Elderly Migration to the Sunbelt:
Seasonal Versus Permanent
Timothy D. Hogan
Arizona State University
Donald N. Steinnes
University of Minnesota—Duluth
A substantial body of research has focused on the interstate migration of elderly households to
the Sunbelt. Most of this research has concentrated on permanent moves, but seasonal migration
of elderly households to Sunbelt locations has become an increasingly important social phenomenon. Although some have suggested that such temporary migration serves as a precursor
ofpermanent locations, recent analyses have found that such seasonal migration constitutes an
alternative elderly life-style. Using 1980 census data, this study empirically examines the
similarities and differences in these two types of elderly migration flows to a Sunbelt state such
as Arizona. The statistical results indicate that seasonal and permanent migration are correlated
in different ways to the variables usually found to be determinants of elderly migration flows
and suggest the two types of elderly migration are related but separate phenomena.
Social scientists from a variety of disciplines have examined elderly
mobility (Longino, 1990), and a substantial portion of this research has
focused on the interstate migration of elderly households to the Sunbelt
region. Some of these elderly households decide to relocate, or migrate,
permanently to the Sunbelt and give up residence in the North. Others may
decide to maintain ties to two locations, one north and one south, during the
year, and these elderly will be referred to as seasonal migrants (Tucker,
Marshall, Longino, & Mullins, 1988). Although such seasonal migrants may
own or rent at either location, where they spend the most time has been used
to define their primary, or permanent, residence (Behr & Gober, 1982;
Sullivan, 1985). Although most studies of seasonal migration, including this
article, concentrate on &dquo;snowbirds&dquo; (northern residents who spend part of the
I
i
AUTHORS’ NOTE: Reprint requests should be addressed to Timothy D.
Business, BA 319, Arizona State University, Tempe, AZ 85287-4406.
Hogan, College of
The Journal of Applied Gerontology, Vol. 12 No. 2, June 1993 246-260
0 1993 The Southern Gerontological Society
246
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
247
winter in the Sunbelt), it should be noted that some southern residents may
spend part of the summer at a northern residence, either owned or rented.
However, these alternative seasonal flows have not generally been identified
in the seasonal migration studies, which have usually been based on snowbird surveys conducted at a Sunbelt destination (Happel, Hogan, & Sullivan,
1983; Hoyt, 1954; Martin, Hoppe, Larson, & Leon, 1987; Monahan &
Greene, 1982; Rush, 1983; Sullivan, 1985; Sullivan & Stevens, 1982), or
among the elderly residents of a northern origin state (Krout, 1983).
The various snowbird surveys have shown that seasonal and permanent
migrants to the Sunbelt states tend to be White, married, and retired (Longino &
Marshall, 1990). Also the migrants are more educated, healthier, and have
higher incomes than nonmovers among the elderly population (Happel et al.,
1983; Krout, 1983; Monahan & Greene, 1982; Sullivan, 1985; Sullivan &
Stevens, 1982). When questioned about the reasons for migrating, however,
permanent (Long & Hansen, 1979) and seasonal (Happel et al., 1983; Krout,
1983; Sullivan & Stevens, 1982) migrants have provided varying answers,
but with life-style reasons dominating (Longino, 1990).
Of the two types of elderly migration to the Sunbelt, much more is known
about the permanent moves than the seasonal flows. The U. S. Bureau of the
Census provides aggregate measures of permanent migration flows and
individual household data via the PUMS data sets. Both have been used by
researchers to profile interstate migrants, local movers, and nonmovers and
to develop and estimate various models explaining migration behavior
(Longino, 1990). In addition, other sources of interstate permanent migration
data have been used (e.g., the Panel Study of Income Dynamics by Henretta,
1986) or identified (e.g., the Social Security Master Beneficiary Record
System by Kestenbaum, 1986). In contrast, the 1980 census was the first to
provide information that could be used to estimate seasonal migration flows,
although problems exist with using these data.
Some analysts have suggested that seasonal migration is a precursor of
permanent relocation (Gober & Zonn, 1983; Wiseman, 1980), but more
recent analyses have found that for the vast majority, elderly seasonal
migration constitutes an alternative life-style rather than a prelude to a
permanent move (Hogan & Happel, 1989; McHugh, 1990; Sullivan, 1985).
Based on such evidence from both domestic flows and from Canadian
seasonal migration, Longino, Marshall, Mullins, and Tucker (1991) issued a
call for more detailed study of what distinguishes seasonal and permanent
migrants. We focus on that objective. Using 1980 census data on elderly
interstate migration to the second most popular Sunbelt destination, Arizona,
this analysis empirically examines the differences in the determinants of
these two types of elderly migration.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
248
Models of Elderly Migration
Having compared and contrasted permanent and seasonal elderly migration primarily from the perspective of survey-based studies, we now consider
models developed and tested using census information. Although census
information does not capture all of the determinants of migration that have
been suggested by the surveys, the data do allow for direct measurement of
the migration rate (for each origin state). Such migration rates are often
dependent variables in regression models that use state characteristics as
determinants, or explanatory variables. Thus the models can consider, albeit
indirectly, the validity of some of the determinants of elderly permanent and
seasonal migration suggested by surveys. In addition, variables such as
distance and climate can be incorporated to further explain variation in
interstate migration flows.
Most empirical models of elderly migration have concentrated on analysis
of interstate permanent migration. Barsby and Cox (1975) identified past
migration and income levels in origin and destination states as important
determinants and found some evidence of attractiveness of states with
recreational amenities. They found little evidence that public sector characteristics had any influence. Cebula (1979) investigated the effects among
quality-of-life factors and found positive effects for good climate and recreational opportunities. Based on a gravity model approach, Rives and Serow
( 1981 ) discovered distance and size of the elderly population to be highly
significant explanatory variables. Several recent studies have focused on the
economic aspects of the migration decision and have identified cost-of-living
differentials as another factor affecting elderly interstate migration (Fournier,
Rasmussen, & Serow, 1986, 1988; McLeod, Parker, Serow, & Rives, 1984;
Serow, Charity, Fournier, & Rasmussen, 1986).
Hogan (1987) examined 1980 interstate seasonal migration flows to
Arizona and Florida and found climate to be the most important determinant
of migration flows. Wealthier states sent more seasonal migrants, but costof-living differentials were not found to be a significant factor. Steinnes and
Hogan (1992) conclude that seasonal, and to a lesser extent permanent,
elderly interstate migration flows to Arizona are a consequence of gains in
unaffordable (origin) housing markets.
Models estimated using interstate census data are essentially crosssectional, so they cannot directly address the issue of whether seasonal migration is a precursor to permanent migration or a separate alternative lifestyle. This issue will be fully resolved only when data are collected for
households over time, or even a lifetime. Indirectly, however, we believe that
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
249
our
results, which will indicate that seasonal and permanent migration are
suggest that seasonal migration is a separate
not determined the same way,
phenomenon, or life-style.
Empirical Analysis
Previous studies of elderly migration have used different specifications of
dependent variable and a wide variety of explanatory variables. This
study analyzes seasonal and permanent migration using migration rates
calculated from 1980 census data and employs the same set of independent
variables in both models. Although some of these measures, such as distance
and climate, have been used in most previous elderly migration models, other
variables might be considered novel, especially when they are hypothesized
to have different signs for seasonal and permanent migration.
the
Dependent Variables
In Table 1, we present data on seasonal and permanent migration flows to
Arizona, one of the prime Sunbelt destination states for elderly migration. To
focus on differences in the incidence of migration (abstracting from the
absolute numbers of migrants from larger states), these migration data have
been represented in terms of elderly migration rates (number of migrants per
1,000 persons 65 and over in the origin state). The rankings of the set of 32
states for migration measures and the differences in rates and rankings have
also been included in Table 1 to make comparisons more convenient. These
migration rates clearly demonstrate that the incidence of seasonal migration
versus permanent migration to Arizona varies considerably among the states.
Our intent is to provide models that examine the determinants of the variations in the interstate rates observed.
Rates of permanent elderly migration to Arizona are based on the conventional census migration data, the ratio of (a) the number of persons 65 or
above residing in Arizona in 1980 who resided in each other state in 1975 to (b)
the 1980 population 65 and over in that state. The seasonal migration rates
are based on the 1980 census data on nonpermanent residents in Arizonathe ratio of the number of nonpermanent residents in Arizona from each state
to that state’s 65 and over population in 1980. Given the difference in scale of
these two sets of migration rates, each series was converted to an index with a
mean of 100 (see Table 2 later) through division by the U. S. average value.
Although census information on permanent migration has been available
and used for many years, the U. S. decennial census offered no data on
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
250
Table 1.
Differences Between Elderly
Origin (selectedb states)
Migration Rates’
SOURCE: U. S. Bureau of the Census (1982, 1984).
a. Migrants per 1,000 people 65 years or older.
b. States with highest seasonal migration rates (over 0.1
c. Rank among 32 states considered.
d. Adjusted to same U.S. mean as seasonal.
to Arizona
).
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
By State
of
251
seasonal migration prior to 1980. However, the 1980 census, for the first time,
compiled data relating to nonpermanent residents (U. S. Bureau of the Census, 1982). This provided a count of those who (on census day, April 1) were
located in a place that was not their permanent residence. Although for many
states the nonresidents present were young and/or in the military, the nonresidents present in Arizona and Florida had an average age of over 65. Thus,
for these two states, the nonresidency status has been used (Hogan, 1987;
Rose & Kingma, 1989; Steinnes & Hogan, 1992) as a measure of elderly
seasonal migration from each origin state. The rates provided are lower than
would be expected because many elderly seasonal migrants to the Sunbelt
would have left by April 1. However, if this undercounting is the same for
each origin, then the rates do measure the variability in flows from each
origin. We have assumed this to be true in that we have scaled this series, as
noted.
The 1980 census data on nonpermanent residency can be interpreted as a
measure of elderly seasonal migration for only two destination states, and we
have chosen arbitrarily to concentrate on Arizona. With only two destination
states, it is not possible statistically to evaluate differences in characteristics
between destination states, so the analysis is focused on differences among
origin states and differences between the origin states and the destination
state. This decision is also based on previous research on these flows to
Arizona and Florida that found statistically significant differences between
migration models for the two states, indicating that a single model pooling
data for the two states would not be appropriate (Hogan, 1987).
Although the permanent migrants could be former seasonal migrants, they
would have had to have done their seasonal migrating prior to their permanent
migration between 1975 and 1980. Thus they could not have been counted
in the seasonal migration, or nonpermanent resident, category in 1980. It
should be understood that the permanent migration rate is for 5 years
(1975-1980), whereas the seasonal migration rate is as of a single date (April
1, 1980). We have adjusted for this difference by converting the two rates to
an index with a mean of 100.
Explanatory Variables
Geographic characteristics of the origin states
Most analyses of interstate migration flows consider various geographic
variables to explain variations in interstate migration rates. Sometimes
referred to as push and pull factors, these variables measure differences
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
252
between the origin and/or destination states. Although these variables can
often be interrelated, the multiple regression approach to be used in the
estimation is designed to isolate the partial effect of each variable on
migration, be it seasonal or permanent. Our hypotheses for these, and other,
explanatory variables are based on our expectation of what will be obtained
in the multiple regression estimation results that follow.
Adjacent state. A binary variable designating the five states adjacent to
Arizona (California, Colorado, New Mexico, Nevada, and Utah) was defined
as one explanatory measure. Differential effects are hypothesized between
this variable and seasonal versus permanent migration. A negative association is hypothesized between adjacency and seasonal migration, given the
reduced differences in climate and other environmental characteristics. For
permanent migration, on the other hand, a positive link is hypothesized.
Distance. Migration research has generally hypothesized a negative relationship between the distance of the move and the size of the migration flow.
To test this relationship, the distance in miles between the largest metropol-
itan
area
in each state and Phoenix, AZ, has been used. Because seasonal
migration involves annual relocation costs, it is further hypothesized that
seasonal migration will be more affected by distance than will permanent
migration.
Temperature difference. The importance of climatic factors in the migration decision of older persons has been widely documented. The difference
in average January temperature between each origin state and Phoenix has
been employed as the climate variable, and a strong positive association is
hypothesized. Because seasonal migrants move each winter to a warm
Sunbelt location to avoid the colder weather of their origin state, a differential
positive relationship between climate and seasonal versus permanent migration is hypothesized.
Demographic and social characteristics of the elderly population
Previous analyses of elderly mobility have identified differences by race,
age, and other characteristics. Surveys undertaken in Arizona (and other
Sunbelt areas) also show that a very high proportion of seasonal migrants to
Arizona are White, retired or semiretired but relatively young, and married.
Four separate variables related to the demographic characteristics of the
origin states’ elderly population have been included in the analysis.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
253
Percentage White. A positive relationship between migration and the
proportion of the origin state’s elderly population that is White is hypothesized for seasonal and permanent migration.
Percentage of the elderly population less than 75. A positive relationship
is
hypothesized between this variable and both types of migration because
migrants to Sunbelt states, such as Arizona, are drawn from the younger
cohorts of the elderly population.
Percentage married. A positive relationship between the proportion married and migration is hypothesized because studies have shown that migrants
to Arizona tend to be married.
Although we believe the last two variables (proportions less than 75 and
married) will be positive for both types of migration, we expect the relationship to be stronger for seasonal migration. One reason is that if seasonal
migration is a precursor to permanent migration, rather than vice versa, then
it follows that people will be younger when they seasonally migrate. Thus
the younger a state’s elderly population the more seasonal, relative to
permanent, migrants we should observe. If, on the other hand, seasonal
migration is not a precursor to permanent migration, then the age variable
should have the same influence on permanent and seasonal migration.
Finally, if permanent migration is a precursor to seasonal migration, then we
should find a stronger (positive) relationship for percentage less than 75 in
the permanent model. Our expectation regarding martial status (percentage
married) is based on surveys showing a stronger tendency for seasonal
migrants to be married than for permanent migrants to be married.
Percentage rural. While the majority of the U. S. population lives in urban
relatively high proportion of seasonal migrants to Arizona reside in
nonurban areas of their origin states (Hogan & Happel, 1990); thus a positive
relationship is hypothesized between rural residence and seasonal migration,
whereas a negative one is hypothesized with respect to permanent migration.
areas, a
Income level in the origin state. Previous literature has demonstrated a
link between income level and the decision to migrate. Treating interstate
migration activity as a normal good, a positive relationship between median
income level in the origin state and migration is hypothesized. Given the
added costs of maintaining two homes and annual trips, a differential positive
effect of income level on seasonal relative to permanent migration is hypothesized. This variable, like the migration rates, has been converted to an index
with a mean of 100 by dividing each state rate by the U. S. average value.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
254
Table 2.
Descriptive Statistics
The descriptive statistics for each variable can be found in Table 2. The
selection of variables as determinants was limited by the information available from the census. Although these variables may not be conceptually the
same as the determinants that have been suggested from surveys, they do
provide the best available proxies. Moreover, many of these variables have
been previously used to explain permanent migration flows, so by using them
we are best able, we believe, to consider in a systematic way the differences
between seasonal and permanent elderly migration.
Estimation Results
The simple correlations between the independent variables are provided
in Table 3. These statistics give some indication of the potential for multicollinearity in the estimated regression equations. Although no severe multicollinearity is indicated, some correlations may restrict the significance (as
measured byt values) of the independent variables in the regression results.
In Table 4, regression results are presented for seasonal and permanent
migration separately (unpooled), whereas in Table 5 (see later) the data are
pooled or combined. When pooled, the dependent variable is the seasonal
migration rate for the first 47 cases and the permanent migration rate for the
second 47 cases of the combined data set. Interaction terms are introduced
in the pooled model (Table 5) by multiplying each independent variable by
a dummy variable (X = 1 if permanent migration; 0 if seasonal migration).
In Table 4, we provide evidence that, in fact, seasonal and permanent
elderly migration are not influenced in the same ways by the set of explana-
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
255
Independent Variables
Table 3.
Correlation Coefficients for
Table 4.
Estimated Seasona)/Permanent
Migration Equations
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
256
tory variables considered. Two variables, adjacent state and proportion rural,
have opposite signs, as was hypothesized. Although the other variables have
the same sign, seasonal migration is more affected than permanent migration
by each of the remaining variables, as expected. However, some of these
variables have lowt values, so these differences may be viewed as indicative
but not definitive. All the variables have the signs hypothesized with the
exception of percentage White. However, this incorrect sign, and its insignificance, may be the result of multicollinearity between this variable and
temperature difference measure.
Although the F values are significant for both the seasonal and permanent
equations in Table 4, more explanation is provided for permanent migration.
However, it must also be recalled that seasonal migration is less accurately
measured (based on nonpermanent residence status) than is permanent
migration, and so this may be partially responsible for the lower explanatory
power of the seasonal migration equation.
Having been provided with evidence of a difference between seasonal and
permanent elderly migration in Table 4, it remains to verify this difference
with a formal statistical test. Table 5, as noted, gives estimation results when
the seasonal and permanent data sets are combined, or pooled. In column 1,
an equation is estimated for the pooled data, which, in effect, assumes that
seasonal and permanent migration rates are the same. It should be recalled
(see Table 2) that both rates were scaled to have the same mean (100). By
pooling the data, most variables are less significant than in Table 4 and less
explanatory power is provided.
In the last two columns of Table 5, interaction terms are added that allow
for the possibility that each independent variable has a differential influence
on either seasonal or permanent migration. The interactions were based on a
dummy variable (X = 1 if permanent; 0 if seasonal) to distinguish the two
pooled data sets. Hence the coefficients in the second column represent the
influence of each independent variable on seasonal migration. The third
column measures the differential influence of each independent variable on
permanent (versus seasonal) migration. Generally, the differences and signs
are consistent with those observed in Table 4 and previously discussed.
One exception is for the proportion of the elderly population less than 75,
which shows a positive interaction coefficient in column 3 of Table 5.
Although insignificant, this result, along with the insignificance of this
variable in Table 4, does not support the idea that seasonal migration is a
precursor to permanent migration. As indicated earlier, the precursor argument would suggest a larger positive coefficient for the age variable in the
seasonal equation.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
257
Table 5.
Estimated
Migration Equations (pooled data)
NOTE: The value of the F test of the overall significance of the interaction terms was
3.21, indicating statistical significance at the .01 level.
a. Each independent variable is multiplied by a dummy variable (X= 1 if permanent; 0
if seasonal) defining the two data sets that have been pooled.
To evaluate whether the determinants of seasonal and permanent migration are equal, a statistical test can be conducted to see if the interaction terms
(column 3), taken together, are significant. The null hypothesis is that all the
coefficients of these interaction terms are zero, or that the data may be pooled
(column 1 is the correct approach). Put simply, the test evaluates whether
additional explanatory power can be provided by adding the interaction
terms-that is, allowing for seasonal and permanent migration rates to be
different. Note that because both rates have the same mean, there is no
difference in the intercept term.
The results of this F test are found in column 3 of Table 5 and are significant at less than the 1% level. This supports, statistically, the hypothesis
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
258
that seasonal and permanent
migration
are,
indeed, related but separate
phenomena.
Summary and Conclusion
Our objective has been to statistically investigate the differences between
seasonal and permanent elderly migration. Using the available census information to estimate models of seasonal and permanent migration with a
common set of explanatory variables, the results suggest that seasonal and
permanent elderly migration are correlated in substantially different ways
with these explanatory factors and, thus, represent separate phenomena.
It follows that the vast amount of research done on permanent migration
may not be applicable to seasonal migration and that further study on the
latter phenomenon is warranted. Unfortunately, the data set on seasonal
migration from the 1980 census used here can reasonably be interpreted as
measuring elderly seasonal migration flows only in the cases of Arizona and
Florida. Also, snowbird surveys conducted in the past provided only fragmented evidence of seasonal migration. To learn more about the elderly
seasonal migration phenomenon, it may be necessary to collect survey data
specifically aimed at the question of their mobility decision. Only then can
the differences between seasonal and permanent migration suggested here
be more fully documented and analyzed.
Although we have not been able to address directly the issue of whether
seasonal migration is a precursor to permanent migration or an altemative
life-style, our results, if anything, support the latter position. We reach this
conclusion because we find, statistically, that the two types of migration have
different determinants. Also, our results with respect to age are not consistent
with what would be expected if seasonal migration is a precursor. However,
this issue will be resolved only when longitudinal data are collected that
follow the migration decisions, seasonal and permanent, of people over a
lifetime.
References
Barsby, S. L., & Cox, D. R. (1975). Interstate migration of the elderly. Lexington, MA: Heath.
Behr, M., & Gober, P. (1982). When residence is not a house: Examining residence-based
migration definitions. Professional Geographer, 34, 178-184.
Cebula, R. J. (1979). The determinants of human migration. Lexington, MA: Heath.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
259
Fournier, G., Rasmussen, D., & Serow, W. (1986). Elderly migration as a response to economic
incentives. Social Science Quarterly, 67, 245-260.
Fournier, G., Rasmussen, D., & Serow, W. (1988). Elderly migration: For sun and money.
Population Research and Policy Review, 7, 189-199.
Gober, P. C., & Zonn, L. E. (1983). Kin and elderly amenity migration. The Gerontologist, 23,
288-294.
S. K., Hogan, T. D., & Sullivan, D. A. (1983). The social and economic impact of
Phoenix area winter residents. Arizona Business, 30, 3-10.
Henretta, J. C. (1986). Retirement and residential moves by elderly households. Research on
Happel,
, 23-27.
Aging, 8
Hogan, T. D. (1987). Determinants of seasonal migration of the elderly to Sunbelt states.
Research on Aging, 9, 115-133.
Hogan, T. D., & Happel, S. K. (1989). Arizona winter resident population is likely to grow.
Arizona Business, 36
, 4-5.
Hoyt, G. C. (1954). Life of the retired in a trailer park. American Journal of Sociology, 2,
361-370.
Kestenbaum, B. (1986). Migration of the elderly from the perspective of the master beneficiary
record. Research on Aging, 8, 329-336.
Krout, J. A. (1983). Seasonal migration of the elderly. The Gerontologist, 23, 295-299.
Long, L. H., & Hansen, K. A. (1979). Reasons for interstate migration: Jobs, retirement, climate
and other influences. Current Population Reports (Series P-23, No. 81). Washington, DC:
U.S. Department of Commerce.
Longino, C. G., Jr. (1990). Geographical distribution and migration. In R. H. Binstock & L. K.
Handbook of aging and the social sciences (3rd ed., pp. 45-63). San Diego,
George (Eds.),
CA: Academic Press.
Longino, C. G., Jr., & Marshall, V. W. (1990). North American research in seasonal migration.
Aging229-235.
,
and Society, 10
Longino, C. G., Jr., Marshall, V. W., Mullins, L. C., & Tucker, R. D. (1991). On the nesting of
snowbirds: A question about seasonal and permanent migrants. Journal of Applied Gerontology,
,
157-168.
10
Martin, H. W., Hoppe, S. K., Larson, C. L., & Leon, R. L. (1987). Texas snowbirds: Seasonal
, 134-147.
migrants to the Rio Grande Valley. Research on Aging, 9
McHugh, K. E. (1990). Seasonal migration as a substitute for, or precursor to, permanent
migration. Research on Aging, 12, 229-245.
McLeod, K., Parker, J., Serow, W., & Rives, N., Jr. (1984). Determinants of state-to-state flows
of elderly migrants. Research on Aging, 6, 372-383.
Monahan, D. J., & Greene, V. L. (1982). The impact of seasonal population fluctuations upon
service delivery. The Gerontologist, 22, 160-163.
Rives, N., & Serow, W. (1981). Interstate migration of the elderly: Demographic aspects.
Research on Aging, 3, 259-278.
Rose, L. S., & Kingma, H. L. (1989). Seasonal migration of retired persons: Estimating its extent
and its implications for the state of Florida. Journal of Economic and Social Measurement,
, 91-104.
15
Rush, C. H. (1983). A survey of winter Texans in the Lower Rio Grande Valley, 1982-83.
Edinburg, TX:
Pan American University, Bureau of Business and Economic Research.
Serow, W., Charity, D., Fournier, G., & Rasmussen, D. (1986). Cost of living differentials and
elderly interstate migration. Research on Aging, 8, 317-327.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016
I
260
Steinnes, D. N., & Hogan, T. D. (1992). Take the money and
sun:
Elderly migration
as a
consequence of gains in unaffordable housing markets. Journal of Gerontology: Social
Sciences, 47, S197-S203.
Sullivan, D. A. (1985). The ties that bind. Research on Aging, 7
, 235-250.
Sullivan, D. A., & Stevens, S. A. (1982). Snowbirds: Seasonal migrants to the Sunbelt. Research
on
Aging, 4,
159-177.
Tucker, R. D., Marshall, V. W., Longino, C. F., Jr., & Mullins, L. C. (1988). Older anglophone
Canadian snowbirds in Florida: A descriptive profile. Canadian Journal on Aging, 7,
218-232.
U. S. Bureau of the Census. (1982). Nonpermanent residents by states and selected counties and
incorporated places: 1980 (Supplementary Report PC80-51-6). Washington, DC: U.S.
Department of Commerce.
U. S. Bureau of the Census. (1984). Census of population, 1980: County to county migration
flows [Machine readable data file]. Washington, DC: U. S. Department of Commerce.
Wiseman, R. G. (1980). Why older people move: Theoretical issues. Research on Aging, 2,
141-154.
Timothy D. Hogan, Professor of Economics at Arizona State University, is Director of
the Center for Busmess Research within the L. William Seidman Research Institute at
ASU. He has research interests in migration, particularly seasonal migration and the
mobility of elderly households, and the economic and demographic factors associated
with regional growth. Previous articles in these areas have appeared in the Journal of
Gerontology, Research on Aging, and American Demographics.
Donald N. Steinnes is a professor of economics at the Umversity of Minnesota-Duluth.
His research mterests include causality and the location of economic activity and the
interrelationships among economic factors and migration, particularly for the elderly
population. He has published articles in the Journal of Gerontology, the Journal of
Regional Science, and the Southern Economic Journal related to these topics.
Downloaded from jag.sagepub.com at PENNSYLVANIA STATE UNIV on September 16, 2016