitting withdrawals) would increase or decrease net
saving in the long run. (In the short run, the effect would most likely decrease net
saving.)
· More broadly, how much is personal saving influenced by taxes, Social
Security, and pension coverage?
How much do behavioral elements, such as mental accounts and rules of
thumb, influence saving behavior? As with retirement and pension acceptance
decisions (see below), adding such factors to models would complicate analysis.
Yet there is evidence from anomalous savings behavior (e.g., low rates of savings
for middle-income families) that it may be important to take account of such
factors.
· Relatedly, how much do families know about their future financial needs
and potential sources of support, and would more knowledge influence their
behavior?
.
The above questions refer to accumulation of savings until retirement age.
Further questions arise about consumption and savings patterns after retirement:
.
Looking at older people, does wealth decline in retirement as much as the
life-cycle model would predict? There is conflicting evidence about the extent to
which wealth, particularly housing equity, is spent down.
· What determines the demand for annuities of different types, and, specifi-
cally, how do couples decide between single and joint life annuities?
.
What are the effects of changing the form of retirement benefits lump
sums as opposed to annuities, or nominal annuities as opposed to indexed annu-
ities on income security over retirees' life spans?
· What are the effects of inflation and nominal interest rates on real con-
sumption patterns in retirement?
· What is the relationship between retirement income programs and support
of the elderly by their families?
There is another problem in projecting the retirement income security impli
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ASSESSING POLICIES FOR RETIREMENT INCOME
cations of people's savings and consumption choices, namely, that of projecting
rates of return on different assets. A major unanswered question is whether rates
of return will be influenced by demographic changes. For example, will baby
boomers' housing lose value, and what will be the likely effects on their post-
retirement standard of living? Also, how much variation will there be in baby
boomers' returns on housing and other assets and what will be the likely distribu-
tional effects on the adequacy of their retirement income?
Data gaps and measurement problems are a major impediment to addressing
all of these questions. People's consumption, savings, and wealth are notoriously
difficult to measure in surveys, although two relatively new surveys the Health
and Retirement Survey (HRS) and the Asset and Health Dynamics Among the
Oldest Old (AHEAD) survey have made significant progress in improving the
measurement of sample members' financial holdings. To the extent that these
surveys accurately measure income and change in net worth, then an estimate of
consumption can be obtained by subtraction. Ideally, direct measures of con-
sumption would be used in order to more accurately assess pre-retirement living
standards, estimate the implications for post-retirement living standards of
people's current savings rates, and help estimate likely future savings rates con-
sistent with the life-cycle model. The third round of HRS and AHEAD includes
a question on total expenditures that may prove useful for such analyses, although
the data will require careful evaluation of their quality.
Better data are also needed on people's information about likely available
sources of retirement income (e.g., their pension rights and anticipated savings)
and their expectations about likely future events (e.g., their own life expectancy,
the likelihood they will continue in good health, the likelihood they will receive
Social Security or pension benefits or an inheritance). More detailed information
on pension plan provisions of workers would also be helpful in determining
factors that influence savings behavior. Linked family data would help deter-
mine the strength of the bequest motive and the factors influencing it. (Some
analysis of savings behavior has been conducted with linked family data from the
Panel Study of Income Dynamics.) This data need may be of lower priority if
policy interest remains generally focused on people with low-to-middle levels of
earnings for whom the prospects of significant bequests are relatively low. How-
ever, linked family data are important for other purposes, such as understanding
care-giving responsibilities and intrafamilial sources of support.
HRS and AHEAD are designed to remedy these kinds of data gaps, and the
two surveys will need to be continued if they are to make possible the develop-
ment of a broadly accepted model of savings and consumption behavior with
high explanatory power. Such a model will most likely retain a basic life-cycle
approach; however, the evidence of substantial heterogeneity among the popula-
tion with regard to savings behavior suggests that a satisfactory model will need
to incorporate multiple savings motives or distinguish among motives for differ-
ent kinds of people.
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KEY RESEARCH ISSUES
49
Retirement income projection models need to take account of personal sav-
ings, particularly given the possibility of cutbacks in Social Security and em-
ployer pension benefits. Although such cutbacks may not occur, they will very
likely be considered, and, hence, policy makers will want estimates of their likely
effects. The lack of agreement on the most appropriate analytical model con-
strains the ability of projection models to estimate the likely effects of policy
changes on personal savings. In the absence of clear directions from research, it
will be very important for projection models to provide sensitivity analyses under
alternative behavioral assumptions.
Labor and Leisure
A great deal has been learned about labor supply and retirement behavior in the
last 20 years; indeed, more is known about labor supply and pension acceptance
decisions than about almost any other aspect of retirement behavior (see
Lumsdaine, 1996~. One reason is that such behavior is easier to measure than, for
example, consumption and savings. Another reason is the availability of rich
longitudinal panel data sets for analysis of labor-leisure choices. Earlier panels,
such as the Retirement History Survey (RHS), lacked detailed information about
workers' pension and health care coverage, but this weakness has been corrected
in the new HRS. Repeated cross-sectional surveys, such as the March Current
Population Survey (CPS), have also provided valuable information on labor sup-
ply trends for population groups.4
What is known about men's retirement behavior underscores the extent of
heterogeneity among workers.5 A large fraction of men (40-50%) work full time
at a career job until their early 60s and then remain out of the labor force for the
rest of their lives. Typically, they apply for Social Security benefits and a pen-
sion, if they have one, at age 62 or at age 65. (Some apply for a pension even
before they are eligible for Social Security at age 62.) Another large fraction
(over 40%) never retire or have complicated in-and-out labor supply patterns.
4The availability of rich data sets in this area resulted from concerns with the trend toward early
retirement in the 1960s that led to support for panel surveys, such as the RHS and the National
Longitudinal surveys of Labor Market Experience (NLS). Two decades later, HRS and AHEAD
were initiated to update the picture on retirement and to respond to concerns about savings behavior
and the health status of an aging population. we argue that a concern with changes in employer
behavior should motivate support for employer-based surveys, in addition to the continuation of
panel surveys of individuals.
5This summary description is based largely on the RHS. More recent studies support the general
characterization, although some trends already evident in the RHS such as the shift in the modal
age of retirement from age 65 to age 62 are more pronounced in later data (see Huron, Haveman,
and O'Donnell, 1995; Karoly and Rogowski, 1994; Peracchi and Welch, 1994). Less is known about
women s retirement behavior because, historically, fewer data have been available.
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ASSESSING POLICIES FOR RETIREMENT INCOME
Very few individuals (less than 5%) phase out of the labor market by gradually
reducing their hours of work, probably because of employer constraints on work
hours. Factors that influence age at retirement include the availability of em-
ployer pensions and retiree health insurance coverage, Social Security provi-
sions, eligibility for Medicare, and the individual's health status.
There is general agreement among researchers that Social Security and Medi-
care provisions have important effects on retirement behavior, at least for the
subset of workers who are not at the upper tail of the income and wealth distribu-
tion and whose Social Security benefits are not dwarfed by employer pensions.
However, there is a large range of uncertainty about the exact magnitude of
individual responses to various policies. Much of this uncertainty stems from
disagreement about the appropriate strategy for modeling individual behavior
whether to use reduced-form statistical models or structural econometric models
(see Lumsdaine, 1996:70-75~.
Reduced-form models do not require the researcher to impose any underly-
ing theory of individual behavior. Hence, they are much easier to formulate and
estimate than are structural models, which are derived from an explicit theory of
individual behavior and make strong, a priori assumptions. Reduced-form mod-
els allow for flexibility in the choice of functional form, which, in turn, makes it
easier than in structural models to learn about the data. However, unless policies
have changed a great deal in the past, reduced-form models cannot estimate the
independent effect of policy parameters on behavior and hence have great diffi-
culty in forecasting how behavior will change under alternative policy regimes.
Structural models can provide estimates of policy effects, even when there
has been little historical variation, because they impose a priori identifying as-
sumptions on the data, which typically involve strong restrictions on the nature of
individual preferences. However, if the model's assumptions are incorrect, then
its predictions are likely to be incorrect. Moreover, there will generally be
several sets of assumptions about preferences that "explain" any given body of
historical data. If each set gives different predictions about the likely effect of a
policy change, there will be little objective basis to choose among them.
Despite these problems, the structural approach does appear to yield accurate
predictions of the effects of policy changes on retirement behavior in the limited
number of out-of-sample predictive tests that have been performed to date. For
example, Lumsdaine, Stock, and Wise (1990) estimated a dynamic structural
model of retirement decisions at a Fortune 500 firm by using data prior to the
introduction of a temporary window plan that created substantial incentives for
workers to leave the firm. The model did a reasonably good job of predicting the
large increase in retirement rates for most people of the relevant ages after the
introduction of the window plan, whereas a variety of reduced-form models
performed poorly in this regard.
Questions about future directions for labor supply and pension acceptance
research include in what ways to pursue the use of complex structural models.
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KEY RESEARCH ISSUES
51
Important issues to be addressed are whether such models should be static or
dynamic, what kinds of uncertainties need to be modeled, and how comprehen-
sive the overall model needs to be.
Another important question is whether structural models should continue to
assume completely optimizing behavior (as defined by economic theory) on the
part of individual workers. Such an assumption can be modified (e.g., by assum-
ing that people use various rules of thumb, such as work until age 65 and then
retire); however, doing so is likely to make the modeling task even more difficult.
Moreover, it is not clear how to specify the ways in which people who are using
rules of thumb or other psychologically influenced decision rules will respond to
policy changes.6
Important aspects of retirement behavior that are not addressed in current
analytical models include workers' decisions to retire or apply for disability
benefits and joint retirement decisions of spouses. Another area for work is to
integrate models of retirement and savings behavior. The usual approach is to
ignore or drastically simplify the nature of consumption and savings choices as
they relate to retirement behavior (e.g., assuming that consumption equals in-
come). There is some theoretical and empirical justification for this approach
(except for the very wealthy), and the practical difficulties of doing otherwise are
formidable. Nonetheless, it may be increasingly important to tackle this problem
in order to answer questions about future trade-offs among savings, consumption,
and work: for example, the extent to which people will save more, consume less,
or work longer if Social Security, employer pensions, or health care benefits
become less generous or more costly.
Finally, it is important to determine the applicability of complex structural
models for policy use. At present, it may not be feasible to incorporate a full-
blown behavioral dynamic programming model of retirement into a microsimu-
lation projection model for estimating the likely effects of alternative Social
Security and employer pension policies on retirement income security. The
question then becomes what kinds of simplifications are necessary in order to
have a practical and usable projection model.
The ability to refine and extend behavioral models of labor supply and retire-
ment depends on the continued availability of rich panel data sets. Data have
been much more plentiful on these topics than on consumption and savings or
employer behavior. Also, the new HRS promises to fill important data gaps in
earlier retirement surveys. But HRS and related surveys must be continued and
enhanced to permit the development of more robust estimates of behavioral pa-
rameters for use in projecting workers' labor supply responses to policy changes.
6Questions may be added to HRS and AHEAD to learn about this issue by asking people directly
how they make decisions and what information they use.
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52
ASSESSING POLICIES FOR RETIREMENT INCOME
DEMOGRAPHIC VARIABLES
Size and Composition of the Population
Projecting the likely costs and effects of current and alternative retirement-in-
come-related policies requires estimating the number of people alive over the
projection period and their distribution by age and other characteristics. In turn,
to generate population projections requires estimating births, net immigration,
and deaths.
For many retirement-income-related analyses, it is not necessary to estimate
future fertility levels because the concern is with the number of retirees (for
which projections can extend as long as 50-60 years on the basis of the population
alive at year 1) or with the numbers of retirees and workers (for which projections
can extend as long as 20 years on that basis). Net immigration is important, but
it is heavily influenced by legislation, which means that immigration can be
included in projection models as a parameter that can readily be given different
values to reflect expected policies.7
Estimates of future mortality levels, on the other hand, are important deter-
minants of Social Security trust fund balances in both the short and long term.
They also affect the viability of employer pension plans. Simulations by the
Social Security actuary show 75-year trust fund balance projections to be more
sensitive to assumptions about the future course of mortality than to any other
demographic or economic variable in the actuary's cost model (see Board of
Trustees [OASDIi, 1994:131-132~. However, the projections do not allow for
extreme assumptions, such as a return to the high birth rates of the 1950s, in
which case mortality might not be the driving variable.
Reasonably good data are available on mortality rates by age and sex. The
problem is what assumptions to apply to historical data to project mortality rates
into the future and how to estimate the uncertainty in the projections, particularly
over the long term. The Social Security Administration (SSA) essentially devel-
ops mortality projections by extrapolating rates of decline in age-specific death
rates for specific causes of death over the previous 20 years. The results, which
are inherent in the methodology, imply a sharp slowing of the rates of decline of
mortality at all ages, relative both to the previous two decades and to longer run
historical trends back to 1900. Lee and Carter (1992), in contrast, project the
rates of decline in age-specific mortality rates observed over the twentieth cen-
tury (not disaggregated by cause of death), which have been fairly steady despite
periods of faster and slower progress. Hence, the Lee and Carter projections (and
those developed by other researchers) imply a larger retirement-age population
than do the SSA projections (or those developed by the Census Bureau).
7The task is somewhat more complicated than indicated in that net immigration must be distrib-
uted into immigration and emigration by such characteristics as age and sex.
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KEY RESEARCH ISSUES
53
A major advantage of the Lee and Carter approach is that it provides prob-
ability intervals describing the uncertainty in their extrapolative method. It does
not, however, allow for the possibility of "structural breaks," such as the sharp
decline in age-specific mortality rates that occurred between the nineteenth and
twentieth centuries, and thus it probably underestimates the extent of uncertainty.
Nonetheless, the stochastic models used by Lee and Carter and others to estimate
the uncertainty in population forecasts represent a major step forward over the
approach that is used by SSA and the Census Bureau to convey estimates of
uncertainty to policy makers. In the SSA and Census Bureau approach, "high"
and "low" scenarios are developed to bound the "intermediate" or expected fore-
cast.
For many policy purposes, it would be highly useful to carry out research
that could support projections of mortality rates for other characteristics on which
mortality is known to vary, in addition to age and sex. In particular, in order to
answer distributional questions, such as the retirement income security of wid-
ows relative to married couples or of low versus high earners, it is important to
have mortality projections by such characteristics as marital status, income level
and other indicators of socioeconomic status, and health or disability status.
However, little work has been done to develop such projections.
Recent studies show that social class differentials in mortality (measured by
educational levels) have widened sharply for men at all adult ages since 1960,
somewhat less so for women (Preston and Elo, 1995~. Data from Social Security
administrative records could provide the basis for an authoritative study of mor-
tality variation over time by earnings history for people of retirement age. It
would also be valuable to use Social Security data to study mortality variation by
marital status. However, at present, marital status is recorded only for people
who are receiving benefits as a spouse or widow or widower; it is not recorded for
people who are receiving benefits on the basis of their own earnings, who may be
married or unmarried. SSA is currently appending information on Social Secu-
rity benefits and mortality to several panels of the Survey of Income and Program
Participation. These files could provide the basis for a study of the relationship of
mortality to income and marital status for a limited sampled
Family History
Marital history can have important effects on the income and wealth of the
elderly, particularly for women. Research has documented significant drops in
economic well-being for women after the divorce or death of a spouse (see, e.g.,
Holden, 1991), and women are more at risk of being widowed and of not remar-
rying after either widowhood or divorce. The result, cross-sectionally, is a highly
8Another source of data on mortality differentials by marital status is the National Longitudinal
Mortality Study, which Preston and Elo (1995) used. It has income data but only for the last year.
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54
ASSESSING POLICIES FOR RETIREMENT INCOME
skewed distribution of income and wealth by marital status and sex among eld-
erly people aged 65 and over. In 1995, the poverty rate for single elderly women
was 5.6 times the rate for married elderly women; the corresponding ratio for
elderly men was 3.0. At the same time, elderly women were more than twice as
likely as elderly men to live without a spouse or other family member (Bureau of
the Census, 1996:Table 2~. Similar patterns are evident for wealth (see Gustman
and Juster, 1996:Tables 2-A4, 2-A5~. There are also differences in income and
wealth by age: older subgroups of the elderly population are poorer and less
wealthy than younger subgroups. It has not been determined how much these
patterns reflect dissaving and outliving sources of income by people as they age
and how much they reflect cohort differences in initial income and wealth levels.
Considerable research has been conducted on models of first marriage and
divorce. Less work has been done on remarriage, and relatively little work has
been done that is directly relevant for retirement income security policy analysis
(see Caldwell, 1993, for a review of the literature; see also McLanahan and
Casper, 1995~. Needed work includes the development of models of marital
behavior and projections of trends that explicitly account for the risks of mar-
riage, divorce, widowhood, and remarriage for people as they approach retire-
ment age and beyond. Such models and projections should take account of trends
in mortality differences by sex and marital status. Work is also needed to draw
out the economic consequences of marital histories for post-retirement income
and wealth levels. Such work should take account of women's increased labor
force participation, which may result in their accumulating higher levels of pen-
sion and other wealth than in the past, and of trends in the form of retirement
benefits (e.g., lump sums versus annuities), which may adversely affect the retire-
ment income security of surviving spouses in particular. Data on former spouses'
rights to pension and other benefits are also important to include in analyses that
link marital histories and retirement income security.
Another way in which people's family histories affect retirement income
security is through the effects on kinship networks. An important policy concern
is whether the baby boom generation, which exhibited higher ages at first mar-
riage and lower fertility rates than the previous generation, will be supported by
as many kin (own children and other relatives). Work on the availability of kin to
provide financial support and care-giving for older people has been hampered
until recently by the design of household surveys, which historically have not
asked about adult children or other relatives not living in the household (see
Wolf, 1994, for a review of the literature in this field). Newer surveys, such as
HRS, AHEAD, and the National Survey of Families and Households, have at-
tempted to remedy this lack with detailed questions about kin networks and
intrafamilial transfers.
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KEY RESEARCH ISSUES
55
Health Status
An important factor to include in retirement-income-related policy projections is
the health and disability status of workers and retirees and the likely trends over
time. A decrease in mortality for older people has different implications for
retirement income security if they suffer from disease or disability or if their
additional years of life are active and healthy, with few expensive medical care
needs and the opportunity to continue to earn income. Similarly, an increase or
decrease in disability levels for workers as they approach retirement has implica-
tions for the extent to which they retire early or, when this is not possible, apply
for benefits from public and private disability programs. Generally, income and
wealth differ markedly by health and disability status. While the direction of
causality is not established, it is clearly important to have indicators of health and
disability status to understand the distributional consequences for retirement in-
come security of many kinds of proposed policy changes.
Unfortunately, the identification of health and disability status, whether cross-
sectionally or longitudinally, is beset with measurement problems and ambigu-
ities of classification. Lee and Skinner (1996), in their review commissioned by
the panel, consider the evidence on trends in disability status defined in several
different ways: objective health measures, incidence of specific diseases, mea-
sures of functional ability, and self-reports. They find a mixed picture, although
there appears to be a long-term trend toward lower overall disability levels among
the elderly. Self-reported health assessments, however, show marked short-term
fluctuations, which may be influenced by such factors as improvements in medi-
cal diagnosis. Also, there is a close match between increases and decreases in
self-reported disability and changes in disability insurance programs, including
changes in the intensity of administrative efforts to ascertain eligibility and to
follow up cases once enrolled, as well as changes in eligibility requirements.
Panel data with improved measures of health and disability status are needed
to model relationships with key behaviors, including decisions about savings,
consumption, and retirement. (The HRS and the AHEAD survey are designed to
serve this purpose.) Panel data are also needed to determine trends in disability
levels over time and whether, as some researchers hypothesize, morbidity will be
"compressed" into the last years of life. Such a compression could lead to
significant reductions in the medical care needs and costs of the elderly, although
this effect could be offset by such factors as an increase in the proportion of the
"oldest old" in the population or a decrease in the availability of family care-
g~vers.
For the purpose of establishing trends, panels need to follow large samples of
people for long periods of time; large samples are required because relatively few
people are disabled. Also, panels of new cohorts must be initiated periodically.
Recent studies that find significant declines in the extent of disability among the
elderly (e.g., Manton, Stallard, and Liu, 1993, who use a measure of functional
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56
ASSESSING POLICIES FOR RETIREMENT INCOME
status) are based on data spanning only 7 to 10 years. More years of data are
required to confirm these results.
HEALTH CARE COSTS
No assessment of retirement income security is complete without consideration
of likely trends in the magnitude and distribution of health care costs. To the
extent that older Americans face rising health care costs that they must finance
through some combination of higher health insurance premiums, taxes, and direct
out-of-pocket outlays, then a retirement income stream that would have been
adequate in the past to cover other needed consumption in addition to medical
care may no longer be adequate.
Projections of likely future trends in aggregate medical care costs and in the
availability of medical care benefits are subject to extreme uncertainty, given the
large number of actors whose behavior must be modeled federal and state agen-
cies, private health insurers, employers, medical care providers, medical care
technology developers, and medical care consumers and the complexities of the
interactions among them. Economic incentives clearly play a role in medical care
consumption. However, there is an argument that in the United States the devel-
opment of new technologies and treatments coupled with a strong disinclination
on the part of providers and consumers to forgo their use, once introduced, is a
driving force for medical care cost increases. The shift to managed care has
reduced the rise in costs, but it is not clear that this trend will continue once
excess capacity is wrung out of the system (see Moon, 1995~.
In short, determining the relative importance of various factors that influence
medical care costs and benefit packages and the role of public or private sector
policy changes in changing relevant behaviors presents an almost overwhelming
research challenge. Research and models that are focused on retirement income
security cannot hope to resolve these issues. What seems most fruitful for retire-
ment-income-related research and modeling to address is the likely distributional
consequences of alternative medical care cost and insurance coverage scenarios
for the retirement income security of groups of the elderly population.
For this purpose, it is important to develop good estimates of the relationship
of health and disability status, insurance coverage, and other individual-level
variables (e.g., age, gender, ethnicity, employment status, income level) to medi-
cal care costs in a relative sense: that is proportionally how much more is spent
on medical care in total and out-of-pocket by people in worse health than by
people in better health. The National Medical Expenditure Survey (NMES) is an
important data source for this purpose. However, it was last conducted in 1987
(see below), and key relationships may have changed since then as a consequence
of major changes that have occurred in health care financing.
Panel data are also needed to determine differences in medical care spending
patterns across the years of retirement, taking account of both acute and long
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KEY RESEARCH ISSUES
57
term care costs. The few available studies suggest that spending patterns are
correlated across time (the small group of people who are high spenders in one
year are high spenders in subsequent years), but that there is a considerable
dropoff in the concentration of spending over a long period. There are plans to
conduct NMES on a continuing basis, beginning in 1996 and renamed the Na-
tional Medical Expenditure Panel Survey (MEPS), but individual sample mem-
bers will only be followed for a 2-year period. The HRS and AHEAD surveys,
when linked with Medicare and Medicaid records, may develop a capability to
provide needed longitudinal data on the distribution of medical care costs across
retirement.
CONCLUSION
This discussion has touched on an array of important topics for understanding
and projecting retirement income security and has identified many gaps in basic
research knowledge. In some areas, such as employer benefit plan decisions,
employer demand for older workers, and savings and consumption choices of
individuals, there is no agreement on the underlying behavioral phenomena. The
primary reason for the lack of agreement is lack of data: key data elements are
missing or grossly inadequate in one or more respects for either cross-sectional or
longitudinal analysis. In other areas, such as labor supply and retirement deci-
sions, better data have been available and more is known. However, there is still
disagreement about the strength of key relationships (e.g., to what extent Social
Security or Medicare influences age at retirement), and there are still areas that
are not fully explored (e.g., the retirement behavior of women and joint decisions
of couples). New panel surveys, such as HRS and AHEAD, promise a rich set of
information with which to refine labor supply models and also to unlock some of
the puzzles in savings and consumption behavior. However, there has been little
opportunity as yet to mine these surveys and determine their power or to identify
enhancements that may be needed. In still other areas, such as mortality projec-
tions, the need is to develop more sophisticated projection models that exploit
existing data and to develop methods for estimating uncertainty in the projec-
tions, which typically extrapolate past trends for long periods into the future.
We believe that little progress can be made in the development of improved
projection modeling tools with which to estimate the likely effects of proposed
changes in retirement-income-related government policies until improved ana-
lytical models are developed and key knowledge gaps are filled. Filling these
gaps requires priority attention to the underlying data needs, the topic of Chapter
4. It also requires systematic research. We end this chapter with a list of priority
topics for policy-relevant basic research that should move forward as new and
improved data become available; see Box 3-1. Research in many of these areas
should be extended to include the experience of other countries with policy
initiatives that may be considered in the United States.
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58
ASSESSING POLICIES FOR RETIREMENT INCOME
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OCR for page 59
KEY RESEARCH ISSUES
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~ se-q- Eences TOE economic weEI-Deln E~ ane-~ rel-l-' -me-' l~
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OCR for page 60
60
ASSESSING POLICIES FOR RETIREMENT INCOME
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-^rr~r~r~r~r~C~1~^ 4~mC~^C~rr~r~TC~ ~ ~^rr~r~r~^r~TC~ m~ Ir~ :mr~ ~At~ITr~
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Representative terms from entire chapter:
income security
