United States

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Economic

Research

Report

Number 79

August 2009 John Cromartie and Peter Nelson

Baby Boom Migration 

and Its Impact on 

Rural America
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Photo credit: John Cromartie, ERS.

 Cromartie, John

 Baby boom migration and its impact on rural America.

 (Economic research report (United States. Dept. of Agriculture. 

 Economic Research Service) ; no. 79)

 1. Baby boom generation—United States. 

 2. Migration, Internal—United States.

 3. Rural development—United States. 

 4. Population forecasting—United States.

 I. Nelson, Peter. II. United States. Dept. of Agriculture. Economic

 Research Service. III. Title. 

 HB1965.A3
United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Abstract

Members of the baby boom cohort, now 45-63 years old, are approaching a period in their 

lives when moves to rural and small-town destinations increase. An analysis of age-specific, 

net migration during the 1990s reveals extensive shifts in migration patterns as Americans 

move through different life-cycle stages. Assuming similar age patterns of migration, this 

report identifies the types of nonmetropolitan counties that are likely to experience the 

greatest surge in baby boom migration during 2000-20 and projects the likely impact on 

the size and distribution of retirement-age populations in destination counties. The analysis 

finds a significant increase in the propensity to migrate to nonmetro counties as people 

reach their fifties and sixties and projects a shift in migration among boomers toward more 

isolated settings, especially those with high natural and urban amenities and lower housing 

costs. If baby boomers follow past migration patterns, the nonmetro population age 55-75 

will increase by 30 percent between now and 2020.

Keywords: Baby boomers, migration, rural development, life-cycle migration, population 

projections.

Acknowledgments

The authors wish to thank the following reviewers for their helpful comments: Robert 

Gibbs, Charlie Hallahan, David McGranahan, and Patrick Sullivan of USDA’s Economic 

Research Service, David Plane of the University of Arizona’s Department of Geography and 

Regional Development, and an anonymous reviewer. Thanks also go to John Weber for his 

editing assistance and Susan DeGeorge for the graphic design and text layout of the report.

About the Authors

John Cromartie is a geographer with USDA’s Economic Research Service. Peter Nelson is 

an associate professor of the Department of Geography, Middlebury College.

John Cromartie, jbc@ers.usda.gov 

Peter Nelson

Baby Boom Migration and Its 

Impact on Rural America

Economic

Research 

Report

Number 79

August 2009
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Why Is Baby Boom Migration Important to Rural America? . . . . . . . . 4

Migration as a Life-Cycle Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Migration Patterns of the Baby Boom Cohort . . . . . . . . . . . . . . . . . . . . . 8

Factors Driving Age-Specific Net Migration, 1990-2000 . . . . . . . . . . . . 10

Net Migration Projections, 2000-20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Appendix: Regression Analysis and Population Projections . . . . . . . . 25

Recommended citation format for this publication:

Cromartie, John, and Peter Nelson. Baby Boom Migration and Its 

Impact on Rural America, ERR-79, U.S. Dept. of Agri., Econ. Res. Serv. 

August 2009.
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Summary

The size and direction of migration patterns vary considerably by age group, 

and baby boomers (those born between 1946 and 1964) have entered a stage 

in which their migration patterns will increase the population of rural and 

small-town settings. Many older boomers are ending child-rearing duties, 

changing housing preferences, and pondering early retirement options. 

Quality-of-life considerations are beginning to replace employment-related 

factors in decisions about when to move and where to live. Within clearly 

marked ranges, this report projects the level of net migration change for baby 

boomers through 2020 and measures its impact on the retirement-aged population in nonmetro areas.

What Is the Issue?

Population change as a result of net migration is unevenly distributed across 

the landscape. Rural jurisdictions face different demands for local goods 

and services and different opportunities for economic expansion, depending 

on population trends. Anticipating the types of areas that will receive 

large numbers of baby boomers in the near future could help communities 

plan for rising demands for housing, transportation, health care, and retail 

infrastructure. 

Economic and social impacts from migration connect to broader age-related 

issues subject to debate at Federal and State levels. Places that emerge as 

migration destinations for Americans approaching or entering retirement 

today will be increasingly influenced by Federal policy decisions regarding 

Social Security adjustment, pension guarantees, workforce issues, and health 

care provision, among others.

What Did the Study Find?

Baby boomers are a heterogeneous group. Younger boomers are still in the 

middle of child rearing and career building, while older boomers are more 

likely to be “empty nesters.” In this decade and the next, this cohort will 

pass through stages when moves to nonmetro counties increase, especially 

to areas with scenic and urban amenities, high second-home concentrations, and lower housing costs. Analysis of county net migration rates shows 

that employment considerations become much less important as migrants 

approach retirement. The influence of employment change on migration for 

60-64 year olds is one-fourth as strong as that on 30-34 year olds.

Baby boomers have already demonstrated more of an affinity for moving to 

rural and small-town destinations than older or younger cohorts. They led a 

short-lived rural “rebound” in the early 1990s despite being at an age when 

career-oriented motivations strongly influence migration decisions. They 

are now poised to significantly increase the population of 55-75 year olds in 

rural and small-town America through 2020, with major social and economic 

implications for their chosen destinations.

Through this decade and the next, baby boomer migration will likely 

contribute to a deconcentration of population growth near metro areas. 

Assuming a midrange projection, net migration to core metro counties will 
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switch from a population gain of 979,000 during the 1990s to a population 

loss of 643,000 during the 2010s. Fringe metro counties had the highest 

rates of baby boomer migration in the 1990s, but these rates are projected 

to drop considerably during the 2010s. Migration gains in nonmetro counties adjacent to metro areas will remain relatively stable over the first two 

decades of this century, while more remote counties will see the most 

dramatic increases. After gaining only 277,000 boomer migrants during the 

1990s, these nonadjacent counties will gain nearly 362,000 and 383,000 new 

boomer residents during the 2000s and 2010s, respectively. 

Regardless of all but the most dire future economic and housing market 

conditions, baby boom migration will increase the overall size of rural 

America’s retirement-age population. Assuming a midrange projection 

scenario and including the effects of migration, the rural and small-town 

population of 55-75 year olds will increase two-thirds, from 8.6 million to 

14.2 million between 2000 and 2020. The rate of growth for this age group 

in nonmetro areas has likely tripled to 31 percent during the current decade, 

compared with that of the 1990s, and will remain close to 30 percent in the 

next decade. Without net migration, the rate of growth for this age group 

would be cut nearly in half to just 18 percent in this decade and 15 percent 

during 2010-20. 

How Was the Study Conducted?

ERS analyzed age-specific net migration rates during the 1990s (the most 

recent data) to determine how net migration shifts geographically with age. 

Migration tends to persistently favor counties with specific attributes—

employment opportunities, scenic amenities, reasonable real estate prices, 

proximity to large cities, among others. Age patterns of those migrating 

fluctuate within relatively narrow confines from one decade to the next, especially for older age groups. Thus, population projections for the baby boom 

cohort were constructed for different types of metro and nonmetro areas 

by asking: What will happen to future population trends if age-specific net 

migration patterns remain close to those measured in the 1990s?

The study used a regression model to measure the combined influence of 

employment trends, housing market characteristics, scenic and urban amenities, and other factors on age-specific net migration rates for different types 

of counties during the 1990s. Separate models by 5-year age groups capture 

the changes in migration patterns as people move through different life-cycle 

stages. Using migration rates derived from the models, the study projected 

the size and distribution of retirement-aged populations up through 2020. A 

range of projections provides upper and lower bounds for the likely size of 

baby boomer migration and its geographic variation.
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Introduction

A dramatic jump in births in mid-1946 marked the beginning of the baby 

boom, as couples affected by the Great Depression and separated by war 

began to make up for lost family-building time. Widespread economic prosperity and family-friendly government programs helped sustain high birth 

rates for nearly 20 years—an unforeseen interruption in long-term U.S. 

fertility decline. Births surpassed 4 million per year between 1954 and 1963, 

and the fertility rate surged to over 3.5 children per woman (fig. 1). Today’s 

83 million baby boomers, ranging in age from 45 to 63 years old, represent 28 

percent of the total U.S. population. Never before has such a large share of the 

U.S. workforce approached retirement. By comparison, 42 million people, or 

17 percent of the population, were in these middle-aged years in 1990.

Between 2010 and 2020, “boomers” will make more than 200 million residential moves. Most moves will be within or between metropolitan (metro) 

regions, where 80 percent of this cohort now reside. However, boomers also 

will increase the size and reshape the demographic character of rural areas 

and small towns throughout the country. Older boomers are moving through 

a life-cycle stage marked by peak employment earnings, the end of childrearing duties, changing housing preferences, and early retirement options. 

Quality-of-life considerations have begun to replace employment-related 

factors in decisions about when to move and where to live. Boomers as a 

group have already demonstrated, at times, a higher preference than older 

or younger cohorts for staying in or moving to nonmetropolitan (nonmetro) 

counties. Demographically speaking, they are poised to move rural and smalltown America in new directions. 

1930 1940 1950 1960 1970 1980 1990 2000

0

0.5

1.5

2.5

3.5

4.5

5.0

4.0

3.0

2.0

1.0

Births (millions) Births per woman

Baby boom cohort

Births

Births per woman

6

5

4

3

2

1

0

Note: Total fertility rate (births per woman) is the sum of age-specific birth rates for women 

ages 15-44.

Source: USDA, Economic Research Service, using data from the National Center for 

Health Statistics.

Figure 1

U.S. live births and total fertility rate
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This report focuses on age-specific population change at the county level, 

particularly on change caused by net migration—the difference between 

the number of people moving into and out of a county. The research has two 

parts:

1. Analysis of age-specific net migration rates during the 1990s (the most 

recent data), which shows how county-level patterns of net migration 

shift with age.

2. Population projections for the baby boom cohort for different types 

of metro and nonmetro areas (see appendix). The projections are 

constructed by asking: What will happen to future population trends if 

age-specific net migration patterns measured in the 1990s stay the same?

The volume of migration flows into and out of rural areas shifts periodically, sometimes quite suddenly, as was the case with the “rural turnaround” 

of the 1970s and its subsequent demise (Beale, 1975; Johnson and Fuguitt, 

2000). However, migration tends to persistently favor counties with specific 

attributes—employment opportunities, scenic amenities, reasonable real 

estate prices, proximity to large cities, among others (Cromartie, 2001). Age 

patterns of those migrating fluctuate within relatively narrow confines from 

one decade to the next, especially for older age groups (Johnson et al., 2005). 

Thus, recent net migration rates can be used to project a range of future 

population outcomes for different types of counties. 

Counties are the unit of analysis because they are the smallest geographic 

unit used to report age-specific, net migration. The selection of counties 

characterized as “rural and small town” for purposes of this study includes 

those designated as nonmetro. Metro counties are divided into two groups—

those that contain a predominantly urban core population and those on 

the periphery that are predominantly rural in character (see box, “County 

Classifications Used in This Report”). 
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County Classifications Used in This Report

The classification of counties as “rural and small town” for purposes of 

this analysis includes a four-level typology that begins with counties 

designated as metropolitan or nonmetropolitan (nonmetro) by the U.S. 

Office of Management and Budget in 1993 (based on data from the 1990 

Census). Projections for 2000 and beyond are based on counties considered to be nonmetro for the 2000 Census. Nonmetro counties are defined 

as those counties lying outside urban cores of 50,000 people or more and 

their immediately adjacent commuting zones. For more information on 

how metro and nonmetro areas are defined, see the Measuring Rurality 

briefing room on the ERS website: www.ers.usda.gov/briefing/rurality/

newdefinitions/

We further divide nonmetro counties into those that are adjacent to metro 

areas and those that are more remote, based on the ERS Rural-Urban 

Continuum Code (Beale, 2004): www.ers.usda.gov/briefing/rurality/

ruralurbcon/

We also divide metro counties into two types: those that include the urban 

core of the region and those on the periphery (“rural metro”), in which a 

majority of the population reside in census-designated rural territory. The 

official, census definition of rural includes people living in open countryside and places with populations less then 2,500. Most U.S. counties 

contain both rural and urban populations, whether or not they are classified as metro or nonmetro. ERS’s Measuring Rurality briefing room 

includes a comparison of rural and nonmetro populations: www.ers.usda.

gov/briefing/rurality/whatisrural/

Most of the 262 rural-metro counties included here were newly reclassified from nonmetro to metro in 1993. Many are in the middle of a transition to a landscape dominated economically and socially by suburbs but 

retain much of their rural character (Cromartie, 2006). They were among 

the most rapidly growing counties in the country in the 1990s. We include 

them in the regression analysis and discussion of results because they 

have been quintessential migration destinations for boomers seeking both 

rural “quality of life” and access to urban amenities.
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Why Is Baby Boom Migration Important to 

Rural America?

The migration of baby boomers, though just one segment of overall rural 

population change, will affect rural economies and rural development policy 

efforts for years to come. Economic and social impacts from migration 

connect to broader age-related issues subject to vigorous debate at Federal 

and State levels. Places that emerge as migration destinations for Americans 

approaching or entering retirement today will be influenced increasingly 

by Federal policy decisions regarding Social Security adjustment, pension 

guarantees, workforce issues (including immigration policy), and health 

care provision, among others. For example, communities that experienced 

retirement-related population growth in the 1990s showed a significant rise in 

government transfer payments as a proportion of their income base (Nelson, 

2005). Also, many rural and small-town destinations for older Americans 

witnessed high levels of migration of Hispanics, who are finding work in 

sectors stimulated by the arrival of retirees (Nelson et al., 2009).

Population change from net migration is unevenly distributed across the 

landscape. Rural jurisdictions will face different demands for local goods 

and services and different opportunities for economic expansion, depending 

on population trends. New baby boom residents are likely to have a positive 

impact on income and employment levels in migration destinations. They 

may also increase infrastructure and social-service costs for local governments and require surrounding areas to make investments in health care 

and related services. Counties experiencing surges in population growth 

sometimes suffer from lack of planning and strained fiscal resources, while 

needs arise quickly for housing, transportation, and retail infrastructure. This 

report provides information that can be used to assess the potential impact of 

baby boom migration on rural and small-town areas. Within clearly marked 

ranges, the report projects the level of boomer migration for counties facing 

different economic or demographic scenarios.
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Migration as a Life-Cycle Event

Each individual or family makes unique migration decisions, but commonalities exist at different life stages that affect the number of people moving 

and their destination choices (Taeuber, 1966; Stapleton, 1980; McHugh and 

Mings, 1996). Migration rates for children (who mostly follow parental decisions) decline from birth to very low levels during high school and then rise 

precipitously (fig. 2). Most migration occurs when people are in their twenties as they finish college, make initial career decisions, serve in the military, 

form families, or simply act out of a sense of restlessness. Nonmetro population loss from net migration is heavily concentrated among young adults, 

beginning with high school graduation (fig. 3). Urban destinations dominate 

among young singles seeking jobs, affordable rental housing, social opportunities, and creative cultural environments. 

Migration rates decrease steadily and shift geographically through the 

working-age years. People in their midforties are half as likely to move as 

people in their early thirties. Individuals and families settle down as career 

decisions become more firm and children enter teenage years. Married 

couples with children place a higher premium on residential space, better 

schools, feelings of personal safety, and other qualities associated with 

suburban settings. Since 1960, nonmetro population loss from net outmigration has dropped to near zero after age 30 and even switched to gains in the 

1970s and 1990s (Johnson et al., 2005). As families settle down, a significant proportion of return migration to nonmetro counties occurs and fewer 

people choose to leave rural and small towns.

For about 15-20 years after people reach their midfifties, the propensity 

to migrate stops decreasing and, in some cases, increases slightly. At the 

same time, the direction of migration shifts markedly toward lower density 

0 10 20 30 40 50 60 70 80

Age (years)

Source: USDA, Economic Research Service, using Pandit (1997a).

Migration rate

High

Low

Figure 2

Percent migrating by age
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settings. Many suburban, “empty-nest” couples downsize and move closer to 

city centers to take advantage of cultural amenities, while others seek recreational opportunities, lower housing costs, increased feelings of personal 

safety, or a perceived slower pace of life by moving to the countryside 

(Glasgow and Brown, 2006; Litwak and Longino, 1987). As they age toward 

retirement, Americans are still much less prone to move than they are in their 

twenties, but they are much more prone to choose rural and small-town destinations when they do move.

Rural migration is highest early in the retirement process and declines 

sharply as physical activity becomes more limited and health care needs 

increase. The oldest baby boomers turn 74 years old in 2020 and, if longterm migration patterns persist, will be less likely to make moves to rural 

settings, will be more likely to move back to metro areas, and often will 

move to be closer to caretaker relatives. 

Many people develop strong ties to particular places over an extended period 

while vacationing or visiting family and friends (Johnson and Rasker, 1995). 

Thus, retirement-related migration may begin long before retirement and 

progress slowly over several years rather than occur as a discrete, one-time 

event. Couples often purchase a second home or simply visit the same location annually or on weekends with their children, then visit more often and 

for longer stretches as children leave home. The Internet has greatly facilitated work from more remote locations and contributed to a notable increase 

in permanent moves to second-home destinations in the 1990s (Beyers and 

Lindahl, 1996; Beyers and Nelson, 2000). 

Age patterns of migration are quite stable across diverse geographic settings, 

but the strong influence of employment change and other economic trends 

makes it difficult to predict all aspects of future population trends. Business 

cycles and industrial restructuring affect the size and direction of migration 

streams more than they affect patterns of fertility or mortality—the other 

two components of population change. Economy-driven migration volatility 

-2.0

-1.5

-1.0

-0.5

0

0 5 10 15 20 25 30 35

5-year age groups

Percent

40 45 50 55 60 65 70 75

0.5

1.0

1.5

Metro

Nonmetro

Figure 3

Average annual net migration rates by age, 1980-2000

Source: USDA, Economic Research Service, using data from USDA-funded cooperative 

agreements (Fuguitt and Beale, 1996; Voss et al., 2004).
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tends to concentrate in younger age groups rather than in the life-cycle stages 

the baby boom cohort is currently entering. However, boomers have demonstrated a marked affinity for pioneering new migration paths that differ from 

those of preceding generations. Their migration history will undoubtedly 

influence their future decisions.
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Migration Patterns of the Baby Boom Cohort

Baby boomers have followed well-established age-related migration patterns 

but have shown an increased preference for rural and small-town destinations, compared with older and younger cohorts. Their early childhoods 

coincided with a massive wave of rural outmigration and suburbanization. 

Many of their parents had come of age in the countryside during the Great 

Depression and World War II. Financial support through the GI Bill and 

federally subsidized mortgages enabled the parents of boomers to leave the 

farm or escape congested urban settings and move to newly constructed 

suburbs (Newman, 1993). The rural connections they maintained while 

raising urban and suburban families influenced the next generation’s subsequent migration decisions.

Baby boomers who entered young adulthood in the 1970s faced increased 

labor and housing market competition due both to economic trends and the 

unprecedented size of their cohort. Extensive plant closures and layoffs 

throughout the northern, urban-industrial belt exacerbated the difficulty 

of absorbing rapidly increasing numbers of new workers (Bluestone and 

Harrison, 1987). In addition, increased housing demand coincided with rising 

interest rates and inflation. These conditions made it harder for many baby 

boomers to match the income expectations set by the previous generation, 

start a family, and buy a home (Plane and Rogerson, 1991; Pandit, 1997b). 

Boomers responded demographically by postponing marriage and remaining 

at home with their parents longer than earlier cohorts. Couples delayed childbearing and adopted multi-earner strategies, significantly increasing female 

labor-force participation, which further increased job competition (Newman, 

1993).

Boomers also responded geographically. Net migration from the Northeast 

and Midwest regions to the South and West tripled in size during the 1970s. 

This sharp rise in what was an already-established population shift from 

“Rust Belt” to “Sun Belt” was tied directly to the migration adjustment of 

boomers in the face of increased labor market competition and escalating 

housing costs (Plane and Rogerson, 1991; Plane, 1992). At the same time, 

rural and small-town America experienced a remarkable demographic turnaround, as nonmetro population gains in the 1970s exceeded those of the 

previous four decades combined (Johnson and Cromartie, 2006). Many 

factors contributed to this “rural renaissance,” including the economic stress 

of baby boomers (McGranahan, 1985). Decreasing urban job opportunities 

inhibited the rural-to-urban flow of young workers. Additionally, boomers 

already living in cities but unable to enter the housing market looked beyond 

metro boundaries to cheaper options in small-town and rural hinterlands. 

Overall, they still favored metro destinations as they aged through their twenties, but their net outmigration rate from nonmetro areas was less than half 

that of similar age groups during the 1960s (Johnson et al., 2005).

The ebbs and flows of nonmetro population change since the 1970s have 

been strongly linked to the migration of baby boomers (Nelson et al. 2004). 

An exceptionally severe farm crisis and economic recessions heavily focused 

on goods-producing industries made it harder for rural areas to retain current 

residents or attract new migrants in the 1980s. Large metro regions in 
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particular regained much of the economic momentum lost in the 1970s. The 

demographic response to these regional economic shifts came largely from 

boomers in their late twenties and early thirties migrating more to increasingly career-friendly urban centers (Nelson and Sewall, 2003). 

Later, baby boomers led a short-lived rural “rebound” in the early 1990s, 

stimulating recreation-based economies and boosting population growth in 

the intermountain West, the southern Appalachians, the Upper Great Lakes, 

and other scenic locations (Nelson et al., 2009). In 1995, baby boomers were 

31-49 years old and still strongly career oriented. Much of their nonmetro 

migration fueled rapid suburban expansion into counties adjacent to metro 

centers. Many of those moving to more remote settings, most notably in the 

intermountain West and other scenic regions with recreation opportunities, 

benefited from expanding airline services and the Internet, which enabled 

them to stay connected to urban-based employers and customers. Whether 

driven by technological changes, increased wealth, changing lifestyle preferences, or a combination of factors, areas once popular as recreation and 

tourist destinations became increasingly popular as permanent residences. If 

these geographic patterns mark a socioeconomic transition toward retirement, 

it is the beginning of a migration trend that will have increased over the 

current decade and will persist well into the next (Bures, 1997).
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Factors Driving Age-Specific 

Net Migration, 1990-2000

Previous studies show that it is possible to measure the degree to which 

factors driving migration shift from one age group to the next (Plane, 1992; 

Plane, 1993; Pandit, 1997b; Pandit, 1997a; Nelson et al., 2004). The regression models estimated in this study measure the combined influence of 

employment and housing market factors, recreation and natural amenities, 

urban influence, demographic characteristics, and regional variation on agespecific, county-level net migration rates during the 1990s (see box, “Data 

Sources”). Separate models by 5-year age groups capture the life-cycle variation of these effects (see appendix).

Employment and housing market factors driving net migration patterns 

include employment change, unemployment, and median home values. 

Job-related factors are expected to be more strongly associated with migration among younger cohorts. Home values reflect the importance of equity 

transfers as older populations capture the gains in highly priced real estate 

markets and move to areas with lower housing costs (Karlgaard, 2004; 

Nelson, 2004).

The importance of recreation and the attraction of scenic landscapes are 

among the most important drivers of nonmetro migration (Rudzitis, 1993; 

McGranahan, 1999; Nelson, 1999; Vias, 1999; Power and Barrett, 2001; 

Reeder and Brown, 2001). To capture these effects, this study uses the ERS 

Natural Amenities Index along with the percent of housing units used seasonally or occasionally.

Three variables capture dimensions of urban influence. The first is a four-tier 

classification of counties along a continuum from metro centers to isolated, 

nonmetro counties, from which three of four dichotomous variables are used: 

rural-metro, nonmetro-adjacent, and nonmetro-nonadjacent dummy variables 

measure the relative proximity to the omitted tier (urban-metro). They distinguish three progressively more isolated sets of counties (see box, “County 

Classifications Used in This Report” on page 3). While this classification 

captures the relative position of counties in the metro-nonmetro hierarchy, 

the other two urban influence variables—percent urban and population 

density—describe the character of the county itself. 

Demographic characteristics include the percent of households made up of 

married couples with no children living at home and the percent foreign born. 

The former captures the extent to which baby boomer migrants are arriving 

in destinations characterized by higher concentrations of “empty nest” households. The latter identifies any linkages between these domestic migration 

streams and streams that may originate from abroad (Walker et al., 1992; 

Wright et al., 1997; Frey and Johnson, 1998; Frey and Liaw, 1998). 

Initial model results based on these variables indicated that regional differences remained in the unexplained portions of cohort migration rates. 

Dummy variables for residence in three census divisions—South Atlantic, 

East South Central, and West South Central—best explained the remaining 

variation and were added to the model. 
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Full results include identical sets of regression coefficients for 11 age groups, 

from 25-30 year olds to 75-80 year olds (app. table 3). The coefficients 

measure the degree to which a given variable, such as county unemployment 

rate, affects the variation in net migration among counties. The coefficients 

allow comparison among these effects, for instance, to determine whether 

unemployment rate shows a stronger effect on net migration than does 

employment change. Most importantly, they measure the shifts in net migration influences over different life-cycle stages.

Data Sources

County-level, net migration estimates by 5-year age groups for 1990-

2000 were tabulated as part of a USDA-funded cooperative agreement that used population data from the U.S. Census Bureau and vital 

statistics from the National Center for Health Statistics (Voss et al., 

2004). Researchers employed a “forward-survival” technique similar to 

previous net migration estimates for each decade since 1950 (Bowles 

and Tarver, 1965; Bowles et al., 1975; White et al., 1987; Fuguitt and 

Beale, 1996). Net migration represents the difference between the actual 

county population for an age group as recorded in the 2000 decennial 

census and an “expected” population. This analysis used birth and death 

data from the 1990 decennial census to estimate the expected number 

of survivors in each age group. If the actual population was higher than 

expected, the difference was attributed to net inmigration. If lower, then 

the residual was counted as net outmigration. For each 5-year age group, 

cohort migration rates were calculated by dividing net migration by 

cohort population (see appendix). 

Most other data used in this report were obtained from the 1990 

and 2000 decennial censuses (Census Bureau, U.S. Department of 

Commerce). We calculated the change in employment from 1990 

to 1993 using data from the Bureau of Economic Analysis, U.S. 

Department of Commerce. In addition to the typologies used to characterize counties along an urban-rural continuum (see box, “County 

Classifications Used in This Report”), the ERS Natural Amenities Index 

was included in the analysis of net migration to measure physical characteristics of a county area that enhance the attractiveness of a location as 

a place to live. The scale combines six measures of climate, topography, 

and water area that reflect environmental qualities most people prefer 

(McGranahan, 1999).

The U.S. Census Bureau’s classification of States into Census Divisions 

was used to measure regional effects on net migration. Three of the nine 

Divisions were included in the analysis:

South Atlantic—Delaware, District of Columbia, Florida, Georgia, 

Maryland, North Carolina, South Carolina, Virginia, and West Virginia. 

East South Central—Alabama, Kentucky, Mississippi, and Tennessee.

West South Central—Arkansas, Louisiana, Oklahoma, and Texas.
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Employment and Housing Market Factors

Populations age 30-34 showed the greatest attraction to areas with strong 

employment growth in the 1990s (fig. 4). Younger baby boomers were still in 

the middle of their working careers and thus strongly affected by job growth. 

However, employment trends exerted a much smaller influence on older 

baby boomers, who were loosening their ties to the job market in the 1990s. 

Models for older ages show that these connections will continue to weaken. 

The influence of employment change on migration for 60-64 year olds is 

one-fourth as strong as that for 30-34 year olds (beta values of 0.07 versus 

0.28, respectively). The effect of unemployment rates on net migration shows 

important age differences as well. Individuals between age 25 and 49 are 

directed away from areas with high unemployment to the greatest extent. As 

individuals reach age 50, migration is actually directed into areas with higher 

unemployment rates. 

If age patterns of migration hold, baby boomers are likely to be strongly 

influenced by housing market characteristics in their future decisions about 

where to live. Model results show that populations age 55 and older are 

moving away from areas with higher median home prices, even when 

controlling for factors such as metro status, employment growth, and scenic 

amenities. The effects are most pronounced for those in their early sixties 

and early seventies, the ages that the largest group of baby boomers will be 

passing through in the coming decade. 

Natural Amenities and Recreation

The effects of both natural amenities and seasonal housing on net migration 

increase dramatically with age, as expected (fig. 5). All ages are attracted to 

areas with higher levels of natural amenities. However, the coefficient for 

25-29 year olds (0.14) is half that of 50-54 year olds (0.28). Seasonal housing 

has virtually no effect on younger cohorts, but the relationship becomes positive and quite strong for populations over age 40. This result suggests that 

25 30 35

5-year age groups

Percent

40 45 50 55 60 65 70 75

0.25

0.15

0.05

0

-0.05

-0.10

-0.15

0.30

0.20

-0.20

0.10

Unemployment rate

Employment change

Median housing price

Figure 4

Effects of employment and housing market factors on net 

migration by age, 1990-2000

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau and 

the Bureau of Economic Analysis.
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areas popular as second-home destinations become destinations for permanent migration. This effect is most pronounced for those age groups (55-64) 

through which older boomers are just now crossing. If these migration 

patterns persist, population in seasonal and recreation-based areas is likely to 

increase substantially in this decade and in the future.

Urban Influence

The ages older baby boomers are currently entering (late fifties and early 

sixties) show the strongest migration toward nonmetro destinations, regardless of adjacency (fig. 6). All things being equal, net migration of boomers 

to suburban destinations (measured here using rural-metro counties) should 

drop substantially during the next 10 years, while nonmetro counties adjacent to metro areas emerge as primary destinations. However, baby boom 

migration to more isolated, nonadjacent counties should increase relative 

to migration to adjacent counties. The effect of remoteness, or nonadjacency, becomes slightly positive for migrants in their sixties, and the gap 

between coefficients for adjacent and nonadjacent counties narrows. If these 

geographic patterns hold, nonmetro population growth in the coming years, 

rather than being concentrated near metro regions, will spill over into the 

areas remote from the metro regions as well.

After controlling for proximity to metro centers, the effects of county population density and percent urban are shown to capture additional aspects of agespecific net migration (app. table 3). Population density is strongly associated 

with net migration only during ages when suburbanization is most likely, 

when people move into counties with relatively lower population densities. Migration to more urbanized counties (controlling for density) drops 

precipitously between ages 30 and 34 but then increases steadily after that. 

Independent of their status as metro or nonmetro, adjacent or more isolated, 

counties with larger shares of their population in urban settings (towns with 

at least 2,500 people) are relatively more attractive for age groups nearing or 

entering retirement. This effect may be capturing increased migration to the 

rural West, where counties can be quite isolated but generally have popula25 30 35

5-year age groups

Standardized regression coefficients

40 45 50 55 60 65 70 75

0.40

0.30

0.15

0

-0.10

-0.05

0.45

0.35

-0.20

0.25

Seasonal housing

Scenic amenities

Figure 5

Effects of recreation (as measured by seasonal housing) and 

scenic amenity factors on net migration by age, 1990-2000

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau.
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tions that are much more concentrated in cities and towns. It also may reflect 

a desire for older cohorts to seek out areas with higher levels of cultural 

amenities, retail services, and hospitals.

Demographic Factors

Demographic variables exert the expected influence on cohort migration 

rates. Age increases the tendency for migration streams to be directed toward 

areas with higher concentrations of “empty nest” households. Surprisingly, 

this variable’s effect on migration is stronger than that of scenic amenities for 

cohorts in their sixties (app. table 3). The presence of foreign-born populations acts to decrease net migration for those in their thirties and, like population density, appears to be associated with the move from central cities to 

suburbs. The effect’s impact on net migration decreases with age but is still 

significantly negative for those approaching retirement. 

Regional Variation

For virtually every age group with the exception of those in their early thirties, the effect of southern destinations is positive, indicating strong regional 

shifts that were not entirely explained by employment, demographic, and 

amenity differences in the 1990s. This finding is consistent with the strength 

and duration of “Sun Belt” migration. In addition, the particularly strong pull 

of Florida and other States in the South Atlantic region reflects a long association with older age migration. The effect is strongest for the 65-74 year-old 

age groups. 

25 30 35

5-year age groups

Standardized regression coefficients

40 45 50 55 60 65 70 75

0

0.15

0.10

0.05

-0.05

-0.10

-0.20

-0.15 Nonmetro nonadjacent

Nonmetro adjacent

Metro rural

Figure 6

Effects of metro and nonmetro county types on net migration 

by age, 1990-2000

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau.
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Net Migration Projections, 2000-20

Population projections answer “what if” questions, in this case, about future 

population change from net migration. Post-2000 data on age-specific, net 

migration will not be available at the county level for several years. Even so, 

it is possible to project population growth from net migration in rural and 

small-town areas, for this decade and the next, using data from the 1990s. 

Net migration projections are combined with projected estimates of the 

overall size of age groups and the effect of immigration to project the size of 

future baby boom cohorts in different types of metro and nonmetro counties 

(see appendix for more details). 

Migration flows between counties are affected by employment trends, 

housing prices, and other factors subject to much uncertainty, especially 

given current economic conditions and prospects. Also, baby boomers may 

pioneer new migration paths that differ from those of preceding generations 

as they age into retirement. Projections provide useful analytical and planning information, but they must be considered within a probable range of 

outcomes. As is typical of most population projections, three scenarios are 

calculated here representing high, medium, and low possibilities in terms of 

nonmetro population change for baby boomers during this decade and the 

next.

Projected Population Change from Net Migration of 

Baby Boomers

Net migration increased the number of baby boomers living in nonmetro 

areas by 1.1 million during 1990-2000. If baby boomers continue to demonstrate an increased proclivity for living in nonmetro areas, as they did in 

the 1970s and 1990s, but otherwise follow the same patterns of migration 

as their predecessors, their presence in nonmetro locations will increase by 

1.5 million in this decade and 1.6 million during the 2010s. If they do not 

continue to exhibit an increased preference for nonmetro areas but simply 

follow the patterns of older cohorts during the 1990s, nonmetro population 

will grow from net migration of boomers by 1.2 million and 1.1 million for 

this decade and the next. A midrange projection can be calculated as the 

average between these two scenarios—1.3 and 1.4 million, respectively.

These projections represent a substantial increase in nonmetro population 

growth for the age groups they represent—45-64 year olds in 2010 and 55-75 

year olds in 2020—compared with growth in previous decades. For the baby 

boom cohort, the midrange projection for each decade represents an increase 

in the cohort’s already large gains during the 1990s, despite the sharp decline 

in the overall propensity to migrate as people age beyond their thirties and 

forties toward and into retirement.

Baby boomers span a wide range of ages and thus will experience their peak 

nonmetro population gains at different times (fig. 7). In the current decade, 

nonmetro population growth among 45-54 year olds will likely increase at a 

higher rate than in the 1990s because of the younger cohort of baby boomers, 

but older boomers will account for the bulk of net migration gains. In the 

coming decade, older boomers will be entering their early seventies, the age 
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when rates of net migration to nonmetro counties begin to fall. The impact 

of this decrease in migration for older age groups will be offset by strong 

net migration growth for the younger boomers, who will then be in their late 

fifties and early sixties. 

During 2000-20, baby boomer migration will likely contribute to a significant deconcentration of the population. Assuming the midrange projection 

between the two sets of outcomes described earlier, baby boomer net migration to core (predominantly urban) metro counties will switch from a gain 

of 979,000 during the 1990s to a loss of 643,000 during the 2010s. Fringe 

(predominantly rural) metro counties experienced the highest rates of baby 

boomer migration in the 1990s (a 17-percent increase, compared with a 

9-percent increase for nonmetro counties) but are projected to see boomer 

migration decline to 8 percent during the 2010s. Fringe counties, along with 

adjacent nonmetro counties, received the bulk of past suburban expansion, 

but movement to metro fringe areas is a declining component of migration 

among baby boomers.

Measured in terms of relative change, populations in more remote (nonadjacent) nonmetro counties will experience the most dramatic changes from 

baby boomer migration. While nonadjacent counties gained 277,000 in 

population during the 1990s from baby boomer net migration, midrange 

projections indicate that boomer net migration will increase these counties’ 

populations by nearly 362,000 and 383,000 during this decade and the next.

Whether adjacent to big cities or less accessible, counties with desirable 

physical attributes—pleasant climates, mountains, beaches, lakes—are likely 

to increase their already high share of baby boomer migration. Among the 

500 nonmetro counties with the lowest ERS Natural Amenities Index scores, 

net migration is projected to decrease from 180,000 in the 1990s to near 

zero in the 2010s (fig. 8). At the same time, boomer net migration in the 500 

counties with the highest scores will increase from 520,000 in the 1990s to 

720,000 in the 2010s. Counties in the third highest quartile of amenity scores 

Net migration

Figure 7

Net migration of baby boomers in nonmetro counties

by 5-year age groups, 1990-2000

5-year age groups

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau and 

the National Center for Health Statistics.

0

500,000

400,000

300,000

200,000

100,000

1990s

2000s

2010s

35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74
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are also projected to experience a large increase in baby boomer population 

during this decade and the next, compared with their increases in the 1990s. 

Differences between projected and actual population outcomes are potentially greater for rapidly growing counties, such as those with scenic amenities and booming recreation-based economies. In the past, net migration has 

declined as such areas “fill up,” often in response to higher housing prices. 

Counties in the third quartile, with attractive physical features but possibly 

not the most desirable qualities, could gain in attractiveness among boomers 

in reaction to this filling up of the highest quartile counties. The current mortgage foreclosure crisis, particularly strong in recreation towns that experienced a recent housing boom, creates uncertainty about filling up and future 

demographic trends in scenic areas. 

Projected Impacts of Baby Boom Migration on the 

Nonmetro Retirement-Age Population

Except under the most dire future economic and housing market conditions, 

baby boom migration will increase the size of rural America’s retirementage population. Assuming a midrange projection, the rural population of 

55-74 year olds will increase by two-thirds, from 8.6 million to 14.2 million, 

between 2000 and 2020 (table 1). The overall rate of growth among this age 

group has probably more than tripled to 30 percent during the current decade, 

compared with growth in the 1990s, and will remain close to 30 percent in 

the next decade. Without net migration, the rate of growth for this age group 

would drop by about half, 18 percent in this decade and 15 percent during 

2010-20.

The coming increase in nonmetro populations age 55-74 will vary geographically. These trends are projected to affect not just traditional retirement 

regions in the South and West but nonmetro areas throughout the country. 

The biggest absolute increases will be in the South, where the nonmetro 

population age 55-74 is projected to increase by almost 2.5 million between 

Net migration

Figure 8

Net migration of baby boomers in nonmetro counties

by natural amenity quartiles, 1990-2000

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau and 

vital statistics from the National Center for Health Statistics. Scenic amenities were 

measured using the ERS Natural Amenities Index (McGranahan, 1999).

0

800,000

700,000

600,000

500,000

400,000

300,000

200,000

100,000

5-year age groups

Low 2nd 3rd High

1990s

2000s

2010s
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2000 and 2020. The largest percentage increase will be in the nonmetro 

Northeast, which is projected to grow slightly faster than the nonmetro West 

during the 2010s. The Midwest is also projected to increase in population 

growth rates among this age group, from just 2 percent in the 1990s to over 

20 percent in both the current and next decades.

Table 1

Recent and projected nonmetro population change among 

55-74 year olds by region

Nonmetro population ages 55-74 Population growth rate

U.S. region 1990 2000 2010 2020 1990s 2000s 2010s

Millions Percent

Northeast 0.886 0.925 1.276 1.686 4.4 37.9 32.1

Midwest 2.633 2.685 3.235 3.944 2.0 20.5 21.9

South 3.480 3.868 4.972 6.272 11.2 28.5 26.1

West 0.957 1.152 1.708 2.251 20.3 48.2 31.8

Total 7.957 8.631 11.191 14.152 8.5 29.7 26.5

Source: USDA, Economic Research Service using data from the U.S. Census Bureau and the 

National Center for Health Statistics.
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Conclusion

Baby boomers today are between the ages of 45 and 63. Many younger 

members of the cohort are still in the middle of child rearing, while those 

in their fifties are more likely to be empty nesters. Separate analyses of net 

migration by 5-year age groups reflect such diverse life-cycle situations. 

Differences by age group in the effects of employment growth, housing 

prices, urban influence, natural amenities, and other socioeconomic characteristics on county-level net migration reflect expected changes in migration 

decisions by age group.

Employment considerations still exert a strong influence on younger 

boomers, but these effects will decrease in the coming decade. Boomers are 

increasingly drawn to areas with a combination of scenic amenities, recreation or cultural opportunities, and reasonable housing costs. Nonmetro 

destinations for baby boomers will likely become more dispersed across the 

landscape and not as concentrated around metro areas. 

The presence of seasonal housing is a particularly strong indicator of an 

area likely to attract retirement-related migration. This association reflects 

the importance of recreation and leisure activities for those building ties 

to future residential destinations. Many people identify a future migration 

destination while vacationing or visiting family and friends. Retirementrelated migration may not be a discrete event that occurs in a finite period of 

time. Individuals or families may purchase a second home to visit for a few 

weeks a year or on weekends. Then, when children leave home, sojourns to 

the second home become more frequent and the “cottage” or condo may be 

renovated into a more substantial residence. Modern telecommunications 

technology and increased airport accessibility have made work from these 

more remote locations possible and increased permanent moves to secondhome destinations in recent years. Projections of baby boomer population 

growth reflect the fact that areas once popular as recreation destinations are 

becoming increasingly popular as permanent residences.

Net migration increased the number of baby boomers living in nonmetro 

areas by 1.1 million during 1990-2000 and is projected to add similar or 

higher numbers of boomers during 2000-10 and 2010-20, despite declines in 

their overall propensity to migrate. The nonmetro population of 55-74 year 

olds is projected to increase two-thirds between 2000 and 2020 as a result of 

net migration trends among baby boomers. 

Anticipating the types of areas where large numbers of baby boomers will 

migrate in the near future could prove useful. Development professionals 

in areas already attracting large numbers of baby boomers often emphasize 

traditional strategies designed to attract manufacturing jobs to their communities. Infrastructure investments geared toward fostering this type of exportbased employment growth likely will have minimal or negative influence on 

the rising number of footloose baby boom migrants more attracted to scenic 

amenities, recreation, or cultural opportunities and affordable housing. Other 

specialists realize that to attract well-to-do boomers, development strategies 

need to be expanded or revised to cater to the interests of boomers. They 

realize this cohort can bring significant new money into a county’s economy 
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and boost development opportunities (Beyers and Nelson, 2000; Nelson, 

2005). Implementing strategies to attract older migrants requires alternative views about what drives regional economies, which could be aided by 

increased knowledge of key factors attracting baby boomers moving to rural 

and small-town America.
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References

Arias, Elizabeth. 2006. “United States life tables,” Vital Statistics of the 

United States 54(14).

Bailey, Adrian. 1993. “Migration history, migration behavior, and selectivity,” The Annals of Regional Science 27: 315-326.

Bailey, Adrian. 1994. “Migration and unemployment duration among young 

adults,” Papers of Regional Science 73: 289-308.

Beale, Calvin. 1975. The Revival of Population Growth in Nonmetropolitan 

America, ERS-605, U.S. Department of Agriculture, Economic Research 

Service.

Beale, Calvin. 2004. “Nonmetro counties vary by urban size and metro proximity,” Amber Waves 2(5): 10.

Beyers, William, and David Lindahl. 1996. “Lone eagles and high fliers in 

rural producer services,” Rural Development Perspectives 11: 2-10.

Beyers, William, and Peter Nelson. 2000. “Contemporary development 

forces in the nonmetropolitan west: New insights from rapidly growing 

communities,” Journal of Rural Studies 16: 459-474.

Bluestone, Barry, and Bennet Harrison. 1987. “The impact of private disinvestment on workers and their communities,” in International Capitalism 

and Industrial Restructuring, R. Peet (ed.), Allen Unwin: Winchester, MA, 

pp. 72-104.

Bowles, Gladys, Calvin Beale, and Everett Lee. 1975. Net Migration of the 

Population 1960-70 by Age, Sex, and Color, U.S. Department of Agriculture, 

Economic Research Service.

Bowles, Gladys, and James Tarver. 1965. Net Migration of the Population 

1950-60 by Age, Sex, and Color, U.S. Department of Agriculture, Economic 

Research Service.

Bures, Regina. 1997. “Migration and the life course: Is there a retirement 

transition?” International Journal of Population Geography 3(2): 109-119.

Cromartie, John. 2001. “Migrants in the south choose urban and natural 

amenities,” Rural America 15: 7-18.

Cromartie, John. 2006. “Metropolitan expansion and nonmetro change in 

the south,” in The Population of Rural America: Demographic Research for 

a New Century, W. Kandel and D. Brown (eds.), Kluwer: New York, pp. 

233-251.

Frey, William, and Kao-Lee Liaw. 1998. “Immigration concentration and 

domestic migrant dispersal: Is movement to nonmetropolitan areas “White 

Flight”?” The Professional Geographer 50: 215-232.
22

Baby Boom Migration and Its Impact on Rural America / ERR-79

Economic Research Service / USDA

Frey, William, and Kenneth Johnson. 1998. “Concentrated immigration, 

restructuring, and the ‘selective’ deconcentration of the United States population,” in Migration into Rural Areas, P. Boyle and K. Halfacree (eds.), 

Wiley: New York, pp. 79-106.

Fuguitt, Glenn, and Calvin Beale. 1996. “Recent trends in nonmetropolitan 

migration: Toward a new turnaround?” Growth and Change 27: 156-172.

Glasgow, Nina, and David Brown. 2006. “Social integration among older 

in-migrants in nonmetropolitan retirement destination counties,” in The 

Population of Rural America: Demographic Research for a New Century, W. 

Kandel and D. Brown (eds.), Kluwer: New York, pp. 177-196.

Johnson, Jerry, and Raymond Rasker. 1995. “The role of economic and 

quality of life values in rural business location,” Journal of Rural Studies 11: 

405-416.

Johnson, Kenneth, and Glenn Fuguitt. 2000. “Continuity and Change in 

Rural Migration Patterns, 1950-1995,” Rural Sociology 65: 27-49.

Johnson, Kenneth, and John Cromartie. 2006. “The rural rebound and its 

aftermath,” in The Population of Rural America: Demographic Research 

for a New Century, W. Kandel and D. Brown (eds.), Kluwer: New York, pp 

25-49.

Johnson, Kenneth, Paul Voss, Roger Hammer, Glenn Fuguitt, and Scott 

McNiven. 2005. “Temporal and spatial variation in age-specific net migration in the United States,” Demography 42: 791-812.

Karlgaard, Richard. 2004. Life 2.0: How People Across American Are 

Transforming Their Lives by Finding the Where of Their Happiness, Crown 

Publishing: New York.

Litwak, Eugene, and Charles Longino. 1987. “Migration patterns among the 

elderly: A developmental perspective,” Gerontologist 27: 266-272.

McGranahan, David, 1985. “Changes in age structure and rural community 

growth,” Rural Development Perspectives 1(3): 21-25.

McGranahan, David, 1999. Natural Amenities Drive Rural Population 

Change. AER-781, U.S. Department of Agriculture, Economic Research 

Service, www.ers.usda.gov/publications/aer781/ 

McHugh, Kevin, Timothy Hogan, and Stephen Happel. 1995. “Multiple residence and cyclical migration: A life course perspective,” The Professional 

Geographer 47(3): 251-267.

McHugh, Kevin, and Robert Mings. 1996. “The circle of migration: 

Attachment to place in aging,” Annals of the Association of American 

Geographers 86(3): 530-550.
23

Baby Boom Migration and Its Impact on Rural America / ERR-79

Economic Research Service / USDA

Nelson, Peter. 1999. “Quality of life, nontraditional income, and economic 

growth: New development opportunities for the rural west,” Rural 

Development Perspectives 14(2): 38-43.

Nelson, Peter. 2004. “Nonearnings income migration in the United States: 

towards a geographical explanation,” paper presented at the North American 

Regional Science Association, Seattle, WA. 

Nelson, Peter. 2005. “Migration and the spatial redistribution of nonearnings 

income in the United States: Metropolitan and nonmetropolitan perspectives 

from 1975-2000,” Environment and Planning A 37: 1613-1636.

Nelson, Peter, Ahn Wei Lee, and Lise Nelson. 2009. “Linking baby boomer 

and Hispanic migration streams into rural America—A multi-scaled 

approach,” Population, Space and Place 15: 277-293.

Nelson, Peter, and Adam Sewall. 2003. “Regional comparisons of metropolitan and nonmetropolitan migration in the 1970s and 1980s: Age and place 

implications,” The Professional Geographer 55(1): 83-99.

Nelson, Peter, E. Hope Stege, and James Nicholson. 2004. “The baby boom 

and nonmetropolitan population change, 1975-1990,” Growth & Change

24(4): 526-544.

Newman, Katherine. 1993, Declining Fortunes: The Withering of the 

American Dream, Basic Books: New York.

Pandit, Kavita. 1997a. “Cohort and period effects in US migration: How 

demographic and economic cycles influence the migration schedule,” Annals 

Association of American Geographers 87(3): 439-450.

Pandit, Kavita. 1997b. “Demographic cycle effects on migration timing and 

the delayed mobility phenomenon,” Geographical Analysis 29(3): 187-199.

Partridge, Mark, Ali Kamar, M. Rose Olfert. 2007. “Voting with their feet: 

Jobs versus amenities,” Growth and Change 38: 77-110.

Plane, David. 1993. “Demographic influences on migration,” Regional 

Studies 27(4): 375-383.

Plane, David. 1992. “Age-composition change and the geographical 

dynamics of interregional migration in the U.S.,” Annals of the Association 

of American Geographers 82(1): 64-85.

Plane, David, and Peter Rogerson. 1991. “Tracking the baby boom, the baby 

bust, and the echo generations: How age composition regulates US migration,” The Professional Geographer 43(4): 416-430.

Power, Tom, and Richard Barrett. 2001. Post-Cowboy Economics: Pay and 

Prosperity in the New American West, Island Press: Washington, DC.

Reeder, Richard, and Dennis Brown. 2001. Recreation, Tourism, and Rural 

Well-Being, ERR-7, U.S. Department of Agriculture, Economic Research 

Service, www.ers.usda.gov/publications/err7/
24

Baby Boom Migration and Its Impact on Rural America / ERR-79

Economic Research Service / USDA

Rudzitis, Gundars. 1993. “Nonmetropolitan geography: Migration, sense of 

place, and the American west,” Urban Geography 14: 574-585.

Stapleton, Clare. 1980. “Reformulation of the life-cycle concept: 

Implications for residential mobility,” Environment and Planning A 12: 

1103-1118.

Taeuber, Karl. 1966. “Cohort migration,” Demography 3(2): 416-422.

Vias, Alex. 1999. “Jobs follow people in the rural Rocky Mountain West,” 

Rural Development Perspectives 14(2): 14-23.

Voss, Paul, Scott McNiven, Roger Hammer, Kenneth Johnson, and Glenn 

Fuguitt. 2004. County-Specific Net Migration by Five-Year Age Groups, 

Hispanic Origin, Race, and Sex, 1990-2000, Center for Demography and 

Ecology, University of Wisconsin, Madison, WI.

Walker, Robert, Mark Ellis, and Richard Barff. 1992. “Linked migration systems: Immigration and internal labor flows in the United States,” 

Economic Geography 68(3): 234-248.

White, Michael, Peter Mueser, and Joseph Tierney. 1987. Net Migration 

of the Populations of the United States 1970-1980, by Age, Race, and Sex: 

United States, Regions, Divisions, States, and Counties, Interuniversity 

Consortium for Political and Social Research, Ann Arbor, MI.

Wright, Richard, Mark Ellis, and Michael Reibel. 1997. “The linkage 

between immigration and internal migration in large metropolitan areas in 

the United States,” Economic Geography 73(2): 234-254.
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Appendix: Regression Analysis and 

Population Projections

Typically, life-cycle perspectives are applied in studies explaining patterns of 

individual-level migration behavior (see, for example, Bailey, 1993; Bailey, 

1994; McHugh et al., 1995). Here, regression analysis is used to study the 

aggregate effects of migration decisions on different types of rural destinations. This analysis builds on previous studies that address this broader, 

age-cohort context in which individual life-cycle paths evolve (Plane, 1992; 

Plane, 1993; Pandit, 1997b; Pandit, 1997a; Nelson et al., 2004). 

Multivariate regression measures the effects of county-level, socioeconomic attributes on net migration in the 1990s. Separate models by 5-year 

age cohorts capture the life-cycle variation of these effects. Using predicted 

cohort migration rates derived from the models’ parameter estimates, along 

with data on expected death rates and immigration, this analysis projects 

the size and distribution of boomer migration to different types of counties 

through 2020. Low-, middle-, and high-range projections provide upper and 

lower bounds for the likely impact on nonmetro counties.

Model Specification and Variables

In these models, county-level, cohort-specific, net migration rates are set as 

the dependent variables. The county migration rates (CMR) were calculated 

for 5-year age cohorts and represent the population growth from net migration (NM) for age group a in county j during the 1990s by ages as of 2000. 

These rates are expressed as a percentage of the population (P) in age group 

a-10 in 1990:

 

Results are reported for 11 age groups, covering 5-year age cohorts from 25 

to 79 years old (app. table 1). The models use the same set of county-level 

Appendix table 1

Cohort migration rates, 1990-2000

Age range (years) Mean Minimum Maximum Standard deviation

 25 to 29 -0.078 -0.909 4.773 0.320

 30 to 34 0.187 -0.876 7.000 0.387

 35 to 39 0.154 -0.557 2.981 0.236

 40 to 44 0.112 -0.636 1.586 0.174

 45 to 49 0.089 -0.755 1.263 0.145

 50 to 54 0.086 -0.645 4.667 0.172

 55 to 59 0.103 -0.399 1.586 0.183

 60 to 64 0.107 -0.500 2.187 0.199

 65 to 69 0.084 -0.545 2.130 0.175

 70 to 74 0.032 -0.414 1.145 0.107

 75 to 79 0.011 -0.548 0.644 0.079

Source: USDA, Economic Research Service, using data from the U.S. Census 

Bureau and the National Center for Health Statistics.

P

NM CMR

j a

j a

j a

, 10,1990

, 1990 2000

, ,1990 2000

−

−

− =
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independent variables, measuring socioeconomic conditions as of 1990, 

which is the beginning of the migration period being analyzed (or 1990-93 

in the case of employment change). Independent variables measure employment and housing market conditions, recreation and natural amenities, urban 

influence, demographic characteristics, and regional location variables representing selected census divisions (app. table 2).The endogeneity of migration and employment change is difficult to disentangle and biases modeling 

results if not adequately addressed. Migrants are simultaneously attracted to 

areas with high employment growth, but their migration stimulates further 

employment growth. Here, the analysis follows established methodology by 

using a lagged variable that measures employment change for the early 1990s 

to “explain” net migration measured for the entire decade (Partridge et al., 

2007). Diagnostics show that this reduces endogeneity sufficiently but still 

captures a strong positive relationship between employment and migration 

among younger age groups. 

Independent variables were converted to z-scores so that the measures could 

be expressed in relative rather than absolute terms. This is necessary because 

migration is a closed system—higher inmigration in one area assumes higher 

outmigration somewhere else. Higher unemployment in a county should not 

affect migration if that county’s position relative to others does not change 

Appendix table 2

Independent variables

Standard 

Mean Minimum Maximum deviation

Employment and housing market factors 

 Percent unemployed, 1990 6.7 0 30.5 3.1

 Employment change, 1990-931 1.5 -12.8 180.3 3.9

 Median home value, 1990 53,670 0 500,000 33,356

Natural amenities and recreation 

ERS Natural Amenity Index 0.1 -6.6 11.2 2.3

 Percent seasonal housing units, 1990 5.7 0 75.4 9.4

Urban influence 

Urban metro, 19902 0.19 0 1 0.35

 Rural metro, 1990 0.08 0 1 0.28

Nonmetro adjacent, 1990 0.32 0 1 0.47

 Nonmetro nonadjacent, 1990 0.41 0 1 0.49

 Percent urban, 1990 36.5 0 100 29.8

 Population density, 1990 226 0.1 68,157 1,705

Demographic characteristics 

 Percent of married couples 

 with no children, 1990 32.7 11.6 51.2 4.2

 Percent foreign born, 1990 2.2 0 45.1 3.6

Regional location

South Atlantic 0.19 0 1 0.38

East South Central 0.17 0 1 0.32

West South Central 0.15 0 1 0.35

1Average annual change in total employment, 1990-93, as a percent of 1990 total employment.

2In the regression analysis, this is the omitted category of the 4-tier urban-rural classification.

Note: For data descriptions, see boxes, “Data Sources” and “County Classifications Used in This Report.”

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau and the 

Bureau of Economic Analysis.
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(i.e., unemployment increases in all counties). But net migration for a county 

would decrease if unemployment grew at a faster rate than in other counties.

Further diagnostics revealed no significant violation of assumptions of 

linearity, independence of the error terms, and error distribution. Variance 

inflation factors showed no signs of significant multicollinearity among 

the predictors. Scatter plots of residuals versus fitted values showed little 

evidence of heteroscedasticity. 

Parameter estimates from variables that have been converted to z-scores 

allow comparison of effects across and within age-specific models (app. 

table 3). There is much variation by age in the overall explanatory power 

of the chosen set of independent variables, as reflected in the (adjusted) 

r-square values. The model explains around 30 percent of the variation in 

cohort migration rates for 25-29 and 30-34 year olds but improves to over 50 

percent for 55-59 and 60-64 year olds. Migration models in general are more 

powerful in explaining the migration flows for those life-cycle stages through 

which boomers are currently passing. The high level of explanatory power 

also may reflect the impact of cohort size on migration “effectiveness,” the 

degree to which population growth from inmigration is not offset by an 

Appendix table 3

Regression coefficients for cohort migration rates, 1990-2000

Age range (years)

Independent variables 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 

Employment and housing 

market factors 

Percent unemployed -0.145 -0.141 -0.047 -0.024 0.011 0.054 0.093 0.128 0.124 0.069 -0.048

Employment change 0.188 0.281 0.253 0.218 0.209 0.148 0.108 0.070 0.068 0.073 0.061

Median home value 0.125 0.088 0.148 0.109 0.052 -0.017 -0.041 -0.051 -0.057 -0.058 0.055

Natural amenities 

and recreation

ERS Natural Amenities Index 0.140 0.174 0.189 0.229 0.255 0.282 0.271 0.261 0.268 0.216 0.036

Percent seasonal

 housing units 0.119 0.072 0.096 0.157 0.215 0.351 0.415 0.408 0.344 0.218 0.027

Urban influence 

Rural metro -0.016 0.021 0.099 0.094 0.062 0.038 0.019 0.015 0.032 0.061 0.081

Nonmetro adjacent -0.087 -0.083 -0.021 -0.007 -0.004 0.005 0.032 0.052 0.061 0.040 -0.005

Nonmetro nonadjacent -0.173 -0.146 -0.143 -0.130 -0.133 -0.099 -0.029 0.002 0.006 -0.029 -0.080

Population density 0.029 0.024 -0.033 -0.033 -0.007 0.027 0.033 0.034 0.013 -0.037 -0.107

Percent urban 0.027 -0.243 -0.187 -0.129 -0.092 -0.059 -0.012 0.039 0.097 0.190 0.251

Demographic factors 

Percent of married couples

 with no children -0.131 0.170 0.253 0.283 0.293 0.297 0.334 0.378 0.378 0.320 0.096

Percent foreign born 0.001 -0.020 -0.099 -0.105 -0.112 -0.081 -0.060 -0.044 -0.041 -0.025 -0.070

Regional dummies 

South Atlantic 0.151 -0.055 -0.024 0.052 0.114 0.170 0.176 0.196 0.212 0.224 0.128

East South Central 0.135 -0.049 -0.018 0.049 0.093 0.126 0.098 0.097 0.091 0.109 0.063

West South Central 0.043 -0.018 -0.016 0.009 0.018 0.038 0.035 0.041 0.029 0.026 -0.023

R-square 0.340 0.289 0.351 0.374 0.406 0.490 0.541 0.538 0.459 0.274 0.121

Note: The number of observations in each model was 3,087. U.S. counties total 3,141, but Alaska and Hawaii were excluded and Virginia independent cities were combined with adjacent counties. Coefficients for rural metro, nonmetro adjacent, and nonmetro nonadjacent dummy variables 

measure effects relative to the omitted category: urban metro. Coefficients in bold were statistically significant at the .05 level.

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau and the Bureau of Economic Analysis.
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equal number of outmigrants. Large birth cohorts tend to have more effective 

migration patterns (Plane and Rogerson, 1991, Plane, 1992, Pandit, 1997a). 

Less efficient migration flows among younger age groups, as well as smaller 

birth cohorts, likely account for the lower goodness-of-fit values for the 

cohorts following the baby boomers.

Projecting Population Growth From Migration

These projections were constructed by asking: “What will future migration 

patterns look like if the most recently measured age-specific migration rates 

(from the 1990s) stay the same?” Although population projections by metro 

and nonmetro categories are uncommon, the methodology employed here is 

similar to that used to project U.S. State populations by the Census Bureau 

(http://www.census.gov/population/www/projections/projectionsagesex.

html). Age affects migration in relatively predictable ways that can be statistically measured. The overall size of age cohorts is also easy to project into 

the future using forward survival methods because age-specific death rates 

are relatively fixed (Arias, 2006). Immigration’s relatively small impact on 

older age groups can be measured using the Census Bureau’s “best guess” 

estimates of future, age-specific immigration flows. Thus, projections can be 

made of the size of future baby boom cohorts in different types of metro and 

nonmetro counties. 

Projections are made of population change from migration for this decade 

and the next, starting with the 1990s regression models: 

where β refers to unique regression coefficients for each cohort a and independent variable i measured for each county j. With the exception of employment change (measured for 1990-93) and the ERS Natural Amenities Index 

(which uses relatively fixed environmental measures), the regression models 

measure independent variables as of 1990 to model 1990-2000 net migration. To project cohort migration rates for 2000-10 for each county j and age 

group a, we compiled the same set of county-level independent variables i 

from the 2000 census and 2000-2003 employment data (app. table 4). Cohort 

migration rates for 2000-10 were estimated by multiplying the regression 

coefficients for a given age group a by the updated independent variables: 

An initial estimate of population change from net migration (NMI) for age 

group a in county j was calculated as:

A final adjustment was needed to ensure that the sum of all net domestic 

migration added to zero. This was accomplished in three steps: first, the 

immigration portion of NMI was subtracted (using Census projections of 

=α + β + ε CMR − Var i j i a j a 1990, , , 1990 2000, , 0

=α + β + ε CMR − Var i j i a j a 2000, , , 2000 2010, , 0

CMR

Population

NMI j a

j a

j a

2000 2010, ,

2000, ,

2000 2010, ,

−

− =
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age-specific immigration); second, a domestic migration weight was calculated (DMW) as each county’s proportion of total domestic net migration 

for each age group; and third, a final estimate of population change from net 

migration (NM) was calculated as: 

For the 2010-20 period, the analysis used the estimated cohort migration 

rates for 2000-10 but applied them to projected 2010 populations. The 2010 

populations were calculated by adding the estimates of net migration and the 

Census Bureau’s estimates of immigration, and subtracting expected agespecific deaths. 

Example of County-Level Net Migration Projection

The projection procedure is shown here for 50-54 year olds (as of 2000) in 

hypothetical “Logan County,” NH (app. table 4). In 1990, Logan County was 

a nonmetro, nonadjacent county with a median home value of $87,000 and 17 

percent of housing units designated for seasonal or occasional use (column 

1). These values were slightly higher than the national average as reflected in 

the z-scores greater than 0 (column 2), and Logan County’s unemployment 

and employment change was lower than average. 

To estimate the 1990-2000 cohort migration rate for Logan County based on 

the regression results, the individual z-scores were weighted by their respective coefficients (column 4) and then summed. This resulted in a predicted 

cohort migration rate for 50-54 year olds of 0.0863. 

(NM DMW ) NM2000 2010, j,a NMI 2000 2010, j,a I 2000 2010, j,a 2000 2010, j,a * − − − − = −

Appendix table 4

An example of cohort migration projection for hypothesized Logan County, New Hampshire

(1) (2) (3) (4) (5) (6) (7)

Actual 1990-2000 Actual 2000-10 

values Regression estimation values estimation 

 (1990) Z-score coefficients (2*3) (2000) Z-score (3*6)

Constant NA NA 0.0728 0.0728 NA NA 0.0728

Percent unemployed 0.06 -0.2232 0.0083 -0.0019 0.04 -0.5120 -0.0042

Employment change 0.13 -0.0868 0.0223 -0.0019 0.12 0.3186 0.0071

Median home value 87000 0.9992 -0.0027 -0.0027 94300 0.2080 -0.0006

Natural amenity index -0.57 -0.2734 0.0424 -0.0116 -0.57 -0.2750 -0.0117

Percent seasonal housing 0.17 1.2270 0.0529 0.0649 0.14 0.8344 0.0442

Percent urban 0.00 -1.2256 -0.0088 0.0108 0.02 -1.2329 0.0109

Population density 37.64 -0.1105 0.0040 -0.0004 40.21 -0.1122 -0.0004

Percent married couples no kids 0.31 -0.3278 0.0456 -0.0149 0.38 0.2400 0.0109

Percent foreign born 0.02 -0.1033 -0.0122 0.0013 0.02 -0.3458 0.0042

Rural metro 0 0 0.0206 0 0 0 0.0000

Adjacent 0 0 0.0015 0 0 0 0.0000

Non-adjacent 1 1 -0.0302 -0.0302 1 1 -0.0302

South Atlantic 0 0 0.0657 0 0 0 0.0000

East South Central 0 0 0.0589 0 0 0 0.0000

West South Central 0 0 0.0159 0 0 0 0.0000

Predicted net migration rate 

(sum of columns) 0.0863 0.1030

Note: NA=not applicable. Regression coefficients shown here are unstandardized versions of the coefficients reported in appendix table 3.

Source: USDA, Economic Research Service, using data from the U.S. Census Bureau and the Bureau of Economic Analysis
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By 2000, Logan County’s median home value had increased to $94,300 but 

had decreased relative to other counties (columns 5 and 6). Employment 

change in 2000-03 had decreased slightly from 1990-93 but had increased 

relative to other counties. Weighting these newer values once again by 

the coefficients generated in the regression analysis gave us a projected 

cohort migration rate for the period 2000-10 of 0.103 for 50-54 year olds 

(as of 2010). The increase in the projected rate from the 1990s to the 2000s 

was driven mostly by the change in relative home values and employment 

conditions, as well as an increase in the percent of married couples with no 

children. 

To calculate population change from net migration during 2000-10, the estimated rate of 0.103 was applied to the cohort that would be 50-54 in 2010. 

The population of 40-44 year olds in Logan County in 2000 was 2,623, indicating that the county had increased its population by 270 in this age cohort. 

A small adjustment to this estimate was made to ensure that total domestic 

net migration adds to zero. Population change during 2010-20 was derived 

by applying the same estimated net migration rate for 50-54 year olds in 

Logan County to the estimated population for the cohort of 40-44 year olds 

in 2010.

Projection Ranges

Almost all projections include low, middle, and high alternatives, based on 

different assumptions about future demographic trends. For instance, the 

U.S. Census Bureau offers different scenarios for the U.S. population in 

2050 depending on potential changes in fertility, mortality, and immigration 

rates (http://www.census.gov/population/www/projections/usinterimproj/

idbsummeth.html). Published statistics usually focus on the middle series 

projections (http://www.census.gov/population/www/projections/projectionsagesex.html). 

For this report, projection alternatives differ in terms of their impact on 

population change among baby boom cohorts in nonmetro locations. A low 

projection comes from a straightforward application of 1990-2000 regression results to 2000 Census indicators and 2000-03 employment changes. 

The findings for baby boomers represent what will happen if they respond to 

county characteristics in a fashion similar to their predecessors in the 1990s. 

This is considered a low expectation because, in the past, baby boomers 

have shown a higher preference for rural and small-town settings, especially 

where scenic amenities are high and housing prices are low. Employment 

change during 2000-03 also was quite low, leading to a more conservative 

projection series. 

The high projection series was constructed by increasing the parameter estimates for the ERS Natural Amenities Index, the percent seasonal housing 

units, and, where significant, the nonmetro location coefficients. It is impossible to predict future employment trends with a high degree of confidence, 

but current economic conditions lend credence to the decision to calculate 

a more conservative projection series by maintaining the low employment 

change conditions of 2000-03. Most of the findings shown in this report are 

from a middle projection series, which was derived as the average of the high 

and low series.