aHT392

.A53

Cl States

ment of

Agriculture

Economic

Research

Service

Rural Development

Research Report

Number 56

What Attracts

New Residents to

Nonmetro Areas?

Linda L Swanson

'I'-5,^ 1

. .^m
WHAT ATTRACTS NEW RESIDENTS TO NONMETRO AREAS?

by Linda L. Swanson. Agriculture and Rural Economics Division, Economic

Research Service, U.S. Department ofAgriculture. Rural DevelopmentResearch

Report No. 56.

Abstract

Most changes in the size and composition of a population occur because of

the movement of residents rather than birth and death rates. Migrants from

metro areas, who have provided much of the growth in rural areas since the

1970's, have tended to move to nonmetro countiesthatranked high in amenities.

Job-related reasons were less important to these new residents. Knowing the

causes ofnonmetro growth is important because today's mobile society makes

planning by small communities difficult. This report assesses migration to

nonmetro counties during 1975-80, whetherthe migrants came from metro or

other nonmetro areas, and to what region they were most likely to move.

Keywords: Migration, retirement, nonmetro population growth.

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Washington, DC 20005-4788 April 1986

in
Summary

New residents from metro areas, rather than a shift in birth or death rates,

contributed most to nonmetro growth during the 1970's. Migrantsfrom metro

areas tended to move to nonmetro countiesthatranked high in amenitiesrather

than in job-related advantages. Movement from one nonmetro county to

another was not strongly based on any specific county characteristic; these

nonmetro migrants mainly avoided metro adjacency and chose counties with high

proportions of agricultural occupations.

Thisreport examines migration to nonmetro counties during 1975-80, whether

the migrants came from metro or other nonmetro areas, and to what region

they were most likely to move.

The presence of a large commimity of retirement-age people was the county

characteristic moststrongly associated with high levels ofmigration from metro

areas. Apparently, once retirement-age people begin moving to a nonmetro

county, other retirement-age people are likely to follow over time.

The steepest growth in metro-to*nonmetro movement occurred in States west

of the Mississippi River and in Florida and New Jersey during 1975-80. The

Pacific Coast States plus Nevada, Arizona, Wyoming, and Alaska posted at

least a 20-percent gain in nonmetro residents who had lived incities orsuburbs

5 years earlier.

Contents

Page

Introduction 1

The Migration Reversal of the 1970's 1

Nonmetro Population Growth and Migration during the 1970's 2

Explanations of Inmigration to Nonmetro Areas 4

Adjacency to a Metro Area 4

Job Opportunities or a Leisure Lifestyle? , 4

Measming Migrant Attraction Using Information

on their Destinations 4

Methods of Analysis 5

Job-related Characteristics 5

Leisure Lifestyle Characteristics 6

Regression Analysis of Inmigration ...,...., 8

Results of the Regression Analysis 9

Regression Analysis Summary 11

Conclusions 11

References 12

Appendices 14

IV
What Attracts New Residents

to Nonmetro Areas?

Linda L. Swanson*

Introduction

America has become such a mobile society that it is

often difficultfor communitiesto anticipate accurately

the makeup of their population in the years ahead.

Nearly 20 percent ofthe Nation's 1980 population had

moved across county lines atleast once in the preceding 5 years. This intercounty mobility pattern has

continued in the 1980's. Migration into and out of an

area can sharply alterthe size and composition of an

area's population in a short period oftime, particularly

in sparsely settled areas or smalltowns. For example,

in the course of a year, 10 families leaving Chicago

for an outlying village of 500 people would scarcely

alter Chicago's population, but would generate a

substantial increase for the village.

This report assesses migration into nonmetropolitan

(nonmetro) counties during 1975-80, whether the

migrants came from metropolitan (metro) or other

nonmetro areas, and in which parts of the country

these rates of inmigration were highest. The author

examined the rates of migration from metro and

nonmetro areasto determine which source was most

strongly associated with population growth. The

report, based on the publicly available 1980 Census

data, shows how the type and rate ofinmigration can

be explained by two categories ofcounty characteristics which the author created for this study: employment-related reasons and opportunities for a leisure

lifestyle. The author examined the characteristics for

theirimpact on movements from either metro or other

nonmetro areas.

A high rate of migration to open country and small

townsfrom anotherregion or a metropolitan area can

create both positive and negative changes in a small

population. For example, more new residents can

boost revenues but severely strain local capabilities

to serve a rapidly expanding population. During the

1970's, when attraction to nonmetro areas was at its

peak, more people moved into nonmetro areas as a

whole than into metro areas. However, data were not

yet available that could indicate which nonmetro

areas gottheir inmigrants primarily from metro areas

and which had people shifting from other nonmetro

areas. Using 1980 Census data, one can assess inmigration during 1975-80 and determine whether the

migrants came from nonmetro or metro areas.

The Migration Reversal of the 1970's

The nonmetro population boom ofthe 1970's caught

many experts by surprise. From the early 1900's to

the 1960's, with a shortrespite during the Depression

in the 1930's, the number ofpeople leaving the countryside for the city far exceeded the number moving

to rural areas from cities. However, several factors

combined to disrupt this country-to-city migration

trend. By the 1960's, mostrural areas had advantages

common to urban areas, such as high-speed highways,

telecommunications, electricity, public watersupply,

and community colleges. Taking advantage of lower

land values and cheaper labor, manufacturing companies found it profitable to place certain types of

operations in nonmetro areas with good transportation access and communications (12, 19, 22, 27)}

Othertypes ofnonmetro employment growth, particularly in service occupations, accompanied population

growth (3, 13, 16). Workforce data show that a net

gain of people in nonmetro areas between 1970 and

1975 was due both to more people staying in nonmetro

areas and to more people moving in from metro areas

(7).

Nationalsurveys ofresidential preferences, conducted

over several decades, showed that an increasingly

large proportion of metro residents preferred to live

in a nonmetro area [26), Both the responsesto survey

questions (6, ÍO, 21, 23) and the association of

noneconomic county characteristics with population

growth in nonmetro counties indicated that much of

the recent nonmetro growth may have been triggered

by noneconomic motives (2, 4, 14, 17). Regarding

employment, itis not clear whether people moved to

nonmetro areasin response to the availability ofjobs,

or whether employers located in areas which attracted

people and provided a pool of labor (8, 23, 24).

Analysts speculate that both processes occurred and

were mutually reinforcing.

By the early 1970's, the nonmetro-to-metro migration

pattern of the century's first 70 years had reversed.

More people were moving from metro to nonmetro

areas than were moving in the opposite direction.

Using data from the 1980 census, we can calculate the

percentage of a nonmetro county's 1980 population

which consisted of people who lived elsewhere in

1975. This gross rate of inmigration may be further

*The author is a sociologist in the Agriculture and Rural

Economics Division's Population Section.

^Italicized numbers in parentheses cite sources listed in the

References section.
analyzed to determine whether people came from a

metro area or from another nonmetro county.

Figures 1 and 2 show the percentage of residents of

nonmetro counties in each State who had recently

come from metro and nonmetro areas, respectively.

The percentage ofthe population that had come from

a metro area during 1975-80 was somewhat higher

than for those who had come from other nonmetro

counties (12.3 percent and 8.3 percent, respectively).

Inmigration from metro areas was highestin the West,

in Florida, and in New Jersey, whose closenessto large

metropolitan areas, such as New York City and

Philadelphia, boosted its population. The highest

regionalinmigrationratesfromnonmetro areas were

in the Mountain and Centralregions, with the highest

State ratesin Montana, Wyoming, and South Dakota.

Exceptfor Wyoming, Üie nonmetro areas ofStates that

had a high percentage of new arrivals from metro

areas did not attract great numbers of migrants from

other nonmetro areas.

Nonmetro Population Growth and

Migration during the 1970's

An area thatis declining in population can, paradoxically, have a large number of people moving in

compared with other counties. Such a situation may

occur when the area*s decline is caused by a higher

number of deaths than births, or more commonly,

when even more people are moving out than in.

The percentage of1980 residents who moved in since

1975 was used as the measure of recent inmigration.

The author divided newcomersinto those who moved

from a metro area and those who came from another

nonmetro county. The migration measures and all but

one of the nonmetro county characteristics were

calculated for all nonmetro counties in the United

States from 1980 census figures.^

Figure 3 shows the relationship between rates of

1970-80 nonmetro population growth and 1975-80

inmigration, from both metro and nonmetro counties.

Growth forthe entire decade was used because it was

likely that migrants not only contributed to the rate

of growth themselves, but also were attracted to

nonmetro counties Üiat were growing and changing.

Counties with high rates of overall growth (ranging

from 20-percent growth to more than doubling in size)

tended to have a high rate of inmigration of people

frommetro areas. However, both the nonmetro coun-

^Countiesin New England are counted as nonmetro "receiving"

counties only if they are entirely nonmetro. Census metro area

boundaries do not follow county lines in New England. Thus, many

counties there have both metro and nonmetro parts.

Figure 1

Metro to Nonmetro Migration, 1975-80^

Alaska: 22.9 percent

Hawaii: 15.0 percent

1/ Percentage of 1980 nonmetro residents who were metro residents In 1975.

U.S. = 12.3 percent

2 Linda L. Swanson
Figure 2

Nonmetro to Other Nonmetro Migration, 1975-80^

Alaska: 13.9 percent

Hawaii: 3.5 percent

1/ Percentage of 1980 nonmetro residents who lived in a different nonmetro county in 1975.

U.S. - 8.3 percent

Figure 3

Growth of Nonmetro Counties and Source of Inmigration

Inmigration from: H Metro ^ Nonmetro

Rate of inmigration, by source, 1975-80

20

15 -

10

5 -

Decline Low to medium

Growtli rate, 1970-80

High

What Attracts New Residents to Nonmetro Areas? 3
tiesthat were growing rapidly and those with decUning populations had aboutthe same rates ofinmigration from other nonmetro counties.

Based on the data, the attractiveness of a nonmetro

county to migrants from metro counties appeared to

have litüe to do with its attractiveness to people

moving from other nonmetro areas. Nonmetro counties that had a high rate ofinmigration from one soruce

(metro or nonmetro) did not necessarily receive many

migrants from the other source.

Explanations of Inmigration

to Nonmetro Areas

People move to an area because it holdssome attraction for them. It can be attractive because they are

familiar with the area, they were born or broughtup

there, or they have family or friends who live there.

Potential migrants also may know about job opportunities or attractive amenities in the area. The area

itself may be well placed geographically to receive

migrants spilling over from a nearby growing area.

Adjacency to a Metro Area

Rapid expansion of a metro area's suburban fringe is

a factor in nonmetro growth. Metro areas may grow

at such a rate that the nearby nonmetro areas begin

to function as suburbs, yet retain their nonmetro

classification in the early-growth stage. However, as

shown in figure 4, less Üian a 5-percent difference

existed between adjacent counties and not-adjacent

counties in the average proportion of residents who

arrived during 1975-80 from a metro area. Migrants

from othernonmetro areas were far more attracted to

counties that were not adjacent to a metro area.

Job Opportunities or a Leisure Lifestyle?

Taken together, the results of research on residential

preferences, on outmigration from metropolitan

areas, and on the recent population growth of nonmetro areasimply that a shiftfrom job-related considerations in destination choice toward Ufestyle considerations occurred during the 1970's. Metro area

residents may have reevaluated the quality ofcity life

and searched for alternatives by moving to nonmetro

areas. If quality-of-life considerations became primary, then the nonmetro areas with characteristics

supportive of a more leisurely lifestyle will mostlikely

attract new residents, particularly from metro areas.

Although job-related advantages may have become

less important, the economic characteristics of a

destination must be taken into account.

Measuring lUigrant Attraction Using

Information on their Destinations

Measuring quality-of-life opportunities based on

county characteristics is difficult because quality is

subjectively interpreted by individuals according to

their values orstage in life. Many ofthe reasons people

give for wanting to move to a nonmetro county, such

as "a better life for children" and "the closeness ofa

small community," are difficultto measure objectively

[20). Otherreasons, however, are more easily reflected

in census-based measures. For instance, "enjoy the

recreation opportunities" or "loved it as a vacation

spot," can be measured as county characteristics by

calculating the proportion of people employed in

recreation and the proportion of houses used as

second homes, both indicators ofwhat could be called

a leisure lifestyle. County characteristics have been

selected to reflect both job-related and leisure lifestyle

opportunities, but neitherset of characteristics can be

regarded as a complete measurement ofeithertype of

opportunity.

The job-related group of characteristics has four

measures. First, median family income in 1979 is a

proxy for the availability of either lucrative employment, or sufficient employment opportunitiesfor one

or more familymembers. The proportion ofresidents

who commute to jobs outside the county gauges how

much a county functions as a residential community

whichhas employment opportunities within commuting distance. Also included in the job-related category

are two specific types of employment, onewhich has

been considered an attraction and one which indicates

poorjob prospects. The difference between 1970 and

1980 in the number ofpeople who work in manufacturing as a proportion of people employed in 1970

denotes the increase (or decrease) in the prevalence

ofa type ofemplo5niient which had been a source of

increasing job opportunity in nonmetro areas.

Nationally, the 1970-80 growth in manufacturing

employment was 7 percent in metro areas and 20

percent in nonmetro areas [14), Employment in agriculture, forestry, or fisheries denotes an economy's

dependence on traditional rural industries which

have declined or have shown little growth.

Leisure lifestyle opportunities are assessed by three

variables: retiree setüement, second homes, and

recreation employment. The net migration rate of

people age 60 and older between 1960 and 1970 is

used as a proxy for tiie presence of a settlement of

people who have moved to the county to retire. Past

research showed that many retirees moved from

metro to nonmetro areas [see 4,10,24), and one would

expect an established community of peers to be

attractive to them.

The proportion of all housing in 1980 that was

seasonal housing or housing held for occasional use

denotes the prevalence of second homes in a county

and indicates the area's desirability as a vacation spot.

Another dimension of recreation was the proportion

ofpeople employed in the recreation or entertainment

industries in 1980. High values in the recreationentertainment variable indicated a resort orrecreation

economy, while a high proportion of second homes

4 Linda L. Swanson
Figure 4

Adjacency Status and Rate of Inmigration from Metro or

Nonmetro Sources, 1975-80

Inmigration from: Metro

Rate of innnigration, by source

15

Nonmetro

10

Not adjacent Adjacent

Adjacency to metro area

identified more informal vacation areas where a resort

economy may not be present.

Because of perceived disparity in quality of life between metro and nonmetro areas and the continuing

lower average income levels in nonmetro areas,

people moving from metro to nonmetro areas were

probably more attracted to nonmetro counties with

quality-of-life or leisure lifestyle opportunities, with

job-related factors less important. People from other

nonmetro counties were probably more affected by

job-related conditionsthan were people moving from

metro areas.

Methods of Analysis

For each county characteristic in the job-related and

leisure lifestyle categories described above, the author

grouped the nonmetro receiving counties by whether

they ranked high, medium, or low on a given characteristic.^ People moving into the counties were

separated by whether they came from another nonmetro county orfrom a metro area, in orderto examine

the patterns of relationship of both types of inmigration to county attributes.

The advantage of this type of analysis is that the

relationship between a county characteristic and

inmigration can be seen one characteristic at a time

in an easy-to-understand format. A problemwith this,

however, is that county characteristics are, in some

instances, related to each other. For example, retirement areas are often vacation areas as well. From a

graph of the association between retirement and

migration, one cannottell whetherretirement was an

importantfactorindependent ofthe recreation opportunities available. A somewhat more complicated

method of analysis, known as ordinary least squares

regression, will be used laterin thisreportto handle

such problems. Because attraction to nonmetro areas

as a whole is the concern, the nonmetro destination

counties were weighted according to their population

size. This was done so that the relationship between

inmigration and county characteristicsin small counties did notinfluence the analysis more than would

be warranted given their size.

^The categories of low, medium, and high for each county

characteristic are defined below. (See Appendix B for the numerical

values of these categories for each variable.)

Low = 1/2 standard deviation or more below the mean,

Medium = within V2 standard deviation above or below the mean,

High = V2 standard deviation or more above the mean.

Job-related Characteristics

An increase in manufacturing employment, a sector

that has recenüy grown faster in nonmetro counties

than in metro areas, was expected to attract migrants

What Attracts New Residentsto Nonmetro Areas? 5
from both metro and nonmetro sources. However,

manufacturing growth did not generally stimulate

migration into nonmetro counties (fig, 5).

Dependence on agriculture was not expected to draw

new people into a county. The second graph offigure

5 shows that the more agricultural a comity, ihe less

likely that metro people would move in, althoughthe

difference in inmigration among levels ofagricultural

employment wasless than 5 percentage points. While

migrantsfi^om other nonmetro counties constituted a

somewhat higher percentage of total population in

counties with greater concentrations of agricultural

employment, these differences were also fairly small.

The median family income of a county signifies the

relative availability of lucrative employment or, in

some cases, itmay indicate thatmore family members

can contribute to the family income. The third graph

offigure 5 showsthat counties with a higherlevel of

median family income were signiñcanüy higher in

their average percentage of recent migrants from

metro areasthan counties with a relatively lowmedian

family income. The average percentage ofnonmeü-oorigin inmigrants, however, was not sufficiently

different between high-income and low-income countiesto enable us to say thatfamily income was clearly

related to migration from other nonmetro counties.

Cause and effect is not easily distinguished because

it is possible that median family income levels were

raised since 1975 by metro-origin inmigrants bringing

with them outside sources of income, such as pensions. Hence, use caution in interpretingthe relationship between metro-origin migrants and county

income levels.

The percentage of residents who commute to jobs

outside their county helps gauge a county's function

as a residential community with a number ofemployment options within commuting distance, even

though few job opportunities may existin the county

itself. Figure 5's last graph shows that Üie difference

in the percentage of inmigrants from either metro or

other nonmetro som-ces among counties, grouped

according to the percentage of the workers who

commute to a job outside the county, wasinsufficient

to support the expectation that nonmetro counties

were functioning simply as residential communities.

Because nonmetro countiesthatlie adjacentto ametro

area are mostlikely to become "bedroom" or commuter commimities, counties were grouped into adjacentto-metro and not-adjacent categories and related

commuting and inmigration. Neitherthe adjacent nor

the not-adjacent groups revealed any substantial

differencesin inmigration among counties with little

commuting versus those with much commuting.

Ofthe job-related county characteristics, only the level

of family income was substantially associated with

the percentage ofrecentinmigrants, and tiiis only with

the percentage of metro-origin inmigrants.

Leisure Lifestyle Characteristics

The three county characteristics chosen to represent

leisure lifestyle were intended to indicate a county's

potential for residents interested in a way of living

that includes a number of amenities. Employment in

recreation or entertainment industries reflects the

extent to w^hich a county has a resort or vacation

atmosphere. The first graph in figure 6 shows that

coimties with a relatively high percentage ofrecreation

employment had a high average percentage of residents who moved in from metro areas. AlÜiough some

migrants may have moved in to take the recreation

jobs, the number of jobs available in recreation was

low even in counties where such employment was

concentrated. Much ofthe recreation attraction probably focused on the amenities themselves while any

employment attraction focused on jobs in trade and

servicesthatthe presence ofrecreationhad generated.

In contrast to the influence on metro-origin inmigration, recreation opportunitiesfailed to atti^actmigrants

from other nonmetro counties.

Another dimension of the appeal of a county as a

vacation spot can be measured by the proportion of

housing that is held for seasonal or occasional use.

Consistentwith our expectations, where this measme

of second homes was highest, metro-origin inmigration was also high (fig. 6). Again, no such relationship

marked inmigration from other nonmetro

counties.

Research and frequent stories in the press have implied that many migrants came to nonmetro areas in

retirement, and one woidd expect an established

community of peers to be attractive to retirees. By

using county rates ofnetmigration during 1960-70 of

people 60 years or older, the presence and size ofan

existing community of retired inmigrants was estimated. Counties with a community ofsuch retirementrelated movers had a much higher average percentage

of residents who were recent inmigrants from metro

areas than did counties with smaller such communities (fig. 6). However, as with the other leisure

lifestyle measures, the presence of retirement communities had Httie association with inmigration from

other nonmetro counties. The retirementnature ofan

area seemed to differentiate counties by their metro/

nonmetro source of newcomers more tiian any other

characteristic.

All of the quality-of-life aspects measured here attracted people from metro areas. By contrast, none

appeared to influence people moving from other

nonmetro counties. However, before one can make a

definitive statement about the comparison between

job-related and leisure lifestyle characteristics and

their association withmigration into nonmetro counties, one mustrule out any possible distorting effects

of the characteristics on one another.

6 Linda L. Swanson
Figure 5

Economic Characteristics of Nonmetro Counties and Rate

of Inmigration from iUietro or Nonmetro Sources, 1975-80

Inmigration from: WE Metro

Rate of inmigration. by source

20

Nonmetro

15

10 -

Low IVIedium High

Change in manufacturing employment, 1970-80

Rate of inmigration, by source

20

15 -

10 -

Low IVIedium High

Employment in agriculture

Rate of inmigration, by source

20

15

10

5 -

Low Medium High

Median family income

Rate of inmigration. by source

20

15

10 -

Low Medium High

Commuting to work

What Attracts New Residentsto Nonmetro Areas? 7
Figure 6

Leisure Lifestyle Cliaracteristics of Nonmetro Counties and

Rate of inmigration from lyietro or Nonmetro Sources, 1975-80^

Inmigration from: Hi Metro

Rate of inmigration, by source

20

15

^ Nonmetro

10

Rate of inmigratbn. by source

20

15

10

5 -

Rate of inmigratioa by source

20

15

10

5 -

Low Medium Hlgli

Recreation employment

1/ See appendix for construction of the ciiaracteristics.

Low Medium High

Second homes

Low Medium

Retirement

High

Regression Analysis of Inmigration

Ordinary least squares regression analysis includes

all ofthe county attributes at the same time inmeasuring their influence on migration. This controls for

their effect on each other. The analysis results show

the independent effect of each characteristic. Another

advantage of regression analysis is that we can use

the exact percentage or level of a characteristic for

each county, ratherthan the rougher grouping ofhigh,

medium, or low status. With this method, the difference between the median family income of $20,000

and that of$50,000 for example, is taken into account

rather than saying that both those counties have

"high" income levels. In this regression analysis,

migration into the countiesisthe dependent variable

on which the independent variables, the county

characteristics, were expected to have some effect.^

The two regression analysesthe author used have the

same set ofindependentvariables (county characteristics) but different dependent variables. The first

regression analysis attempts to explain migration

from metro areas (fig. 7), the second, migration from

^See Appendix C for a more detailed explanation of regression

analysis.

othernonmetro coimties (fig. 8). All ofthe job-related

and leisure lifestyle measures covered earlier, as well

as a third group of measures, were included in the

two regression equations.

Thisthird group of county characteristicsreflected the

presence of institutions which pulled people from

outside the county. These institutions included colleges and universities, military bases, mental hospitals, and prisons. The population in these institutions

tendsto reflect tiie predominantly metro character of

the Nation and most States. A false impression could

have been given ofthe county's attractivenessto metro

residents if the presence of such institutions had not

been taken into consideration, because, for example,

few of the residents of prisons chose their place of

residence.

The institutions category contains three measures.

The author assessed the presence and size ofcolleges

orimiversities by the number ofresidentsenrolled in

college as a proportion of all residents who were

enrolled in school ofany type. The proportion ofthe

laborforce that wasin the armed forcesreflected the

existence and size of a military base in or near the

county. The author gauged the presence and size of

other institutions drawing people from outside the

8 Linda L. Swanson
Figure 7

Standardized Regression Coefficients of Inmigration, 1975-80,

from l\/letro Areas to Nonmetro Counties, by Cliaracteristics

of tlie Destination Counties

Inmigration from metro areas

0.4

0.2

-0.2

Change in Employment Commuting Median

manufacturing in to family

employment agriculture work income

Job-related

Recreation Second

employment homes

Retirement Metroadjacency

College

enrollment

Military Institutions

Leisure lifestyle Institutions

county by calculating the proportion of the total

population residing in correctional institutions; psychiatric hospitals; or in homes, schools, hospitals, or

wards for juveniles, the chronically ill, or the physically or mentally handicapped.

Results of the Regression Analysis

Figure 7 showsthe effect ofeach county characteristic

on inmigration from metro areas, which can be compared with its effect on inmigration from other nonmetro counties, shown in figure 8. Each barrepresents

a standardized coefficient for a county characteristic

from the results ofthe regression analysis. The coefficients have been standardized (in a process somewhat

similar to finding a common denominator for a set of

fractions) so thatthe effect of one county characteristic

on inmigration can be compared to that of another.^

The coefficients are directional in thé sense that a

positive coefficient, appearing above the zero line in

^The size of these standardized coefficients cannot be strictly

compared between equations which use different dependent

variables as figures 7 and 8 do because the standardization is based

on the variation within each equation. See appendix table C forthe

numerical values of metric coefficients (which can be compared

between equations) and of standardized coefficients for both

migration equations.

figures 7 and 8, indicates that when values ofthat

county characteristic are high, the percentage ofnew

residents is also likely to be high. A negative coefficient appearing below the zero line, indicates a reverse

relationship, where a low value on that county characteristic is associated with a high percentage ofrecent

inmigrants and vice versa.

Job-related factors comprised the first set of county

characteristics in figures 7 and 8. Median family

income was positively related to inmigration from

both metro and nonmetro sources. Recall, however,

that when figures 5 and 6 took one variable at a time,

there appeared to be almost no association between

income and nonmetro-origin inmigration. A strong

relationship existed between adjacency to metro areas

and median family income, with higher income in

adjacent counties. The effect of income had been

suppressed by adjacency, a disadvantage of looking

at one characteristic at a time when the variables are

related to each other. Median family income was

higher in nonmetro areas which were adjacent to

metro areas, but nonmetro migrants were more likely

to choose countiesthatwere not metro-adjacent. Note

in figure 8 thatthe negative association with adjacency

wasthe strongestfactorin explaining migration from

other nonmetro areas. Although median family income was higher in nonagricultural areas, migrants

What Attracts New Residentsto Nonmetro Areas? 9
Figure 8

Standardized Regression Coefficients of Inmigration, 1975-80,

from One Nonmetro County to Another, by Characteristics

of the Destination Counties

Inmigration from metro areas

0.4

0.2

0

-0.2 -

-0.4

IS s s \ s 'U.

Change in Employment Commuting Median

manufacturing in to family

employment agriculture work income

Job-related

Recreation Second

employment homes

Retirement Metroadjacency

College

enrollment

Military Institutions

Leisure lifestyle Institutions

from nonmetro areastended to choose counties with

higher proportions of agricultural occupations. Note

that prevalence of agricultural occupations was second only to adjacency in explaining nonmetro-origin

inmigration. The regression analyses indicate that, all

other things (such as adjacency and industrial composition) being equal, migrants from both nonmetro

and metro areas were attracted to nonmetro counties

with relatively high median income.

Migrants from metro areas apparently avoided agricultural counties. However, based on statisticsrelated to

the regression analysis, this avoidance occurred

because of the relationship between agriculture and

several other important county characteristics, not

because of an aversion to things agricultural. Migration from metro areas tended to be directed toward

counties which were adjacentto metro areas and those

with recreation opportunities. Heavily agricultiu-al

counties are not usually adjacentto a metro area, nor

are they likely to have high recreation employment or

a high percentage of second homes. Melro-origin

migrants also went to nonmetro counties with high

median family incomes and high college enrollments.

High college enrollment wasrelatively rare in heavily

agricultural counties as were higherlevels ofmedian

family income. Thus, it was not the avoidance of

agriculture itselfthat kept metro-origin migrantsfrom

going to heavily agricultural counties but the attraction to factors thatwere not usually found in agricultural areas.

Conversely, ñgures 5 and 6 show a weak tendency for

migrants from other nonmetro cireas to move to

heavily agricultural counties. However, because

nonmetro-origin migrants were also attracted to

counties with highmedian family incomes and large

local colleges, neither of which tend to be found in

heavily agricultural areas, the attraction ofagriculture

to these migrants was understated. Had adjacency not

been included in the same regression equation, the

argument could have been made that the apparent

association ofagriculture and nonmetro-origin inmigration was simply because of the strong tendency of

these migrants to choose counties away from metro

areas. However, metro adjacency was includedin the

analysis, indicating that even among not-adjacent

counties, nonmefro-origin migrants had a tendency to

move to where agriculture, forestry, and fisheries

were prevalent. The reasons forsuch an attraction are

not clear. While the steep decline in these economic

sectors during the 19B0's was over by the 1970's,

growth remained sluggish during this period, and

employment opportunities in these sectors probably

did not account for the attraction of the majority of

10 Linda L. Swanson
noninetro inmigrants. Some people were possibly

return migrants who left during the decline of the

previous decade but returned to family and friends.

In explaining metro-origin inmigration, all of the

leisure lifestyle variables are important factors, with

retirement by far the strongest. This essentially confirms the impression given by the characteristic-bycharacteristic analysis of the strong role of leisure

lifestyle factors. The authorincluded metro adjacency

as a leisure lifestyle factor because once commuting

had been accounted for in the equation, the remaining

importance of metro adjacency was proximity to

metro amenities, such as shopping, sports, or cultural

events. Metro adjacency may be construed as an

amenity factor for former metro residents, and was

strongly related to inmigration from metro areas, as

was recreation and the proportion of second homes.

For nonmetro-origin inmigrants, metro adjacency was

the only amenity factor measured here thatinfluenced

destination choice, butthe relationship is negative. Of

the measures included in the study, not being nextto

a metro area seems to be the mostimportant attribute

a nonmetro county can have for these migrants.

The third set of explanatory factors, that ofinstitutions

within the coimty, was associated with inmigration

from both metro and nonmetro sources, although

more strongly with metro-origin inmigration. The

presence and size of colleges and universities, as

measured by the percentage ofschool-going residents

who were enrolled in college, pulls people in from

both metro and other nonmetro areas. To a lesser

degree than colleges, military installations (measured

by percentage of the labor force in the military) also

bring metro-origin people into the county. Military

installations play a marginal role in bringing people

in from other nonmetro areas. Other institutions,

perhaps because oftheirlow tiurnover rate, have little

effect on the number of people arriving from other

counties, metro or nonmetro.

According to the R^ summary measure, 62 percent of

the variation in the percentage of metro-origin

inmigrants was accounted for by the county characteristics included in the equation. This indicates a

fairly good explanatory model. The capacity of the

comity attributes measured here to explain migration

from other nonmetro areas wasratherlow, however,

with only 35 percent of the variance explained.

Regression Analysis Summary

Table 1 summarizes the relative contribution of

job-related, leisure lifestyle, and institutional characteristicsto the overall explanation ofinmigration to a

nonmetro county. Leisure lifestyle attributes were

stronger contributorsto the explanation ofmetro-origin inmigration to nonmetro areas than were the

job-related characteristics. Even the presence of

Table 1-Summary of job-related, leisure lifestyle, and

institutionai contributions to regressions explaining

migration into nonmetro counties

(N = 2,380 nonmetro counties)

Independent

variable sets

Job-related

Leisure lifestyle

Institutional

Total R2

Explanation of

Explanation of percentage of residents

percentage of residents from other nonmetro

from metro areas areas

R^ Change^

0.03

.31

.21

.62

0.10

.13

.09

.35

""The R2 change is how much would be lost to the overall regression

equation's R^ if this set of variables were removed from the analysis.

institutions within the county added much more to

explaining metro-origin inmigration than did jobrelated factors.

The metro-origin inmigration associations contrast

with those of nonmetro-origin inmigration where

leisure lifestyle (almost entirely driven by nonadjacency), job-related factors (income and agriculture),

and die presence ofinstitutions made roughly equivalent, modest contributions to the equation. The fact

that the power of explanation was relatively low

(0.35), even when all three categories were added

together, indicates that county characteristics not

measured here, or perhaps personal factors not easily

assessed with county-level data, should be examined

to explain the movement ofpeople from one nonmetro

county to another.

Conclusions

Most people who moved from a metro area to a

nonmetro county during the 1970*s were apparently

less motivated by job-related reasons than by a lifestyle revolving around amenities. Because recent

nonmetro county growth has been associated with

inmigration from metro areas, people who must make

decisions based on a nonmetro county's projected

population growth should note the lifestyle factors

which provide the strongest lure.

People who moved from one nonmetro county to

anotherseemed more motivated by job opportunities

than amenities, although county characteristics in

general were not particularly useful in explaining

such moves. These people may have been returning

or moving to the county because of personal ties, or

may have been moving short distances across neighboring county lines. Further research should help

determine what motivated nonmetro residents to

relocate in both growing and declining nonmetro

areas diu-ing an overall resurgence of nonmetro

growth.

What Attracts New Residents to Nonmetro Areas? 11
Note that 1975-80 isthe latest periodfor which detailed

migration data will be available until 1990. Since

1980, the surge of nonmetro growth beyond metro

growtii has abated. Nonmetro counties in the South

and West still show healthy growth. Sa3dng with

certainty howmigration patterns have changed in Ihe

1980's is difficult without information for this most

recent period on where people have come from when

they airive in a nonmetro county or where they go

when they leave. That the movement from metro to

nonmetro areas has dropped from its height in the

early 1970's, however, is certain, though we do not

know exactly by how much. Nonmetro countiesthat

have shown the most promising signs ofcurrent and

continuing growth are those wiüi recreation opportunities and/or retirement communities (5). Both the

presence of recreation and retirement are inherently

attractive and create employment opportimities by

bringing population and financial resources to the

coimty which fosters growth and stability.

References

(1) Beale, Calvin L. TheRevival ofPopulation Growth

in Nonmetropolitan America. ERS-605. U.S. Dept

Agr., Econ. Res. Serv., June 1975.

(2)-

(9)- -. "The Economics ofthe Ruralto Urban

"The Recent Shift of United States

Population to Nonmetropolitan Areas, 1970-1975,"

InternationalRegional Science Review, Vol. 2,1977,

pp. 113-22.

(3) Rural and Small Town Population

Change, 1970-80. ESS-5. U.S. Dept. Agr., Econ. and

Stat. Serv., Feb. 1981.

(4)^ -, and Glenn V. Fuguitt. _ . "The New

Pattern of Nonmetropolitan Population Change,"

Social Demography, chap. 8. Karl E. Taeuber, Larry

Bumpass, and James A. Sweet (eds.). New York:

Academic Press, Inc., 1978,

(5) "Metropolitan and NonmetropoHtan

Growth Differentialsin the United States since 1980, "

GeneralConference ofthe International Union forthe

Scientific Study of Population, Florence, Italy, June

1985.

(6) plackwood, Lairy G., and Edwin H. Carpenter.

"The Importance of Anti-urbanism in Determining

Residential Preferences and Migration Patterns,"

Rural Sociology, Vol. 43, No. 1, 1978, pp. 31-47.

(7) Brown, David L. "Spatial Aspects of Post-1970

Work Force Migration in the United States," Growth

and Change, Vol. 12, No. 1, 1981, pp. 9-20.

(8) Chalmers, James A., and Michael J. Greenwood.

"Thoughts on the Rural to Urban Migration Turnaround," InternationalRegional Science Review, Vol.

2, No. 2, 1977, pp. 167-70.

Migration Turnaround," Social Science Quarterly,

Vol. 61, Nos. 3 and 4, 1980, pp. 524-44.

(10) Diliman, Don A. "Residential Preferences, Quality

of Life, and the Population Turnaround," American

Journal ofAgriculturalEconomics, Vol. 61,1979, pp.

960-66.

(11) Glasgow, Nina. "The Older Metropolitan Origin

Migrant as a Factor in Rural Population Growth,"

Rebirth ofRuralAmerica, chap. 9. AndrewJ. Sofi^anko

and James D. Williams (eds.). Ames, Iowa: North

CentralRegionalCenterforRural Development, 1980.

(12) Haren, Claude C, andRonald W. HoUing. "Industrial Development in Nonmetropolitan America: A

Locational Perspective," Nonmetropolitan Industrialization, chap. 2. Washington, DC: V.H. Winston and

Sons, 1979.

(13) Long, Larry H., and Diana DeAre. "Repopulating

the Countryside: A1980 Census Trend," Science, Vol.

217, 19Ö2, pp. 1,111-6.

(14) McCarthy, Kevin F., and Peter A. Morrison. ''The

Changing Demographic and Economic Structure of

Nonmetropolitan Areasin the United States, R-2399-

EDA. Santa Monica, Calif: Rand Corporation, 1979.

(15) McGranahan, David A., and others. " Social and

EconomicCharacteristics oftíie Population in Metro

and Nonmetro Coimties, 1970-80," Rural Development Research Report 58, U.S. Dept. Agr., Econ.

Res. Serv. 1986.

(16) Menchick, Mark D., "The Service Sector," Nonmetropolitan America in Transition, chap. 6. Amos

H. Hawley and Sara M. Mazie (eds.). ChapelHill, N.C:

The University of North Carolina Press, 1981.

(17) Roseman, Curtis C. "Labor Force Migration,

Non-labor Force Migration, and Non-employment

Reasons for Migration," Socio-Economic Planning

Sciences, VoL 7, Nos. 5 and 6, (1983), pp. 303-12.

(18)- _, and James D. Williams. "Metropolitan to Nonmetropolitan Migration: A Decision-making

Perspective," Urèaiî Geography, Vol. 1, No. 4, 1980,

pp. 283-94.

(19) Sternlieb, George, and James W. Hughes. "Metropolitan Decline and Inter-regional Job Shifts," The

Fiscal Crisis ofAmerican Cities. R.E. Alcaly and D.

Mermelstein (eds.). New York: Vintage Books, 1977,

pp. 145-64.

(20) Summers, Gene F., and Thomas A. Hirschl.

"Retirees as a Growth Industry, " Rural Development

Perspectives, Vol. 1, Issue 2, U.S. Dept. Agr., Econ,

Res. Serv., Feb. 1985, pp. 13-16.

12 Linda L. Swanson
(21) Swanson, Linda L. Nonmetropolitan-destined

Migration and Metropolitan Area Attributes, Pub.

Order No. DA8304611, University MicrofilmsInternational, Ph.D dissertation, Univ. of Michigan, Ann

Arbor, 1982.

(25) _, and Andrew J. Sofranko. "Motiva-

(22). , , "SMSA Characteristics and Nonmetropolitan-destined Outmigration. " Presented at Population Association of America meeting, Pittsburgh,

Apr. 15, 1983.

(23) Wardwell, John M. "Rural Development Policy

and Riu-al Growth." Presented at Southern Regional

Demographic Group AnnualConference, Little Rock,

1981.

(24) Williams, James D. "The Nonchanging Determinants ofNonmetropolitan Migration," Rural Sociology,

VoL 46, No. 2, 1981, pp. 183-202.

tions for the Inmigration Component of Popidation

Turnaround in Nonmetropolitan Areas," Demography, Vol. 16, No. 2, 1979, pp. 239-55.

(26) Zuiches, JamesJ. "Residential Preferencesinthe

United States," Nonmetropolitan America in Transition, chap. 2. Amos H. Hawley and Sara Mills Mazie

(eds.). Chapel Hill, N.C.: The University of North

Carolina Press, 1981.

(27) _ -, and Michael L. Price. "Industrial

Dispersal and Labor-force Migration: Employment

Dimensions of the Population Tumaround in Michigan," NewDirectionsin Urban-RuralMigration, New

York: Academic Press, Inc., 1980,

What Attracts New Residents to Nonmetro Areas? 13
Appendix A

The measuresin this article came from decennial U.S.

Census coiuity-level data for each county defined as

being nonmetro in 1980. Construction ofthe variables

is detailed below:

Change in manufacturing — Number of manufacturing employees in 1980 and the number of manufacturing employees in 1970 divided by the

employed population in 1970.

Agriculture occupations — Number in agriculture,

forestry, or fisheries in 1980 divided by the

employed population in 1980.

Median family income — Taken fromthe 1980 census

measures.

Commute to vvrork — Number working in another

county in 1980 divided by the employed population in 1980.

Recreation employment — Number employed in

recreation in 1980 divided by the employed

population in 1980.

Second homes — Number of housing units either

seasonal or held for occasional use divided by

total housing units in 1980.

Retirement — Number of inmigrants minus number

of outmigrants (age 60 or older, 1960-70)

divided by the total population in 1960.

College enrollment — Number enrolled in college or

university in 1980 divided by the total schoolenrolled population in 1980.

Military — Number in armed forces in 1980 divided

by the employed population in 1980.

Institutions — Number in correctional, psychiatric

institutions; homes, schools, hospitals, or wards

forjuveniles and the handicapped in 1980 divided

by the total population in 1980.

Inmigration from metro areas — Number of 1980

residents who lived in a metro area in 1975

divided by the total population in 1980.

Inmigration from nonmetro areas — Number of 1980

residents who lived in another nonmetro county

in 1980 divided by the total population in 1980.

Appendix B

Following are the numerical boundaries for the categories of the explanatory variables in figures 3, 5, and 6:

Change in percentage of

manufacturing employment

Percentage ofworkersin

agricultural occupations

Median family income

(1,000 dollars)

Percentage ofworkers

commuting outside the county

Percentage ofworkers

employed in recreation

Percentage ofhousing units

as second homes

Netinmigration rate ofpeople,

aged 60 and over, 1960-70

Population growth rate

Low Medium High

-28.6to3.6 3.7 to 10.0 10.1 to 55.4

.32 to 4.4 4.5 to 10.6 10.7 to 70.3

7.2tol4.7 14.8 to 19.3 19.3 to 80.6

.71 to 13.5 13.6 to 26.38 26.4to80.3

Oto 3.0 3.1 to 4.9 5.0 to 30.4

Otol.9 2.0 to 10.5 10.6to74.3

Oto 9.9 10.0 to 14.9 15.0 or more

Decline Lowto Medium High

-44.5 too .01 to 50.0 50.1 to 232.0

14 Linda L. Swanson
Appendix C

As an illustration, a regression equation explaining

migration on the basis of the county characteristics,

a) percentage of work commuters, b) percentage

employed in recreation, and c) percentage of houses

which are second homes, could be represented as

follows:

Percentage of 1980 residents who lived elsewhere in

1975 =

+

ai

(percentage who commute to work in another county]

+

a2

(percentage employed in recreation)

+

a3

(percentage of houses which are second homes)

+

(error term, or variation left unexplained)

The coefficients (a^, a2,0.3] for each county characteristic show how much that characteristic contributed to

the explanation of migration. The intercept (a^,) provides a baseline indicating what the dependent variable would be if the value of all the independent

variables were zero. The intercept, plus the coefficients for the three independent variables, plus the

error term, or remainder, equals the variation found

in the dependent variable among the counties observed. The results of the analysis would include a

summary value (R^) indicating how well the three

county characteristics worked togetherin accounting

for the variation in inmigration among nonmetro

counties. Ifall the variation in inmigration to counties

had somehow been accounted for by the county

characteristics, the R^ statistic would equal 1.00. The

R^ value is the proportion of the variation explained

by the analysis. If, in the above example, an R^ of0.31

were obtained, a little lessthan a third ofthe variation

in inmigration would have been explained by the

three independent variables.

Appendix table 1 — Coefficients in regressions of the percentage of

1980 nonmetro residents who recently (1975-80) moved into the county,

based on county characteristics (N = 2,380 nonmetro counties)

Percentage of residents Percentage of residents

Independent from metro areas from other nonmetro areas

variables b B b B

Metric standardized IVIetric standardized

(1) (2) (3) (4)

Job-related:

Change in manufacturing

employment 0.079 0.071 0.040 0.062

Agriculture occupation -.005 -.004 .205 .304

Commute to work .024 .044 .001 .003

Median family Income

(1,000 dollars) .236 .150 .163 .179

Leisure lifestyle:

Recreation employment .728 .189 .069 .031

Second homes .141 .170 -.027 -.056

Retirement 3.836 .332 .187 .028

Metro-adjacency (1 = Adj.) 2.681 .188 -3.162 -.385

Institutions:

College enrollment .??? .351 .107 .293

Military .454 .299 .104 .119

Institutions .440 .092 .024 .009

Constant -2.432 3.594

R2 .62 .35

What Attracts New Residents to Nonmetro Areas? 15
Economic Growth is Good For Everyone« Right?

Not neœssarlty, according to recant findings from USDA's Economic Research Service. Find

out from these related reports Just what can happen when rapid economic growth comes to

a rural area.

mBtmutíútt of Empipyment Qmwth In W Georgia

CúUñtíBB: A Cmm Study, by James D.

Schaub and Victor J. Olivelra, SN: 001-019^^412^6.

Rapid économie growth in a 10<ounty rural area in

south Georgia during 197S-81 favored employment

of whites, mBn, and inmîgrants. They earned higher

average weekly salaries than btací<s» women, and

long-teiTO residents. This study of growth In a mixed

manufacturing- and agricuttura(*ased economy flows

from a reses^ch project on the Impatäs of economic

expansion in nonmetro economies with different industrial bases. The Georgia area's job growth was

greatest in the trades and services sectors. Few

businesses used public sec^r funds to si^rt or expand their operations. Government employed 26 percent of the area's wage and salary worl<ers.

Distribution of&mpioym^nt Qmmti in Ninw tC^ntutíky Cauntim: A Case Stutíy, by Stan G.

Datmrkow, Donald K. Larson, Revert Coltrane, and

Thomas A. Cartin, SN: 001-01S-Ô0337-5.

Rapid employment growth beti/^een 1974 and 1979

in a nine-county ^udy area of south central Kentucky provided Job opisK^rtuníties toth for local

residents and for persons with limited labor force ex«

perience- But, recent Inmigrante held a disproportionate share of better paying executive Jobs, This

case study, which examines the distributional effects

of rapid employment growth In a nonmetro ^ea,

shows that Inmigrante also held a disproportionate

share of jobs In growing business establishments.

Although manufacturing was the major economic

force in the study area in January 1980, ^bs In the

private ser^^ce sector Increased more than In other

sectors.

Distribution of Burst Employmsnt Growtit by Uses:

A Csss Study, by Victor J. Oliveira.

SN: 001^019^00422^3.

Whites benefit more from rural economic growth

than do blacks, based on the findings of a survey of

adults In 10 rural counties in southern Georgia. From

1976-81, a period of rapid employment growth, the

percentage of white women with jobs in the study

area Increased, while the percentage of black mBñ

with jobs actually decreased. Among employed persons, whites increased their share of higher wage

jobs. Persons who moved into the area obtained

higher paying jobs than did other resident; these Inmigrante» most of whom were white, generally took

larger shares of the new lobs than did long4erm

residents of both racial groups. Improving the education and |ob training of poor residents, especially

blacks, is essential to distributing economic benefits

more equally.

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