Determinants of planned retirement age
ABSTRACT The tradeoff between risk and return in equity markets is well established. This paper examines the existence of the same tradeoff in the single-family housing market. For home buyers, who constitute about two-thirds of U.S. households, the choice about how much housing and which house to buy is a joint consumption/investment decision. Does this consumption/investment link negate the risk/return tradeoff within the single-family hosuing market? Theory suggests the link still holds. This paper supplies empirical evidence in support of that theoretical result.
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ABSTRACT: This study develops a framework that can be used to examine the retirement savings decision. Using Retirement Confidence Survey data (N = 751), this analysis determined that respondents with higher education levels, higher income, a smaller household size, and favorable financial attitudes tend to currently have a retirement savings program in place. Those who are exposed to workplace financial education are more likely to have a retirement savings program and having a retirement savings program related positively to retirement confidence.
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ABSTRACT: We examine a sample of retirees who either remain fully retired or return to work in some capacity. Surprisingly, we find that those who rejoin the labor force are no less financially prepared for retirement than are their counterparts who remain retired. Instead, the factors that significantly influence the decision to return to work are the availability of health insurance, whether the initial decision to retire is freely chosen, and the degree of satisfaction with retirement. However, the factors that significantly affect the decision to return to work differ between those who wanted to retire and those who were forced into retirement.
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ABSTRACT: The relationship between meeting the Capital Accumulation Ratio Guideline and retirement adequacy was investigated. About 63% of the households had a consistent relationship between meeting the 25% ratio guideline and being adequately prepared for retirement, with 46% of households both meeting the 25% ratio guideline and being prepared for retirement and 17% not meeting the guideline and not being adequately prepared for retirement. However, 37% of households did not have a consistent relationship. Meeting the 25% ratio guideline does not appear to be an accurate indicator of retirement adequacy. The 25% guideline was a better indicator than the 50% guideline.
N0RrH'H0[1-«AND Financial Services Review 9 (2000) I-15
Determinants of planned retirement age
Catherine Phillips Montalto""*, Yoonkyung Yuhb, Sherman Hannac
“Assistant Professor Consumer and Textile Sciences Department, The Ohio State University,
1787 Neil Avenue,
Columbus, OH 43210-1295, USA
bLecturer in Consumer Sciences, 203-Dong 1403-Ho, Hyundai Apartment,
Dowha—dong, Mupo—ku, Seoul Korea
°Professor, Consumer and Textile Sciences Department, The Ohio State University, 1787 Neil Avenue,
Columbus, 01143210-1295, USA
Determinants of planned retirement age are analyzed. The prediction equation indicates that
planned retirement age increases substantially as people get older, and increases somewhat with
higher noninvestment income. Social Security reform should recognize that the capacity to
continue working and the ability to afford to retire both inﬂuence the age at which people plan to
retire. The range of planned retirement ages suggests that research on the adequacy of retirement
preparation should focus on planned retirement age. Financial planners should consider the ﬁnding
that planned retirement age increases with age. © 2000 Elsevier Science Inc. All rights reserved.
./EL classn‘ication.' D12; J26
Keywords: Planned retirement age; Retirement adequacy
The ﬁnancial viability of the Social Security program in the United States is being debated
in response to the aging of the population. The percentage of the U.S. population made up
of persons 65 years of age and over is 15% today and is projected to increase to 20% over
the next 30 years (U.S. Bureau of the Census, 1998). The demographic pressures of
* Corresponding author. Tel.: +1-614-292-4571; fax: +l-614-688-8133.
E-mail address: firstname.lastname@example.org (C.P. Montalto).
1057-0810/00/$ — see front matter © 2000 Elsevier Science Inc. All rights reserved.
2 C.P. Montalto et al. /Financial Services Review 9 (2000) I—I5
population aging will require forward—looking action from policy makers to preserve the
ﬁnancial viability of the Social Security program. One proposed change is to raise the age of
eligibility for full retirement beneﬁts further or more rapidly than the currently planned
gradual increase from 65 to 67 over the next 25 years. Proposals have also been made to
increase the early retirement age from age 62 to age 65 (Mitchell & Quinn, 1995).
The rationale for increasing retirement age is to reduce the long—term deﬁcit in the Social
Security Trust Fund. Increasing retirement age would increase the number of years a worker
spends in the workforce thereby increasing the amount the worker contributes to the Trust
Fund. Additionally, increasing the retirement age would decrease the number of years a
retiree spends in retirement thereby reducing the beneﬁts drawn out. The primary justiﬁca—
tion given for increasing the retirement age is the longer life expectancy and improved health
of the nation’s elderly.
Raising the retirement age may improve the ﬁnancial solvency of the Social Security
System, but it will also affect the economic well-being of individuals. Implications for
individual well-being depend on the importance of Social Security income, the impact of
delayed receipt of Social Security income. the ability to continue working to the age of
eligibility, and individual preferences related to retirement age. Additionally, raising the age
of eligibility for Social Security beneﬁts has possible spill-over effects to other government
programs, such as Supplemental Security Income and Disability Insurance (Bovbjerg, 1998).
Previous research documents that a worker’s decision to retire is inﬂuenced by rules
governing pensions and Social Security beneﬁts, wealth, characteristics of jobs held by
elderly workers, health insurance coverage, and social norms (Fields & Mitchell, 1984; Hurd,
1997). During this century, social norms and enacted legislation have resulted in retirement
at earlier ages. Labor force participation rates of older males declined throughout most of this
century, and then stabilized in the mid—l980s. Today, older men (55 to 64 years of age)
frequently leave full—time career jobs, but continue working part—time or part—year rather than
completely withdrawing from the labor force (Employee Beneﬁt Research Institute, 1999).
Although age 65 is currently the age of eligibility for full retirement beneﬁts under the Social
Security program, everyone does not plan to retire at age 65. The range of planned retirement
ages is quite large. Five percent of today’s workers plan to retire before age 55, while 22
percent plan to stay in the labor force until at least age 66 (Retirement Conﬁdence Survey,
Despite evidence of variation in the age at which individuals plan to retire, some recent
studies (Mitchell & Moore, 1997; Bernheim, 1996) evaluating retirement wealth adequacy of
preretirees assume age 65 as the retirement age. The standardized assumption of retirement
at age 65 without allowing for individual differences can result in signiﬁcant overestimation
or underestimation of the adequacy of retirement wealth. Clearly, assumptions made about
planned retirement age are critical in determining whether people have saved ‘enough’ for
retirement. Yuh, Montalto, and Hanna (1998) and Yuh, Hanna, and Montalto (1998) ﬁnd that
planned retirement age has a substantial impact on the estimated adequacy of preparation for
retirement. Even though about 75% of workers elect to retire before age 65, there are
proposals to increase the minimum age to receive any Social Security retirement pension
(Apfel, 1998). Therefore, it is worthwhile to study factors related to planned retirement age
to see which types of workers would be impacted most by increases in the minimum age for
__”M __,__ ,, Copyright © 2QOQ_. All ri hts reserved. ___
C.P. Morztalta et at. /Financial Services Review 9 (2000) 1-15 3
receiving Social Security beneﬁts. Additionally, planned retirement age is an important
variable in developing rational savings plans for retirement, so improving understanding of
planned retirement age has implications for ﬁnancial planning.
This research investigates the determinants of planned retirement age. Few studies have
addressed this issue directly. Although previous studies on retirement behavior have ana-
lyzed the observed age of retirement among retirees ex-post, little research has focused on the
planned retirement age of preretired workers ex—ante. Understanding the determinants of the
age that current workers plan to retire is important because the planned retirement age of
preretirees is a crucial factor affecting saving and investment decisions during the working
years. Additionally, proposed increases in the minimum age for receiving Social Security
beneﬁts will have the most impact on workers that plan to retire before the minimum age.
The paper is organized as follows. Section 2 provides a review of relevant literature and
presents the conceptual framework underlying our estimation of determinants of planned
retirement age. The methodology is presented in Section 3. and the results and discussion are
provided in Section 4. The summary and policy implications are presented in Section 5.
2. Literature review
There have been many studies on retirement issues since the 1970s. Typically, actual
retirement has been treated as a choice variable in the literature, and various economic factors
have been shown to play an important role in the retirement decision.
2.]. Related empirical research
Boskin (1977) tries to explain the long—term decline in the labor—force participation of all
male age—groups. Using data from the Panel Study of Income Dynamics for 1968 through
1972, he ﬁnds that the value of current annual Social Security retirement beneﬁts has a
pronounced effect on the decision to retire. The level of net earnings has a strong negative
effect on the probability of retirement. Quinn (1977) examines the microeconomic determi-
nants of early retirement among white married men aged 58-63 using the 1969 Retirement
History Study. The relative impact of three sets of factors in explaining older men’s
labor—force participation decisions are investigated: personal and ﬁnancial characteristics,
local labor market conditions, and certain attributes of the individual‘s job. Quinn ﬁnds that
health status and current eligibility for Social Security and other pensions are the most
important determinants of retirement, and that there is a deﬁnite interaction between the
two—persons in poor health are more likely to retire in response to ﬁnancial incentives from
Social Security and other private pensions.
Kotlikoff ( 1979) estimates a model for expected age of retirement using data from the
National Longitudinal Survey (NLS) of Older Men. Private pension coverage is an important
predictor of expected retirement age. Coverage under a private pension plan is associated
with expected retirement 1.2 years earlier; for government pension coverage the impact is 1.8
reserved" ’ ""‘” “"““""“"
4 C.P. Montalto et al. /Financial Services Review 9 (2000) 1-15
years. Age has a positive and signiﬁcant effect, and the health and employment attitudinal
variables all have the anticipated negative effects.
Diamond and Hausman (1984) examine factors that affect the actual retirement decision
using the National Longitudinal Survey of Older Men. The presence of pensions and Social
Security beneﬁts, the level of permanent income, and poor health have strong, positive
effects on the probability of retirement. They argue that planned retirement dates change over
time. In fact, while planned retirement age has some predictive power for actual retirement
age, much unexplained variance remains. Honig (1996) uses data from the ﬁrst wave of the
Health and Retirement Survey and ﬁnds evidence that expected and observed retirement
functions are similar. Honig suggests that retirement expectations may accurately forecast
Burtless and Mofﬁtt (1985) develop and estimate a model of the joint choice of retirement
age and post retirement hours of work by the aged population using data from the Longi-
tudinal Retirement History Survey (LRHS). They ﬁnd that Social Security inﬂuences both
retirement age and choice of post retirement hours of work, but the magnitude of the effect
on the age of retirement is small. They also ﬁnd that an earlier retirement age is related to
poor health, lower levels of education, and higher pre retirement wage rates. Burtless (1986)
develops a retirement age model and estimates the model using the Longitudinal Retirement
History Survey. Poor health, being married, household size, and wealth in excess of $25,000
all reduced the age of retirement.
Samwick (1998) investigates the incentive effects of Social Security and pension beneﬁts
on retirement using data from the 1983 Survey of Consumer Finances and the corresponding
Pension Provider Survey. The results suggest that the retirement decision is much more
sensitive to changes in retirement wealth than to the level of retirement wealth. Further,
changes in retirement wealth are primarily determined by pensions, and not Social Security.
Samwick ﬁnds small effects of Social Security on retirement, and much more substantial
effects of pensions.
Uccello (1998) examines the relative importance of health status, income, employment
characteristics, and demographic characteristics in the decision to retire using data from the
1990 Survey of Income and Program Participation and the 1994 wave of the Health and
Retirement Survey. Simulations reveal that health insurance coverage solely through
one’s employer and presence of a working spouse have the largest negative effects on the
expected level of retirement. Pension coverage, employment in a physically demanding
occupation, and being nonwhite have the largest positive impact on the expected level of
The previous research focuses primarily on ex—p0st analyses of the observed retirement
age of retirees using a work—leisure model or a life cycle labor supply model. Typically, data
on actual retirement behavior is used to estimate the probability of being retired as a function
of Social Security and pension beneﬁts, and other demographic characteristics. The results
consistently conﬁrm that higher earning power and good health reduce the probability of
retirement, while eligibility for and higher levels of retirement beneﬁts, and higher ﬁnancial
wealth increase the probability of retirement. Previous research has not analyzed factors
affecting the age that currently employed workers plan to retire.
C.P. Montalto et al. /Financial Services Review 9 (2000) 1-15 5
2.2. Conceptual model
A currently employed individual choosing a planned retirement age must consider
whether resources will be adequate, whether working will be possible, and also his or her
individual preferences for leisure. The ability to afford to retire is inﬂuenced by the
individual’s accumulated ﬁnancial resources as well as the earned retirement beneﬁts. The
ability to continue to work is inﬂuenced by individual productivity and health, as well as
characteristics of jobs. Preferences for leisure may be inﬂuenced by social norms, but also
vary across individuals at a given point in time. The data set used in the empirical analysis
allows examination of several of these factors.
A deﬁnition of retirement is required before the determinants of planned retirement age
can be analyzed. However, there is no consensus in the literature on the deﬁnition of
retirement (Gustman, Mitchell & Steinmeier, 1995). Various deﬁnitions of retirement have
been used by economists and other social scientists, including: self-reported retirement;
termination of work or looking for work; termination of full—time work: working less than a
given number of hours; leaving the main employer (a long—term job); and receipt of an
employer—provided pension or Social Security beneﬁts. This study deﬁnes retirement as
occurring when an individual stops working full—time which is the deﬁnition most commonly
used in empirical studies (Sickles & Taubman. 1986; Diamond & Hausman, 1984).
3 . I . Data
Data for this study are drawn from the public use tape of the 1995 Survey of Consumer
Finances (Kennickell, Starr—McCluer & Sundén, 1997). The Survey of Consumer Finances
(SCF) is a triennial survey sponsored by the Federal Reserve with the cooperation of the
Department of the Treasury. The purpose of the SCF is to provide comprehensive and
detailed information on the ﬁnancial characteristics of U.S. households. A total of 4,299
families were interviewed in the 1995 SCF survey. The 1995 SCF has ﬁve complete data sets
called “implicates” as a result of multiple imputation to handle missing data. This study uses
repeated—imputation inference (RII) techniques to combine the ﬁve different data sets to
make valid inferences (Rubin, 1987; Montalto & Sung, 1996).
The Survey of Consumer Finances was chosen for this study because it provides infor-
mation on a broad age—range of the U.S. population. and it speciﬁcally asks currently
employed respondents to provide their planned retirement age. To analyze determinants of
planned retirement age, heads of household age 35 to 70 years who were currently working
full—time were selected, resulting in a sample of 1,607 individuals. Fig. 1 shows the
cumulative distribution of the planned retirement age. About 17% of the sample planned to
retire by age 55, 35% planned to retire before age 62, and 51% planned to retire by age 62.
Almost all respondents (89%) planned to retire by age 65, and 91% planned to retire by age
Copyright © 20 .
6 C.P. Mantalta et (11. /Financial Services Review 9 (2000) 1-15
40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80
Planned Retirement Age
Fig. 1. Cumulative distribution of planned retirement age.
3.2. Sample selection
Planned retirement age in this study is deﬁned as the age at which the individual plans to
stop working full—time. Thus, the variable of interest is only observed for those individuals
currently working 35 hours per week or more. If current hours of work and planned
retirement age are correlated, then analysis of planned retirement age using only the sample
of individuals currently working full—time will produce inconsistent estimates of the param-
eters of the planned retirement age equation. A positive correlation between current hours of
work and planned retirement age is plausible, since a “taste” for work would likely result in
more hours of work and a later age of planned retirement. If this “taste” for work is not
controlled in the planned retirement age equation, a speciﬁcation error is committed by
omitting a relevant variable. This type of speciﬁcation error is commonly referred to as
selection bias. In other words, an estimation of the effect of age on planned retirement age
may be biased because older workers with a preference for earlier retirement are selected out
of the sample of individuals currently working full—time.
Heckman’s (1979) two—step estimation procedure is used to estimate a planned retirement
age equation that includes a variable to correct for potential selection bias. In the ﬁrst step,
probit analysis is used to estimate the probability of working full—time for the full sample of
heads of household age 35 to 70 years. The probit results are then used to calculate the
selection bias correction variable (also referred to as the inverse Mills ratio) for each
observation. In the second step, the determinants of planned retirement age are estimated by
ordinary least squares on the sub sample of heads of household age 35 to 70 years who were
currently working full—time. The selection bias correction variable is used as an independent
C.P. Montalm er al. /Financial Services Review 9 (2000) l«]5 7
variable in this equation, thereby producing consistent estimates of the parameters of the
planned retirement age equation.
3.3. Probability of working full-time equation
The probability of currently working full—time is estimated with a probit regression on the
sample of heads of household age 35 to 70 years (N = 2,731). The dependent variable is a
dichotomous variable equal to one if the respondent is currently working full—time, zero
otherwise. Independent variables include variables capturing potential barriers to full—time
employment as well as the standard human capital variables. The probit estimating equation
contains eighteen independent variables and can be represented as
FULL—TIME = [30 + [31 Black non—Hispanic + B2 Hispanic + [33 Other
+ 34 Unmarried male, living alone + [35 Unmarried female, living alone
+ [36 Unmarried male, living with others + [37 Unmarried female, living with others
+ [38 Poor health + B9 Children under 6 years + B”, Children 6 to 17 years
+ B” High school + B,2 Some college + B” College grad + [314 Age
+ B,5 Age over 45 + [316 Age over 55 + B” Age over 65 + ,3”, Eixperience (1)
Potential barriers to full—time employment are measured with categorical dichotomous
variables for race/ethnicity and marital status/living arrangement, and dichotomous variables
for self—reported poor health of the respondent, presence of children under age 6 in the
household, and presence of children 6 to 17 years of age in the household. Human capital is
measured with categorical dichotomous variables for education, a spline variable for respon-
dent’s age (Suits, Mason & Chan, 1978), and a continuous variable measuring the respon-
dent’s previous years of full—time work experience. Means for the continuous variables and
percentages for the dichotomous variables are presented in Table 1 .
The probit equation is estimated on the combined data from the ﬁve implicates of the
Survey of Consumer Finances, resulting in unbiased coefficient estimates. In order to correct
the standard errors for imputation error, the estimated covariance matrix of the estimated
coefﬁcients is needed. The Probit procedure in SAS does not generate this matrix when the
model includes dichotomous variables as dependent or independent variables (SAS Institute
Inc., 1990, p. 1338). As a result, the standard errors cannot be corrected for imputation error,
and the statistical signiﬁcance of the coefﬁcient estimates may be overestimated. However,
since the purpose of the probit equation is to generate the selection bias correction variable,
the criteria of unbiased coefﬁcient estimates is relatively more important, and the signiﬁ-
cance of relationships of lesser importance in this application.
3.4. Planned retirement age equation
The determinants of planned retirement age are estimated by ordinary least squares on the
sub—sample of heads of household age 35 to 70 years who were currently working full—time.
Repeated-imputation inference (RII) techniques are used to combine data from all ﬁve
implicates of the Survey of Consumer Finances to generate the coefﬁcient estimates of the
_._.-. A .-,-i__.£2opyrJght©2000. All r
8 CP. Morzzalto et :11. /Financial Services Review 9 (2000) 1-15
Descriptive statistics and probit analysis of the probability of working full-time
Variable Mean Probit regressionz
percent] Estimate Std. error P-value‘?
Working full-time (dependent variable) 67.4%
Intercept 0.5026 0.6099 0.4099
Respondent’s race/ethnicity (reference category: White non—Hispanic)
Black non—Hispanic 12.6% ~0.2844 0.11 16 0.0l09*
Hispanic 5.7% ~0.l621 0.1540 0.2926
Other races 4.2% 0.1842 0.1566 0.2396
Marital status/living arrangement (reference category: Married or living with partner)
Unmarried male, living alone 7.3% -0.242] 0.1189 0.04l7*
Unmarried female, living alone 11.9% —0.2662 0.1207 0.0274*
Unmarried male, living with others 3.9% —0.3032 0.1782 0.0889
Unmarried female, living with others 13.1% —0.2832 0.1088 0.0092**
Respondent self reports poor health 6.3% ~1.40l6 0.1607 0.000l***
Presence of children < age 6 in the household 13.4% —0.0097 0.1081 0.9288
Presence of children 6 to 17 in the household 35.9% 0.2058 0.0823 0.0l24*
Respondent’s education (reference category: Less than high school graduate)
High school graduate 30.6% 0.2418 0.1042 0.0203*
Some college education 23.2% 0.2924 0.1091 0.0073**
College graduate or more 27.6% 0.5225 0.0992 0.0001***
Respondent age (spline variable)
Years of age 49.77 —0.0l08 0.0148 0.4646
Years of age over 45 6.87 -0.0515 0.0241 0.0328*
Years of age over 55 2.49 —0.1280 0.0236 0.0001***
Years of age over 65 0.31 0.1050 0.0452 0.0201*
Respondents previous years of full-time 25.6 0.0452 0.0041 0.0001 ***
' Descriptive statistics are weighted and estimated using RI] techniques.
2Probit analysis is unweighted and estimated on the pooled sample; standard errors are not corrected for
imputation error and the statistical significance of the coefficient estimates may be overestimated.
3*p < .05. **p < .01, ***p < .001.
Source: 1995 Survey of Consumer Finances, combined data set, N = 13,655 (2,731 in each implicate).
planned retirement age equation. Standard errors are corrected for imputation error to enable
valid tests of signiﬁcance of coefﬁcients.
The dependent variable is the planned retirement age of the respondent. The independent
variables are selected in accordance with the conceptual model where a currently employed
individual considers the adequacy of retirement resources, the feasibility of continued
employment, and individual preferences for leisure when selecting the age at which retire-
ment will occur. The independent variables include ﬁnancial variables and variables captur-
ing access to resources, characteristics of employment, and respondent demographic char-
acteristics and perceptions. The selection bias correction variable is included as an
independent variable to correct for potential selection bias. Means for the continuous
variables and percentages for dichotomous variables are presented in Table 2 . The ordinary
least squares regression equation contains twenty seven independent variables and can be
Co ri ht © 2000. All ri hts reserved. __A__M__»_m__m____ _ k W __
C.P. Montalto et al. /Financial Services Review 9 (2000) 1-15 9
Planned retirement age = a0 + oz, ln Noninvestment income + 012 ln Financial assets
(13 ln Nonﬁnancial assets + a4 ln Debt + a5 ln IRA/KEOGH
a6 ln Deﬁned contribution + a7 Deﬁned beneﬁt + as Employed spouse/partner
019 Household size + am Retirement saving goal + an Poor health
an Self—employed + 0113 Technical + 0:14 Service + (115 Precision/Repair
am Operators + 0117 Farming + (X18 Life expectancy + alg Age
0120 Age—squared + am Black non-Hispanic + (122 Hispanic + C223 Other
0424 High school + 0:25 Some college + 0426 College graduate
0:27 Selection bias correction variable (2)
The ﬁnancial variables include amounts of noninvestment income, ﬁnancial assets (ex-
cluding IRA/KEOGH and deﬁned contribution values), nonﬁnancial assets, deﬁned contri—
bution beneﬁts, IRA/KEOGH, and debt. These amounts are measured as the natural loga-
rithm (ln) to reduce heteroskedasticity (unequal variance of the disturbances). An indicator
variable is included for ownership of a deﬁned beneﬁt plan. Access to resources is measured
with an indicator variable for an employed spouse or partner, a continuous variable for
household size, and an indicator variable equal to one if retirement is one of the top three
household saving goals. Higher levels of ﬁnancial variables, lower levels of debt, and
increased access to resources through family members or saving behavior increase the ability
to “afford” to retire and are expected to decrease the planned age of retirement. Alternatively,
employment of a spouse or partner may suggest interdependent decision making regarding
the timing of retirement and may increase the planned retirement age. Larger household size
may also increase the level of resources needed in retirement, thus increasing the planned age
Characteristics of the respondent’s employment are measured with indicator variables for
self—reported poor health, and for self—employment of the respondent, and categorical di-
chotomous variables for respondent’s occupation. The occupation controls are rather crude
since the information in the data set only identiﬁes six broad categories of occupation. This
information is used in an attempt to control for differences across these occupation categories
in the characteristics of jobs held by elderly workers. Poor health may reduce the ability to
continue working thus lowering the planned retirement age. The effect of health problems on
the ability to work may also depend on the type of job one has, the opportunities for
accommodating health problems, and the opportunities to switch to less demanding jobs.
Some of this effect may be picked up by the occupation variables. Self-employment may
enable one to extend the working life at their own discretion, and is expected to be positively
associated with planned retirement age.
Respondent’s demographic characteristics are measured with linear and quadratic terms
for respondent’s current age, and categorical dichotomous variables for race/ethnicity, and
for the highest level of educational attainment. The respondent’s perception of life expect-
ancy is measured with a continuous variable. The availability of reduced Social Security
beneﬁts at age 62, and the increase in the beneﬁt level per year that receipt is deferred (up
to age 65) is actuarially fair for a person with average life expectancy. and better than fair
for someone with longer than average life expectancy. However, for persons whose life
expectancy is lower than the average, Social Security wealth decreases the longer they
10 C.P. Montalto et al. /Financial Services Review 9 (2000) 1-15
Descriptive statistics and ordinary least squares regression of planned retirement age
Variable Mean OLS regressionz
percent] Estimate Std. error P-va1ue3
Planned retirement age (dependent variable) 61.97
Intercept 69.3167 6.7182 0.0001 ***
Financial variables/access to resources
Log(non—investment income) 10.72 0.3885 0.1781 0.0309*
Log(ﬁnancial assets excluding IRA/KEOGH 8.70 —0.1797 0.0720 0.0127*
and deﬁned contribution)
Log(nonﬁnancial assets) 10.99 —0.l976 0.0833 0.0179*
Log(debt) 9 .24 0.0448 0.0415 0.2812
Log(IRA/KEOGH) 3.44 -0.0759 0.0357 0.0335*
Log(deﬁned contribution) 4.28 —0.0520 0.0311 0.0954
Deﬁned beneﬁt ownership 34.2% —0.7557 0.341 1 0.0268*
Employed spouse/partner 47.3% 0.3024 0.3162 0.3388
Household size 3.0 0.0769 0.1 168 0.5102
Retirement is a saving goal 34.1% —O.4l73 0.3179 0.1893
Characteristics of employment
Respondent self-reports poor health 1.0% 0.5543 1.9092 0.7716
Respondent is self employed 10.8% 0.3040 0.3985 0.4456
Respondent’s occupation (reference category: Managerial and professional specialty)
Technical, sales, administrative support 24.6% 0.4040 0.4192 0.3355
Service 9.0% — 1.4772 0.7020 0.0354*
Precision production. craft and repair 12.9% ~ 1.2691 0.6032 0.0355*
Operators, fabricators, and laborers 20.0% -0.033] 0.5526 0.9522
Farming, forestry, and ﬁshing 1.8% *2.0350 1.1511 0.0772
Respondent demographic characteristics and perceptions
Respondent’s life expectancy (years) 80.22 0.0511 0.0163 0.0024**
Age of respondent 46.09 —0.8195 0.2483 0.0010**
Age of respondent squared 2184.14 0.0116 0.0026 0.0001***
Respondent’s race/ethnicity (reference category: White non-Hispanic)
Black non-Hispanic 10.7% —2.4825 0.6583 0.0002***
Hispanic 5.1% —2.3700 0.8655 0.0066**
Other races 4.7% —0.9494 0.7092 0.1813
Respondent’s education (reference category: Less than high school graduate)
High school graduate 30.2% 0.7226 0.6793 0.2877
Some college education 25.6% 1.3220 0.7007 0.0593
College graduate or more 33.6% 1.7421 0.7366 0.0181*
Selection bias correction variable 0.28 0.6124 1.1247 0.5861
Model F—statistic = 16.0438 (p-value = 0.0001).
Adjusted R-square ranges from 0.2093 to 0.2194.
' Descriptive statistics are weighted and estimated using RII techniques.
2 Regression analysis is unweighted and estimated using RII techniques.
3*p < .05, **p < .01, ***p < .001.
Source: 1995 Survey of Consumer Finances (1,607) households in each implicate).
postpone beneﬁts beyond age 62, creating an incentive to begin taking beneﬁts at age 62
rather than later (Economic Report of the President, 1999). Lower life expectancy is thus
expected to decrease the planned retirement age, and therefore we expect a positive rela-
tionship between life expectancy and planned retirement age.
Copyright © 2000. All rights reserved.
C.P. Mantalto er al. /Financial Services Review 9 (2000) 1~15 ll
4. Results and discussion
4.1 . Probability of working full—time
The probit results for the probability of working full—time are consistent with a priori
expectations and previous research (Table 1). For a 40 year old, white, married respondent,
in good health, with no dependent children, a high school diploma, and 22 years of previous
full—time work experience, the probability of working full—time is 90%. The probability of
currently working full—time increases with education and work experience. For the reference
case, the probability of working full—time is only 86% for someone who has not completed
high school, and increases to 94% for a college graduate. Each additional year of previous
full—time work experience increases the probability of currently working full—time by 0.76
percentage points. For the reference case, 25 years of previous full—time work experience
increases the probability of currently working full—time to 92.5%. Age is inversely related to
the probability of currently working full—time within the sample of people 35 to 70 years old,
with the most noticeable declines occurring after age 50. For the reference case, the
probability of currently working full—time is 92% at age 35, 90% at age 40, 89% at age 45,
82% at age 50, then declines to 73% at age 55, 37% at age 60, 10% at age 65, and 4% at age
The probability of currently working full—time is lower for Black, non—Hispanic respon-
dents than for otherwise similar White, non—Hispanic respondents (85% vs. 90% given the
characteristics of the reference case). Compared to respondents who are married or living
with a partner, unmarried respondents living alone, and unmarried female respondents living
with at least one other person, are less likely to currently work full—time. Self reported poor
health of the respondent also reduces the probability of currently working full—time. For the
reference case, poor health reduces the probability of currently working full—time to only
4.2. Planned retirement age
The retirement age prediction equation explains approximately 21% of the variance, and
13 of the 27 variables are signiﬁcant at the 0.05 level or better (Table 2). The levels of
ﬁnancial assets (excluding IRA/KEOGH and deﬁned contribution values). nonﬁnancial
assets, and other private pension funds signiﬁcantly lower the planned retirement age. Levels
of ﬁnancial assets and nonﬁnancial assets lower the planned retirement age relatively more
than levels of IRA/Keogh accounts or deﬁned-contribution pension plans. Ownership of a
deﬁned—beneﬁt pension signiﬁcantly decreases the planned retirement age. Because the
dependent variable is planned retirement age, the coefﬁcients of dummy variables can be
interpreted as the effect on planned retirement age, holding all other variables constant. For
instance, all other things equal, those who have a deﬁned beneﬁt pension have a predicted
retirement age 0.76 years lower than otherwise similar households without a deﬁned beneﬁt
Planned retirement age does not vary much across the six broad occupation categories.
More detailed information on occupation may be necessary to accurately measure this effect.
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12 CP. Morztalto et at. /Financial Services Review 9 (2000) 1-15
03 O’) \l
03 on O
Planned Retirement Age
60 , r 4 1 l l T 9 4
35 40 45 50 55 60 65 70
Fig. 2. Predicted planned retirement age by current age.
Being employed in less—skilled occupations (service; precision production, craft and repair)
relative to managerial and professional specialty occupations decreases the planned retire-
ment age by less than 1.5 years. Being Black non—Hispanic, or Hispanic relative to White
non—Hispanic decreases the planned retirement age signiﬁcantly. The planned retirement
ages for Black non—Hispanic respondents and Hispanic respondents are 2.4 and 2.2 years
lower, respectively, than that of otherwise similar White non—Hispanic respondents.
Planned retirement age increases with noninvestment income. anticipated life expectancy
of the householder, the combined effect of linear and quadratic age variables, and education.
The selection bias correction variable is not statistically signiﬁcant. The effect of noninvest-
ment income, although signiﬁcant, is generally small. For instance, at the mean values of
other variables, the predicted effect of income increasing from $10,000 per year to $50,000
per year is a 0.63 year increase in planned retirement age; but an increase from $50,000 per
year to $100,000 per year is only a 0.27 year increase in planned retirement age. Planned
retirement age of respondents who have graduated from college is 1.7 years higher than that
of otherwise similar respondents who have not ﬁnished high school. Fig. 2 shows the effect
of current age on planned retirement age. At the mean value of other variables, the effect of
increasing current age from 35 to 45 is a 1.08 year increase in planned retirement age, while
an increase from 45 to 55 is a 3.41 year increase, and an increase from 55 to 65 is a 5.72 year
5. Summary and implications
The regression results suggest that ﬁnancial preparation for retirement, as well as demo-
graphic characteristics and perceptions, including current age and anticipated life expect-
Co ri ht©2000. All ri h»t_s.,Le§.e_.r.\./_e.cl.. .......... -.
C.P. Montalto et al. /Financial Services Review 9 (2000) 1-15 13
ancy, strongly affect planned retirement age. The results also suggest that adjustments to
planned retirement age take place over time. These adjustments may be in response to the
realization that accumulated resources are not adequate to meet needs in retirement, thus
causing workers to postpone retirement. An alternate explanation is that a generational
change may result in lower planned retirement ages for younger cohorts of workers since
norms regarding retirement age and ﬁnancial instruments used to save for retirement have
changed over time. These differences may produce systematic differences between younger
cohorts and older cohorts of workers in the age at which they plan to retire. Either
explanation has implications for proposed changes in government policy.
5.2. Implications for ﬁnancial planning
As Fig. 1 demonstrates, there is a wide range of planned retirement ages. The effect of
current age on planned retirement age (Fig. 2) suggests that some workers’ plans are not
achieved, or that there is a generational change in planned retirement ages. Financial planners
should try to assess the likelihood that a client’s planned retirement age can be achieved, both
in terms of the risks of loss of a high income job before the planned retirement age and the
chance that the client will have to work longer than planned. If there is a generational
reduction in planned retirement ages, ﬁnancial planning for retirement will become more
challenging and perhaps further increase the need for financial professionals to assist
5.3. Implications for public policy and future research
The ability of workers to adapt to further increases in Social Security retirement age
depends on their capacity to extend their working lives and to accumulate enough savings to
offset a delay or reduction in Social Security income. Alternatively, workers face retirement
with reduced income. Adequacy of retirement resources is inﬂuenced by family income and
wealth. Policies to increase pension coverage as well as private savings would help counter
the negative effects of a decrease in Social Security income. The ability to extend the
working life is inﬂuenced by health status as well as characteristics of the job. Some workers
will have difﬁculty extending their working lives. Important questions to address with
additional research include: Will employers be willing to retain and/or hire older workers‘?
What will happen to older men and women who are not healthy enough to work full—time or
who are unable to ﬁnd jobs? What will happen to older workers in physically demanding
The relationship between age and planned retirement age should be further explored with
different data sets, including the 1998 Survey of Consumer Finances, in order to test the
generational explanation of the positive relationship between age and planned retirement age.
There are implications for proposed changes in government policy whether workers adjust
their planned retirement age over time, or more recent cohorts of workers plan to retire at
younger ages than earlier cohorts.
This study focuses on ﬁnancial, employment and demographic characteristics as deter-
minants of planned retirement age. However, attitudinal and psychological factors might also
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14 C.P. Montalto et al. /Financial Services Review 9 (2000) 1-15
affect planned retirement age. In reality, individual responses to work and retirement
incentives often vary substantially even among persons who appear to have much in common
in terms of background characteristics and ﬁnancial circumstances. Thus, unobserved,
unmeasured individual differences might play an important role in retirement decisions. A
comprehensive theory of work and retirement should be able to explain the substantial
variations in retirement decisions that are observed among apparently similar individuals
(Leonesio, 1996). Research that improves our understanding of factors related to planned
retirement age will improve our ability to analyze policy issues, including proposed changes
to the Social Security program, as well as provide better insight into how to inﬂuence
individual behavior related to planning and saving for retirement.
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