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Recommended citation format for this publication:

McGranahan, David, John Cromartie, and Timothy Wojan. Nonmetropolitan 

Outmigration Counties: Some Are Poor, Many Are Prosperous, ERR-107, 

U.S. Dept. of Agri., Econ. Res. Serv. November 2010.
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Nonmetropolitan Outmigration Counties: Some Are Poor, Many Are Prosperous / ERR-107

Economic Research Service / USDA

United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Research 

Report

Number 107

November 2010

Nonmetropolitan Outmigration 

Counties: Some Are Poor, Many 

Are Prosperous

Abstract

Population loss through net outmigration is endemic to many rural areas. Over a third of 

nonmetro counties lost at least 10 percent of their population through net outmigration 

over 1988-2008. Some of these counties have had very high poverty rates, substantial 

loss in manufacturing jobs, and high unemployment. Lack of economic opportunity 

was likely a major factor in their high outmigration. Most high net outmigration counties, however, are relatively prosperous, with low unemployment rates, low high school 

dropout rates, and average household incomes. For these counties, low population 

density and less appealing landscapes distinguish them from other nonmetro counties. 

Both types of outmigration counties stand out on two measures, indicating that qualityof-life factors inhibit inmigration: a lack of retirees moving in and local manufacturers 

citing the area’s unattractiveness as a problem in recruiting managers and professionals. 

David McGranahan, dmcg@ers.usda.gov

John Cromartie, jbc@ers.usda.gov

Timothy Wojan, twojan@ers.usda.gov
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Contents

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

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Poverty and High Net Outmigration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Who Leaves and Who Moves In? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Characterizing Outmigration Counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Trends in Poverty and Unemployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Local Problems Facing Rural Manufacturers . . . . . . . . . . . . . . . . . . . . . . . 22

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Appendix Table 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
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Summary

What Is the Issue? 

Population loss is a longstanding concern among rural development practitioners. Nearly half of today’s nonmetropolitan counties lost population through 

net outmigration over the past 20 years; for over 700 counties, this loss has 

exceeded 10 percent. Population loss tends to increase tax burdens, reduce 

property values, and reduce both the demand for and supply of local goods 

and services. Rural outmigration is also troublesome because it is highly 

concentrated among young adults, especially those possessing or acquiring 

education and skills. But what makes outmigration counties different from 

nonmetro counties that gained from migration or at least had a more manageable loss over the past 20 years? 

This report identifi es 733 nonmetro counties with an estimated net loss in 

population due to net outmigration of over 10 percent between 1988 and 

2008, and compares their characteristics with other nonmetro counties. 

What Did the Study Find?

Outmigration counties fall into two distinct types with very different sets of 

characteristics. One type, with poverty rates exceeding 25 percent in 1999, is 

clearly hampered by a lack of economic opportunities. High school completion rates are low, poverty rates average over 30 percent, and unemployment 

rates are chronically high in these high-poverty outmigration counties. The 

other set of counties, however, is generally prosperous. Overall, these counties have higher educational attainment and lower unemployment than rural 

counties without high outmigration. These counties tend to be remote, thinly 

settled, and lacking in scenic appeal for prospective residents or tourists. 

Quality-of-life considerations appear to be a main drawback for these (lowpoverty outmigration) counties. 

Age cohort migration. In general, young adults leave rural areas to attend 

college, serve in the military, or see the world. Rural areas gain population 

through the inmigration of young families, midlife career changers, and 

retirees. Outmigration counties tend to have greater net outfl ow of young 

adults than other nonmetro counties, losing, on average, the equivalent of 

over 6 percent of their population this way in 1990-2000. During this period, 

the low-poverty outmigration counties regained about 1 percent of their 

population through the net infl ow of young families, but had little gain or 

loss among older cohorts. High-poverty outmigration counties lost young 

families, probably due to a lack of economic opportunity, but they, too, had 

little gain or loss among older cohorts. Most of the young adults moving 

into outmigration counties appear to be return migrants and related family 

members, with social ties a major draw. 

County characteristics. Outmigration counties with high poverty share some 

characteristics with low-poverty outmigration counties. Both groups tend to 

have less manufacturing than other nonmetro counties, and both are rarely 

classifi ed as recreation counties. Otherwise, differences are more striking 

than similarities. Outmigration counties with high poverty tend to have 

working-age populations with low rates of high school completion, very high 
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unemployment, low rates of self-employment, and other conditions refl ecting 

socioeconomic hardship. Outmigration counties with less poverty, however, 

have working-age populations with higher educational attainment than other 

nonmetro counties and higher employment rates. These counties are disadvantaged by their remoteness and low population density, their lack of forest 

cover, and lack of public land. Most low-poverty outmigration counties are in 

the lowest third of all nonmetro counties in landscape appeal. Thus, although 

favored by the level of human resources, these counties have diffi culties 

attracting industries or people without long-term ties to the area.

Local problems facing manufacturers. A 1996 ERS survey asked manufacturers about local factors impeding their competitiveness, and the results 

show why manufacturers (and other employers) have avoided outmigration 

counties. In high-poverty outmigration counties, the factor most often cited 

as a major problem, by 41 percent of local manufacturers, was the quality of 

local schools. Area attractiveness to managers and professionals was reported 

as a major problem by 28 percent of manufacturers in these counties, and 

8 out of 10 of these manufacturers also reported the quality of schools as a 

major problem. In the low-poverty outmigration counties, only 8 percent 

cited the quality of local schools as a problem. Nonetheless, 25 percent of 

the manufacturers reported the unattractiveness of the area to managers and 

professionals as a major problem. In these counties, the problem appears to 

stem from their remoteness, small population size, and lack of landscape 

amenities. In addition, population loss itself may create an unattractive physical environment, characterized by empty commercial and residential buildings or public property with insuffi cient upkeep. Localities unattractive to 

manufacturing managers and professionals are likely unattractive to talented 

business owners and professionals in general.

How Was the Study Conducted?

ERS used annual estimates of net migration from the U.S. Census Bureau to 

identify nonmetro counties that lost 10 percent or more of their population 

through net outmigration. Statistical comparisons were made across a range 

of geographic, demographic, and socioeconomic indicators to determine 

what characteristics distinguish these counties from other nonmetro counties. Data from a variety of sources were used, including the 2000 Census of 

Population, the Bureau of Labor Local Area Unemployment System fi les, 

and the ERS Rural Manufacturing Survey.
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Introduction

Population loss through net outmigration has long been a fact of life in many 

rural areas. Over a third of nonmetropolitan (nonmetro) counties lost over 10 

percent of their population through net outmigration between 1988 and 2008 

(fi g. 1).1 This includes most counties in the Great Plains and many in the 

Midwestern Corn Belt, as well as signifi cant clusters in southern Appalachia, 

the Mississippi Delta, Alabama’s “Black Belt,” the southern Rio Grande 

Valley, and a scattering of counties in the West, some of them former mining 

counties. A few counties experience short-term periods of high outmigration, when industries shut down or during natural disasters, for instance. But 

in the vast majority of cases, high net outmigration is a chronic condition. 

Many if not most of these counties have been losing population for over half 

a century. 

Rural development programs that promote entrepreneurship and stimulate 

business recruitment attempt to counter job losses that contribute to high 

outmigration, while infrastructure and housing programs can help communities adjust to changing demographic realities. In targeting assitance, however, 

Federal programs rely largely on traditional measures of local distress, such 

as unemployment and low income. rather than outmigration or population 

loss. USDA Rural Development funds tend to target outmigration counties 

only indirectly, through use of population size or density criteria in deter1In this report, rural areas are defi ned 

using counties classifi ed as nonmetropolitan by the Offi ce of Management 

and Budget (see glossary).
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mining program eligibility. Only a few Federal programs include explicit 

measures of population loss to target assistance funds, 

Recent efforts have begun to emphasize the unique conditions and needs of 

rural outmigration counties. The 2002 Farm Act established the Northern 

Great Plains Regional Authority to spur economic development in rural 

communities suffering population loss. More recently, members of Congress 

representing Great Plains States have proposed a New Homestead Act that 

combines fi nancial support for new residents in high outmigration areas with 

tax incentives meant to expand business and job opportunities.

All things being equal, people tend to “vote with their feet,” leaving areas 

that are weak economically or that lack prized amenities. Residents typically view population loss as a strong indicator of distress because it tends 

to increase tax burdens, reduce property values, and reduce both the demand 

for and supply of local goods and services. Rural outmigration is highly 

concentrated among young adults, especially those possessing or acquiring 

education and skills. While some analysts argue that chronic population loss 

refl ects healthy adjustment to both technological change in resource-based 

industries and the growing economic power of urban agglomerations, others 

sense that rural communities do not have the knowledge, organization, or 

fi nancial resources to deal effectively with the broader forces affecting their 

livelihoods. But what makes “outmigration” counties different from other 

nonmetro counties, the ones that gained from migration or at least had a more 

manageable loss over the past 20 years? 

For this study, we defi ne “outmigration” counties as those that lost at least 10 

percent of their population due to net outmigration between 1988 and 2008. 

Using the 2003 defi nition of nonmetropolitan (nonmetro) areas, 733 or over a 

third of all nonmetro counties fall into the outmigration category.2 

2The use of a 1988 delineation of 

metropolitan and nonmetropolitan 

counties would have been more logical on statistical grounds if we were 

primarily interested in calculating factors leading to high outmigration. This 

report, however, focuses on what makes 

current nonmetro high outmigration 

counties different from other nonmetro 

counties. We focus on characteristics in 

2000, roughly midway in the 20-year 

migration period. Some characteristics 

may be symptomatic of high outmigration rather than a cause.
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Background

Rural population loss has historically been ascribed to weather conditions for 

crops (primarily drought-related) and changes in farm structure (Fuguitt and 

Beale, 1978; Frisbie and Poston, 1978; Albrecht, 1993). McGranahan and 

Beale (2002) argue, however, that what distinguishes counties with population loss today is less the fortunes of agriculture than remoteness and the lack 

of natural amenities. Whereas weather and changes in farm technology were 

particularly important in earlier decades, amenities have become more relevant, especially since 1970 (Fuguitt, 1981; Heaton et al., 1981; Cromartie, 

1998; Gutmann et al., 2005).

Embedded in these analyses is a chicken-egg question that has long 

confounded regional economics: does migration lead to changes in jobs or 

do changes in jobs lead to migration (Muth, 1971)? The question is critical 

from a policy perspective. Policies promoting job creation through business 

recruitment or entrepreneurship will falter if quality of life is the central 

issue, and programs targeting quality of life (to attract migrants) are futile if 

jobs will not follow. Research suggests that there is no single answer. Some 

nonmetro counties may be losing many more people than they attract because 

they lack economic opportunities and others because they lack residential 

appeal, and this may change over time. 

Counties lose population in two ways: they may experience more deaths 

than births (natural decrease) or they may have more people moving out 

than moving in (net outmigration). Birth and death rates change slowly over 

time and exhibit little geographic variation. Net migration is more volatile, 

as people choose where to live in response to changes in local conditions 

and their stage in the life cycle (Clark, 1982; McHugh and Gober, 1992). 

This explains the greater focus on net migration in both research and policy 

spheres, and why we choose in this report to characterize counties based on 

net outmigration rather than overall population loss. 

Historically, net outmigration has been the primary driver of rural population loss. At the peak of rural net outmigration in the 1950s and 1960s, 

natural decrease was quite rare. Fewer counties lost population because 

natural increase often exceeded net migration. Over time, as fertility rates 

declined and protracted outmigration aged the population (because most rural 

outmigrants are in their late teens or twenties), natural decrease emerged in 

hundreds of counties throughout the Midwest and Great Plains (Johnson, 

1993). Concern over net outmigration may be heightened today because it is 

more commonly tied with an overall loss of population.

Age exerts a strong infl uence on the overall level of migration and the 

propensity to reside in larger or smaller places (Plane and Heins, 2003; 

Cromartie and Nelson, 2009; Plane and Jurjevich, 2009). Motivations for 

moving are quite different among young adults than among empty nesters 

or retirees. Population loss in rural America is heavily concentrated among 

those in their late teens and twenties, but outmigration declines and inmigration increases as people reach their late 20s and 30s. Plane and Jurjevich 

(2009) show that this shift in the direction of migration is driven not just by 
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suburbanization but by migration across the entire urban-rural spectrum, even 

toward sparsely settled counties outside metropolitan areas. 

Understanding these age-related dynamics is important when considering 

migration-related economic development policy, because different qualityof-life factors attract different age groups. For instance, it may be more 

important to understand what makes rural communities attractive to older 

adults with families than to try to woo recent high school graduates (Gibbs 

and Cromartie, 1994). In addition, differences in preference among age 

groups are likely to be heightened by education (Whisler et al., 2008). More 

educated populations are more likely to migrate in the fi rst place and to move 

longer distances when they do move (Artz, 2003). Recent research suggests 

that rural outmigration among college grads is increasing as knowledgebased economies provide higher returns to those with professional skills and 

creativity (Domina, 2006). High rates of high school graduation and college 

attendance characterize most high outmigration counties, suggesting that 

these age-related migration patterns are particularly salient to their economic 

prospects.
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Poverty and High Net Outmigration

In general, one would expect poverty and high outmigration to be closely 

related. However, this relationship holds only at the very high end of the 

poverty range, above an apparent 25-percent threshold (fi g. 2). On average, 

counties with poverty rates between 20 and 25 percent in 1999 were no more 

likely to be outmigration counties than counties with poverty rates below 

10 percent—in both groups, about 35 percent of counties were classifi ed as 

having high outmigration. At poverty rates above 25 percent, the proportion 

of outmigration counties is nearly double, 60 percent. Of the 107 “highpoverty” outmigration counties, some are in the Great Plains, including many 

with substantial Native American populations, but most are well outside this 

region—in Appalachia, in former plantation regions in the Southeast, and in 

Hispanic areas of Texas along but not necessarily on the border with Mexico 

(fi g. 1).

The high poverty rates in these counties signal lack of economic opportunity. 

However, many counties with very low rates of poverty have also lost population through outmigration. Comparing high- and low-poverty outmigration 

counties with each other and with other nonmetro counties highlights the 

differing circumstances under which high net outmigration can arise. 

Figure 2

Only counties with very high poverty rates have an aboveaverage proportion with high outmigration 

Percent with high outmigration

Source: ERS, based on U.S. Census of Population, 2000, SF3 files.

Total

36 34 34

60

35

30

Under 10 10-15

County poverty rate (percent)

15-20 20-25 25 or more
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Who Leaves and Who Moves In?

Rural areas generally lose population as young adults leave to go to college, 

join the armed services, or see the larger world. These areas then tend to 

gain population through the inmigration of both families with children and 

retirees. In this section, we examine how high- and low-poverty outmigration 

counties may differ from this pattern. Since people seek different residential 

and economic environments over the life cycle, migration comparisons by 

age cohort indicate the particular weaknesses of outmigration counties in 

maintaining their populations. 

Average population change, 1990-2000, due to migration in each age cohort 

is presented in fi gure 3 for both high- and low-poverty outmigration counties 

and the remainder of nonmetro counties (more recent data are not yet available). Although all three sets of counties show a substantial loss of young 

adults, their migration patterns are distinct. On average, the counties that did 

not have high outmigration gained the equivalent of 1.5 percent of their 1990 

population through the net inmigration of children born during the course 

of the decade (age 0-9 in 2000) and another 1 percent through the inmigration of children age 0-4 in 1990 (10-14 in 2000). They lost about 2 percent 

of their population through the outmigration of those ages 20-29 in 2000. 

However, for each cohort between age 30 and age 64, these counties gained 

the equivalent of about 1 percent through net inmigration as families, midlife 

career changers, and retirees moved in. Overall, these nonmetro counties had 

an average gain of over 9 percent of their population over the decade. 

The low-poverty outmigration counties also gained through the inmigration 

of children and lost through the outmigration of young adults, but the gain 

in children was considerably smaller and the loss of young adults greater. 

During 1990-2000, these counties lost the equivalent of over 6 percent of 

their population through the net outmigration of residents age 15-29 in 2000. 

These outmigration counties had a small gain in population age 30-39 in 

2000, likely parents responsible for the gain in children.3 For older adults, 

there was little net change in population due to migration. Overall, lowpoverty outmigration counties had a net loss of about 6 percent of their 

population from net migration during 1990-2000, virtually all from the net 

outmigration of young adults.

While some of the outmigration of young adults may have stemmed from a 

lack of economic opportunities, it seems unlikely that this is severe, given 

the apparent net inmigration of young families. At the same time, the absence 

of any net inmigration by retirees, who are much more sensitive to qualityof-life considerations than economic opportunities, suggests that the lowpoverty outmigration counties are less attractive as places to live than are 

other nonmetro counties.

The central difference between the high- and low-poverty outmigration 

counties is the net outmigration of young families and their children in the 

high-poverty counties, an indication of poor economic opportunities. These 

counties lost 1 percent of their population in 1990-2000 through the net 

outmigration of children age 0 to 14. At the same time, these counties are 

not attracting retirees. Overall, the high-poverty outmigration counties lost 

3This gain in children and adults in 

their 30s was not apparent in another 

data set focusing on migration 1995-

2000 and using a different methodology to estimate migration (See 

McGranahan et al., Amber Waves, Nov. 

2010). One source of discrepancy may 

be that the Amber Waves study did not 

include migration to or from abroad, 

but the data are also from different time 

periods and based on different estimation methods.
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an average of nearly 10 percent of their population between 1990 and 2000 

because more people moved out than in.

This analysis of migration by age has focused on migration in the 1990s—a 

period of rapid national economic growth. Urban areas during this period 

grew through immigration from abroad, while rural areas as a whole had a 

net gain in population from internal migration as more people moved from 

urban to rural areas than in the other direction.

The 2000-2010 decade has been much more uncertain, even before the 

current recession started. While some net migration into rural areas may have 

occurred in the middle of the decade, net gains were not characteristic of the 

decade as a whole. Thus, the patterns of age cohort migration just depicted 

have not necessarily carried over to 2000-2010. 

Return Migration

People often view the loss of young people right after high school as the critical migration issue facing rural America. For most communities, however, 

population growth and economic development depend less on retaining high 

school grads than on attracting newcomers or former residents back later 

in life (Cromartie and Gibbs, 1994). Return migration—usually defi ned as 

an individual moving back to a hometown or other previous place of residence—is a major component of inmigration to most U.S. counties. To 

more accurately gauge its importance to rural and small-town America, the 

defi nition may be broadened to include spouses, children, or others who are 

technically newcomers to a county but are moving as part of return migrant 

households (Cromartie and Stack, 1989). For the United States as a whole, 

just under 50 percent of 25- to 44-year-olds who migrated across county lines 

during 1995-2000 were part of return migration streams—reversing the paths 

taken by the same cohort 10 years earlier.4 For nonmetro, high outmigration 

counties, well over 80 percent of migrants arrived as part of return migration 

streams. 

4 Return migration, whether defi ned 

narrowly or broadly, cannot be measured directly at the county level with 

existing census data. Here we report 

indirect estimates calculated by comparing outmigration streams at one time 

period with inmigration counterstreams 

at a later date—see box, “Measuring 

Return Migration.”

−4

−3

−2

−1

0

1

2

0-

9

10-

14

20-

24

25-

29

30-

34

35-

39

40-

44

45-

49

50-

54

55-

59

60-

64

65-

69

70-

74

75-

79

80-

84

15- 85+

19

Figure 3

Age-cohort migration, 1990-2000, relative to nonmetro county 

population, 1990 

Average percen county population change due to migration

Source: ERS, based on estimates generated by Johnson et al., 2005.

Cohort age, 2000

Low poverty

Outmigration counties

High poverty

Other nonmetro
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Migrants returning to home communities tend to be in their late 20s to 

mid-30s, as they “settle down” to raise families and build careers based on 

education and experience gained elsewhere. Return migration may replenish 

human capital typically lost through the post-high-school exodus of young 

adults, shoring up the economic vitality of outmigration counties. The 

proposed Rural Homestead Act encourages residential relocation as a key 

feature of its economic development strategy. Similar strategies adopted in 

other countries, notably in Japan, and several Midwestern States are encouraging return migration by various means.5

As remoteness and other geographic disadvantages typical of outmigration 

counties increase, the overall rate of inmigration decreases. An analysis of 

outmigration streams of 15- to 34-year-olds during 1985-1990 compared 

with inmigration streams of 25- to 44-year-olds during 1995-2000 (the same 

cohorts 10 years older) shows this relationship. For every 100 migrants that 

left low-poverty outmigration counties during the late 1980s, only 51 inmigrants were counted 10 years later. For high-poverty outmigration counties, 

5Iowa Governor Vilsack initiated 

a program to encourage ex-Iowans to 

come home: http://www.iowalifechanging.com/toolbox/downloads/iccprogramoverview.pdf; other programs 

include the Come Home to Kansas 

campaign (http://www.ktec.com/sec_

press/coverage/kcstar_chtk.pdf) and 

South Dakota’s Dakota Roots program 

(http://www.dakotaroots.com/about/). 

Measuring Return Migration

Census data may be used to measure return migration at the State level but not at the county or community level. Statelevel return migration may be determined by comparing a person’s current location with information on two previous 

locations provided by the Census: State of birth and residence 5 years prior. For example, data from the 2000 census 

could be used to estimate how many people were (1) born in Missouri, (2) lived in Chicago in 1995, and (3) lived in 

Branson, Missouri in 2000. We can count this as return migration to Missouri but not necessarily to Branson. Here 

we measure return migration for different types of nonmetro counties using an indirect method that does not depend 

on place-of-birth data. Instead, age-specifi c outmigration streams from county x to county y during one 5-year period 

(1985-1990) are compared with the matching counterstream from county y back to county x 10 years later (1995-2000). 

From the earliest work on basic laws of migration, research shows that every major migration stream generates a counterstream. Large fl ows from point A to point B all but guarantee partially offsetting fl ows from point B to point A. 

Though not exclusively composed of returnees, counterstreams tend to be dominated by returnees together with newcomers who are moving as part of return migration households, most typically spouses and children of returnees. Analysis of counterstreams provides an untapped method for measuring the relative importance of return migration to different types of nonmetro counties, especially when measured for specifi c age cohorts to and from specifi c counties (or 

county groupings) over time. This analysis compares the migration of those age 15-34 in 1990 to those age 25-44 in 

2000 (separately by 5-year age groups). To reduce the number of matched migration streams and counterstreams, counties within the same metropolitan or micropolitan areas were combined to create one inmigration or outmigration unit.

Regression analysis shows a strong correlation, as expected, between outmigration streams and their matched counterstreams 10 years later (the adjusted r-square was .44). The beta coeffi cient indicates that, on average, an additional 100 

people in the 1985-90 outmigration stream was associated with an additional 64 migrants in the 1995-2000 counterstream. 

Residuals from the regression analysis were used to identify 1995-2000 counterstreams that were larger than expected 

(above .25 standard deviations of the mean) and thus were assumed to contain a larger proportion of newcomers than 

returnees. Counterstreams that were highly correlated or lower than expected were labeled as return migration streams.

Although we have conceptualized migration fl ows as discrete types, they obviously fall along a continuum between a 

complete lack of infl ow at t + 10 despite an earlier outfl ow and an infl ow despite a lack of outfl ow at t. There are no 

hard-and-fast rules guiding the selection of thresholds, and different thresholds yield different results on the relative proportion of return migration streams to a given county group. In general, a “large” positive residual indicates a counterstream fl ow likely dominated by newcomers, whereas a “large” negative residual implies a counterstream fl ow with a 

much higher proportion of returnees and related newcomers. For high outmigration counties, the percentage of migrants 

in return migration streams remains much higher than other nonmetro counties across different threshold selections.
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the ratio was even lower, 43 per 100 (table 1). In contrast, the ratio of late 

1990s inmigrants to late 1980s outmigrants was well over 80 per 100 for both 

nonmetro counties not classed as outmigration and for metro counties. 

Inmigration to any county consists of some mix of newcomers and returnees. 

As the overall rate of inmigration decreases with increasing geographic 

disadvantage, the ratio of returnees to newcomers increases. This is particularly true with the broadened concept of return migration used here—

outmigration counties depend very heavily on return migrants and related 

household members for maintaining populations. Over 85 percent of 25- to 

44-year-old inmigrants to outmigration counties were part of return migration counterstreams during 1995-2000, compared with 66 percent for other 

nonmetro areas.6 The percentages were only marginally different between 

high-poverty and low-poverty outmigration counties (table 1). Migration 

streams dominated by newcomers were more typical of migration to metro 

counties, where less than 40 percent of inmigrants were identifi ed as part of 

return migration counterstreams.

Survey studies are beginning to show what anecdotal evidence has indicated 

for years: return migrants and their families use their education, experience, and commitment to place to enhance the economic and social wellbeing of rural communities. Return migrants are attracted to many of the 

same features of rural areas as newcomers, such as a slower pace of life and 

access to outdoor activities. Most return migrants need employment and, like 

other migrants, often trade larger paychecks for quality-of-life gains. Return 

migrants differ in the strength of family-related factors motivating migration. 

These include relying on family support in times of economic stress, joining 

(or re-joining) family businesses, taking care of aging parents, raising children in safe, familiar environments with access to extended family members, 

and providing children good educational and sports opportunities (von 

Reichert and Arthun, 2009).

6 The relative importance of return 

migrants is greater in outmigration 

counties because few others chose to 

move there. People who leave outmigration counties are less likely to return 

than people who leave other nonmetro 

counties. While we have a record of 

outmigration for 1985-1990 only and 

many could have left in later years, 

the ratio of return migration to the 

1985-1990 outmigration is higher in 

other nonmetro counties (.56) than in 

low-poverty (.44) or high-poverty (.38) 

outmigration counties, suggesting that 

return migration occurs less often in the 

outmigration counties. 

Table 1

Migration patterns by county type for population age 25-44 in 2000 

Nonmetro 

High outmigration

 Other Low High

 Metro nonmetro poverty poverty

Outmigration, 1985-1990 13,880,445 2,966,450 912,339 169,915

Inmigration, 1995-2000 12,135,804 2,524,557 466,400 73,732

Ratio of inmigration to outmigration 0.87 0.85 0.51 0.43

Number in return migration streams, 1995-2000 4,567,262 1,668,827 397,125 65,012

Percent in return migration streams, 1995-2000 37.6 66.1 85.1 88.2

Note: Population was 15-34 years old when outmigration was measured in 1990. Return migration was estimated by comparing outmigration streams during 1985-90 with inmigration counterstreams during 1995-2000 by 5-year age groups (see box, “Measuring Return 

Migration”). 

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

In this section, we examine what makes outmigration counties (high- and 

low-poverty) different from other nonmetro counties—those with less loss 

from migration or actual gain. The comparisons cover a wide range of 

measures (table 2). Some are standard indicators used in socioeconomic 

comparisons (e.g., employment by industry, median household income, 

high school completion rates). Others are less traditional, including business 

start-ups and self-employment, creative class, landscape, and community 

prosperity. For both high- and low-poverty outmigration counties, logistic 

regression was used to test the statistical signifi cance of differences from 

other nonmetro counties. For each type of outmigration county, the six 

measures that most distinguish the counties from other nonmetro counties are 

bold-underlined. The measures used for comparison are divided into seven 

subject areas—geography, landscape, demography, education, economy, 

entrepreneurship and creative class, and socioeconomic conditions.

Geography

Remoteness and sparse settlement have long handicapped rural areas. Larger 

towns and proximity to metropolitan areas mean greater accessibility to 

producer services for employers and to health, retail, and entertainment 

services for residents. Outmigration counties, whether high-poverty or not, 

are typically far from metropolitan areas and are thinly settled compared with 

other nonmetro counties. However, low population density is particularly 

characteristic of the low-poverty outmigration counties. 

For many residents of outmigration counties, neither their own county nor 

neighboring counties display much economic growth, so commuting is 

seldom an answer. Many outmigration counties are surrounded by other 

outmigration counties (fi g. 1). Outmigration counties, particularly those with 

high rates of poverty, are much more likely than other counties to fall in the 

bottom quarter of nonmetro counties in terms of job growth in neighboring 

counties. 

Landscape

Scenic landscapes—with varied topography, a mix of forest and open 

country, little cropland, and access to water (lakes, ponds, or ocean)—have 

had a strong bearing on rural migration in recent decades (McGranahan, 

2008, 1999). People moving to rural areas are often making a signifi cant 

economic sacrifi ce to obtain a higher quality of life and secure access to the 

rural outdoors. A landscape index encompasses topography, land cover, and 

water area.7 The highest scoring counties in this index are those with varied 

topography; lakes, ponds, or ocean; and a mix of forest and open country. 

Thus, counties along the mountain ranges of the West generally scored high, 

while counties in the Great Plains and Corn Belt tended to score low (fi g. 4).

The low-poverty outmigration counties tend to score quite low on scenic 

qualities—although there are exceptions. These counties tend to have little 

forest—62 percent of the counties have less than 5 percent forest cover 

compared with only 12 percent of other nonmetro counties. They also tend 

to have a relatively large amount of cropland (table 2). As a result of these 

7This index is derived from the net 

migration side of a 3SLS simultaneous equation of nonmetro employment 

growth and net migration in 1990-

2000. The landscape measures include 

proportion of land in forest, the square 

of that term, the proportion of cropland, 

a measure of topographic variation, 

and the proportion of the county that 

was water area (including ocean), 

which was capped at 25 percent. The 

landscape index was constructed as the 

sum of the products of the coeffi cients 

and variable levels. See McGranahan 

(2008).
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Table 2

Nonmetro outmigration counties differ from other nonmetro counties—and from each other, depending on poverty level

Outmigration counties

 Other Low High

County characteristics nonmetro poverty poverty

Percent

Geography

 Lowest 1/3 in population density (% of counties) 20.1 60.7 34.6

 Not adjacent to a metro area (% of counties)1 37.7 67.6 64.5

Landscape

 Forestland under 5% of total land (% of counties)2 12.0 61.9 29.2

 Percent cropland (1997)3 26.3 46.6 27.3 ns

 Lowest 1/3 in landscape score (% of counties)4 16.3 70.0 31.1

 Highest 1/3 in landscape score (% of counties)4 44.5 12.0 19.8

 Public land under 2% of total land (% of counties)2 42.9 73.9 56.6

Demography

 Minority population over 50% (% of counties) 3.6 3.0 ns 58.9

 Natural population change, 1988-2008 (%) 5.0 1.9 11.3

 Natural population decrease (% of counties) 26.7 46.2 8.4

Education (ages 25-64)

 No high school degree (%) 19.3 14.9 30.1

 College diploma (%) 15.7 16.8 12.6

 Lowest 1/3 in college diploma (% of counties) 38.8 17.7 57.0

 Median school grades completed, 1950, age 25 and over 5.1 5.6 3.6

Economy

 Agriculture and forestry employment (%) 5.6 13.3 8.7

 Manufacturing employment (%) 18.1 11.9 11.7

 Recreation county (percent of counties)5 19.0 5.0 5.0

Entrepreneurship and creative class 

 Top 1/3 in nonfarm business start-ups 

 per 100 nonfarm jobs, 1991-2000 (% of counties)6 31.5 38.5 27.4 ns

 Net change in number of establishments, 1990-2008, 

 per 1,000 1990 nonfarm business jobs7 26.1 2.9 -9.1

 Top 1/3 in creative class occupations (% of counties)8 40.8 21.5 8.4

 Top 1/3 in both creative class and start-ups 16.2 3.3 1.9

Socioeconomic conditions

 Employment rate (age 21-64) 70.8 75.2 58.7

 Unemployment rate (age 20-29) 8.9 7.8 19.0

 Median household income ($1,000) 32.9 31.9 22.8

 Median value of single family houses ($1,000) 81.1 54.9 50.0

 Subpar housing (% of counties)9 13.6 2.3 52.8

 Prosperity county (% of counties)10 19.7 32.4 0.0

Number of counties 1317 626 107

Note: Unless otherwise noted, 2000 Census of Population data fi les are the original data source. Differences from “other nonmetro” 

counties are signifi cant at p<.05 (based on Wald statistics from logistic regression) level unless noted with “ns.” For each set of outmigration counties, the six factors most distinguishing them from other nonmetro counties are bold underlined (based on relative size of 

the Wald statistic). 

1Source: ERS 2004 rural-urban continuum code, available at: www.ers.usda.gov/Data/TypologyCodes/.

2Source: ERS, based on USDA Forest Service, Northern Research Station, St. Paul, MN, forest inventory, 2003.

3Source: ERS, based on Census of Agriculture, 1997, data fi les. 4Scale based on water area (lakes, ponds, and ocean), topographic variation, forest cover and crop cover, weighted by strength of relation to nonmetro county net migration in 1990-2000 (see McGranahan, 2008). 5Source: ERS 2004 recreation counties, available at: www.ers.usda.gov/Data/TypologyCodes/

6Source: ERS, based on Bureau of the Census, Statistics of U.S. Businesses, special tabs (available only through 2006). 

7Source: ERS, based on Bureau of the Census, County Business Patterns data fi les.

8Source: ERS, based on creative class defi nition in McGranahan and Wojan (2007).

9Source: ERS, based on Isserman (2009). Subpar housing exists when the proportion of housing units with one or more problems is 

higher than the national proportion, with problems defi ned as incomplete plumbing, incomplete kitchen facilities, more than 1.01 people 

per room, and monthly housing costs exceeding 30 percent of income.

10ERS, based on Isserman (2009). Prosperity is defi ned as lower rates of unemployment, poverty, school dropouts, and subpar housing 

than in the Nation as a whole (see Isserman et al., 2009). 
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scenic disadvantages, 70 percent of low-poverty outmigration counties score 

in the bottom third on the composite landscape scale. These counties also 

tend to have little of their land in the public domain. For people involved 

in agriculture, farming landscapes can be attractive and reassuring. But for 

others, these landscapes tend to present little ecological variation, few scenic 

vistas, and limited access to the rural outdoors. Thin settlement and lack of 

scenic appeal may also create problems for employers. 

While most low-poverty outmigration counties score low on landscape 

attractiveness, 12 percent of the counties are actually in the top third on this 

measure. Many are or were mining counties, which may benefi t from attractive landscapes and varied topography if the mining has not been ecologically disruptive.8

By most landscape measures, the high-poverty outmigration counties tend to 

fall between the low-poverty outmigration counties and the other nonmetro 

counties. For instance, the proportion with little or no forest (29 percent) is 

less than half that of the low-poverty outmigration counties (62 percent), but 

substantially higher than in other nonmetro counties. In general, the cause 

of net outmigration for high-poverty counties is low levels of schooling and 

poor socioeconomic conditions, not geography.

8Mining counties are defi ned periodically by ERS, based on county earnings 

by industry (see ERS County Typology 

Codes).
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Demography

Population change is an outcome of both migration and “natural” population change stemming from resident births and deaths. In many of the lowpoverty outmigration counties, population is diminished by both outmigration 

and natural decrease. The average rate of natural population change in these 

counties was only 2 percent over 1998-2008, and nearly half the counties 

experienced a natural decrease (table 2). The frequency of losing population 

through natural decrease in low-poverty outmigration counties is twice that 

found in other nonmetro counties; in part, this refl ects the relatively old age 

of the population stemming from long histories of net outmigration.

In contrast, the high-poverty outmigration counties tend to have relatively 

high rates of natural increase for the study period (11 percent), more than 

twice the average for other nonmetro counties (5 percent). Few of these 

counties had natural decrease (8 percent), compared with other nonmetro 

counties (27 percent). In 13 percent of the high-poverty outmigration counties, natural increase was suffi ciently high that these counties actually gained 

population in 1988-2008, despite high net outmigration. However, for 69 

percent of the high-poverty outmigration counties, the combination of migration and natural change still left the population over 10 percent smaller in 

2008 than it had been in 1988. 
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Nonmetro poverty counties tend to have distinct county racial and ethnic 

makeups.9 The high-poverty outmigration counties are not exceptional 

in this regard. Hispanics, Blacks and/or Native Americans comprise the 

majority of the population in nearly 60 percent of these counties, with each 

group in a distinctly different area (fi g. 5). Almost all of the high-poverty 

outmigration counties in the northern Great Plains—and Alaska—are associated with Native American populations. Those in the Rio Grande area of 

southern Texas are primarily Hispanic, while those in the Mississippi Delta 

are predominately Black. Most high-poverty outmigration counties not 

associated with a minority are in eastern Kentucky and the West Virginia 

Highlands.

The differences in racial and ethnic composition—and the associated differences in location, history, and culture—make for different rates of natural 

increase. For instance, while the average rate of natural increase in highpoverty outmigration counties was 11 percent, the rate was as high as 33 

percent among the primarily Native American counties. With this rate of 

increase, most Native American high poverty outmigration counties actually gained population over 1998-2008, despite high net outmigration. At the 

other extreme, the Appalachian and other high-poverty outmigration counties 

with relatively small minority populations had average natural increases of 5 

percent—the same as in other nonmetro counties. None gained population in 

1988-2008.

Education Completed

The argument that education is a key to rural development seems valid when 

examining high-poverty outmigration counties. Many of the working-age 

population in these counties lack high school degrees—an average of 30 

percent compared with less than 20 percent in other nonmetro counties. Fiftyseven percent of high-poverty outmigration counties also fall in the bottom 

third in terms of college completion rates (table 2).

While low education levels in high-poverty outmigration counties may 

impede local growth in population or jobs, relatively high education levels in 

low-poverty outmigration counties are not suffi cient in themselves to attract 

or generate rural growth. Residents of these low-poverty counties tend to 

have higher educational attainment than other nonmetro residents, despite the 

outmigration of young adults. This attainment arguably contributes to population loss as parental households with high levels of schooling are more apt 

to encourage and more able to support the outmigration of young adults to 

further their education and enhance employment opportunities. 

Nonmetro differences in levels of schooling are longstanding. While 

schooling levels in 1950 were everywhere low by contemporary standards 

(table 2), even then the high-poverty outmigration counties were considerably behind in educational attainment, while low-poverty outmigration counties were ahead of other rural counties.10 

Industry

Outmigration counties had little manufacturing employment or recreational 

activity in 2000, compared with other nonmetro counties (table 2). The 

9See ERS Briefi ng Room: Rural 

Income, Poverty, and Welfare: HighPoverty Counties http://www.ers.usda.

gov/briefi ng/incomepovertywelfare/

highpoverty/.

10As a group, the predominately Native 

American high-poverty outmigration 

counties tend to have education levels 

comparable to other nonmetro counties, 

and did so even in 1950.
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low-poverty outmigration counties continued to rely on agriculture, the 

mainstay of many rural areas for much of the past century. The high-poverty 

outmigration counties were relatively specialized in health, education, and 

government, all activities either in the public sector or largely dependent on 

public-sector spending. 

American Community Survey data are not yet available post-2000 for counties with small populations, a characteristic of many outmigration counties. 

Administrative data based on business establishments indicate change in 

industry structures and jobs, with the disadvantages that self-employment 

is excluded (except for farmers) and that no distinction is made between 

part- and full-time jobs. Figure 6 presents industry structures for 1990 and 

2006 defi ned by employment shares according to the three types of nonmetro 

counties: low-poverty outmigration, high-poverty outmigration, and other 

nonmetro counties.

Other nonmetro counties have been more dependent on manufacturing and 

less dependent on agriculture than outmigration counties over the last 20 

years. Manufacturing hung strong during the rural population exodus of the 

1950s and 1960s, and continued to shift to nonmetro areas through the 1990s, 

enabling some rural areas to maintain population despite shedding labor from 

farming. Since 2000, the contraction of manufacturing has been pervasive, 

but especially pronounced in the high-poverty outmigration counties (table 

3). Manufacturing’s continuing importance in low-poverty outmigration 

counties is not due to its own resilience but to the relative lack of growth in 

other sectors. The evolution of industrial structure between 1990 and 2006 

(fi g. 6) suggests a growing dissimilarity between outmigration counties over 

time.

By 2006, education and health services had become the largest industrial 

sector for all three types of nonmetro counties. These activities are often 

in the public sector or largely supported by public funds. The much larger 

employment share in high-poverty outmigration counties suggests a continued 

inability of the private sector to create jobs. In fact, the job shares of all 

other sectors—with the exception of government—are smallest in highpoverty outmigration counties. Farming remains the clear specialization of 

low-poverty outmigration counties relative to all other nonmetro counties. 

Table 3

Change in number of nonmetro jobs, 1990-2006

 High outmigration

 Other Low High

Sector nonmetro counties poverty poverty

 ----------------------Percent----------------------

Farming -7.32 -11.78 -16.93

Manufacturing -16.55 -16.17 -39.01

Trade, transport, utilities -1.81 -18.40 -18.16

Information/prof services 66.09 42.69 41.69

Education and health 62.26 38.69 38.61

Other services 54.89 33.11 41.53

Government 43.92 20.69 64.85

Total 28.70 12.45 9.81

Source: Unpublished Quarterly Census of Employment and Wages (BLS) and Regional 

Economic Information System (BEA) data.
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Productivity gains in agriculture suggest further job losses, although the recent 

integration of agriculture into energy markets through biofuels may stem or 

reverse this trend in some areas. Despite attempts to develop value-added 

industries, outmigration counties have not been immune to declines in the 

manufacturing sector. And recreation, given the levels of landscape appeal in 

most outmigration counties, is unlikely to emerge as an economic base.

0%

5%

10%

15%

20%

25%

30%

Manufacturing

Trade, transport, 

utilities

Information and 

professional services Education and 

health services 

Other services

Government

Farming

Other nonmetro Low-poverty outmigration High-poverty outmigration

Source: Unpublished Quarterly Census of Employment and Wages (BLS) and Regional Economic 

Information System (BEA).

Figure 6b

Radar chart of economic structure for high-migration counties with high 

and low levels of poverty and all other nonmetro counties, 2006 

0%

5%

10%

15%

20%

25%

30%

Farming

Manufacturing

Trade, transport, 

utilities

Information and 

professional services Education and 

health services 

Other services

Government

Other nonmetro Low-poverty outmigration High-poverty outmigration

Figure 6a

Radar chart of economic structure for high-migration counties with high 

and low levels of poverty and all other nonmetro counties, 1990 
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Given the production sector declines and high outmigration, one would 

expect to see a decline in total jobs in nonmetro outmigration counties over 

1990-2006. In fact, the total number of jobs grew 12.5 percent in low-poverty 

outmigration counties and 9.8 percent in high-poverty counties during this 

period (table 3). While this growth rate is less than half that experienced 

in other nonmetro counties (28.7 percent) over the same period, outmigration counties did experience job growth in the most dynamic sectors of the 

economy: professional, information, health, and educational services. This 

more than compensated for job losses in farming and manufacturing, as well 

as in trade, transport, and utilities. However, whereas manufacturing tends to 

offer full-time jobs, many of the emerging sectors are characterized by parttime jobs with lower wages.

Entrepreneurship and Creative Class 

With the decline in rural manufacturing and the shift of many branch operations overseas, greater emphasis is being given to homegrown paths of 

development, particularly through boosting local business formation. New 

business formation may lead to growth, if there are untapped local opportunities and the new businesses are able to exploit them. But business formation 

itself does not ensure an effective exploitation of opportunities. Business 

start-ups may simply replicate ongoing businesses (resulting in an overabundance of hair salons, for instance) without adding anything new to the local 

economy (Van Stel and Storey, 2004; McGranahan et al., 2010). 

Establishment start-ups occurred at about the same average rates over 

1990-2006 in outmigration as in other nonmetro counties. Low-poverty 

outmigration counties actually had more than their share of counties in the 

top third ranked by start-up rates. But the net change in the number of business establishments between 1990 and 2008 varied sharply across county 

groups. Counties that had little loss or gain through net migration had an 

average of 26 percent more establishments in 2008 than they had in 1990. 

The low-poverty outmigration counties tended to maintain their establishment numbers, despite the outmigration. But even with comparable rates of 

establishment formation, high-poverty outmigration counties generally had 

substantial net losses in number of establishments over 1990-2008. 

If the formation of business establishments is not in itself suffi cient for 

growth, what conditions are conducive to sustained growth through establishment formation? 

A recent study of homegrown paths to rural economic growth identifi ed three 

qualities that, particularly when found together, enhance gains in establishments and jobs: creative class presence, entrepreneurial context, and outdoor 

amenities (McGranahan et al., 2010). The creative class thesis—that places 

need to attract people in creative occupations, such as engineers, architects, 

scientists, and artists—in order to compete in today’s knowledge economy 

was developed by Florida (2002) with cities in mind, but it may be particularly relevant to rural areas. Rural areas lose much of their talent as young 

adults move to urban areas to further their training or pursue other goals. 

Rural areas need to attract talent to grow or even maintain their populations. They need to bring in new knowledge and skills. Research suggests 

that presence of the creative class talent in entrepreneurial settings reduces 
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the likelihood that new businesses are simply replications of existing businesses, resulting in greater net gains in establishments. Absent entrepreneurship, however, the creative class talent may not become directly involved in 

the local economy, staying within a local college or university, for instance. 

Outdoor amenities make it likely that this creative class-entrepreneurship 

dynamic can be sustained, by both drawing more creative class migrants and 

creating new business opportunities. 

Outmigration counties, particularly those with high poverty rates, are much 

less likely than other nonmetro counties to be in the top third in creative class 

share of employment. While over 40 percent of the other nonmetro counties 

fall in the top third, this proportion drops to 22 percent for the low-poverty 

outmigration counties and only 8 percent for the high-poverty outmigration counties. Counties in the top third in both establishment birth rates and 

creative class—“creative-entrepreneurial” counties—are rarely outmigration 

counties.

Creative-entrepreneurial counties had considerably greater gain in establishments over 1990-2008 than other nonmetro counties (fi g. 7). This was 

surprisingly true not only in the counties not experiencing high outmigration 

but also in the very few (20) low-poverty high outmigration counties characterized by both creative class presence and high establishment birth rates. 

These counties with gains in the number of establishments are, however, a 

particular group—in the West rather than the Midwest and with landscape 

scores in the top third rather than in the bottom third, as typifi es the lowpoverty outmigration counties. More generally, outmigration counties do not 

lack in the formation of new businesses, but tend to lack in creative class and 

attractive landscapes, conditions that lead to net gains in new businesses. 

Other nonmetro Low poverty High poverty

−10

0

10

20

30

40

50

60

Creative−entrepreneurial1

Entrepreneurial

Not entrepreneurial

59

28

25

9

-7 -5 -1 -2

Figure 7

Creative-entrepreneurial counties have had greater gains in numbers 

of establishments than counties without this combination

Median gain in number of establishments,1990-2008, per 1,000 1990 jobs

Note: Only 2 high-poverty outmigration counties were creative-entrepreneurial.

1

Counties in nonmetro top 1/3 in both creative class and start-ups.
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Socioeconomic Conditions

High- and low-poverty outmigration counties have starkly different socioeconomic conditions by several measures. In the high-poverty outmigration counties, over 40 percent of the working age population (ages 21-64) 

were not employed in 2000, 10 percentage points more than the working age 

population in other nonmetro counties. Unemployment rates among those 

ages 20-29 were over twice the rates in other nonmetro counties, and median 

household incomes were nearly 50 percent lower. Housing values were also 

relatively low, which reduced the cost of living, but, perhaps due to housing 

quality, this was not enough to entice retirees.11

In contrast, socioeconomic conditions in the low-poverty outmigration counties tended to exceed the conditions in the nonmetro counties with little or 

no outmigration. Employment rates were higher and relatively few people in 

their 20s considered themselves unemployed in 2000. Household incomes 

were nearly as high as in other nonmetro counties, while housing values were 

much lower (although not low enough to attract many retirees). Isserman et 

al. (2009) defi ned prosperous counties as those with below the national rates 

of poverty, unemployment, high school dropouts, and housing problems.12

Nearly a third of the low-poverty outmigration counties are prosperous counties according to this indicator. Less than a fi fth of other nonmetro counties 

were so classifi ed. 

The dichotomy is further highlighted by examining the relationship between 

unemployment and outmigration (fi g. 8). For counties with unemployment 

rates of 6-8 percent or more, the expected economic relationship holds—the 

higher the unemployment, the more likely a county is to have high outmigration. Most of the counties with very high unemployment are also highpoverty outmigration counties. 

11The racial/ethnic make-up of the 

high-poverty outmigration counties 

generally had little bearing on socioeconomic conditions. The labor force 

participation rates of working-age 

adults were generally comparable 

whatever the composition. The major 

difference was a very high unemployment rate (27 percent) in predominately 

Native American counties for people 

age 20-29 in 2000.

12Isserman et al. (2009) followed the 

2000 census in defi ning a housing problem as one or more of the following: incomplete plumbing, incomplete kitchen 

facilities, more than 1.01 occupants per 

room, and monthly housing costs over 

30 percent of household income.

Under 2 2-4 4-6 6-8 8-10 10-12 Over 12

Low poverty

Outmigration county type

High poverty

Unemployment rate (percent), 2000

0

10

20

30

40

50

60

70

80

90

81

48

27 25

33

44

63

Figure 8

High outmigration counties are prevalent where unemployment 

is high—and where unemployment is low 

Percent with high outmigration

Source: ERS, based on 2000 Census of Population SF3 files.
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For counties with unemployment rates of 6-8 percent or less, however, the 

relationship is turned on its head: the lower the unemployment rate, the 

more likely a county is to have high outmigration. Low unemployment does 

not appear to misrepresent socioeconomic conditions: the majority of the 

counties with unemployment rates under 4 percent were classifi ed as highly 

prosperous. No current labor market theory can explain how prosperity and 

very low unemployment leads to high outmigration. The causal direction is 

most likely the other way: high outmigration from these counties leads to 

low unemployment. Most of the counties with unemployment rates below 4 

percent are in the bottom quarter of the landscape scale and many are remote 

and thinly settled. The prosperity, coupled with strong school systems and a 

culture of education, means that young adults have the means to leave and 

fl ourish elsewhere.13 

13Statistical evidence for this line of 

reasoning is presented in binomial 

logistic regressions in appendix table 1. 

For the 1,164 nonmetro counties with 

under 6 percent unemployment in 2000, 

the bivariate results (equation 1) show 

that the higher the unemployment, the 

less likely a county is to be an outmigration county, which is consistent with 

fi gure 8. However, once high school 

education, agricultural and forestry 

employment, population density, and 

landscape score are taken into account 

statistically (equation 2), the expected 

positive relationship between unemployment and outmigration is obtained. 

As indicated by the relative size of the 

Wald statistics, the most infl uential 

measures in this analysis are population 

density and the landscape score. 
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Trends in Poverty and Unemployment

The analysis thus far has drawn largely on data from 2000, roughly the 

midpoint in the 20-year span considered in defi ning outmigration counties. 

The general assumption has been that the characteristics identifi ed are longterm characteristics. This assumption is reasonable for most geographic, 

landscape, and human resource characteristics, but may be less likely to 

hold for poverty and unemployment, especially given the current recession. 

Fortunately, recent estimates of poverty and unemployment are available to 

examine how the recession affected outmigration and other nonmetro counties, at least through 2008.

In general, there appear to have been fairly universal gains in the 1990s, 

when poverty and unemployment rates fell in both high- and low-poverty 

outmigration counties and in other nonmetro counties as well (table 4). The 

strong national growth during this period likely benefi ted people in most 

counties. National growth was slow during 2000-08, although the effects of 

the current recession were not apparent until 2009. 

Only the counties without high net outmigration showed a substantial 

increase in poverty from 1999 to 2008. The 2009 (2nd quarter) unemployment statistics indicate even more strongly that the outmigration counties 

were less hard-hit than other nonmetro counties—although unemployment 

rates were everywhere higher in 2009 than they had been 10 years earlier.

Part of the explanation appears to be that rural manufacturing has been 

particularly affected by the recession, and outmigration counties have relatively little manufacturing. Where outmigration counties had manufacturing, 

their unemployment rates did jump. For instance, ERS classifi ed only 12 

high-poverty outmigration counties as “manufacturing-dependent” in 2000. 

In these counties, 2nd quarter unemployment jumped from 9 percent in 1999 

to 16 percent in 2009, much more than the average reported in table 4.14 14For more on the effect of the recent 

recession, see Parker et al. (2010).

Table 4

Poverty and unempoyment in outmigration counties (by poverty level) 

versus other nonmetro counties 

 Outmigration counties

 Other Low High

County characteristics nonmetro counties poverty poverty

Average poverty rate1 -------------------Percent-------------------

 1989 17.8 16.7 37.3 

 1999 15.0 14.0 31.7 

 2008 16.5 14.3 30.0 

Average unemployment rate2

 1989 6.7 5.4 10.7 

 1999 5.3 4.8 9.4 

 2009 9.7 6.7 10.9

Note: All differences among county groups are statistically signifi cant at the p< .001 level for all 

years, based on Wald statistic tests. 

1ERS, based on Censuses of Population, 1990, 2000, and Bureau of the Census estimates, 

2008.

2ERS, based on Bureau of Labor Statistics Local Area Unemployment fi les. Data are for 2nd 

quarter of each year.
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Local Problems Facing Rural Manufacturers 

The 1996 ERS Rural Manufacturing Survey demonstrates why manufacturers (and other employers) may avoid outmigration counties.15 Funded in 

part by USDA’s Rural Development mission area, this national survey was 

prompted by concern over rural manufacturing competitiveness amid globalized markets and technological change. As part of the survey, the manufacturers were asked if various factors associated with their location were 

problems inhibiting their competitiveness. The 21 factors included tended to 

focus on problems of access—to customers, equipment suppliers, fi nancial 

institutions, and so forth. Somewhat surprisingly, few manufacturers, even 

ones located in remote areas, reported major problems of access. Instead, 

the factors most frequently cited tended to be related to human capital 

(McGranahan, 1998a).

The six factors most frequently cited as major problems by nonmetro manufacturers are listed in descending order in table 5. The local attribute most 

often reported to be a major problem in counties not classifi ed as outmigration counties was the quality of available labor (35 percent), which far 

outweighed the two next most frequently reported problems: State and local 

taxes (23.2 percent) and environmental regulation (22.6 percent).16 

The attractiveness of the area to managers and professionals was the local 

factor fourth most cited as a major problem by manufacturers in non-outmigration counties. This was followed by access to training courses and the 

quality of primary and secondary schools. These three items were more likely 

to be cited as major problems by manufacturers using advanced technologies 

and management practices.

Manufacturers’ responses in outmigration counties were different than in 

other nonmetro counties, and suggest why these counties have attracted 

few manufacturers (or other employers). Manufacturers in the low-poverty 

outmigration counties were less likely than other manufacturers to report 

the quality of available labor as a major problem. And despite often being 

15This survey has not been repeated, 

although ERS is conducting an establishment survey of manufacturing and 

selected other industries. 

16It should not be inferred that poor 

labor quality is in itself the major 

problem facing rural manufacturers. 

The responses refl ect rather, diffi culties with the quality available at the 

price that the manufacturers can or 

want to pay. Thus, manufacturers who 

paid higher wages and hired more high 

school graduates were less likely to 

report the quality of available labor as a 

major problem.

Table 5

Local factors most often cited by manufacturers as major problems in 

their ability to compete 

 Outmigration counties

 Other Low High

Local factor nonmetro counties poverty poverty

Percent reporting as a major problem

Quality of available labor 35.4 28.8 30.1 

State and local taxes 23.2 19.9 19.7 

Environmental regulations 22.6 21.9 19.8 

Attractiveness of area to 

 managers and professionals 13.5 24.9 28.0

Access to training courses 9.7 7.8 19.9

Quality of local schools 9.5 8.0 41.4 

Number of respondents 1,757 394 67

1Statistically signifi cant differences (p<.05) from other nonmetro counties are in bold underline, 

based on Wald statistic tests. 

Source: ERS Rural Manufacturing Survey, 1996.
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relatively remote, manufacturers in these counties were not more likely 

than others to report access to training courses as a major problem. These 

responses are consistent with the relatively good school systems in these 

counties and the low dropout rates. However, the manufacturers in lowpoverty outmigration counties were nearly twice as likely as manufacturers in other nonmetro counties to report the unattractiveness of the area 

to managers and professionals as a major problem. This was their second 

most often reported major problem. Part of the explanation appears to be 

the combination of thin settlement with low landscape amenities in many of 

these counties, but population loss itself may create an unattractive physical 

environment (such as empty commercial and residential buildings, public 

property with insuffi cient upkeep, and distant schools).17

For manufacturers in the high-poverty outmigration counties, human 

resource issues are paramount. For these manufacturers, the most frequently 

cited major problem with their location was the quality of local schools. Poor 

schools affect labor force skills, as access to training courses was reported 

to be a major problem more than twice as often as in other nonmetro counties. But it also apparently affects the attractiveness of the area to managers 

and professionals. Over 80 percent of the manufacturers in the high-poverty 

outmigration counties who reported the unattractiveness of the area to 

managers and professionals to be a major problem also reported local school 

quality as a major problem. The issue of attracting highly trained people 

likely extends beyond manufacturing to the attraction of the creative class in 

general. 

17For an analysis of problems facing 

manufacturers in the Great Plains, the 

location of many low-poverty outmigration counties, see McGranahan, 

1998b.
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Conclusions

Nonmetro counties that lost over 10 percent of their population through net 

outmigration between 1988 and 2008 are a highly diverse group. Remoteness 

and a lack of landscape amenities are key to explaining high outmigration in 

most, but not all, cases. Many outmigration counties are found in Mississippi, 

eastern Kentucky, and other rural areas with long histories of poverty, which 

is less characteristic of counties in the Great Plains. From a policy perspective, the distinction between high-poverty and other outmigration counties 

is an important one, as these two types of outmigration counties face very 

different circumstances.

Outmigration counties with high poverty, identifi ed here as having a poverty 

rate of at least 25 percent in 2000, conform to the idea that population loss 

through outmigration refl ects economic distress. With the partial exception of 

Native American counties, these counties have low educational attainment, 

high unemployment, and poor housing conditions. They are a relatively small 

subset of outmigration counties and are often overlooked by researchers 

studying the causes and consequences of depopulation, perhaps because they 

are not already thinly settled. While outmigration from these high-poverty 

counties appears to be jobs-driven rather than amenity-driven, inadequate 

education and poor schools appear to be the central development issue in 

most of these counties, both for attracting jobs and attracting the people who 

create jobs.

The other set of outmigration counties, however, tends to show fewer signs 

of economic distress than nonmetro counties with little or no outmigration. 

Their residents have relatively high education, low unemployment, and better 

housing conditions than counties with little or no outmigration. The fact that 

traditional distress indicators used in rural development programs did not 

target many of the outmigration counties identifi ed here was a key motivation 

in the establishment of the Northern Great Plains Regional Authority in 2002 

and work on the New Homestead Act. 

These counties tend to be relatively remote and thinly settled, which discourages manufacturers and other employers as well as potential residents. And 

they generally have few landscape amenities to draw families and retirees 

from elsewhere. People moving to rural areas are generally sacrifi cing 

income and access to services to improve their quality of life. For some, this 

may mean returning to family and friends. For others, however, it means 

access to the rural outdoors. Without a hometown or family connection, 

people generally are not going to be drawn to rural areas without interesting 

landscapes. And employers are less likely to go where they cannot attract 

skilled workers without paying high premiums. The outmigration from lowpoverty counties appears to be more lifestyle-driven than jobs-driven. 
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Glossary

Nonmetropolitan—Our classifi cation of counties as “rural” for purposes 

of this analysis includes counties designated as nonmetropolitan (nonmetro) 

by the U.S. Offi ce of Management and Budget in 2003 (based on data from 

the 2000 census). Nonmetro counties are defi ned as those counties lying 

outside urban cores of 50,000 people or more and their immediately adjacent 

commuting zones. For more detail on how nonmetro areas are defi ned, and 

how they differ from the U.S. Census Bureau’s defi nition of rural, see the 

ERS briefi ng room, “Measuring Rurality: What Is Rural?” http://www.ers.

usda.gov/Briefi ng/Rurality/WhatIsRural/.

Poverty rate—A household with an annual income less than the amount 

deemed suffi cient to purchase basic needs of food, shelter, clothing, and 

other essential goods and services for its member(s) is classifi ed as “poor.” 

The poverty threshold is set by the Offi ce of Management and Budget and 

varies by household size, constituency and, over time, with the cost-of-living 

index. In the 2000 census, information on income was collected for 1999. 

The threshold for a family of four, including two children, was $16,985. For 

further information on the defi nition of poverty, see http://www.census.gov/

hhes/www/poverty/povdef.html.

Data Sources and Methods

High outmigration counties—those with 10 percent or higher population loss from net migration, July 1988-

July 2008—were identifed using annual county estimates of net migration from the U.S. Census Bureau’s 

Federal-State Cooperative Program for Population Estimates (http://www.census.gov/popest/overview.

html). The net migration rate was calculated as the sum of net migration over the 20-year period divided by 

the county’s estimated 1988 population. In this analysis, net migration includes both domestic migration and 

immigration.

The 1990-2000 cohort migration analysis comes from data prepared at the University of Wisconsin under a 

cooperative research agreement with the USDA's Economic Research Service. County-level, net migration 

estimates by 5-year age groups for 1990-2000 were tabulated using population data from the U.S. Census 

Bureau and vital statistics from the National Center for Health Statistics—population change not accounted for 

by births and deaths was assumed to result from net migration (Voss et al., 2004). Other data are from a variety 

of sources: the 2000 Census of Population, the Bureau of Labor's Local Area Unemployment System fi les, the 

1950 Census of Population, the ERS Rural Manufacturing Survey, and three composite measures based on 

published journal articles—a scale of landscape amenities (McGranahan, 2008), a measure of creative class 

(McGranahan and Wojan, 2007), and a prosperity index (Isserman et al., 2009).

The statistical analysis in the report is based on asking whether either of the two types of outmigration counties—those with and without high poverty—differ from other nonmetro counties for a broad array of characteristics. We used the logistic regression program from SPSS to answer this question for each measure included 

in the study, testing how well each measure was able to distinguish the respective outmigration counties from 

other nonmetro counties. The denotations in table 2 of the measures most distinguishing the high- and lowpoverty outmigration counties from other nonmetro counties are based on the size of the Wald statistics output 

as part of this program.
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Appendix table 1

Binomial logistic regression of outmigration county status on selected measures, nonmetro counties with 2000 unemployment rates under 6 percent (N=1,164)

Equation 1 Equation 2

 Wald Wald 

County measures, 2000 B statistic Sig. B statistic Sig.

Unemployment rate -0.539 101.2 <.0001 0.629 37.48 <.0001

No high school degree

 (%, ages 25-64) -0.074 27.63 <.0001

Agriculture and forestry 

 employment (%) 0.060 8.14 0.0040

Population density (loge) -1.142 77.91 <.0001

Landscape score -75.08 157.25 <.0001

Intercept 1.713 57.487 <.0001 -2.712 30.752 <.0001

Nagelkerke R2 0.13 0.67 

Source: Measures are described in table 2.