The Gerontologist
Vol. 48, No. 2, 190–202

Copyright 2008 by The Gerontological Society of America

Actuation of Mobility Intentions Among the
Young-Old: An Event-History Analysis
Don E. Bradley, PhD,1 Charles F. Longino, Jr., PhD,2
Eleanor P. Stoller, PhD,3 and William H. Haas, III, PhD4
Purpose: Although migration decision making is
central to understanding later-life migration, the critical
step between migration intentions and mobility outcomes has received only limited empirical attention.
We address two questions: How often are intended
moves actuated? What factors condition the likelihood
that mobility intentions will be actuated? Design and
Methods: We employ data from the 1994–2002
Health and Retirement Study, which is a nationally
representative panel targeting households containing
persons aged 53 to 63 years at baseline. Event-history
techniques are used to examine the link between
reported mobility intentions at baseline and mobility
outcomes across the study period, net of relevant
controls. We conduct separate household-level analyses for couple and noncouple households and recognize three types of moves: local, family oriented, and
nonlocal. Results: Findings conﬁrm the utility of
mobility expectations as a predictor of future mobility.
More importantly, results highlight the complex nature
of later-life mobility. The actuation of mobility intentions
appears to operate differently in couple than in
noncouple households. Moreover, our ﬁndings suggest
that the role of several key variables depends on the
type of move under consideration. Implications: The
ability to identify potential ‘‘retirement migrants’’ may
be of practical importance for state and local government ofﬁcials as well as developers interested in
recruiting or retaining young-old residents. Our study
We gratefully acknowledge support under Grant R03-AG023813
from The National Institute on Aging, which that made this research
possible. D. E. Bradley extends additional thanks for support from East
Carolina University’s Faculty Senate in the form of a Research and
Creative Activity Grant. We also appreciate Ms. Peggy Beckman for the
administrative assistance she has provided.
Address correspondence to Don E. Bradley, PhD, Department of
Sociology, East Carolina University, 442-A Brewster Building, Greenville, NC 27858. E-mail: bradleyd@ecu.edu
1
Department of Sociology, East Carolina University, Greenville, NC.
2
Reynolda Gerontology Program, Wake Forest University, WinstonSalem, NC.
3
Department of Sociology, Wake Forest University, Winston-Salem, NC.
4
Department of Sociology and Anthropology, University of North
Carolina at Asheville.

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offers insight on the interpretation of stated mobility
intentions. Moreover, consistent with early theoretical
work in the ﬁeld, our analysis suggests that empirical
studies must account for heterogeneity among older
movers in order to avoid misleading results.
Key Words: Migration, Residential mobility,
Retirement migration

An established and vibrant research literature
examines the causes, patterns, and consequences of
later-life migration (for reviews, see Longino &
Bradley, 2006; Walters, 2002). Indeed, the geographic
mobility of older adults is an important topic worthy
of attention for at least two reasons. First, available
evidence suggests that the number of older adults
who move is large and likely to increase in the
foreseeable future. Data from the 2000 U.S. Census
suggests that about 23% of those persons 65 years
and older and 26% of those aged 55 to 64 had moved
within the previous ﬁve years, most relocating to
a new residence within the same county (He &
Schachter, 2003). Moreover, as a result of the aging
of the ‘‘baby boom’’ generation, the absolute number
of older movers should increase substantially over
coming decades, assuming that the rate of spatial
mobility holds relatively steady, as it has with respect
to interstate moves since 1960 (Longino, 2006).
Second, later-life in-migration may be an important
driver of structural change in receiving communities.
For example, elder in-migrants appear to stimulate
local economies, in part by purchasing local goods
and services with earnings generated outside the
region (e.g., Reeder, 1998; Sastry, 1992). At the
same time, economic and social impacts depend on
in-migrant characteristics (Stallmann, Deller, &
Shields, 1999) and are not expected to be uniformly
positive (e.g., McHugh, Gober, & Borough, 2002;
Reeder; Rowles & Watkins, 1993).
Why do older people move? Migration decision
making is commonly portrayed as a dynamic multistage process involving three moments: (a) an initial
The Gerontologist

desire to move, (b) the formation of migration intentions, and (c) mobility behavior (Rossi, 1955). Despite
the fact that later-life migration decision making has
occupied a central place in the research literature (e.g.,
Cuba, 1991; Haas & Serow, 1993; Wiseman, 1980),
the critical step between migration intentions and
mobility behavior has received only limited empirical
attention. As a result, scholars know little about the
actuation of migration intentions among older adults.
This is an important gap in the literature, and not
merely for academic reasons. Developers as well as
state and local planners have a vested interest in
identifying potential movers. Migration intentions
manifest a complex decision process, but they are both
relatively easy to measure and the most proximate
predictor of voluntary migration behavior (DeJong,
1999, 2000). This would suggest the utility of concentrating marketing, retention, and recruitment
efforts on seniors that anticipate moving. Unfortunately, existing literature provides little guidance on
interpreting the migration intentions of seniors.
In the present study we focus on the ‘‘young-old’’
(see Neugarten, 1974), and we ask two questions.
First, how often are intended moves actuated? Second, what factors condition the likelihood that
intended moves will be actuated? We examine these
questions by using data from the Health and Retirement Study, which is based on a nationally representative sample of households containing at least one
person born between 1931 and 1941, that is, aged 53
to 63 years at study baseline in 1994. Event-history
techniques are used to examine the link between
measured mobility intentions in 1994 and migration
outcomes over the study period ending in 2002.

Background
Drawing from the general migration literature
(Brown & Moore, 1970; Rossi, 1955; Yee and Van
Arsdol, 1977), Wiseman (1980) posits a range of
‘‘triggering mechanisms’’ (e.g., retirement, death of
a spouse) that prompt seniors to consider moving.
Of critical importance, the character of these
triggering mechanisms and the subsequent migration
decision varies substantially. For this reason later-life
migration is characterized by heterogeneity.
The variety of later-life movers is typically
categorized along two dimensions: distance and
motivation (see Wiseman & Roseman, 1979). As to
distance, local moves are especially likely to reﬂect
dissatisfaction with housing or neighborhood conditions, whereas long-distance moves are additionally inﬂuenced by remote pull factors, such as lower
cost of living. As to motivation, life-course transitions are thought to generate three broad types of
older movers (see Litwak & Longino, 1987; Walters,
2000; Wiseman & Roseman). Amenity-oriented
movers, nearing retirement or newly retired, relocate
to improve their lifestyle: either locally to a nicer
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191

neighborhood or nonlocally to a community offering
an agreeable climate, scenic environs, and an array
of recreational and cultural opportunities. Assistance
movers relocate to be closer to younger family
members in response to negative life circumstances
(e.g., the death of a spouse, declining health, low
income). Finally, for other movers, severe disability
may trigger a move into an institutional setting
where constant care can be provided.

The Actuation of Mobility Intentions
Review of Research.—What does the existing
literature tell us about the actuation of mobility intentions among older adults? Ferraro (1981) examines the
fulﬁllment of relocation desires, though this is a
qualitatively different question than the one posed
here. Only a handful of studies employ the sort of
longitudinal design necessary to link migration intentions at baseline and subsequent mobility (Colsher &
Wallace, 1990; Goldscheider, 1966; Lawton, Kleban, &
Carlson, 1973; Robison & Moen, 2000; Teaford,
1992; Yee & Van Arsdol, 1977). Although it generally
suggests that a minority of intended movers actually
move, the relevant literature is limited in several
respects.
First, all of the studies in question are based on
data from regional or community samples, most of
which are quite small. Both Goldscheider (1966) and
Yee and Van Arsdol (1977) rely on data from
a probability sample of households in the Los
Angeles area that included 386 respondents aged 50
or older. In two rural Iowa counties, Colsher and
Wallace (1990) surveyed 3097 residents aged 65 or
older. Teaford (1992) followed 277 widows she
recruited in Franklin County, Ohio, whereas Lawton
and colleagues (1973) studied 115 older residents in
one of Philadelphia’s inner-city neighborhoods.
Robison and Moen (2000) employed two waves of
data from the Cornell Retirement and Well-Being
Survey of 762 retirees and workers aged 50 to 72
randomly chosen from six selected employer organizations in upstate New York. Consequently, the
generalizability of reported ﬁndings from existing
studies cannot be taken for granted.
Second, nearly all studies linking migration
intentions and mobility behavior among older adults
conduct follow-up interviews after only 1 or 2 years.
In those instances in which migration intentions at
baseline are expressed in terms of an unbounded
future (Robison & Moen, 2000), ﬁndings are difﬁcult
to interpret. However, even when migration intentions are speciﬁed over some time period (e.g., ‘‘How
likely are you to move within the next 2 years?’’),
ending observation after that time period has elapsed
ignores the possibility that some elders may delay
expected moves. Relatively short follow-up periods
may underestimate the extent to which migration
intentions are actuated.

Third, available studies that examine the actuation of migration intentions among seniors do not
adequately capture variation in strength of mobility
expectations; it is typically operationalized as a dichotomous variable identifying those planning
a move (Colsher & Wallace, 1990; Goldscheider,
1966; Lawton et al., 1973; Yee & Van Arsdol, 1977).
However, the formation of migration intentions is
a dynamic process, as a part of which individuals
may entertain ‘‘remote thoughts’’ of relocating for
many years prior to actually moving (e.g., Haas &
Serow, 1993). Treating mobility expectations as
dichotomous ignores this process, aggregating those
with modest to weak expectations of moving
together with those not actually considering a move.
Fourth, the relevant literature largely overlooks
the heterogeneity of older movers, described by early
scholarship; with one exception (Colsher & Wallace,
1990), existing studies examine change of residence
irrespective of the type of move completed. This is an
important shortcoming, because the process of
migration decision making appears to operate differently for different types of moves (Wiseman &
Roseman, 1979). Regarding the decision to move, for
example, personal resources such as health and
income may facilitate an amenity-oriented move but
reduce the likelihood of an assistance move (see
Meyer & Speare, 1985). The problem here is that
where characteristics predicting the actuation of
intentions are examined, results are difﬁcult to interpret. Goldscheider (1966), for example, shows an
inverse relationship between socioeconomic status
and the likelihood of successfully anticipating a
move. However, these ﬁndings may be driven by a
greater preponderance of local or within-area moves
motivated by neighborhood or housing dissatisfaction, thus obscuring the processes governing the
actuation of out-of-area moves. Failure to distinguish between mover types may be less problematic
in those studies that focus on population subgroups
especially likely to complete family- or assistanceoriented moves (i.e., Lawton et al., 1973; Teaford,
1992), but there is no information provided regarding the actual character of the move completed.
Fifth and ﬁnally, existing studies regarding the
actuation of mobility intentions among older adults
give little attention to conceptualizing the link
between migration intentions and migration outcomes. Empirical analyses do not appear to be
guided by conceptual models that speciﬁcally address
the capacity of older adults to translate mobility
expectations into actual moves. Limited theoretical
development makes it difﬁcult to generate reasonable
expectations and interpret results.
The present study offers several contributions.
First, our analysis employs a nationally representative panel of households. Second, we link migration
intentions at baseline to migration outcomes over an
8-year study period. Third, we employ measures that
capture substantial variation in the strength of
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mobility intentions. Fourth, our analysis accounts
for heterogeneity among older movers, constraining
analysis to a young-old panel and recognizing three
types of movers. Fifth, our analysis is guided by
a speciﬁc conceptual framework regarding factors
that may condition the likelihood that households
will actuate mobility intentions.
Conceptual Framework.—Among intended movers, are there characteristics that might predict the
likelihood of actually moving? Migration intentions
reﬂect expectations that a move will facilitate the
attainment of one or more valued goals. According
to value-expectancy theory, the greater the value
placed on a particular goal or set of goals and the
higher the expectancy that moving to a particular
location will lead to attaining that goal or set of
goals, the stronger the intention to move (DeJong,
1999, 2000; DeJong & Fawcett, 1981). All other
factors being equal, at any given point in time those
with stronger mobility intentions should be more
likely than others to actually move.
At the same time, individuals form migration
intentions based on imperfect knowledge as to future
conditions, as is obvious. Value-expectancy theory
argues that the actuation of mobility intentions
depends, in part, on ‘‘unanticipated constraints and
facilitators’’ (DeJong & Fawcett, 1981). Intended
movers, for example, may underestimate the costs of
moving, both ﬁnancial and otherwise. To the extent
that expected moves are voluntary and desired, the
likelihood of actually moving should depend on
exposure to unanticipated constraints and the
capacity to overcome unanticipated constraints.
Consistent with value-expectancy theory (DeJong,
1999), ﬁnancial resources may enhance a household’s
capacity to overcome unanticipated constraints.
Among those individuals considering a move, wealth
and income should be positively linked with the
likelihood of actually moving. Moreover, intended
movers in relatively good health may be better
prepared to perform the tasks necessary to complete
a move and less likely to experience the sort of
reversals that would constrain them from actually
moving (see Wiseman, 1980).
At the same time, the difﬁculties associated with
moving, and consequently the importance of ﬁnancial resources and health, should depend on the
character of the move contemplated. The effective
cost of a move is partly a function of distance
between origin and destination (e.g., Lee, 1966;
Speare, Goldstein, & Frey, 1975). Irrespective of
distance, friends and relatives in the receiving area
often sponsor migrants, thereby allowing persons
with modest ﬁnancial resources and limited human
capital to make long-distance moves (e.g., Massey,
Alarcon, Durand, & Gonzalez, 1987; Massey et al.,
´
´
1993). Financial resources and health may be
particularly salient to nonlocal moves completed
without the support of family at destination. This
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expectation makes sense because local movers and
movers with family at destination, irrespective of
distance moved, are likely to have better information
concerning the destination and thus should be less
likely to encounter unanticipated constraints. Moreover, local movers and family-supported movers
should be well positioned to rely on social resources
at destination in order to overcome unanticipated
constraints that do arise.
Methods
Data
The present study exploits data from the Health
and Retirement Study (HRS). Supported in large part
by the National Institute on Aging and conducted by
the University of Michigan, the HRS is an ongoing
panel study designed to follow respondents and their
spouses through the transition to retirement (see
Juster and Suzman, 1995). Our data extract is partly
derived from the RAND HRS data ﬁle, a cleaned
user-friendly version of the HRS produced with
funding from the National Institute on Aging and the
Social Security Administration (RAND, 2004).
The core sample for the HRS is based on
a stratiﬁed multistage area probability design,
similar to that employed in several federal government-sponsored data-collection efforts (e.g., the
Health Interview Survey and the Current Population
Survey). It is representative of U.S. households
containing at least one person born between 1931
and 1941. At study baseline in 1994, age-eligible
panel members were 53 to 63 years old. Since the ﬁrst
wave of data collection in 1992, about one third of
the HRS sample members had been lost to attrition
by 2002. However, the analysis by Cao and Hill
(2005) of the HRS data suggest that (a) attrition
across waves has not undermined the representativeness of the sample and (b) panel members lost for
reasons other than mortality appear to be quite
similar to those remaining in the study.
Migration decision making is properly conceived
as a household-level process (Brown & Moore, 1970;
DeJong & Fawcett, 1981; Rossi, 1955; Stark &
Bloom, 1985). Moreover, decision making in couple
households is likely to be qualitatively different from
decision making for single individuals. In a couple
household, the anticipated costs and beneﬁts of
a move should depend on the characteristics of both
household members (e.g., a wife’s retirement may be
less likely to trigger a long-distance move when the
husband is still working). Further, reported mobility
expectations for members of a couple are likely to
inform one another. Within couples, reported
mobility intentions for any individual are likely to
include an assessment of the spouse or partner’s
desire or willingness to move. Individual-level
analyses, in this context, would be problematic
because correlated residuals within households
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generate downwardly biased estimates of the standard errors (e.g., Berry, 1993).
In light of these considerations, in the analysis to
follow we treat households as the unit of analysis
and disaggregate couple households (composed of
two married or partnered age-eligible persons or an
age-eligible person and his or her age-ineligible
spouse) and single households (composed of a
single age-eligible respondent). We apply household
weights in order to account for oversampling among
African Americans, Hispanics, and Florida residents,
effectively excluding a trivial number of households
with nonpositive weights (i.e., 1 single household
and 20 couple households).
The couple household analysis is limited to
heterosexual couples in which both members (a)
considered themselves married or partnered and (b)
were interviewed in 1994 (n = 4,302). In the analysis
and discussion to follow, the term husband refers to
the male member of a married or partnered couple
and wife refers to the female member of a married or
partnered couple. Respondents in single households
are included, if they responded at the initial wave of
data collection in 1992 and provided a full interview
in 1994 (n = 2,144).
Analytical Strategy
We examine the actuation of mobility intentions
by using Cox’s proportional hazards models. Analysis estimates the hazard or risk that a move will
occur within a given month, conditional on the
household being ‘‘at risk’’ of experiencing a move at
the beginning of the month. Households are considered at risk of moving from the baseline interview
in 1994 until moving or being censored. Households
are censored upon dissolving as a result of death or
divorce, or attrition, or otherwise at the end of the
study period in 2002. We chose 1994 as the baseline
because limited information is available to characterize moves made between 1992 and 1994.
A simple proportional hazards model with no
time-dependent covariates takes the following form:
hi ðtÞ ¼ k0 ðtÞ 3 expðb1 x1i þ b2 x2i þ . . . þ bk xki Þ: ð1Þ

For our purposes, let hi(t) represent the hazard of
mobility for household i at month t, given that the
individual is still in the risk set at the beginning of
month t. Proportional hazards models assume that
the baseline function of time, k0(t), is a constant and
maximize a partial likelihood function in order to
estimate the inﬂuence of covariates. Model coefﬁcients, b1, . . ., bk, indicate the proportionate adjustment to the baseline hazard rate that is implied by
changes in selected independent variables, x1, . . . , xk.
Exponentiated coefﬁcients, termed hazard ratios, are
comparable with odds ratios. Thus, with respect to
ordinal or interval measures, 100 3 (exp bi À 1)
provides the anticipated percentage change in the

estimated hazard of some event for each unit change
in a given covariate (Allison, 1995). We conduct the
analyses by using PROC PHREG within the SAS
program. Following a procedure outlined by Boudreau and Lawless (2006), we adjust estimated
standard errors to account for the complex multistage sampling process used to select HRS panel
members.
Theory and previous research suggest heterogeneity among later-life movers. We partly constrain
heterogeneity by focusing on the young-old, who are
likely to be transitioning into retirement during the
study period. This constraint minimizes the danger of
obscuring relationships by aggregating households
where the migration decision-making process is
fundamentally dissimilar. As compared with the
young-old, persons aged 70 or older may be especially
likely to be pushed into an assistance move by some
combination of widowhood, disability, and ﬁnancial
constraints (see Choi, 1996; Litwak & Longino, 1987;
Meyer & Speare, 1985; Walters, 2000). Under these
circumstances, migration decision making may be
shared with or transferred to family members at
alternative destinations (Wiseman & Roseman, 1979)
and depend on the capacity and willingness of others
to provide assistance and support (e.g., Miller,
Longino, Anderson, James, & Worley, 1999).
In addition, our analyses recognize that households
are at risk for different types of moves. Speciﬁcally, we
distinguish between local moves, nonlocal moves, and
family-oriented moves, as described in the following
paragraphs. Following Allison (1995), we estimate
a series of separate models that focus on each type of
move in turn, treating alternative event types as a form
of censoring.
Measures
Dependent Variable.—Spatial mobility is measured by a series of questions, available at each wave
from 1996 to 2002. A move occurs when the
household has changed residence since the previous
wave interview. Date of move information is used to
compute the number of months between the date of
interview in 1994 and the ﬁrst subsequent move.
Our analysis identiﬁes three types of moves. Local
moves involve a change of residence in which (a) the
new residence is ‘‘in or around’’ the primary address
city reported at the previous wave and (b) there are
no children or parents living with or within 10 miles
of the household at destination. Family-oriented
moves involve any change of residence, in which
children or parents are living with or within 10 miles
of the household at destination. A change of
residence in which (a) the new residence is no
longer ‘‘in or around’’ the primary address city
reported at the previous wave and (b) there are
no parents or children living with or within 10 miles
of the household at destination is coded as a
nonlocal move.
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Family-oriented moves include both within-area
moves and between-area moves in which immediate
family members are resident at the destination. This
approach is justiﬁed on two grounds. First, the
destination for a move toward family members is
constrained by the location of eligible family
members (Wiseman & Roseman, 1979), so that
distance moved may not discriminate distinct types
of family-oriented moves. Second, models estimating
the hazard of a within-area family-oriented move
would likely be misspeciﬁed. Only those individuals
and couples having parents or children living in the
area would be at risk of making a within-area
family-oriented move. However, it is not possible to
properly construct the risk set, because the HRS
measures parents and children within 10 miles,
which will not reliably be coterminous with the
boundaries of the address city or area of residence.
The operationalization of mover types we employ
is an important improvement over previous studies
that do not account for the heterogeneity of older
movers. Even so, we do not claim to fully represent
the full range of variability among later-life movers.
We constructed mover types that aggregate households sharing a reasonably similar underlying causal
structure with respect to the actuation of mobility
intentions. For this reason, family-oriented moves
include those individuals and couples moving toward
family and others who may be moving with family or
even moving from one set of family to another.
Theoretical arguments already presented suggest that
overcoming unanticipated constraints and actualizing mobility intentions depends on the support of
family at destination rather than whether or not the
members of a household are moving closer to family.
In addition, the reader should be aware of certain
data-imposed limitations that constrain our ability
to measure the supply of supportive social ties at
destination. Nonlocal moves, for example, include
movers with siblings or other extended family
members in the destination area along with those
who move to a community where they have no
existing social ties. Along these same lines, we were
unable to disaggregate movers returning to their
community of origin (Longino & Serow, 1990;
Wiseman & Roseman, 1979), some of whom will
be motivated by assistance concerns and others by
amenity considerations. Furthermore, measures
available in the HRS identify those households
with children or parents living within 10 miles but
fail to discriminate among households with immediate family living 11 or more miles away. Some local
and nonlocal movers identiﬁed here may be living
close to their children but not within 10 miles.
Independent Variables.—We have argued that
the actuation of mobility intentions should depend
on the strength of mobility intentions, health, and
ﬁnancial resources. The HRS includes suitable
measures for each of these concepts. Expected
The Gerontologist

mobility at baseline is captured with the following
item from the 1994 interview: ‘‘Now, using the same
scale as before where ‘0’ is absolutely no chance and
‘100’ means that it is absolutely certain, please tell
me what you think are the chances that you will
move in the next 2 years?’’
Self-assessed health is measured by using the
following item: ‘‘Next I have some questions about
your health. Would you say your health is excellent,
very good, good, fair, or poor?’’ Responses were
coded 1 to 5, where 5 indicates excellent perceived
health. Although it is not included in the present
study, functional disability appears to be an
important predictor of geographic mobility in older
age cohorts (e.g., Miller et al., 1999). The vast
majority of HRS panel members were free of
functional health problems at baseline, as few
reported any difﬁculty with either basic activities of
daily living (bathing, dressing, and eating) or
instrumental activities of daily living (using the
telephone, managing money, or taking medications).
Not surprisingly, exploratory analyses in both noncouple and couple households failed to suggest any
link between functional disability measures and
geographic mobility.
Household wealth measures the total value of
diverse assets, such as real estate, vehicles, a business
or farm, retirement accounts, bonds, and other
savings vehicles. Household income assesses total
earnings from a variety of sources, including salary
or wages, unemployment compensation, retirement
pensions and annuities, and social security income.
Data on household economic resources were collected from a designated ﬁnancial respondent within
each household. We exploit RAND-constructed
measures of household wealth and household income. For both measures, RAND used regression
modeling to impute values where the original HRS
data were incomplete (RAND, 2004). Given that
wealth and income measures are typically skewed,
we employ the natural log of both household wealth
and household income in our models. In those cases
in which reported household wealth or household
income was zero, we coded the corresponding logged
variable as zero. Where household income or
household wealth values were negative, we multiplied by À1 in order to reverse the sign, took the
natural log, and then multiplied by À1 again.
Our analysis includes a range of additional
control variables suggested by value-expectancy
theory (e.g., DeJong, 1999) and previous migration
research. We include the following demographic
measures: race and ethnicity, age, gender (in noncouple households), marital status, and education.
Accounting for demographic characteristic is important, in part because awareness of available destination opportunities and access to alternative
destinations may vary across population subgroups.
Education, for example, may expand an individual’s
‘‘awareness space,’’ thereby enhancing the likelihood
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of successfully ﬁnding a suitable destination (e.g.,
Speare et al., 1975). Moreover, single women and
minorities may be especially likely to face housing
discrimination as an unanticipated constraint, thus
blocking efforts to actualize mobility intentions (e.g.,
Crowder, 2001).
Consistent with previous later-life migration research (e.g., DeJong, Wilmoth, Angel, & Cornwell,
1995; Miller et al., 1999; Speare, Avery, & Lawton,
1991), our research also includes statistical controls
accounting for the inﬂuence of person-ties and placeties at origin. As moorings hold boats in the harbor,
so person-ties and place-ties at origin (i.e., familiarity
and associations with a given place generated
through residential experience) are expected to
decrease the likelihood of a later-life move (see
Longino, Perzynski, & Stoller, 2002 for a review).
Speciﬁcally, we measure familiarity with neighbors,
home ownership, duration of residence (i.e., whether
household members are native to the community of
origin or not), labor force participation, and family
propinquity.
Finally, previous migration experience may allow
households to form migration intentions based on
better information as to (a) the difﬁculties associated with moving and (b) the range of available
destination opportunities. Consistent with valueexpectancy theory (e.g., DeJong, 1999), this would
lead us to suspect that intended migrants with higher
levels of previous migration experience should experience fewer unanticipated constraints. We control
for veteran status because older veterans are likely
to have substantial migration experience and may
be especially likely to move (see Cowpers et al.,
2000).
Descriptive statistics and details regarding the
construction of included measures for noncouple and
couple households are presented in Tables 1 and 2.
As noted in Table 2, among couple households we
employ a number of couple-summary measures.
With respect to mobility intentions, for example,
we use the couple-average expected mobility value,
unless only one member provided a valid response,
in which case we code the single valid response for
the household. For each construct where husband
and wife values were available, we compared models
employing two terms (e.g., highest expected mobility
within couple and the difference between high and
low expected mobility within couple) with models
using a single couple-summary value. We use couplesummary measures for those constructs where we
ﬁnd no signiﬁcant improvement in model ﬁt
associated with the use of two terms.
These preliminary analyses lead us to exclude the
item Wife Retired or Not in Labor Force from the
full models presented in the following paragraphs.
Although unanticipated, this result makes sense in
light of the literature suggesting that, within couples,
the partner with greater earning capacity will exert
disproportionate inﬂuence on household decisions,

Table 1. Descriptive Statistics for Single Households

Variable
Expected mobility
Logged household wealth
Logged household income
Self-assessed health
African American
Hispanic origin
Age (years)
Female
Formerly marrieda
Newly marriedtb
Education (years)
Know neighborsc
Homeowner
Area natived
Retiredte
Children nearf
Parents nearg
Veteran

M

SD

23.2
7.730
9.077
3.1
0.308
0.090
58.0
0.709
0.858
0.044h
11.8
1.5
0.572
0.400
0.547h
0.590
0.194
0.156

34.134
6.172
2.293
1.246
3.212

3.296
0.876

Table 2. Descriptive Statistics for Couple Households

No.
Missing
44
0
0
1
0
1
0
0
0

Variable
Average expected mobility
Logged household wealth
Logged household income
Wife’s self-assessed health
Husband’s self-assessed health
African-American couplea
Hispanic coupleb
Average age (years)
Partnered couple
Average education (years)
Know neighborsc
Homeowner
Area native coupled
Husband retired or out of labor
forcete
Children nearf
Parents nearg
Husband veteran

0
7
19
0
0
0
0

Notes: Standard deviations are omitted for binary
variables. Mean value for binary variables is the proportion of
households with that attribute.
a
1 = divorced, separated, or widowed; 0 = otherwise.
b
1 = married by the beginning of month t; 0 = otherwise.
c
Measured with ‘‘Among your nearby neighbors, how many
of the adults would you know by name if you met them on
the street?’’ Responses coded from 0 to 3 (0 = none of them
to 3 = all of them).
d
1 = responded ‘‘yes’’ to this question: ‘‘Are you currently
living in the same general area where you were born?’’ (0 =
otherwise).
e
1 = retired or not in the labor force by the beginning of
month t; 0 = otherwise.
f
1 = any child living in the household or within 10 miles of
the household; 0 = otherwise.
g
1 = any parent living in the household or within 10 miles
of the household; 0 = otherwise.
h
value at 24th month.

including the decision to make a long-distance move
(see Smits, Mulder, & Hooimeijer, 2003 for a review), and the fact that across couple households,
husbands report substantially higher earnings than
do their wives (average husband advantage =
$13,915; median husband advantage = $4,000).
Findings
Figures 1 and 2 graph the probability of having
moved at least once, across months since the 1994
baseline interview, for noncouple and couple households respectively. Figures are based on Kaplan–
Meier estimates of the survivor function, and results
are broken out by mobility expectations at baseline.
Not surprisingly, the probability of moving differs
substantially depending on expected mobility. In
those households certain of an upcoming move
(expected mobility = 100), the estimated probability
of having moved 2 years after the initial interview is
.48 in single households and .55 for couple households. By contrast, in households with low mobility
196

M

SD

17.3
11.069
10.563
3.5
3.4
0.157
0.093
57.4
0.029
12.2
1.7
0.885
0.856

27.659
3.746
1.202
1.104
1.149

0.686h
0.694
0.308
0.578

4.854
2.832
.847

No.
Missing
14
0
0
0
2
0
0
0
0
28
8
8
0
0
0
22

Notes: Standard deviations are omitted for binary
variables. Mean value for binary variables is the proportion of
households with that attribute.
a
1 = at least one member of couple African American; 0 =
otherwise.
b
1 = at least one member of couple of Hispanic origin; 0 =
otherwise.
c
Measured with ‘‘Among your nearby neighbors, how many
of the adults would you know by name if you met them on
the street?’’ Responses coded from 0 to 3 (0 = none of them
to 3 = all of them).
d
Sum of couple members who responded ‘‘yes’’ to this
question: ‘‘Are you currently living in the same general area
where you were born?
e
1 = Husband retired or not in the labor force by the
beginning of month t; 0 = otherwise.
f
1 = any child living in the household or within 10 miles of
the household; 0 = otherwise.
g
1 = any parent living in the household or within 10 miles
of the household; 0 = otherwise.
h
Value at the 24th month.

expectations at baseline (expected mobility = 1–50),
the estimated probability of having moved in the
subsequent 24 months is .12 for noncouple households and .05 for couple households. Notably,
among both noncouple and couple households
assessing the chances of mobility at or above 50%,
the rate of change in the probability of having moved
appears to ﬂatten somewhat 2 years after the
baseline interview. This makes intuitive sense given
that the expected mobility item speciﬁcally asks
about the chances of moving within the next 2 years.
Tables 3 and 4 report results from a series of
proportional hazards models for noncouple and
couple households, respectively, considering a move.
As we already noted, the impact of expected mobility
appears to be nonconstant over time, weakening after
2 years. In order to model this dynamic, we create
a time-dependent covariate after 2 years (1 = more
than 24 months since initial interview; 0 = 24 or fewer
months since initial interview), and we enter a multiplicative interaction term, expected mobility 3 after 2
The Gerontologist

Figure 1. Noncouple households categorized by expected mobility, estimated probability of moving at least once by months since
baseline interview.

years, into the proportional hazards models. We
represent mobility expectations by using two terms, as
follows:
exp½b1 expected moblity
þ b2 expected moblity 3 after 2 yearsðtÞ:

The effect of mobility expectations during the ﬁrst 2
years is represented by the hazard ratio for expected
mobility (i.e., exponentiated b1). After the ﬁrst 2
years, the estimated effect of mobility expectations is
the product of the hazard ratios for expected
mobility and expected mobility 3 after 2 years (i.e.,
exponentiated b1 3 exponentiated b2). The calculated value is presented at the bottom of both Tables
3 and 4. The p value for expected mobility 3 after
2 years evaluates the null hypothesis that there is no
difference between the slope of expected mobility on
the hazard of moving after the ﬁrst 2 years as
compared with that during the ﬁrst 24 months.
In Table 3, results are presented for noncouple
households considering a move at baseline (i.e., expected mobility . 0). Model 1 assesses the net effect
of covariates on the hazard of moving, regardless of
the type of move reported. Models 2, 3, and 4
analyze three types of moves: local moves, familyoriented moves, and nonlocal moves, respectively.
Vol. 48, No. 2, 2008

197

Among noncouple households considering a move,
how does the strength of mobility expectations
impact mobility behavior? In Model 1, each unit
increase in expected mobility is associated with a 2%
increase in the hazard of making some type of move
during the ﬁrst 2 years. However, after the ﬁrst 2
years, expected mobility is signiﬁcantly weaker and
no longer signiﬁcant (hazard ratio = 1.002, p =
.867). A similar pattern is apparent across types of
moves. Expected mobility predicts all three types of
moves during the ﬁrst 2 years after the baseline
interview, but there is no evidence of an effect in the
25th and following months.
There is some apparent variation in the impact of
expected mobility. Each unit increase in expected
mobility is associated with an estimated 3.9% increase
in the hazard of a nonlocal independent move during
the ﬁrst 24 months after the initial interview. This is
a substantial effect, and it implies that every 20-point
increase in expected mobility more than doubles the
hazard of a nonlocal independent move during the ﬁrst
24 months. By contrast, each unit increase in expected
mobility is associated with an estimated 1.6% increase
in the hazard of a family-oriented move. Each 20-point
increase in expected mobility according to these
results implies an approximate 37% increase in the
hazard of a family-oriented move. We calculated Wald

Figure 2. Couple households categorized by average expected mobility, estimated probability of moving at least once by months
since baseline interview.

chi-square values comparing the estimated net effect
of expected mobility on family-oriented as compared
with nonlocal moves, and we found that the difference
is statistically signiﬁcant (v2 = 3.884, p = .0489).
Do personal resources increase the likelihood of
moving among noncouple households considering a
move? Net of covariates, neither household wealth,
household income, nor self-assessed health increase
the overall hazard of moving, in Model 1. However,
household wealth is negatively associated with the
hazard of a nonlocal move in Model 4, where results
suggest that each percentage increase in household
wealth is associated with an estimated 5.8% decrease
in the hazard of making a nonlocal move.
It is noteworthy that education in Model 1
increases the hazard of moving in single households
considering a move at baseline. Education’s inﬂuence, however, appears to be limited to Model 4,
where each additional year of schooling is associated
with an estimated 29.4% increase in the hazard of
a nonlocal independent move. An effect of this size
implies, for example, that net of covariates, a college
graduate with 16 years of education is about 2.8 times
more likely to make a nonlocal move as a comparable
single individual with a high school degree with 12
years of education. Wald chi-square tests suggest that
education is a signiﬁcantly stronger predictor of
nonlocal independent moves as compared with both
198

family-oriented moves (v2 = 9.122, p = .003) and
local moves (v2 = 4.943, p = .026).
Results for couple households considering a move
at baseline (i.e., average expected mobility . 0) are
presented in Table 4. Findings in Model 1 suggest
that, during the ﬁrst 2 years after baseline, expected
mobility is strongly associated with the overall
hazard of moving. Moreover, the fact that expected
mobility 3 after 2 years is signiﬁcant indicates that
the effect of expected mobility on the overall hazard
of moving weakens after the ﬁrst 2 years. Even so,
Model 1 implies that in the 25th and subsequent
months, expected mobility remains signiﬁcant and
positive (hazard ratio = 1.013, p , .0001).
Models 2, 3, and 4 present proportional hazards
regression results for alternative move types. The
estimated effect of expected mobility in the ﬁrst 2
years is substantial and consistent across models. By
way of illustration, Model 4 results indicate that, in
the initial 24 months, the expected hazard of a nonlocal independent move for couples at the 75th
percentile of the distribution of expected mobility
(i.e., expected mobility = 50) is approximately four
times greater than that of comparable couples at the
25th percentile of the distribution (i.e., expected
mobility = 10). Expected mobility effects of similar
magnitudes are observed for the hazards of a local
move and the hazards of a family-oriented move.
The Gerontologist

Table 3. Results of Proportional Hazards Models Predicting First Move for Noncouple Households
Considering a Move at Baseline (expected mobility > 0), 1994 to 2002 (n = 890)
Model 1
Variable
Expected mobility
Exp. mobility 3 after 2 years
Logged household wealth
Logged household income
Self-assessed health
Hispanic origin
African American
Age (years)
Female
Formerly married
Newly marriedt
Education (years)
Know neighbors
Homeowner
Area native
Retiredt
Children near
Parents near
Veteran
À2 log likelihood
Likelihood ratio v2
Model-implied effect of expected mobility
in the 25th and following months

Model 2

Model 3

Model 4

HR

p

HR

p

HR

p

HR

p

1.020
0.981
0.979
0.993
1.033
0.970
0.885
1.020
1.191
0.991
3.112
1.085
0.964
0.361
0.785
1.401
1.038
0.909
0.524
1,094.4
168.1

.000
.000
.121
.857
.594
.914
.625
.377
.402
.958
.000
.001
.591
.000
.159
.036
.804
.585
.013

1.030
0.972
0.978
0.965
1.100
0.691
0.875
1.023
1.111
0.547
1.923
1.038
1.308
0.235
.955
1.937
0.192
0.360
0.438
174.0
75.8

.002
.035
.434
.564
.501
.624
.798
.742
.826
.077
.518
.641
.095
.005
.884
.097
.002
.111
.124

1.016
0.983
0.987
1.011
1.025
1.039
0.934
1.011
1.259
1.848
3.293
1.049
0.847
0.315
0.703
1.288
2.034
1.105
0.570
626.0
131.2

.001
.035
.444
.801
.729
.928
.810
.692
.379
.048
.000
.170
.101
.000
.171
.256
.001
.665
.069

.000
.005
.022
.724
.225
.408
.673
.370
.786
.633
.001
.000
.441
.295
.688
.491
.020
.082
.354

,.0001

1.039
0.969
0.942
0.977
1.180
0.581
0.807
1.055
1.127
0.790
5.217
1.294
1.118
0.644
0.868
1.273
0.464
0.480
0.607
200.9
54.6

0.999

0.8119

1.007

1.001

,.0001
0.867

1.001

,.0001
0.93

,.0001
0.259

Note: Model 1, 2, 3, and 4 = any type of move, local move, family-oriented move, and nonlocal move, respectively. HR =
hazard ratio.

After the ﬁrst 2 years, the net effect of expected
mobility weakens signiﬁcantly with respect to
family-oriented and nonlocal moves, but not local
moves. Nevertheless, across types of move, expected
mobility retains positive signiﬁcance even 2 years
after the initial interview. Thus, in couple households there is evidence suggesting a residual effect of
expected mobility beyond the 2-year period speciﬁed
by the question item.
Do resources enhance the likelihood of mobility
among couples considering a move at baseline?
Household wealth is signiﬁcantly associated only
with the hazard of a nonlocal independent move in
Model 4. Here each percentage change in household
wealth is associated with a 13.1% increase in the
hazard of a nonlocal independent move. Comparing
couple households at the 75th and 25th percentiles of
household wealth, Model 4 results imply that net of
covariates those reporting a net worth of $332,500
are approximately 26% more likely to make a nonlocal independent move as compared with those
reporting $52,900 in household wealth. According to
Wald chi-square tests, the effect of household wealth
is signiﬁcantly more positive with respect to nonlocal
as compared with family-oriented moves (v2 =
5.611, p = .018), but not signiﬁcantly different
between nonlocal and local independent moves (v2 =
2.854, p = .091).
Similarly, wife’s self-assessed health predicts only
nonlocal moves. Each unit change in wife’s subVol. 48, No. 2, 2008

199

jective health is associated with a 29.3% increase in
the hazard of a nonlocal move according to Model 4.
According to these results, among couples considering a move, those in which the wife reports excellent
health are approximately 2.8 times more likely to
make a nonlocal move as compared with couples in
which the wife assesses her health as poor, net of
expected mobility and other covariates. Finally, the
slope coefﬁcient for wife’s self-assessed health is
signiﬁcantly different with respect to nonlocal independent moves as compared with both familyoriented (v2 = 5.881, p = .015) and local (v2 = 4.315;
p = .038) independent moves, according to Wald
chi-square tests.
We estimated a number of ancillary models in order
to evaluate the possibility that, in noncouple households, migration decision making operates differently
for men and women. Findings suggest that among
those considering a move at baseline, divorced men
may be signiﬁcantly less likely than divorced women
to report a local move. Analyses failed to support the
notion that the proximity of children and parents
affects single men and women in different ways.
Discussion and Conclusion
This study examines the actuation of migration intentions among the young-old. Longitudinal studies
of older adults that link mobility intentions to future
mobility are few in number and characterized by

Table 4. Results of Proportional Hazards Models Predicting First Move for Couple Households
Considering a Move at Baseline (i.e., avg. expected mobility > 0), 1994 to 2002 (n = 2,010)
Model 1
Variable
Average expected mobility
Avg. exp. mobility 3 after 2 years
Household wealth
Household income
Wife’s self-assessed health
Husband’s self-assessed health
Hispanic couple
African-American couple
Average age (years)
Partnered couple
Average education (years)
Know neighbors
Homeowner
Area native couple
Husband retired or out of labor forcet
Children near
Parents near
Husband veteran
À2 log likelihood
Likelihood ratio v2
Model-implied effect of expected mobility
in the 25th and following months

Model 2

Model 3

Model 4

HR

p

HR

p

HR

p

HR

p

1.033
0.981
0.991
1.055
1.057
1.035
0.495
0.807
0.982
1.275
0.986
0.977
0.505
1.018
1.123
0.834
1.065
0.911
2849.4
380.3

.000
.000
.544
.448
.255
.474
.005
.257
.149
.383
.542
.694
.000
.793
.345
.095
.570
.378

.000
.172
.652
.949
.757
.215
.658
.901
.585
.278
.914
.827
.001
.035
.414
.003
.016
.703

,.0001

1.035
0.981
1.135
0.927
1.293
1.020
0.081
0.674
1.009
1.355
0.938
0.927
0.792
0.794
0.937
0.504
0.670
1.393
723.7
147.9

.000
.004
.036
.592
.003
.834
.007
.358
.696
.660
.173
.536
.412
.096
.799
.001
.083
.131

,.0001

1.035
0.978
0.977
1.103
0.984
1.003
0.691
0.898
0.966
1.058
0.996
0.999
0.441
1.042
1.164
1.323
1.477
0.816
1534.1
244.0

.000
.000
.189
.317
.823
.966
.172
.628
.043
.880
.913
.987
.000
.618
.327
.043
.006
.103

,.0001

1.029
0.988
1.013
1.014
0.967
1.136
0.745
0.944
0.984
1.726
1.006
0.970
0.334
1.352
1.330
0.477
0.483
0.916
475.6
104.4

,.0001

1.014

,.0001

1.017

.0073

1.011

,.0001

1.016

.0001

Note: Model 1, 2, 3, and 4 = any type of move, local move, family-oriented move, and nonlocal move, respectively. HR =
hazard ratio.

several limitations. We seek to improve on these
studies in the following ways. First, we use panel
data from a large nationally representative sample.
Second, our study follows panel-member households
over an 8-year study period. Third, we measure
mobility intentions with greater precision than that
offered by discrete and ordinal items used in
previous research. Fourth, our study accounts for
heterogeneity among older movers by restricting the
analysis to young-old households and by modeling
three types of moves (i.e., local, family oriented, and
nonlocal). Findings reported here address two
questions: How often are intended moves actuated?
What factors condition the likelihood that intended
moves will be actuated?
With respect to the ﬁrst question, among those
households at baseline certain of moving within
2 years, the estimated probability of actually completing a move during that interval is .48 for single
households and .55 for couple households. Lower
rates of mobility were observed as migration intentions weakened. Consistent with existing research,
the link between expected mobility and actual mobility is strong but imperfect; this is not surprising,
given that migration intentions shift in response to
changing conditions and perceptions (DeJong, 1999).
What factors shape the chances of actually
moving for those individuals and couples considering
a move at baseline? First, our ﬁndings suggest that
the strength of mobility expectations is of substantial
importance, with one exception. In single house200

holds, expected mobility is signiﬁcantly weaker as
a predictor of family-oriented moves as compared
with nonlocal moves. This result is not entirely
counterintuitive. It may be that the actuation of
family-oriented migration intentions among single
elders is especially dependent on support from family
at destination, support that may be volatile and
difﬁcult to judge in advance as compared with the
kinds of conditions facilitating other types of moves
(e.g., warm climate, nicer neighborhood).
Second, we argued that among those persons and
couples considering a move, economic resources and
health should facilitate the actuation of migration
intentions, especially with respect to nonlocal moves.
Contrary to expectations, for single households our
analysis suggests that, net of expected mobility and
other covariates, increasing household wealth diminishes the likelihood of a nonlocal move among those
individuals considering a move at baseline. The most
obvious explanation is that for many young-old
singles, expected moves are not necessarily desired
moves and wealthier individuals are better prepared
to avoid moving. This interpretation makes sense
to the extent that unmarried elders appear to be
especially likely to move toward kin (Cuba, 1992;
DeJong et al., 1995; Litwak & Longino, 1987),
motivated in part by assistance needs, whether
perceived or anticipated (Wiseman, 1980; Meyer &
Speare, 1985). It may be that, for many single elders,
anticipated assistance needs serve as a motivation to
move toward kin, but those with greater wealth are
The Gerontologist

better able to delay or avoid actually moving. More
speciﬁcally, wealthier singles may be better equipped
to purchase or otherwise secure access to formal
services.
Findings with respect to couple households
considering a move at baseline are broadly consistent
with expectations and suggest that household wealth
and wife’s self-assessed health increase the likelihood
of a nonlocal move, net of average expected mobility
and other covariates. Moreover, the effects of both
household wealth and wife’s self-assessed health are
signiﬁcantly more positive with respect to nonlocal
as compared with family-oriented moves.
Our results conﬁrm the utility of migration
intentions as a predictor of future mobility, at least
among young-old households. At the same time,
ﬁndings suggest important differences in the role
of economic resources and health between types of
households and across different types of moves. Of
course, there are several limitations that readers
should keep in mind. Our analysis and interpretation
assumes that the actual moves across the study
period are of the same character as the moves under
consideration at baseline. This is not an unreasonable assumption, especially for moves completed
soon after the baseline interview, given evidence to
suggest that elders may contemplate moves many
years in advance (e.g., Gober & Zonn, 1983; Haas &
Serow, 1993). Even so, future research might generate additional insight by exploiting more speciﬁc
measures of migration intentions than we employ
here (McHugh, 1984).
There are a range of additional questions raised
by our results which will have to await future
research. Speciﬁcally, our ﬁndings apply to youngold individuals and couples, but how might migration decision making may play out differently in age
cohorts who are older? Future research might
proﬁtably exploit data from the Asset and Health
Dynamics panel, which targets persons born before
1923 and includes measures of migration intentions
and outcomes.
Moreover, there may be substantial heterogeneity
among single elders with respect to the nature and
quality of family relationships and the kinds of needs
or demands that may motivate a move. Nevermarried elders, for example, may be especially likely
to move toward nieces, nephews, and siblings in light
of perceived or anticipated assistance needs. Moreover, divorce may have implications for ties to one’s
children, thus altering the migration calculus.
In addition, it is not immediately clear why only
the wife’s or female partner’s subjective health
directly impacts the hazard of making a nonlocal
move, though this ﬁnding may not be entirely counterintuitive. Executing a long-distance move without
family support at destination obviously requires a
substantial amount of planning and work. Within
couples it may be that a disproportionate share of
the planning and work of moving is shouldered by
Vol. 48, No. 2, 2008

201

women, which, if true, would help us to understand
the singular importance of the wife’s self-assessed
health. The search for a new residence, for example,
may typically fall to wives and female partners, and
healthier women may be more likely to have sufﬁcient energy to successfully execute a search. Future
research should explore the way in which the work
of moving is typically divided in couple households.
Future studies should also pay attention to other
kinds of intracouple dynamics that may condition
migration decision making and mobility behavior.
Couples that enjoy spending time together, for
example, may be more likely to make a long-distance
move involving the disruption of existing social
network ties as compared with couples sharing a less
supportive less intimate relationship.

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Received January 16, 2007
Accepted July 11, 2007
Decision Editor: William J. McAuley, PhD

The Gerontologist

