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Health, Retirement, and Migration from Metro Counties: Evidence from the Health and Retirement Study Nan E. Johnson
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Department of Sociology , Michigan State University , East Lansing , Michigan , USA Published online: 12 Nov 2013.
To cite this article: Nan E. Johnson (2013) Health, Retirement, and Migration from Metro Counties: Evidence from the Health and Retirement Study, Biodemography and Social Biology, 59:2, 127-140, DOI: 10.1080/19485565.2013.833800 To link to this article: http://dx.doi.org/10.1080/19485565.2013.833800
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Biodemography and Social Biology, 59:127–140, 2013 Copyright © Society for Biodemography and Social Biology ISSN: 1948-5565 print / 1948-5573 online DOI: 10.1080/19485565.2013.833800
Health, Retirement, and Migration from Metro Counties: Evidence from the Health and Retirement Study NAN E. JOHNSON
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Department of Sociology, Michigan State University, East Lansing, Michigan, USA Event history analyses and difference-in-proportions tests are used to analyze 1994−2003 data from the Health and Retirement Survey. For young-old metropolitan adults who had never retired, self-rated health (SRH) was unrelated to the odds of becoming a migrant, but for those who had retired, better SRH raised the odds. Neither SRH nor its interwave change was related to the risk of a nonmetro or metro destination. Metro-metro and metro-nonmetro migrants were indistinguishable in their recalled reasons for migration. The implications of the findings for theory and future research are discussed.
Introduction Most research on migration in the twentieth-century United States has focused on adults of labor-force ages and has ignored older adults. In part, this selective inattention by the scientific community reflects the much lower percentage of senior adults (ages 65 and older) migrating compared to younger adults (Frey 2001; He et al. 2005). The few studies on the migration of senior adults have sought to explain the push factors from their origins and the pull factors to their destinations as affected by their retirement and health statuses. These factors deserve much more attention, since the baby boom birth cohorts of 1946−1964 will swell the ranks of senior adults eligible for postretirement migration during most of the next two decades. For nearly half a century, there has been a persistent net in-migration to nonmetropolitan (nonmetro) U.S. counties by young-old adults (individuals aged 65−74) and a steady net in-migration to metropolitan (metro) U.S. counties by old-old adults (individuals aged 75-plus) (Schachter et al. 2003; Johnson et al. 2005). Maybe the choices made by senior adults (individuals aged 65 and older) of whether and where to migrate are shaped by whether they think they are healthy. It is possible that if older metro adults think they have good or excellent health and have undergone a retirement from the labor force, they feel free to migrate to a destination that is culturally and economically quite different or geographically distant from their current metro county of residence (a nonmetro county or a An earlier draft of this article was presented at the annual meeting of the Population Association of America in Detroit, Michigan, on May 1, 2009. David Weir constructively commented on that draft. This research was funded by a grant to the author through Michigan AgBioResearch Project, MICL01874. Address correspondence to Nan E. Johnson, Professor Emerita, Michigan State University, Department of Sociology, Berkey Hall, Room 317, 509 East Circle Drive, East Lansing, MI 48824-1111. E-mail: [email protected]
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metro county in another metropolitan statistical area [MSA], respectively). No previous study has examined this possibility. In the present investigation, I review what is known about the interplay of health and retirement statuses on the migration of older adults, as evidenced from census data and longitudinal surveys. After identifying the gaps in the literature, I frame five hypotheses that I test with 10 years’ worth of longitudinal data tracking the same individuals from ages 53−63 to ages 63−73. In the final section, I explore the implications for future research on older-adult migration.
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Evidence from Decennial Censuses Litwak and Longino (1987) hypothesized that many young-old, “amenity-seeking” adults make a “first move” around the time of retirement to natural amenities−rich nonmetro places in order to maintain friendship networks and pursue leisure activities. If their first move takes them away from children, then their second move might take them back. The later move is thought to be triggered by the onset of disabling medical conditions that compromise the elder’s ability to manage the instrumental activities of daily living (IADLs; e.g., cooking, cleaning, shopping, driving, using the telephone, and taking medications without help). The assistance-seeking move is usually to a metro destination, where 80 percent of U.S. residents now live (Schachter et al. 2003) and where most secondary and tertiary medical care is now provided. A third move might be to a nursing home or an assisted living center when the older adult becomes disabled in some of the activities of daily living (ADLs; e.g., bathing, dressing, eating, toileting, and walking indoors without help) and requires more assistance than close relatives can informally give on a daily basis. However, Litwak and Longino (1987:269) argue that a third move is usually local (e.g., from one metro county to another in the same MSA) rather than long distance (e.g., from one metro county to another in a different MSA). To contrast nonmetro-metro and metro-nonmetro migration streams in more detail, Longino (1980) used the April 1970 U.S. Census, in which people were asked to report their county of residence in April 1965 if it was not their county of residence in April 1970. Longino contrasted the intercounty migrants aged 60 and over who were metrononmetro migrants with those who were nonmetro-metro migrants. The two groups of older, cross-type intercounty migrants had about the same average age (69−70 years, p > .05). Compared to the nonmetro-metro migrants, their metro-nonmetro counterparts reported lower percentages of widows or widowers, lower percentages of older adults living with someone other than a spouse or partner, a higher mean family income, and a higher percentage of homeowners. Longino (1980) concluded that the metro-nonmetro migrants fit the demographic profile of “amenity seekers” who would gentrify their nonmetro destinations, while the nonmetro-metro migrants were likely those “who need more care than they can get in nonmetropolitan settings” (215). However, Longino could not directly contrast the health characteristics of older people in the two migration streams, because the 1970 U.S. Census lacked measures of health. This data limitation does not apply to the U.S. censuses of 1980, 1990, and 2000. Conway and Rork (2011) used data on individuals in the Integrated Public Use Microdata Series (IPUMS), constructed from a 5 percent random sample of questionnaires returned to these three U.S. censuses. The three censuses asked a comparably worded question on work-related disability concerning any chronic physical or mental health condition that caused difficulty in working, limited the amount or type of work that could be done, or prevented any work from being done. These kinds of work-related disabilities could force
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a retirement, as well as a “return migration” to a place of origin where there might be relatives who could offer practical assistance. The three censuses posed a question about an individual’s state and county of residence five years before the census date, which allowed Conway and Rork (2011) to identify those who had been interstate migrants at least once in the five years preceding the census. Interstate ”return migration,” used as a proxy for assistance-seeking migration, was defined as an interstate migration in which the state of residence five years before the census was not the state of residence at the time of the census, with the latter state being the migrant’s state of birth. The researchers found that having a work-related disability (versus its absence) raised the odds that an older adult would become an interstate migrant rather than continue living in the same state of residence as five years prior and increased the odds that those living outside their birth state would migrate back to it rather than continue living outside it. The association between the disability and interstate migration grew stronger across the three censuses. Conway and Rork (2011) speculate that the importance of the level of disability as a precursor of interstate migration might also apply to migration across urban versus rural/suburban areas but did not explore this possibility. Moreover, Conway and Rork (2011) acknowledge their inability to explore the effects of changes in disability status on migration (since their IPUMS data did not allow them to track the same individuals between 1980 and 2000). The current study, based on a longitudinal survey of the same individuals over a 10-year period, overcomes these limitations. Brown et al. (2011) used the 1990 and 2000 U.S. censuses to find the characteristics distinguishing those nonmetro counties that had net in-migration of young-old adults (aged 65−74) in the 1990s. Nonmetro counties with higher (rather than lower) rates of net migration in 1990−2000 by young-old adults, higher percentages of residents aged 65 or older in the 1990 census, a boundary on a metro county line (“metro” being defined using the 2000 census), or a hospital with at least 100 beds within the 10-mile radius had a higher (rather than lower) net in-migration rate in 1990−2000. This study was important for its finding that available health infrastructure can make a nonmetro county an attractive destination for potential residents. A limitation of this study was that its focus on counties as the analytical unit precluded a look at how the current level of, or a recent change in the level of, the migrant’s health could be a distinguishing factor.
Evidence from Longitudinal Surveys Longitudinal studies of health level, health change, and migration by young-old adults generally do not face the previously discussed limitations inherent in census data. For example, Halliday and Kimitt (2008) used the 1984−1993 Panel Study of Income Dynamics (PSID) to track the same respondents initially aged 60-plus over this decade. The researchers found that men had a higher propensity to become interstate migrants in a given year if their SRH had been above or below average (rather than average) in the preceding year. The researchers speculated that having above-average SRH may have reduced the perceived costs of interstate migration, while having below-average SRH may have raised the perceived benefits. These conclusions show that SRH can be a factor shaping the chance of migration and thus might steer migrants to different types of destinations. Perhaps men with better-than-average SRH make a first move after retirement to seek environmental amenities, and maybe men with worse-than-average SRH make a second move in quest of personal assistance for disabilities. These possibilities were not explored by Halliday and Kimmitt (2008), because they did not distinguish first from second moves. The present study makes that distinction by focusing only on the first move.
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Sabia (2008) used the PSID to track household heads and their spouses from 1972−1992. The dependent variable was the hazard rate of making the first residential move, regardless of whether it was interstate, intercounty within the same state, or within the same county or city. Consistent with a first-stage (amenity-seeking) move, respondents aged 61 or older in 1972 had double the odds of residential mobility if they had retired from the labor force. Consistent with a second-stage (assistance-seeking) move, those aged 71-plus in 1972 who had an onset of a physical limitation after starting the study limitationfree had greater odds of moving than their peers who remained disability-free. These results prompt the question: Is it the level or a recent change in the level of a health measure that becomes a trigger for migration? Drawing from the 1984−1986 waves of the Longitudinal Study of Aging (LSOA), Longino et al. (1991) concluded that both health measures are important. But the LSOA was confined to disabled adults aged 70 or older, who would likely have been making second moves. These studies were updated by Longino and colleagues’ (2008) analysis of the 1994−2002 waves of the longitudinal U.S. Health and Retirement Study (HRS). They tracked the residences of adults who were aged 53−63 in 1994 until they were aged 61−71 in 2002, with the latter age span providing brackets around the average age of retirement for men and women (64 years; Wiatrowski 2001). The strongest predictor of the odds of migration was retirement within the past year. Unexpectedly, the level of SRH did not influence the odds of making a first move. Somewhat surprisingly for this research team, it did not look at the influence of a change in SRH on the odds of a first move. However, it is possible that the SRH or changes in it had become irrelevant to older-adult migration after the early 1990s, when the longitudinal data sets analyzed by Halliday and Kimmitt (2008) and Sabia (2008) terminated. In addition, none of the previously cited studies has ever assessed how the level of SRH or its recent change might filter older metro outmigrants to a nonmetro versus metro destination. The current study fills that research gap. The importance of looking at the interrelationships of older adults’ current health, a recent change in it, and their migration to a nonmetro county has been emphasized in recent longitudinal analyses by Glasgow and Arguillas (2008) and Johnson (2012). Glasgow and Arguillas (2008) explored these relationships in 14 counties that the U.S. Department of Agriculture defined as rural retirement destinations (RRDs) after the 2000 census. The researchers called someone who had lived no longer than five years in one of the 14 RRD counties a “migrant.” Interviewing the same respondents in 2002 and 2005 and comparing the SRH of migrants and longer-term residents for these two time points, the researchers were surprised to find only small differences between the average SRH of the two groups in both years. The SRH was slightly better in both years for the migrants than for the longer-term residents, but Glasgow and Arguillas had expected much larger differences, favoring a better SRH for the migrants. Glasgow and Arguillas acknowledged that the short time frame (three years) in which to observe changes in SRH, along with sample attrition between 2002 and 2005, could have minimized their chance to observe large differences between migrants and longer-term residents in the level of, and the change in the level of, SRH. The cautious reader will note that the current study overcomes these two limitations by analyzing 10 years’ worth of longitudinal data from the same individuals using an event history analysis that controls the biases created by sample attrition. After Longino and colleagues’ (2008) analysis of the 1994−2002 waves of the HRS, Johnson (2012) used one more wave (2004) to study the first migration around the time of retirement by people initially aged 53−63, following them until they were aged 63−73. Older-adult migrants at worse levels of SRH had higher odds than those at better levels to migrate within the same type of county (nonmetro-nonmetro or metro-metro) than across
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different types of county (nonmetro-metro or metro-nonmetro). However, that analysis, like all other studies reviewed previously, did not consider the chance that the effects of SRH might interact with a respondent’s retirement status. In addition, it did not decompose the odds of a respondent migrating to a metro destination into the chance of going to another metro county within the same MSA or a different MSA. The present investigation will decompose those odds. The 1990s witnessed a major new trend in the type of destination chosen by senioradult migrants: suburban metro counties (Frey 2001). It heralded the importance of studying the interplay of retirement and health in affecting whether or not an older adult migrates and urged a more complex breakdown of the type of metro county of destination. The present investigation focuses on older adults who migrate from a metro origin to three types of destination: a nonmetro county, a metro county within the same MSA, or a metro county in a different MSA. It is plausible that the greater the change in lifestyle represented by the contrast between the origin and destination, the more favorably the migrant would need to rate his or her own health in order to cope with the physical and mental stresses of migration. This possibility likely explains why worse health reduces the odds of an older adult making a metro-nonmetro move (Johnson 2012). Because Litwak and Longino (1987) argue that poorer health should motivate shorter-distance moves by senior citizens, it is reasonable to think that worse SRH would produce a higher chance of senior citizens moving within their own MSA than to another MSA, if they move. These possibilities prompt five hypotheses.
Statement of Hypotheses Hypothesis 1 (H1): Older metro adults who have retired, have better SRH, or report that their SRH has not declined recently have higher odds than their peers of making a first move (versus remaining in the same metro county of residence). Hypothesis 2 (H2): Ceteris paribus, metro adults with better SRH have the greatest odds of becoming migrants to nonmetro destinations, intermediate odds of becoming migrants to metro counties in other MSAs, and the lowest odds of becoming migrants to other metro counties in the same MSA, when compared to metro adults with worse SRH. Hypotheses 3−5: When they recall reasons for this first migration, metrononmetro migrants should be more likely than metro-metro migrants (within or across MSAs) to state that the natural environment or leisure activities were important (H3), less likely than their metro-metro peers to report a desire to live nearer relatives or friends (who could function as caregivers) (H4), and less likely to report a health-related concern (H5).
Methods The Data I used the biennial waves of the HRS, which began in 1992. The original target respondents represented a national stratified random sample of people born between 1931 and 1941 (and thus aged 51−61 at Wave 1 in 1992), plus their spouses, regardless of the latter’s year of
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birth. Blacks, Hispanics, and residents of Florida were oversampled (see Juster and Suzman [1993] for details on the sampling design). The original respondents have been re-interviewed, if possible, every two years since 1992. Three important control variables measuring travel experience (argued by Longino et al. [2008] to be a spur to migration) were available only in 1992, and two important control variables measuring community integration (argued by Longino et al. [2008] to be a barrier to migration) were available only in 1994 (see Table 1). Thus, to be included in the present study, respondents needed to have participated in both the 1992 and the 1994 waves. Also, it was necessary to use the 2004 wave in order to know whether respondents surviving to the 2002 wave who had not yet migrated had done so between 2002 and 2003. Thus, this Table 1 Study variables: Health and Retirement Study, 1994−2003 Variable Wave year Self-rated healtht Change in SRHt Retiredt (%) White (%) Female (%) Age (y) in 1994 Widowed/divorced/separatedt (%) Education (y) Own a second home in 1992 (%) Regular vacation destination in 1992 (other than second home) (%) Own recreational vehicle in 1992 (%) Religious service attendance in 1994 Own homet (%) Area native in 1994 (%) Children living with R or within 10 milest Parents living with R or within 10 milest
Mean
Standard error
1998.04 2.60 3.07
.014 .005 .004
1 = yes; 0 = no
44.28 86.72 52.93 57.77 22.69
.002 .001 .002 .016 .002
1 = yes; 0 = no
12.66 16.06
.014 .002
1 = yes; 0 = no
35.15
.002
1 = yes; 0 = no
10.09
.001
3.27
.007
84.51 39.91 1.36
.002 .002 .007
0.26
.003
Description Annual, 1994−2003 1 (excellent) to 5 (poor) 1 (much better) to 5 (much worse) 1 = yes; 0 = no 1 = yes; 0 = no 1 = yes; 0 = no
1 = > once/wk; 2 = once/wk; 3 = 2−3 times/mo; 4=1 or more times/y; 5 = otherwise 1 = yes; 0 = otherwise 1 = yes; 0 = otherwise
Notes: “Children” includes biological children, stepchildren, and children-in-law. “Parents” includes biological parents, stepparents, and parents-in-law. Total N = 49,637 person-years. Subscript “t” indicates the variables updatable at each wave (1994, 1996, 1998, 2000, 2002, and 2004). Weights constructed from the March 1994 Current Population Survey are used to correct for the sampling design.
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analysis defines the observation decade for migration as January 1, 1994, to December 31, 2003; uses information from the 1992−2004 waves of the HRS; and applies population weights calculated from the March 1994 U.S. Current Population Survey in constructing all of the following tables.
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Dependent Variables At each interview, the numerical Federal Information Processing Standards (FIPS) code, uniquely identifying the county and state of the interview, was entered into each respondent’s record. The county of interview was not necessarily the county of the main residence. The U.S. Census Bureau defines “migration” as the relocation of a main residence across a county line. Thus, it was necessary to construct a definition of “migration.” The definition is as follows. In 1996, the respondent was asked for the first time: “In what month and year did you move to your current home in [ASSIGN MAIN RESIDENCE]”; this question has appeared in every subsequent wave. Hence, I define a “migrant” as a respondent who affirmed having moved to a new main residence since the previous interview, who stated a valid month and year as the date of that move, whose migration date was between the previous and the current interview, and whose FIPS codes were different at the two interviews. The respondents’ FIPS codes at each wave were matched to the 2004 County Typology Codes, available at the website of the U.S. Department of Agriculture (see www.ers.usda. gov/Data/TypologyCodes/). These codes label counties as “nonmetro” or “metro” according to the results of the 2000 U.S. Census of Population. The base population of the current analysis consists of the 5,916 people (weighted N) who responded to the 1994 wave from a metro county. Of these, there were 729 migrants (weighted N) over the next decade (12.32%). Once the first migration was observed, the respondent was dropped from the analysis. Of the remaining respondents, 1,717 (29.02%) refused to participate further, could not be located, or died before December 31, 2003; 3,470 (58.65%) were re-interviewed in the 2004 wave without having migrated since January 1, 1994. This retention rate in a longitudinal survey of older adults is excellent in view of the health challenges that can lead to attrition. For H1, the dependent variable is a trichotomy: metro nonmigrant, metro outmigrant, and attriter. For H2, there are five categories of the dependent variable: attriter, metro nonmigrant, and the three categories of metro outmigrants according to county type of destination (nonmetro, another county within the same MSA, another county in a different MSA). This detail will allow an exploration of whether SRH or a recent change in SRH affects the destination of older adults’ first migration around the time of retirement. For H3−H5, the dependent variables are the reasons recalled by migrants for their migration. Starting with the 1996 wave, those who had changed their main address during the past two years were asked why. The maximum number of reasons coded for a respondent was five in the 1996 and 2000 waves, six in the 1998 wave, and two in the 2002 and 2004 waves. I group these reasons into three categories: a desire to enjoy natural environments or to engage in leisure activities, a desire to live with or nearer relatives or friends, and a concern about health or health change. For each category, the migrant was scored 1 if he or she reported any reason that fell into that category and 0 otherwise. The reason(s) for migration reportedly occurring in an odd-numbered year were stated in the wave conducted in the following year (always even-numbered). The reasons for migration in an even-numbered year were taken from the wave conducted in that year if the migration
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month preceded or was the same as the interview month; otherwise, they were taken from the next wave.
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Independent Variables Two independent variables for H1 and H2 refer to the level of SRH at a given wave and the change in that level since the previous wave two years prior. At each wave, the respondent was asked to rate his or her health on a five-point scale ranging from 1 (excellent) to 5 (poor). The respondent was also asked to compare his or her health now to what it was two years prior. The response ranged from 1 (much better now) to 5 (much worse now). As the subscript “t” denotes in Table 1, these two health measures were updated every two years, when the respondent was interviewed at the next wave. The annual average score for SRH over the decade (2.60) was between “very good” and “good” (see Table 1, Row 2). The mean score of 3.07 for the interwave change in SRH represents “about the same” (see Table 1, Row 3). These indicators typify a population whose average age at the beginning of the observation decade (1994) was about 58 years (Table 1). In the multivariate analyses outlined in the following sections, the natural logarithm of age is used as a predictor variable, since an older age does not have a linear effect on the odds of older-adult migration (Litwak and Longino 1987). The third independent variable for H1 and H2 is retirement status. At each interview, respondents were asked if and when they had ever retired. Their replies were dichotomized, with 1 meaning yes, they had retired at some point, and 0 meaning otherwise. During the observation decade, an average of 44.28 percent of respondents had retired for the first time (Table 1). Finally, the independent variable for H3−H5 is the metro status (1 = metro; 0 = nonmetro) of the destination county of the metro outmigrant. The destination county was treated as a factor that preselects older-adult migrants according to their reasons for migration. Control Variables Several control variables (Table 1) were held constant in the regressions for H1 and H2. Longino et al. (2008) showed that being white, having more years of education, or more travel experience (as indexed by having a regular vacation destination or owning a second home) boosted the odds of making a nonlocal move. Also, indicators of local community integration (owning one’s residence; being familiar with nearby neighbors; having more children, children-in-law, stepchildren, parents, parents-in-law, or stepparents living with the respondent or within 10 miles; and being a native of the area where living in 1994) predicted a lower risk of making a nonlocal move. These variables were controlled here so that their effects would not confound the interrelationships of retirement, health, and health change on the odds of a first peri-retirement move and its destination. Statistical Analysis A discrete-time event history analysis was used to test H1 and H2. The unit of analysis was the person-year. Each respondent contributed one person-year of observation for every year up through, but not after, the year when he or she migrated for the first time (reported by respondent), became unlocatable, declared a desire to leave the study, or died (reported by a knowledgeable informant in an exit interview). The time of attrition from the study for
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reasons other than migration or death was defined as the year after the last interview. Those who did not experience any of these events contributed the maximum of 10 person-years each. The total weighted number of person-years (N = 49,721) constitutes the study sample on which the tests of H1 and H2 are based. The N in Table 1 is slightly below this N as a result of listwise deletion of cases with missing data. Because the person-years contributed by respondents from 1994−2003 are a discrete, time-ordered series, these units of observation were sorted and entered into the multinomial logistic regressions that predicted the categories of the dependent variable for H1 and H2. The regressions were computed with the “mlogit” routine in the STATA 11.0 software package and weighted with a factor computed from the March 1994 Current Population Survey (Tables 2 and 3). The “cluster” function was used to correct standard errors of the beta coefficients for intercorrelations arising when respondents lived together in the same family households. For discussions of the statistical theory of discrete-time event history analysis, see Allison (1984) and Blossfeld, Hamerle, and Mayer (1989). For H3−H5, the unit of analysis was the migrant. The test variable was the type of county of destination (nonmetro or metro), because it was hypothesized to select migrants according to their reasons for migration (the dependent variables). I used the “proportion” routine in the STATA 11.0 software package to compute the proportion of migrants in the category of the test variable who migrated to a nonmetro (or metro) county for a particular reason. Note that a migrant could state more than one reason for residential relocation. Table 2 Multinomial logistic regression predicting odds of a first migration (1994−2003) from a metro county: Health and Retirement Study Migrant Predictor variable Self-rated health (SRH) Change in SRH since last wave Ever retired (1 = yes) White (1 = yes) Female (1 = yes) log(age) in 1994 Widowed/divorced/separated (1 = yes) Education (y) Vacation destination in 1992 (1 = yes) Second home in 1992 (1 = yes) Own recreational vehicle in 1992 (1 = yes) Frequent religious attendance in 1994 Own home (1 = yes) Native of 1994 area (1 = yes) Number of children living with/near R Number of parents living with/near R Retired X SRH
Attriter
OR
t
OR
t
1.00 1.06 3.31∗∗∗ 1.53∗∗ 1.13 0.01∗∗∗ 1.12 1.05∗∗ 1.20† 1.23† 0.77 1.03 0.40∗∗∗ 0.71∗∗∗ 0.81∗∗∗ 0.69∗∗∗ 0.87†
−0.03 0.94 5.18 2.93 1.52 −5.18 1.12 2.84 1.94 1.70 −1.48 0.78 −7.98 −3.53 −4.82 −3.33 −1.70
1.16∗∗∗ 1.04 0.43∗∗∗ 0.74∗∗∗ 0.78∗∗∗ 4.34∗∗ 1.13† 0.99 0.91 0.94 0.96 1.09∗∗∗ 0.95 1.00 0.96† 1.01 1.16∗∗
4.14 0.92 −5.01 −4.09 −4.97 2.57 1.82 −1.32 −1.34 −0.68 −0.33 3.79 −0.66 −0.00 −1.81 0.18 2.86
Notes: N = 49,721 person-years. Wald chi-square = 487.89, degrees of freedom = 34, p < .0001. The omitted category is the metro nonmigrant. OR = odds ratio. Weights constructed from the March 1994 Current Population Survey are used to correct for the sampling design. †p < .10; ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001.
136 0.99 1.04 5.19∗∗ 2.84∗∗ 1.05 0.04 1.02 1.03 0.92 1.19 1.15 1.02 0.58∗ 0.47∗∗∗ 0.70∗∗∗ 0.66 0.84
OR
OR 0.99 1.15 1.98 1.67† 1.04 0.05† 1.19 1.02 1.30 0.96 0.63 0.97 0.43∗∗∗ 0.83 0.97 0.67† 0.81
t −0.06 0.31 3.01 3.01 0.29 −1.62 0.08 0.78 −0.41 0.65 0.42 0.22 −2.08 −3.23 −3.25 −1.55 −0.89
−0.10 1.05 1.49 1.88 0.26 −1.71 0.91 0.84 1.38 −0.14 −1.13 −0.42 −3.82 −.99 −.44 −1.90 −1.32
t
Within same MSA
0.99 1.03 3.41∗∗∗ 1.27 1.19† 0.004∗∗∗ 1.13 1.07∗∗ 1.29∗ 1.35† 0.68 1.06 0.34∗∗∗ 0.76∗ 0.76∗∗∗ 0.70∗ 0.92
OR −0.09 0.38 4.06 1.25 1.68 −4.99 0.86 2.78 2.01 1.88 −1.60 1.19 −7.10 −2.07 −4.07 −2.33 −0.76
t
To different MSA
t 4.14 0.92 −5.01 −4.09 −4.97 2.57 1.82 −1.32 −1.33 −0.68 −0.33 3.79 −0.66 −0.00 −1.81 0.19 2.86
OR 1.16∗∗∗ 1.04 0.43∗∗∗ 0.74∗∗∗ 0.78∗∗∗ 4.34∗∗ 1.13† 0.99 0.91 0.94 0.96 1.08∗∗∗ 0.95 1.00 0.96† 1.01 1.16∗∗
Attriters
Notes: N = 49,721 person-years. Wald chi-square = 547.24, degrees of freedom = 68, p < .0001. The omitted category is the metro nonmigrant. OR = odds ratio. Weights constructed from the March 1994 Current Population Survey are used to correct for the sampling design. †p < .10; ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001.
Self-rated health (SRH) Change in SRH Ever retired (1 = yes) White (1 = yes) Female (1 = yes) log(age) in 1994 Widowed/divorced/separated (1 = yes) Education (y) Vacation destination in 1992 (1 = yes) Second home in 1992 (1 = yes) Own recreational vehicle in 1992 (1 = yes) Frequent religious attendance Own home (1 = yes) Native of 1994 area (1 = yes) Number of children with/near Number of parents with/near Retirement X SRH
Predictors
Metro-nonmetro stream
Table 3 Multinomial logistic regression predicting odds of a first migration from a metro county (1994−2003) by stream type: Health and Retirement Study
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Findings
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H1: If older adults have retired, have better SRH, or report that their health has not declined recently, they have higher odds than their age peers of making a first move (versus remaining in the same county of residence). A self-reported change in health during the past interwave interval does not affect the risk of migration (OR = 1.06; p > .10; see Table 2). However, there is a statistically significant interaction between retirement status and the level of SRH upon the odds of migrating from a metro county (versus not migrating; OR = 0.87, p < .10; see Table 2, Row 17). For older metro adults who have never retired, SRH is unrelated to the odds of migration out of their 1994 county of residence (Figure 1, Panel 1). But for older metro adults who have ever retired, the odds of outmigration are higher at each of the more favorable levels of SRH (Figure 1, Panel 2). The interaction term has a modest effect (significant at p < .10 instead of the conventionally used .05 level). Yet it merits scientific attention, since the failure of previous research to consider this interaction may have resulted in the debate over the importance of SRH as a trigger of older adults’ propensity to migrate (Halliday and Kimmitt [2008] and Longino et al. [1991] vs. Longino et al. [2008]). H2: Ceteris paribus, metro adults with better SRH have the greatest odds of becoming migrants to nonmetro destinations, intermediate odds of becoming migrants to metro counties in other MSAs, and the lowest odds of becoming migrants to other metro counties in the same MSA, when compared to metro adults with worse SRH.
Figure 1. Log odds of migration by self-rated health and retirement status.
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The current study extends all previously cited works by examining whether SRH is connected to the type of county of destination when a first migration out of a metro county occurs around retirement. This innovation was prompted by previous works emphasizing good health as a critical push factor for migration into natural environments offering outdoor sports (metro-nonmetro migration) and poor health as a push factor for local migration offering closer proximity to caregivers (metro-to-local-metro migration; e.g., Johnson 2012; Litwak and Longino 1987). Table 3 (first, second, and last rows) shows that the level of SRH, its perceived change between waves, and its interaction with retirement status are all unrelated to the risk of migrating from a metro county to any of the three possible destinations (a nonmetro county, another metro county in the same MSA, or another metro county in a different MSA) relative to continuing to live in the same metro county. These results contradict H2. In sum, it seems that if an older metro adult outmigrant thinks that he or she has better (rather than worse) health, he or she will have an equal chance of migrating to any of these three destinations. However, retirement status is the most important predictor of the destination (Table 3). Compared to those who have never retired from a job, retirees have 5.19 times (p < .01) the odds of becoming a metro-nonmetro migrant and 3.41 times (p < .001) the odds of migrating to a metro county in a different MSA rather than remaining in the same metro county of residence (Table 3). Retirement does not affect the odds of intra-MSA migration (vs. remaining within the same metro county). Older intra-MSA migrants can probably change their type of housing without needing to change their labor-force status. H3: When they recall reasons for this first migration, metro-nonmetro migrants should be more likely than metro-metro migrants (within or across MSAs) to state that the natural environment or leisure activities were important factors. A smaller percentage (11.1%) of metro-nonmetro migrants than of metro-metro migrants (16.1%) said that their reasons for migrating were to enjoy natural amenities and leisure opportunities at the destination (data not presented here). But the 5 percent difference is not statistically important (z = −1.49; p < .135). H3 is rejected. H4: The metro-nonmetro migrants should be less likely than the metro-metro migrants to report a desire to live nearer relatives or friends (who could function as caregivers). The desire to live closer to relatives or friends was the most popular reason given for migration. As hypothesized by Litwak and Longino (1987), the percentage of metrononmetro migrants reporting this reason was slightly lower than that for metro-metro migrants (21.9% and 24.8%, respectively). However, the difference of 2.9 percent is not statistically significant (p < .469). These results refute H4. H5: The metro-nonmetro migrants should be less likely than the metro-metro migrants to report a health-related concern. The least popular category of reasons reported for migration was SRH or a recent change in it. Congruent with H5, it was reported by a somewhat smaller percentage of metro-nonmetro migrants than metro-metro migrants (4.2% and 5.6%, respectively). Yet the 1.4 percent difference is not statistically important (z = −0.67; p < 0.504). H5 is contradicted.
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Conclusions The present study makes five significant contributions to the published literature on biodemography and social biology. First, it synthesizes studies based on cross-sectional censuses or longitudinal surveys to identify gaps in what is known about older adults’ health, health change, and migration. It suggests the importance of looking not only at the level of an older adult’s health (Conway and Rork 2011; Halliday and Kimitt 2008; Johnson 2012) but also at any change in that level before the risk period for migration (Longino et al. 1991; Sabia 2008). An unanswered research question uncovered by the literature review is which of those two health factors is more important for adults making a peri-retirement migration. Thus, the second contribution of this study is the search for an answer to this question using the nationally representative U.S. HRS. I tracked the first migration by metro older adults (ages 53−63, with an average age of 58, in 1994) over the next decade (1994−2003). I found that until a first retirement occurs, the odds of making a first migration from a metro county versus remaining a resident of that county are even. But once an older adult has retired for the first time, the risk of the first migration from a metro county is higher if the level of SRH is more favorable. Change in the level of SRH during the prior two years is unrelated to the risk of a first migration during the next two years. Consequently, a perceived change in one’s health may be an important push factor only for later migrations following the first one after retirement. This finding would be consistent with Litwak and Longino’s (1987) theory of older adults’ sequential migrations. Third, this is the first study to explore whether the level of SRH affects the choices made by young-old metro outmigrants when deciding among three types of destination counties (rank-ordered from the destination affording the greatest change in lifestyle to the destination requiring the smallest change): nonmetro, metro in another MSA, or metro within the same MSA. Although better SRH raises the odds that an older adult will become a first-time migrant after retirement (Table 2), it does not affect the risk of choosing any of the three possible destinations (Table 3). Because of the sparse number of migrants reporting reasons for their first migration, I used the nonmetro-metro dichotomy to assess respondents’ reasons for selecting their destination county. Contrary to Litwak and Longino’s (1987) theory, metro-nonmetro migrants were not more likely than migrants to metro counties to cite the natural environment or leisure activities as important motivations, and metro-metro migrants were not more likely than migrants to nonmetro destinations to cite a health concern or a desire to live closer to relatives or friends. Maybe it is not until the second or third migration after someone retires that his or her failing health or wish to live closer to potential caregivers highlights the choices between nonmetro and metro destinations. Fourth, the findings imply consequences for the destinations of older adults who migrate out of metro counties. A better SRH score predicts their greater odds of migration anywhere, but their retirement at least one time predicts where they go. The retired metro migrant’s greatest odds are to go to (in decreasing order) a nonmetro destination, a different MSA, and a metro county in the same MSA. Thus, nonmetro counties can compete against metro counties for retirees who will bring their pensions or Social Security checks with them. Finding whether their nonmetro destinations are more likely than not to be adjacent to metro counties or their childhood homes will shed new light on the reasons why older adults migrate soon after retirement. Fifth, the current investigation raises the question, Is the net in-migration of older adults to nonmetro counties ending after more than half a century? The current analysis
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ends in 2004, before the Great Recession started in late 2007. Adults near retirement in or after 2007 and at favorable levels of SRH may be putting off retirement in order to increase their savings (see Hurd and Rohwedder 2010). Those who do retire may be unable to sell their current homes or to buy new ones in other counties because of the crash in housing values and the difficulty in obtaining mortgages. Either way, there could be a postponement of the first postretirement migration until the senior citizen is older or unhealthier or until the Great Recession ends.
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References Allison, P. D. 1984. Event history analysis: regression for longitudinal event data. Beverly Hills, CA: Sage. Blossfeld, H-P., A. Hamerle, and K. U. Mayer. 1989. Event history analysis: statistical theory and application in the social sciences. Hillsdale, NJ: Lawrence Erlbaum. Brown, D. L., B. C. Bolender, L. J. Kulcsar, N. Glasgow, and S. Sanders. 2011. Intercounty variability of net migration at older ages as a path-dependent process. Rural Sociol 76: 44–73. Conway, K. S., and J. C. Rork. 2011. The changing roles of disability, veteran, and socioeconomic status in elderly interstate migration. Res Aging 33: 256–285. Frey, W. H. 2001. Seniors in suburbia. Am Demogr 23(11): 18–21. Glasgow, N., and M-J. Arguillas. 2008. Social integration and health of older in-migrants to rural retirement destinations. In Rural retirement migration, ed. D. L. Brown and N. Glasgow, 117–140. New York: Springer. Halliday, T. J., and M. C. Kimmitt. 2008. Selective migration and health in the USA, 1984−93. Popul Stud 62(3): 321–334. He, W., M. Sengupta, V. A. Velkoff, and K. A. DeBarros. 2005. 65+ in the United States: 2005. Current population reports: special studies. Washington, DC: U.S. Census Bureau. Hurd, M., and S. Rohwedder. 2010. The effects of the economic crisis on the older population. Working Paper # 2010-231. Ann Arbor: Michigan Retirement Research Center. Johnson, K. M., P. R. Voss, R. B. Hammer, G. V. Fuguitt, and S. McNiven. 2005. Temporal and spatial variation in age-specific net migration in the United States. Demography 42(4): 791–812. Johnson, N. E. 2012. Self-rated health and the “first move” around retirement: a longitudinal study of older Americans. J Rural Health 28(2): 183–191. Juster, F. T., and R. Suzman. 1993. The health and retirement survey: an overview. Health and Retirement Study Working Paper Series Core Paper No. 94-1001. Ann Arbor, MI: Institute for Social Research. Litwak, E., and C. F. Longino, Jr. 1987. Migration patterns among the elderly: a developmental perspective. Gerontologist 27(3): 266–272. Longino, C. F., Jr. 1980. Residential relocation of older people: metropolitan and nonmetropolitan. Res Aging 2(2): 205–216. Longino, C. F., Jr., D. E. Bradley, E. P. Stoller, and W. Haas, III. 2008. Predictors of non-local moves among older adults: a prospective study. J Gerontol Soc Sci 63B(1): S7–S14. Longino, C. F., Jr., D. J. Jackson, R. S. Zimmerman, and J. E. Bradsher. 1991. The second move: health and geographic mobility. J Gerontol 46(4): S218–S224. Sabia, J. J. 2008. There’s no place like home: a hazard model analysis of aging in place among older homeowners in the PSID. Res Aging 30(3): 3–35. Wiatrowski, W. J. 2001. Changing retirement age: ups and downs. Month Labor Rev (April):3–10.
Biodemography and Social Biology Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hsbi20
Health, Retirement, and Migration from Metro Counties: Evidence from the Health and Retirement Study Nan E. Johnson
a
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Department of Sociology , Michigan State University , East Lansing , Michigan , USA Published online: 12 Nov 2013.
To cite this article: Nan E. Johnson (2013) Health, Retirement, and Migration from Metro Counties: Evidence from the Health and Retirement Study, Biodemography and Social Biology, 59:2, 127-140, DOI: 10.1080/19485565.2013.833800 To link to this article: http://dx.doi.org/10.1080/19485565.2013.833800
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Biodemography and Social Biology, 59:127–140, 2013 Copyright © Society for Biodemography and Social Biology ISSN: 1948-5565 print / 1948-5573 online DOI: 10.1080/19485565.2013.833800
Health, Retirement, and Migration from Metro Counties: Evidence from the Health and Retirement Study NAN E. JOHNSON
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Department of Sociology, Michigan State University, East Lansing, Michigan, USA Event history analyses and difference-in-proportions tests are used to analyze 1994−2003 data from the Health and Retirement Survey. For young-old metropolitan adults who had never retired, self-rated health (SRH) was unrelated to the odds of becoming a migrant, but for those who had retired, better SRH raised the odds. Neither SRH nor its interwave change was related to the risk of a nonmetro or metro destination. Metro-metro and metro-nonmetro migrants were indistinguishable in their recalled reasons for migration. The implications of the findings for theory and future research are discussed.
Introduction Most research on migration in the twentieth-century United States has focused on adults of labor-force ages and has ignored older adults. In part, this selective inattention by the scientific community reflects the much lower percentage of senior adults (ages 65 and older) migrating compared to younger adults (Frey 2001; He et al. 2005). The few studies on the migration of senior adults have sought to explain the push factors from their origins and the pull factors to their destinations as affected by their retirement and health statuses. These factors deserve much more attention, since the baby boom birth cohorts of 1946−1964 will swell the ranks of senior adults eligible for postretirement migration during most of the next two decades. For nearly half a century, there has been a persistent net in-migration to nonmetropolitan (nonmetro) U.S. counties by young-old adults (individuals aged 65−74) and a steady net in-migration to metropolitan (metro) U.S. counties by old-old adults (individuals aged 75-plus) (Schachter et al. 2003; Johnson et al. 2005). Maybe the choices made by senior adults (individuals aged 65 and older) of whether and where to migrate are shaped by whether they think they are healthy. It is possible that if older metro adults think they have good or excellent health and have undergone a retirement from the labor force, they feel free to migrate to a destination that is culturally and economically quite different or geographically distant from their current metro county of residence (a nonmetro county or a An earlier draft of this article was presented at the annual meeting of the Population Association of America in Detroit, Michigan, on May 1, 2009. David Weir constructively commented on that draft. This research was funded by a grant to the author through Michigan AgBioResearch Project, MICL01874. Address correspondence to Nan E. Johnson, Professor Emerita, Michigan State University, Department of Sociology, Berkey Hall, Room 317, 509 East Circle Drive, East Lansing, MI 48824-1111. E-mail: [email protected]
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metro county in another metropolitan statistical area [MSA], respectively). No previous study has examined this possibility. In the present investigation, I review what is known about the interplay of health and retirement statuses on the migration of older adults, as evidenced from census data and longitudinal surveys. After identifying the gaps in the literature, I frame five hypotheses that I test with 10 years’ worth of longitudinal data tracking the same individuals from ages 53−63 to ages 63−73. In the final section, I explore the implications for future research on older-adult migration.
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Evidence from Decennial Censuses Litwak and Longino (1987) hypothesized that many young-old, “amenity-seeking” adults make a “first move” around the time of retirement to natural amenities−rich nonmetro places in order to maintain friendship networks and pursue leisure activities. If their first move takes them away from children, then their second move might take them back. The later move is thought to be triggered by the onset of disabling medical conditions that compromise the elder’s ability to manage the instrumental activities of daily living (IADLs; e.g., cooking, cleaning, shopping, driving, using the telephone, and taking medications without help). The assistance-seeking move is usually to a metro destination, where 80 percent of U.S. residents now live (Schachter et al. 2003) and where most secondary and tertiary medical care is now provided. A third move might be to a nursing home or an assisted living center when the older adult becomes disabled in some of the activities of daily living (ADLs; e.g., bathing, dressing, eating, toileting, and walking indoors without help) and requires more assistance than close relatives can informally give on a daily basis. However, Litwak and Longino (1987:269) argue that a third move is usually local (e.g., from one metro county to another in the same MSA) rather than long distance (e.g., from one metro county to another in a different MSA). To contrast nonmetro-metro and metro-nonmetro migration streams in more detail, Longino (1980) used the April 1970 U.S. Census, in which people were asked to report their county of residence in April 1965 if it was not their county of residence in April 1970. Longino contrasted the intercounty migrants aged 60 and over who were metrononmetro migrants with those who were nonmetro-metro migrants. The two groups of older, cross-type intercounty migrants had about the same average age (69−70 years, p > .05). Compared to the nonmetro-metro migrants, their metro-nonmetro counterparts reported lower percentages of widows or widowers, lower percentages of older adults living with someone other than a spouse or partner, a higher mean family income, and a higher percentage of homeowners. Longino (1980) concluded that the metro-nonmetro migrants fit the demographic profile of “amenity seekers” who would gentrify their nonmetro destinations, while the nonmetro-metro migrants were likely those “who need more care than they can get in nonmetropolitan settings” (215). However, Longino could not directly contrast the health characteristics of older people in the two migration streams, because the 1970 U.S. Census lacked measures of health. This data limitation does not apply to the U.S. censuses of 1980, 1990, and 2000. Conway and Rork (2011) used data on individuals in the Integrated Public Use Microdata Series (IPUMS), constructed from a 5 percent random sample of questionnaires returned to these three U.S. censuses. The three censuses asked a comparably worded question on work-related disability concerning any chronic physical or mental health condition that caused difficulty in working, limited the amount or type of work that could be done, or prevented any work from being done. These kinds of work-related disabilities could force
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a retirement, as well as a “return migration” to a place of origin where there might be relatives who could offer practical assistance. The three censuses posed a question about an individual’s state and county of residence five years before the census date, which allowed Conway and Rork (2011) to identify those who had been interstate migrants at least once in the five years preceding the census. Interstate ”return migration,” used as a proxy for assistance-seeking migration, was defined as an interstate migration in which the state of residence five years before the census was not the state of residence at the time of the census, with the latter state being the migrant’s state of birth. The researchers found that having a work-related disability (versus its absence) raised the odds that an older adult would become an interstate migrant rather than continue living in the same state of residence as five years prior and increased the odds that those living outside their birth state would migrate back to it rather than continue living outside it. The association between the disability and interstate migration grew stronger across the three censuses. Conway and Rork (2011) speculate that the importance of the level of disability as a precursor of interstate migration might also apply to migration across urban versus rural/suburban areas but did not explore this possibility. Moreover, Conway and Rork (2011) acknowledge their inability to explore the effects of changes in disability status on migration (since their IPUMS data did not allow them to track the same individuals between 1980 and 2000). The current study, based on a longitudinal survey of the same individuals over a 10-year period, overcomes these limitations. Brown et al. (2011) used the 1990 and 2000 U.S. censuses to find the characteristics distinguishing those nonmetro counties that had net in-migration of young-old adults (aged 65−74) in the 1990s. Nonmetro counties with higher (rather than lower) rates of net migration in 1990−2000 by young-old adults, higher percentages of residents aged 65 or older in the 1990 census, a boundary on a metro county line (“metro” being defined using the 2000 census), or a hospital with at least 100 beds within the 10-mile radius had a higher (rather than lower) net in-migration rate in 1990−2000. This study was important for its finding that available health infrastructure can make a nonmetro county an attractive destination for potential residents. A limitation of this study was that its focus on counties as the analytical unit precluded a look at how the current level of, or a recent change in the level of, the migrant’s health could be a distinguishing factor.
Evidence from Longitudinal Surveys Longitudinal studies of health level, health change, and migration by young-old adults generally do not face the previously discussed limitations inherent in census data. For example, Halliday and Kimitt (2008) used the 1984−1993 Panel Study of Income Dynamics (PSID) to track the same respondents initially aged 60-plus over this decade. The researchers found that men had a higher propensity to become interstate migrants in a given year if their SRH had been above or below average (rather than average) in the preceding year. The researchers speculated that having above-average SRH may have reduced the perceived costs of interstate migration, while having below-average SRH may have raised the perceived benefits. These conclusions show that SRH can be a factor shaping the chance of migration and thus might steer migrants to different types of destinations. Perhaps men with better-than-average SRH make a first move after retirement to seek environmental amenities, and maybe men with worse-than-average SRH make a second move in quest of personal assistance for disabilities. These possibilities were not explored by Halliday and Kimmitt (2008), because they did not distinguish first from second moves. The present study makes that distinction by focusing only on the first move.
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Sabia (2008) used the PSID to track household heads and their spouses from 1972−1992. The dependent variable was the hazard rate of making the first residential move, regardless of whether it was interstate, intercounty within the same state, or within the same county or city. Consistent with a first-stage (amenity-seeking) move, respondents aged 61 or older in 1972 had double the odds of residential mobility if they had retired from the labor force. Consistent with a second-stage (assistance-seeking) move, those aged 71-plus in 1972 who had an onset of a physical limitation after starting the study limitationfree had greater odds of moving than their peers who remained disability-free. These results prompt the question: Is it the level or a recent change in the level of a health measure that becomes a trigger for migration? Drawing from the 1984−1986 waves of the Longitudinal Study of Aging (LSOA), Longino et al. (1991) concluded that both health measures are important. But the LSOA was confined to disabled adults aged 70 or older, who would likely have been making second moves. These studies were updated by Longino and colleagues’ (2008) analysis of the 1994−2002 waves of the longitudinal U.S. Health and Retirement Study (HRS). They tracked the residences of adults who were aged 53−63 in 1994 until they were aged 61−71 in 2002, with the latter age span providing brackets around the average age of retirement for men and women (64 years; Wiatrowski 2001). The strongest predictor of the odds of migration was retirement within the past year. Unexpectedly, the level of SRH did not influence the odds of making a first move. Somewhat surprisingly for this research team, it did not look at the influence of a change in SRH on the odds of a first move. However, it is possible that the SRH or changes in it had become irrelevant to older-adult migration after the early 1990s, when the longitudinal data sets analyzed by Halliday and Kimmitt (2008) and Sabia (2008) terminated. In addition, none of the previously cited studies has ever assessed how the level of SRH or its recent change might filter older metro outmigrants to a nonmetro versus metro destination. The current study fills that research gap. The importance of looking at the interrelationships of older adults’ current health, a recent change in it, and their migration to a nonmetro county has been emphasized in recent longitudinal analyses by Glasgow and Arguillas (2008) and Johnson (2012). Glasgow and Arguillas (2008) explored these relationships in 14 counties that the U.S. Department of Agriculture defined as rural retirement destinations (RRDs) after the 2000 census. The researchers called someone who had lived no longer than five years in one of the 14 RRD counties a “migrant.” Interviewing the same respondents in 2002 and 2005 and comparing the SRH of migrants and longer-term residents for these two time points, the researchers were surprised to find only small differences between the average SRH of the two groups in both years. The SRH was slightly better in both years for the migrants than for the longer-term residents, but Glasgow and Arguillas had expected much larger differences, favoring a better SRH for the migrants. Glasgow and Arguillas acknowledged that the short time frame (three years) in which to observe changes in SRH, along with sample attrition between 2002 and 2005, could have minimized their chance to observe large differences between migrants and longer-term residents in the level of, and the change in the level of, SRH. The cautious reader will note that the current study overcomes these two limitations by analyzing 10 years’ worth of longitudinal data from the same individuals using an event history analysis that controls the biases created by sample attrition. After Longino and colleagues’ (2008) analysis of the 1994−2002 waves of the HRS, Johnson (2012) used one more wave (2004) to study the first migration around the time of retirement by people initially aged 53−63, following them until they were aged 63−73. Older-adult migrants at worse levels of SRH had higher odds than those at better levels to migrate within the same type of county (nonmetro-nonmetro or metro-metro) than across
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different types of county (nonmetro-metro or metro-nonmetro). However, that analysis, like all other studies reviewed previously, did not consider the chance that the effects of SRH might interact with a respondent’s retirement status. In addition, it did not decompose the odds of a respondent migrating to a metro destination into the chance of going to another metro county within the same MSA or a different MSA. The present investigation will decompose those odds. The 1990s witnessed a major new trend in the type of destination chosen by senioradult migrants: suburban metro counties (Frey 2001). It heralded the importance of studying the interplay of retirement and health in affecting whether or not an older adult migrates and urged a more complex breakdown of the type of metro county of destination. The present investigation focuses on older adults who migrate from a metro origin to three types of destination: a nonmetro county, a metro county within the same MSA, or a metro county in a different MSA. It is plausible that the greater the change in lifestyle represented by the contrast between the origin and destination, the more favorably the migrant would need to rate his or her own health in order to cope with the physical and mental stresses of migration. This possibility likely explains why worse health reduces the odds of an older adult making a metro-nonmetro move (Johnson 2012). Because Litwak and Longino (1987) argue that poorer health should motivate shorter-distance moves by senior citizens, it is reasonable to think that worse SRH would produce a higher chance of senior citizens moving within their own MSA than to another MSA, if they move. These possibilities prompt five hypotheses.
Statement of Hypotheses Hypothesis 1 (H1): Older metro adults who have retired, have better SRH, or report that their SRH has not declined recently have higher odds than their peers of making a first move (versus remaining in the same metro county of residence). Hypothesis 2 (H2): Ceteris paribus, metro adults with better SRH have the greatest odds of becoming migrants to nonmetro destinations, intermediate odds of becoming migrants to metro counties in other MSAs, and the lowest odds of becoming migrants to other metro counties in the same MSA, when compared to metro adults with worse SRH. Hypotheses 3−5: When they recall reasons for this first migration, metrononmetro migrants should be more likely than metro-metro migrants (within or across MSAs) to state that the natural environment or leisure activities were important (H3), less likely than their metro-metro peers to report a desire to live nearer relatives or friends (who could function as caregivers) (H4), and less likely to report a health-related concern (H5).
Methods The Data I used the biennial waves of the HRS, which began in 1992. The original target respondents represented a national stratified random sample of people born between 1931 and 1941 (and thus aged 51−61 at Wave 1 in 1992), plus their spouses, regardless of the latter’s year of
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birth. Blacks, Hispanics, and residents of Florida were oversampled (see Juster and Suzman [1993] for details on the sampling design). The original respondents have been re-interviewed, if possible, every two years since 1992. Three important control variables measuring travel experience (argued by Longino et al. [2008] to be a spur to migration) were available only in 1992, and two important control variables measuring community integration (argued by Longino et al. [2008] to be a barrier to migration) were available only in 1994 (see Table 1). Thus, to be included in the present study, respondents needed to have participated in both the 1992 and the 1994 waves. Also, it was necessary to use the 2004 wave in order to know whether respondents surviving to the 2002 wave who had not yet migrated had done so between 2002 and 2003. Thus, this Table 1 Study variables: Health and Retirement Study, 1994−2003 Variable Wave year Self-rated healtht Change in SRHt Retiredt (%) White (%) Female (%) Age (y) in 1994 Widowed/divorced/separatedt (%) Education (y) Own a second home in 1992 (%) Regular vacation destination in 1992 (other than second home) (%) Own recreational vehicle in 1992 (%) Religious service attendance in 1994 Own homet (%) Area native in 1994 (%) Children living with R or within 10 milest Parents living with R or within 10 milest
Mean
Standard error
1998.04 2.60 3.07
.014 .005 .004
1 = yes; 0 = no
44.28 86.72 52.93 57.77 22.69
.002 .001 .002 .016 .002
1 = yes; 0 = no
12.66 16.06
.014 .002
1 = yes; 0 = no
35.15
.002
1 = yes; 0 = no
10.09
.001
3.27
.007
84.51 39.91 1.36
.002 .002 .007
0.26
.003
Description Annual, 1994−2003 1 (excellent) to 5 (poor) 1 (much better) to 5 (much worse) 1 = yes; 0 = no 1 = yes; 0 = no 1 = yes; 0 = no
1 = > once/wk; 2 = once/wk; 3 = 2−3 times/mo; 4=1 or more times/y; 5 = otherwise 1 = yes; 0 = otherwise 1 = yes; 0 = otherwise
Notes: “Children” includes biological children, stepchildren, and children-in-law. “Parents” includes biological parents, stepparents, and parents-in-law. Total N = 49,637 person-years. Subscript “t” indicates the variables updatable at each wave (1994, 1996, 1998, 2000, 2002, and 2004). Weights constructed from the March 1994 Current Population Survey are used to correct for the sampling design.
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analysis defines the observation decade for migration as January 1, 1994, to December 31, 2003; uses information from the 1992−2004 waves of the HRS; and applies population weights calculated from the March 1994 U.S. Current Population Survey in constructing all of the following tables.
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Dependent Variables At each interview, the numerical Federal Information Processing Standards (FIPS) code, uniquely identifying the county and state of the interview, was entered into each respondent’s record. The county of interview was not necessarily the county of the main residence. The U.S. Census Bureau defines “migration” as the relocation of a main residence across a county line. Thus, it was necessary to construct a definition of “migration.” The definition is as follows. In 1996, the respondent was asked for the first time: “In what month and year did you move to your current home in [ASSIGN MAIN RESIDENCE]”; this question has appeared in every subsequent wave. Hence, I define a “migrant” as a respondent who affirmed having moved to a new main residence since the previous interview, who stated a valid month and year as the date of that move, whose migration date was between the previous and the current interview, and whose FIPS codes were different at the two interviews. The respondents’ FIPS codes at each wave were matched to the 2004 County Typology Codes, available at the website of the U.S. Department of Agriculture (see www.ers.usda. gov/Data/TypologyCodes/). These codes label counties as “nonmetro” or “metro” according to the results of the 2000 U.S. Census of Population. The base population of the current analysis consists of the 5,916 people (weighted N) who responded to the 1994 wave from a metro county. Of these, there were 729 migrants (weighted N) over the next decade (12.32%). Once the first migration was observed, the respondent was dropped from the analysis. Of the remaining respondents, 1,717 (29.02%) refused to participate further, could not be located, or died before December 31, 2003; 3,470 (58.65%) were re-interviewed in the 2004 wave without having migrated since January 1, 1994. This retention rate in a longitudinal survey of older adults is excellent in view of the health challenges that can lead to attrition. For H1, the dependent variable is a trichotomy: metro nonmigrant, metro outmigrant, and attriter. For H2, there are five categories of the dependent variable: attriter, metro nonmigrant, and the three categories of metro outmigrants according to county type of destination (nonmetro, another county within the same MSA, another county in a different MSA). This detail will allow an exploration of whether SRH or a recent change in SRH affects the destination of older adults’ first migration around the time of retirement. For H3−H5, the dependent variables are the reasons recalled by migrants for their migration. Starting with the 1996 wave, those who had changed their main address during the past two years were asked why. The maximum number of reasons coded for a respondent was five in the 1996 and 2000 waves, six in the 1998 wave, and two in the 2002 and 2004 waves. I group these reasons into three categories: a desire to enjoy natural environments or to engage in leisure activities, a desire to live with or nearer relatives or friends, and a concern about health or health change. For each category, the migrant was scored 1 if he or she reported any reason that fell into that category and 0 otherwise. The reason(s) for migration reportedly occurring in an odd-numbered year were stated in the wave conducted in the following year (always even-numbered). The reasons for migration in an even-numbered year were taken from the wave conducted in that year if the migration
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month preceded or was the same as the interview month; otherwise, they were taken from the next wave.
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Independent Variables Two independent variables for H1 and H2 refer to the level of SRH at a given wave and the change in that level since the previous wave two years prior. At each wave, the respondent was asked to rate his or her health on a five-point scale ranging from 1 (excellent) to 5 (poor). The respondent was also asked to compare his or her health now to what it was two years prior. The response ranged from 1 (much better now) to 5 (much worse now). As the subscript “t” denotes in Table 1, these two health measures were updated every two years, when the respondent was interviewed at the next wave. The annual average score for SRH over the decade (2.60) was between “very good” and “good” (see Table 1, Row 2). The mean score of 3.07 for the interwave change in SRH represents “about the same” (see Table 1, Row 3). These indicators typify a population whose average age at the beginning of the observation decade (1994) was about 58 years (Table 1). In the multivariate analyses outlined in the following sections, the natural logarithm of age is used as a predictor variable, since an older age does not have a linear effect on the odds of older-adult migration (Litwak and Longino 1987). The third independent variable for H1 and H2 is retirement status. At each interview, respondents were asked if and when they had ever retired. Their replies were dichotomized, with 1 meaning yes, they had retired at some point, and 0 meaning otherwise. During the observation decade, an average of 44.28 percent of respondents had retired for the first time (Table 1). Finally, the independent variable for H3−H5 is the metro status (1 = metro; 0 = nonmetro) of the destination county of the metro outmigrant. The destination county was treated as a factor that preselects older-adult migrants according to their reasons for migration. Control Variables Several control variables (Table 1) were held constant in the regressions for H1 and H2. Longino et al. (2008) showed that being white, having more years of education, or more travel experience (as indexed by having a regular vacation destination or owning a second home) boosted the odds of making a nonlocal move. Also, indicators of local community integration (owning one’s residence; being familiar with nearby neighbors; having more children, children-in-law, stepchildren, parents, parents-in-law, or stepparents living with the respondent or within 10 miles; and being a native of the area where living in 1994) predicted a lower risk of making a nonlocal move. These variables were controlled here so that their effects would not confound the interrelationships of retirement, health, and health change on the odds of a first peri-retirement move and its destination. Statistical Analysis A discrete-time event history analysis was used to test H1 and H2. The unit of analysis was the person-year. Each respondent contributed one person-year of observation for every year up through, but not after, the year when he or she migrated for the first time (reported by respondent), became unlocatable, declared a desire to leave the study, or died (reported by a knowledgeable informant in an exit interview). The time of attrition from the study for
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reasons other than migration or death was defined as the year after the last interview. Those who did not experience any of these events contributed the maximum of 10 person-years each. The total weighted number of person-years (N = 49,721) constitutes the study sample on which the tests of H1 and H2 are based. The N in Table 1 is slightly below this N as a result of listwise deletion of cases with missing data. Because the person-years contributed by respondents from 1994−2003 are a discrete, time-ordered series, these units of observation were sorted and entered into the multinomial logistic regressions that predicted the categories of the dependent variable for H1 and H2. The regressions were computed with the “mlogit” routine in the STATA 11.0 software package and weighted with a factor computed from the March 1994 Current Population Survey (Tables 2 and 3). The “cluster” function was used to correct standard errors of the beta coefficients for intercorrelations arising when respondents lived together in the same family households. For discussions of the statistical theory of discrete-time event history analysis, see Allison (1984) and Blossfeld, Hamerle, and Mayer (1989). For H3−H5, the unit of analysis was the migrant. The test variable was the type of county of destination (nonmetro or metro), because it was hypothesized to select migrants according to their reasons for migration (the dependent variables). I used the “proportion” routine in the STATA 11.0 software package to compute the proportion of migrants in the category of the test variable who migrated to a nonmetro (or metro) county for a particular reason. Note that a migrant could state more than one reason for residential relocation. Table 2 Multinomial logistic regression predicting odds of a first migration (1994−2003) from a metro county: Health and Retirement Study Migrant Predictor variable Self-rated health (SRH) Change in SRH since last wave Ever retired (1 = yes) White (1 = yes) Female (1 = yes) log(age) in 1994 Widowed/divorced/separated (1 = yes) Education (y) Vacation destination in 1992 (1 = yes) Second home in 1992 (1 = yes) Own recreational vehicle in 1992 (1 = yes) Frequent religious attendance in 1994 Own home (1 = yes) Native of 1994 area (1 = yes) Number of children living with/near R Number of parents living with/near R Retired X SRH
Attriter
OR
t
OR
t
1.00 1.06 3.31∗∗∗ 1.53∗∗ 1.13 0.01∗∗∗ 1.12 1.05∗∗ 1.20† 1.23† 0.77 1.03 0.40∗∗∗ 0.71∗∗∗ 0.81∗∗∗ 0.69∗∗∗ 0.87†
−0.03 0.94 5.18 2.93 1.52 −5.18 1.12 2.84 1.94 1.70 −1.48 0.78 −7.98 −3.53 −4.82 −3.33 −1.70
1.16∗∗∗ 1.04 0.43∗∗∗ 0.74∗∗∗ 0.78∗∗∗ 4.34∗∗ 1.13† 0.99 0.91 0.94 0.96 1.09∗∗∗ 0.95 1.00 0.96† 1.01 1.16∗∗
4.14 0.92 −5.01 −4.09 −4.97 2.57 1.82 −1.32 −1.34 −0.68 −0.33 3.79 −0.66 −0.00 −1.81 0.18 2.86
Notes: N = 49,721 person-years. Wald chi-square = 487.89, degrees of freedom = 34, p < .0001. The omitted category is the metro nonmigrant. OR = odds ratio. Weights constructed from the March 1994 Current Population Survey are used to correct for the sampling design. †p < .10; ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001.
136 0.99 1.04 5.19∗∗ 2.84∗∗ 1.05 0.04 1.02 1.03 0.92 1.19 1.15 1.02 0.58∗ 0.47∗∗∗ 0.70∗∗∗ 0.66 0.84
OR
OR 0.99 1.15 1.98 1.67† 1.04 0.05† 1.19 1.02 1.30 0.96 0.63 0.97 0.43∗∗∗ 0.83 0.97 0.67† 0.81
t −0.06 0.31 3.01 3.01 0.29 −1.62 0.08 0.78 −0.41 0.65 0.42 0.22 −2.08 −3.23 −3.25 −1.55 −0.89
−0.10 1.05 1.49 1.88 0.26 −1.71 0.91 0.84 1.38 −0.14 −1.13 −0.42 −3.82 −.99 −.44 −1.90 −1.32
t
Within same MSA
0.99 1.03 3.41∗∗∗ 1.27 1.19† 0.004∗∗∗ 1.13 1.07∗∗ 1.29∗ 1.35† 0.68 1.06 0.34∗∗∗ 0.76∗ 0.76∗∗∗ 0.70∗ 0.92
OR −0.09 0.38 4.06 1.25 1.68 −4.99 0.86 2.78 2.01 1.88 −1.60 1.19 −7.10 −2.07 −4.07 −2.33 −0.76
t
To different MSA
t 4.14 0.92 −5.01 −4.09 −4.97 2.57 1.82 −1.32 −1.33 −0.68 −0.33 3.79 −0.66 −0.00 −1.81 0.19 2.86
OR 1.16∗∗∗ 1.04 0.43∗∗∗ 0.74∗∗∗ 0.78∗∗∗ 4.34∗∗ 1.13† 0.99 0.91 0.94 0.96 1.08∗∗∗ 0.95 1.00 0.96† 1.01 1.16∗∗
Attriters
Notes: N = 49,721 person-years. Wald chi-square = 547.24, degrees of freedom = 68, p < .0001. The omitted category is the metro nonmigrant. OR = odds ratio. Weights constructed from the March 1994 Current Population Survey are used to correct for the sampling design. †p < .10; ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001.
Self-rated health (SRH) Change in SRH Ever retired (1 = yes) White (1 = yes) Female (1 = yes) log(age) in 1994 Widowed/divorced/separated (1 = yes) Education (y) Vacation destination in 1992 (1 = yes) Second home in 1992 (1 = yes) Own recreational vehicle in 1992 (1 = yes) Frequent religious attendance Own home (1 = yes) Native of 1994 area (1 = yes) Number of children with/near Number of parents with/near Retirement X SRH
Predictors
Metro-nonmetro stream
Table 3 Multinomial logistic regression predicting odds of a first migration from a metro county (1994−2003) by stream type: Health and Retirement Study
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Findings
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H1: If older adults have retired, have better SRH, or report that their health has not declined recently, they have higher odds than their age peers of making a first move (versus remaining in the same county of residence). A self-reported change in health during the past interwave interval does not affect the risk of migration (OR = 1.06; p > .10; see Table 2). However, there is a statistically significant interaction between retirement status and the level of SRH upon the odds of migrating from a metro county (versus not migrating; OR = 0.87, p < .10; see Table 2, Row 17). For older metro adults who have never retired, SRH is unrelated to the odds of migration out of their 1994 county of residence (Figure 1, Panel 1). But for older metro adults who have ever retired, the odds of outmigration are higher at each of the more favorable levels of SRH (Figure 1, Panel 2). The interaction term has a modest effect (significant at p < .10 instead of the conventionally used .05 level). Yet it merits scientific attention, since the failure of previous research to consider this interaction may have resulted in the debate over the importance of SRH as a trigger of older adults’ propensity to migrate (Halliday and Kimmitt [2008] and Longino et al. [1991] vs. Longino et al. [2008]). H2: Ceteris paribus, metro adults with better SRH have the greatest odds of becoming migrants to nonmetro destinations, intermediate odds of becoming migrants to metro counties in other MSAs, and the lowest odds of becoming migrants to other metro counties in the same MSA, when compared to metro adults with worse SRH.
Figure 1. Log odds of migration by self-rated health and retirement status.
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The current study extends all previously cited works by examining whether SRH is connected to the type of county of destination when a first migration out of a metro county occurs around retirement. This innovation was prompted by previous works emphasizing good health as a critical push factor for migration into natural environments offering outdoor sports (metro-nonmetro migration) and poor health as a push factor for local migration offering closer proximity to caregivers (metro-to-local-metro migration; e.g., Johnson 2012; Litwak and Longino 1987). Table 3 (first, second, and last rows) shows that the level of SRH, its perceived change between waves, and its interaction with retirement status are all unrelated to the risk of migrating from a metro county to any of the three possible destinations (a nonmetro county, another metro county in the same MSA, or another metro county in a different MSA) relative to continuing to live in the same metro county. These results contradict H2. In sum, it seems that if an older metro adult outmigrant thinks that he or she has better (rather than worse) health, he or she will have an equal chance of migrating to any of these three destinations. However, retirement status is the most important predictor of the destination (Table 3). Compared to those who have never retired from a job, retirees have 5.19 times (p < .01) the odds of becoming a metro-nonmetro migrant and 3.41 times (p < .001) the odds of migrating to a metro county in a different MSA rather than remaining in the same metro county of residence (Table 3). Retirement does not affect the odds of intra-MSA migration (vs. remaining within the same metro county). Older intra-MSA migrants can probably change their type of housing without needing to change their labor-force status. H3: When they recall reasons for this first migration, metro-nonmetro migrants should be more likely than metro-metro migrants (within or across MSAs) to state that the natural environment or leisure activities were important factors. A smaller percentage (11.1%) of metro-nonmetro migrants than of metro-metro migrants (16.1%) said that their reasons for migrating were to enjoy natural amenities and leisure opportunities at the destination (data not presented here). But the 5 percent difference is not statistically important (z = −1.49; p < .135). H3 is rejected. H4: The metro-nonmetro migrants should be less likely than the metro-metro migrants to report a desire to live nearer relatives or friends (who could function as caregivers). The desire to live closer to relatives or friends was the most popular reason given for migration. As hypothesized by Litwak and Longino (1987), the percentage of metrononmetro migrants reporting this reason was slightly lower than that for metro-metro migrants (21.9% and 24.8%, respectively). However, the difference of 2.9 percent is not statistically significant (p < .469). These results refute H4. H5: The metro-nonmetro migrants should be less likely than the metro-metro migrants to report a health-related concern. The least popular category of reasons reported for migration was SRH or a recent change in it. Congruent with H5, it was reported by a somewhat smaller percentage of metro-nonmetro migrants than metro-metro migrants (4.2% and 5.6%, respectively). Yet the 1.4 percent difference is not statistically important (z = −0.67; p < 0.504). H5 is contradicted.
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Conclusions The present study makes five significant contributions to the published literature on biodemography and social biology. First, it synthesizes studies based on cross-sectional censuses or longitudinal surveys to identify gaps in what is known about older adults’ health, health change, and migration. It suggests the importance of looking not only at the level of an older adult’s health (Conway and Rork 2011; Halliday and Kimitt 2008; Johnson 2012) but also at any change in that level before the risk period for migration (Longino et al. 1991; Sabia 2008). An unanswered research question uncovered by the literature review is which of those two health factors is more important for adults making a peri-retirement migration. Thus, the second contribution of this study is the search for an answer to this question using the nationally representative U.S. HRS. I tracked the first migration by metro older adults (ages 53−63, with an average age of 58, in 1994) over the next decade (1994−2003). I found that until a first retirement occurs, the odds of making a first migration from a metro county versus remaining a resident of that county are even. But once an older adult has retired for the first time, the risk of the first migration from a metro county is higher if the level of SRH is more favorable. Change in the level of SRH during the prior two years is unrelated to the risk of a first migration during the next two years. Consequently, a perceived change in one’s health may be an important push factor only for later migrations following the first one after retirement. This finding would be consistent with Litwak and Longino’s (1987) theory of older adults’ sequential migrations. Third, this is the first study to explore whether the level of SRH affects the choices made by young-old metro outmigrants when deciding among three types of destination counties (rank-ordered from the destination affording the greatest change in lifestyle to the destination requiring the smallest change): nonmetro, metro in another MSA, or metro within the same MSA. Although better SRH raises the odds that an older adult will become a first-time migrant after retirement (Table 2), it does not affect the risk of choosing any of the three possible destinations (Table 3). Because of the sparse number of migrants reporting reasons for their first migration, I used the nonmetro-metro dichotomy to assess respondents’ reasons for selecting their destination county. Contrary to Litwak and Longino’s (1987) theory, metro-nonmetro migrants were not more likely than migrants to metro counties to cite the natural environment or leisure activities as important motivations, and metro-metro migrants were not more likely than migrants to nonmetro destinations to cite a health concern or a desire to live closer to relatives or friends. Maybe it is not until the second or third migration after someone retires that his or her failing health or wish to live closer to potential caregivers highlights the choices between nonmetro and metro destinations. Fourth, the findings imply consequences for the destinations of older adults who migrate out of metro counties. A better SRH score predicts their greater odds of migration anywhere, but their retirement at least one time predicts where they go. The retired metro migrant’s greatest odds are to go to (in decreasing order) a nonmetro destination, a different MSA, and a metro county in the same MSA. Thus, nonmetro counties can compete against metro counties for retirees who will bring their pensions or Social Security checks with them. Finding whether their nonmetro destinations are more likely than not to be adjacent to metro counties or their childhood homes will shed new light on the reasons why older adults migrate soon after retirement. Fifth, the current investigation raises the question, Is the net in-migration of older adults to nonmetro counties ending after more than half a century? The current analysis
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ends in 2004, before the Great Recession started in late 2007. Adults near retirement in or after 2007 and at favorable levels of SRH may be putting off retirement in order to increase their savings (see Hurd and Rohwedder 2010). Those who do retire may be unable to sell their current homes or to buy new ones in other counties because of the crash in housing values and the difficulty in obtaining mortgages. Either way, there could be a postponement of the first postretirement migration until the senior citizen is older or unhealthier or until the Great Recession ends.
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References Allison, P. D. 1984. Event history analysis: regression for longitudinal event data. Beverly Hills, CA: Sage. Blossfeld, H-P., A. Hamerle, and K. U. Mayer. 1989. Event history analysis: statistical theory and application in the social sciences. Hillsdale, NJ: Lawrence Erlbaum. Brown, D. L., B. C. Bolender, L. J. Kulcsar, N. Glasgow, and S. Sanders. 2011. Intercounty variability of net migration at older ages as a path-dependent process. Rural Sociol 76: 44–73. Conway, K. S., and J. C. Rork. 2011. The changing roles of disability, veteran, and socioeconomic status in elderly interstate migration. Res Aging 33: 256–285. Frey, W. H. 2001. Seniors in suburbia. Am Demogr 23(11): 18–21. Glasgow, N., and M-J. Arguillas. 2008. Social integration and health of older in-migrants to rural retirement destinations. In Rural retirement migration, ed. D. L. Brown and N. Glasgow, 117–140. New York: Springer. Halliday, T. J., and M. C. Kimmitt. 2008. Selective migration and health in the USA, 1984−93. Popul Stud 62(3): 321–334. He, W., M. Sengupta, V. A. Velkoff, and K. A. DeBarros. 2005. 65+ in the United States: 2005. Current population reports: special studies. Washington, DC: U.S. Census Bureau. Hurd, M., and S. Rohwedder. 2010. The effects of the economic crisis on the older population. Working Paper # 2010-231. Ann Arbor: Michigan Retirement Research Center. Johnson, K. M., P. R. Voss, R. B. Hammer, G. V. Fuguitt, and S. McNiven. 2005. Temporal and spatial variation in age-specific net migration in the United States. Demography 42(4): 791–812. Johnson, N. E. 2012. Self-rated health and the “first move” around retirement: a longitudinal study of older Americans. J Rural Health 28(2): 183–191. Juster, F. T., and R. Suzman. 1993. The health and retirement survey: an overview. Health and Retirement Study Working Paper Series Core Paper No. 94-1001. Ann Arbor, MI: Institute for Social Research. Litwak, E., and C. F. Longino, Jr. 1987. Migration patterns among the elderly: a developmental perspective. Gerontologist 27(3): 266–272. Longino, C. F., Jr. 1980. Residential relocation of older people: metropolitan and nonmetropolitan. Res Aging 2(2): 205–216. Longino, C. F., Jr., D. E. Bradley, E. P. Stoller, and W. Haas, III. 2008. Predictors of non-local moves among older adults: a prospective study. J Gerontol Soc Sci 63B(1): S7–S14. Longino, C. F., Jr., D. J. Jackson, R. S. Zimmerman, and J. E. Bradsher. 1991. The second move: health and geographic mobility. J Gerontol 46(4): S218–S224. Sabia, J. J. 2008. There’s no place like home: a hazard model analysis of aging in place among older homeowners in the PSID. Res Aging 30(3): 3–35. Wiatrowski, W. J. 2001. Changing retirement age: ups and downs. Month Labor Rev (April):3–10.
