Economic Development Options and Constraints in Remote Rural Counties: A Case Study of the Great Plains Region

CRS Report for Congress
Economic Development Options and
Constraints in Remote Rural Counties:
A Case Study of the Great Plains Region
April 29, 2004
Tadlock Cowan
Resources, Science, and Industry Division

Congressional Research Service ˜ The Library of Congress

Economic Development Options and Constraints
in Remote Rural Areas: A Case Study of the
Great Plains Region
Summary
Although many rural areas fared relatively well over the past decade, there
remain wide swathes of rural America that continue to decline. One of the more
significant indicators of this selective decline is the population out-migration in
remote rural areas of the Great Plains where agriculture and natural resource-based
economies are predominant. Congress is concerned about theses areas and has
proposed legislation to address the decline. Current conditions in much of the Great
Plains suggest a continuing and deepening decline in the absence of new sources of
competitive advantage. This is especially true for remote counties, which present^st
distinct challenges for rural development policy in the 21 century. This report
discusses socioeconomic characteristics and trends of 242 remote rural counties in
seven states of the Great Plains region stretching from Texas to the Dakotas. Remote
counties are defined here as those with populations under six persons per square mile
and on the extreme end of two widely used scales of rurality that categorize counties
based on the extent to which they are influenced by urban areas or larger population
centers. Appendices provide individual county level data on socioeconomic trends
in population, education, employment, and income for the 242 remote counties.
Remote rural counties in the Great Plains experienced extensive population out-
migration over the 1990s. With few employment alternatives in the private sector to
replace the exodus of jobs from agriculture, mining, and timbering, remote counties
are a particular concern to policymakers because the remaining population is
disproportionately elderly, low-income, low-wage, and more dependent on
agriculture and other natural resources than other rural areas. While the Great Plains
area receives higher per capita federal funding than the country as a whole, most of
the funds direct payments to individuals, e.g., Social Security, disability, farm
subsidies rather than to capital-generating areas. This does not represent a difference
from federal spending in non-metro areas generally, but in the Great Plains, programs
that promote rural economic development may be even more important than in rural
areas with more diversified economies, such as those within commuting distance to
urban areas. The possibilities of regionally based solutions are discussed.
Most rural development researchers agree that the great diversity exhibited
from one rural area to another makes crafting effective federal policy especially
difficult. Contributing to this difficulty is the relative dearth of research that might
help policymakers understand why some policies seem to work well in some rural
areas and not so well in others. The United States Department of Agriculture
classifications of rural areas into particular policy types and their dominant economic
activity are two of the more widely cited efforts to impose some analytic order on the
diversity of rural areas. Yet they still paint rural areas with a very broad brush.
Understanding the particular characteristics and economic conditions of remote rural
areas in the Great Plains may help legislators in making rural economic development
policies to better deal with the circumstances of that region.
This report will not be updated.

Contents
In troduction ......................................................1
The Great Plains...............................................1
Rural Definitions..........................................4
Background ..................................................6
Population Change in Non-Metro Areas During the 1990s..........8
The Declining Opportunity Structure of Remote Rural Areas of
the Great Plains...............................................9
Human Capital Issues in Remote Rural Areas...........................13
Federal Funding in the Great Plains...................................19
Policy Options for Remote Rural Areas of the Great Plains................21
A Continuing Role for Agriculture?..............................22
Structural Changes in Agriculture............................23
Overcoming Remoteness: An Interstate Skyway System..............26
Regional Approaches to Rural Economic Development...............27
In troduction .............................................27
The Northern Great Plains Regional Authority (NGPRA).........28
Legislation in the 108^th Congress.................................29
New Homestead Act (H.R. 2194 and S. 602)...................30
New Homestead Economic Opportunity Act (H.R. 1686).........31
Rural America Job Assistance and Creation Act (H.R. 137)........31
Status of Legislation......................................31
Conclusion ......................................................32
Appendix A. Measuring Rurality....................................35
Appendix B. Top 25 Industries in Low-Wage Rural Counties..............40
Appendix C. Remote Kansas Counties................................43
Appendix D. Remote Montana Counties...............................46
Appendix E. Remote Nebraska Counties...............................50
Appendix F. Remote North Dakota Counties...........................53
Appendix G. Remote Oklahoma Counties..............................56
Appendix H. Remote South Dakota Counties...........................57
Appendix I. Remote Texas Counties..................................60

Figure 1. Demographic Decline in the Great Plains.......................2
List of Tables
Table 1. Remote Great Plains County Population.........................3
Table 2. Great Plains Population ....................................10
Table 3. Great Plains Age and Education Structure, 2000..................11
Table 4. Poor Great Plains County Rankings (1999)......................12
Table 5. Remote County Household Income, Poverty, and
Unemployment Rates, 2000-2001................................13
Table 6. Remote County Employment Structure.........................16
Table 7. USDA Classification of Non-Metro Counties by Economic Type....36
Table 8. USDA Classification of Non-Metro Counties by Policy Type.......37
Table 9. Rural-Urban Continuum Codes...............................38
Table 10. Urban Influence Codes.....................................39

Economic Development Options and
Constraints in Remote Rural Areas:
A Case Study of the Great Plains Region
Introduction
Congressional interest in rural policy involves a wide range of issues, including
agriculture, forestry, and mining production, community infrastructure, natural
resource conservation and management, and socioeconomic development. Current
challenges to and reform of existing federal rural policies are evolving in an
environment of increasing concern about national competitiveness, new federal
political strategies that devolve more power to state and local areas, deregulation of
financial markets, budget constraints, and the increasing degree of separation
between farm policies and rural economic development policies. Global
socioeconomic changes are being felt especially in rural areas that have historically
depended on natural resource based economies, including agriculture. A changing
rural America is also producing pressures for different policies and raising new
questions about the role Congress might play in shaping effective rural development
policies for the future.
The Great Plains
Much of rural America lying outside urban commuting zones faces significant
economic development challenges as the United States has increasingly become a
largely urban/suburban and increasingly high-technology, bi-coastal economy.¹ Yet,
the myriad problems facing rural America are often invisible to an urban and
suburban world. Faced with weaknesses in the farm economy, persistent poverty,
and the loss of manufacturing jobs to lower labor costs abroad, large expanses of
rural America, especially those areas sparsely populated and remote from population
centers, are falling farther behind their urban and suburban counterparts. This trend
is not new nor are spatial inequalities a new phenomenon. Spatially unequal
development characterizes virtually all countries in the developed world as well as
the developing world. But these spatial inequalities have grown more pronounced
in some U.S. regions over the past decade as the United States makes adjustments to
the internationalization of markets and the division of labor. These patterns are
visible throughout the United States. Nowhere is this perhaps more pronounced than

¹ U.S. economic activity is overwhelmingly concentrated along the country’s ocean coasts
and Great Lakes coasts, as well as its navigable rivers. See Jordan Rappaport and Jeffrey
Sachs, “The United States as a Coastal Nation,” Center for International Development,
Harvard University, July 2001.

in the Great Plains region, especially in that region’s remote rural counties. (See
definition and description of “remote rural areas” on page 5).
Remote rural areas of the Great Plains Region present distinctive spatial and
socioeconomic dynamics that offer a stark example of the significant difficulties
facing many other rural areas. Unlike many other rural areas, however, much of the
Great Plains is undergoing significant population out-migration (see Figure 1). In
this report, data on 242 remote rural counties in seven states of the Great Plains
(Texas, Oklahoma, Nebraska, Kansas, South Dakota, North Dakota, and Montana)
are examined. In Appendices C-I, individual remote county level data are provided
on population change, household income, employment, and other socioeconomic
variables.
Figure 1. Demographic Decline in the Great Plains
The remote counties discussed here have experienced significant population
loss, highlighting the fact the non-metro Great Plains counties have seen a relatively
steady population decline. While the total population of these 242 counties is just
under a million, representing a little over 12% of the non-metro population in the
seven states, these counties might be regarded as an extreme case of more general
phenomena accompanying widespread population decline in the region as a whole
(see Table 1).

Table 1. Remote Great Plains County Population
State StateState Non-Remote CountyPercentage of
Population,MetroPopulationRemote to State
2000 P o pul at i o n Non-Metro
Population
Kansas2.7 million1.17 million92,0137.9%
Montana 902,195 692,486 275,393 39.8%
Nebraska1.4 million811,42590,39411.1%
Nor t h 642,200 347,724 131,877 37.9%
Dakota
Oklahoma3.4 million1.35 million29,9652.2%
South 754,844 493,867 144,368 29.2%
Dakota
Texas20.8 million3.16 million209,6996.6%
Total30.5 million8.0 million973,70912.2%
^Source:^C^eⁿ^s^u^s^{2000, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^{, U}^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^rce.
As used here, the Great Plains region includes parts or all of Texas, Oklahoma,
Nebraska, Kansas, North Dakota, South Dakota, and Montana stretching from central
Texas to the Canadian border. Cycles of growth and decline have long characterized
the region. In the late 1870s and early 1880s, an abnormal abundance of rain led
many settlers to the region only to experience, a few years later, the blizzards of 1887
followed by a decade of withering drought. Periods of drought and depression lasted
until the beginning of the 20^th century, which ushered in a period of high agricultural
commodity prices and good crop years that lasted through the first World War.²
Following the Depression and World War II, another period of strong growth in the
agricultural sector again made the Great Plains an economically competitive region.
Since then, the steady decline in numbers of farms and the limited creation of non-
farm jobs has left the region searching for new ways to rejuvenate local economies.
Over 60% of the counties in the Great Plains region had population declines
from 1990-2000. In North Dakota, the state as a whole had a negative growth rate
while the United States grew at an average of over 13% between 1990-2000. As
younger persons migrate from many of these areas in the Great Plains, the elderly
population increases proportionately, the tax base dwindles, public services decline,
employers close shop, and small communities disappear. When Frederick Jackson
Turner presented his landmark essay, The Significance of the Frontier in American
History, to the American Historical Association at the Chicago World’s Fair in 1893,
an area of 6 or fewer persons was the defining criterion of frontier territory. Despite

²See Walter Prescott Webb, The Great Plains (Blaisdell Publishing Co.: Waltham, Mass.,

1931); James R. Dickenson, Home on the Range: A Century on the High Plains (New York:

Scribner, 1995).

the large population growth of the U.S. during the 20^th century, there are remote rural
areas that have fewer persons living there today than lived there in 1890.³
These counties are also more dependent on farming than other non-
metropolitan counties. While non-metro counties in general suffer higher rates of
poverty than metro-counties, have lower wages than metro areas, low population
densities, and/or less diversified economies, these 242 counties may fall into a
distinct category that warrants special attention from policymakers. Although other
states may have low-density rural areas and significant out-migration (e.g., Delta
South and Central Appalachia), the remote rural areas of the Great Plains represent
a distinct geographic region in the central part of the United States where farming is
still important, out-migration is significant , employment opportunities are limited,
environmental amenities are few, and the challenges of rural economic development
particularly significant.
Rural Definitions. Rural areas, when compared to urban and suburban areas,
are characterized by sparse populations, often great distances to population centers,
and, accordingly, low scale efficiencies that make the provision of public and private
services costly. Rural areas, according to the U.S. Census, comprise open country
and settlements with fewer than 2,500 residents. The formal definition of rural is
essentially a residual category: Rural areas consist of all territory outside of Census
Bureau-defined urbanized areas and urban clusters. Urbanized areas have an urban
nucleus of 50,000 or more people. They may or may not contain individual cities
with 50,000 or more. In general, they must have a core with a population density of
1,000 persons per square mile and may contain adjoining territory with at least 500
persons per square mile. The same computerized procedures and population density
criteria are used to identify urban clusters of at least 2,500 but less than 50,000
persons. This delineation of built-up territory and small towns and cities is new for
the 2000 census.
Metro and non-metro areas are defined by the Office of Management and
Budget. Metropolitan Statistical Areas and Micropolitan Statistical Areas are
collectively referred to as Core Based Statistical Areas (CBSAs). Metro areas consist
of (1) central counties with one or more urbanized areas and (2) outlying counties
that are economically tied to the core counties as measured by worker commuting
data. Outlying counties are included if 25% of workers living there commute to the
core counties, or if 25% of the employment in the county consists of workers coming
from the central counties. Non-metro counties are outside the boundaries of metro
areas and are further subdivided into micropolitan areas centered on urban clusters
of 10,000 or more residents, and all remaining “non-core” counties.⁴

³This result has led to the counter-argument to Turner that the U.S. frontier not only remains
but is growing. See Deborah E. Popper and Frank Popper, “The Great Plains: From Dust
to Dust,” Planning 53, no. 12, 1987.
⁴For statistical details concerning the Census Bureau’s formal definitions of rural and urban,
see Federal Register, vol. 67, no. 51, March 15, 2002, pp. 11663-11670. In June 2003, the
Office of Management and Budget promulgated revised definitions of Metropolitan
Statistical Areas (MSAs). See OMB Bulletin no.03-04, June 6, 2003.

Metropolitan and Micropolitan Statistical Areas do not equate to an urban-rural
classification. All counties included in CBSAs, as well as “non-core”counties,
contain both rural and urban territory and populations. Based on the most recent
definitions above, there were 59.1 million rural residents of whom 49.2% lived in
non-metro counties in 2000. There were 49.2 million non-metro county residents,

59% of whom lived in rural areas. Nationally, 17% of the population lived in non-

metro counties and 21% lived in rural areas in 2000.⁵ For programmatic as opposed
to statistical analysis and demographic modeling purposes, however, “rural” most
often refers to socioeconomic trends and conditions in non-metropolitan areas.⁶ For
example, statutory language in the 2002 farm bill (P.L.107-171, Sec. 6020) defines
rural and rural area as any area other than an area with a city or town over 50,000 and
the “urbanized area contiguous and adjacent to such a city or town.” In this report,
the terms rural, rural area, and non-metropolitan will be used interchangeably to refer
to non-metropolitan areas unless otherwise specified to include the rural residents of
metropolitan counties. Similarly, metropolitan and urban areas will be used
interchangeably unless a specific reference is made to rural areas within metropolitan
counties.⁷
Remote rural areas are defined in this report as (1) those with county population
densities of 6 or fewer persons per square mile and (2) on the far end of a rural-urban
continuum scale and a scale measuring the degree of urban influence on a rural area.⁸
These remote counties are, arguably, even more vulnerable to the “tyranny of
distance” when it comes to attracting residents and businesses that might provide the
basis for creating new sources of economic growth and development. During the
1990s, some rural areas such as the Mountain West, while sparsely populated and
with few large population centers, have seen significant population growth stemming
from the presence of attractive environmental amenities. However, in the Great
Plains region, containing a high proportion of farm-dependent counties,
socioeconomic conditions have continued to decline.⁹

⁵John Cromartie, “Measuring Rurality: What Is Rural?” USDA-ERS Briefing Room, 2003,
[http://www.ers.usda.gov/Briefing/ Rurality].
⁶The Bureau of the Census and Office of Management and Budget definitions are created
solely for the purposes of demographic measurement and analysis. Keenly aware that this
purpose can conflict with policies that target specific populations and geographic regions,
these agencies have long recommended and encouraged other agencies that use their
definitions to modify them to serve the objectives of their particular programs. See Federal
Register, vol.65, no. 249, December 27, 2000, pp. 82228-82238.
⁷Most (91%) metro county residents are urban area residents.
⁸Appendix A describes two widely used scales developed by USDA’s Economic Research
Service (ERS) that measure rural non-metropolitan counties by their population, their
proximity to metropolitan areas, and the relative size of population centers within the non-
metro county. In addition, Appendix A also describes two ERS typologies that categorize
non-metro counties on the basis of economic and policy types.
⁹Farming-dependent counties are defined by USDA as those where 20% or more of total
labor and proprietors’ income stems from agriculture. Inflation adjusted total personal
income in farm-dependent counties grew 13% between 1990 and 1998, compared to 21%
growth in other non-metro counties. See Fred Gale, “How Important Are Farm Payments
(continued...)

Background
An important issue for many rural areas is how to create new sources of rural
competitive advantage beyond the traditional economies based on commodity
agriculture, resource extraction, and peripheral manufacturing jobs. A recent survey
indicates that, while state leaders regard rural economic development as vital to their
respective states, actual legislative priorities have not placed rural development as
a central issue of their states’ legislative agendas. Approximately half the national
sample of rural, suburban, and urban legislators reported that they personally dealt
with rural issues. However, these legislators also noted that urban and suburban
issues often took priority in the legislative agenda. They cited lack of opportunity for
young people as the most important rural problem followed by decline of family
farming. ¹⁰ Yet, when asked what legislative work occupied most of their agenda,
84% of the legislators reported that quality of education attracted the most legislative
attention. Other areas cited were, the environment (70%), access to technology
(69%), access to healthcare (64%) and access to transportation (59%). While some
of these concerns are relevant to rural areas, economic development issues per se
scored considerably lower on the legislative agenda: Only a third of the legislators
cited lack of opportunity for young people as a key legislative concern.
The focus on rural oriented economic development policies has not been among
the highest federal priorities, as measured by federal initiatives, since the 1960s and
1970s. Much national rural policy attempts either to reinvigorate traditional
production spheres, such as agriculture, to build or improve physical infrastructure,
and to create or preserve small businesses. Analysis by USDA’s Economic Research
Service (ERS)of data in the Consolidated Federal Funds Reports show that when
compared to metropolitan areas, rural areas receive fewer federal funds per capita for
funding that might be characterized as capital investment and more funds that are
income support payments.¹¹ Although non-metro areas, in general, receive somewhat
less funding per capita than metro areas, rural areas also often have even more
limited access to important private investment resources because their remoteness
and low population densities may increase project risks. Credit in rural areas, for
example, can often be more expensive and offer fewer financial product options than
those available in metropolitan areas. Rural communities also may have more
difficulty in financing infrastructure projects and providing rental and middle income
housing construction. Moreover, smaller rural communities often have limited
taxing and repayment capacity. Large infrastructure projects, for example, may have

⁹ (...continued)
to the Rural Economy?” Agriculture Outlook, October 2000.
¹⁰The other eight most highly ranked rural problems, in descending order, are access to
healthcare, low-wage jobs, quality of education, sprawl, access to technology, access to
transportation, breakup of the family, and the environment. See W. K. Kellogg Foundation,
Perception of Rural America: National State Legislator Survey, Battle Creek, MI,
November 2002.
¹¹Richard Reeder, F. Bagi, and S. Calhous, “Who’s Vulnerable to Federal Budget Cuts?”
Rural Development Perspectives 11(2), June 1996.

the effect of raising taxes disproportionately for small rural communities, simply
because there are fewer people over which costs can be spread.
Analysts have sometimes asked the question: “Why invest in rural America?”¹²
America today is a suburban nation and becoming more so by the decade.¹³ When
the United States was younger, the rural sector was the “Frontier.” In the early 20^th
century, it became the “Storehouse,” the geography providing the raw commodities
to support a growing urban industrial population.¹⁴ In both periods, the rationale for
investment and public support was clear. The importance of rural areas to the nation
as a whole today appears to be more ambiguous.
In the 1960s and 1970s, the urban cores of many major U.S. cities, e.g., Detroit,
Chicago, Cleveland, New York, Boston, Washington, D.C., were faced with the
challenges of serious social and physical decline. Newspapers, news magazines, and
television were awash in stories about urban decline, which generated a national
debate about its causes and solutions. Over several years, Congress responded with
a broad range of innovative polices aimed at reversing the deterioration and
reinvigorating much of the country’s older centers. The long-term decline of rural
America, on the other hand, is happening relatively more quietly and often out of the
public eye. Yet some would argue that rural challenges today are as great on a
community level as were the challenges of cities 35 years ago. According to the
General Accounting Office, the patchwork of programs that constitutes rural policy
today is not the outcome of comprehensive and systematically crafted policy goals
targeted to rural areas as much as it is extensions and modifications of programs
designed for urban areas.¹⁵ The agricultural and manufacturing sectors remain the
primary foci in terms of amounts spent on rural areas. Some have questioned
whether financial support to production agriculture necessarily translates into
economically diverse and viable rural communities. Similarly, low-wage and low-
skill manufacturing that often predominates in many rural areas may be unable to
provide these areas with the capacity to rebuild local economies for the future,
particularly with globalization and outsourcing of production.

¹² See Karl N. Stauber, “Why Invest in Rural America — and How? A Critical Public Policy
Question for the 21^st Century.” Paper presented at Exploring Policy Options for a New
Rural America, Center for the Study of Rural America, Kansas City Federal Reserve Bank,
May 2001.
¹³Some observers even argue that the United States has now entered a “post-suburban” era
of development. See Rob Kling, Spencer Olin, and Mark Poster, Postsuburban California:
The Transformation of Orange County Since World War II, (Berkeley: University of
California Press, 1995).
¹⁴Ib i d .
¹⁵See General Accounting Office, Rural Development: Patchwork of Federal Programs
Needs to Be Reappraised, GAO/RCED-94-165, July 1994.

Population Change in Non-Metro Areas During the 1990s.
Approximately 49.2 million persons resided in non-metropolitan areas in 2003,¹⁶
17.4% of the U.S. population. After years of little or no growth in population, rural
and small towns grew faster than suburban and urban areas in the 1970s. In the
1980s, however, this trend reversed with the 1981-1982 recession and the farm
financial crisis, and a decline in number of retirees — a major source of rural
population growth — moving to rural areas. A shift occurred again during the 1990s
when most non-metro counties either increased their growth rates, shifted from a

1980s loss to a gain, or, continued a decline, although at a somewhat reduced rate.

Population growth was highest in the Mountain West and lowest or non-existent in
the Great Plains, Mississippi Delta, and Corn Belt. Non-metro counties adjoining
metro areas accounted for almost two-thirds of all non-metro growth, increasing
about 12% on average. Much of this growth stemmed from metro residents
relocating to the adjoining non-metro areas and from other sources of immigration.
Despite this net inflow of people from metro areas, the rate of net migration into rural
areas, which had steadily increased during the early and mid-1990s, dropped to one-¹⁷
half of 1% during 1997-1999. Because many low-growth farming areas, such as
those in the Great Plains, lack the attraction of amenities such as those found in the
Mountain West or Florida, it is hard to see how they will experience future
population growth without new sources of employment.
During the 1990s, population remained stable or grew in those rural areas and
small communities able to attract jobs in the service sector, the major source of
employment growth in non-metro economies. Farm-dependent counties generally
saw little or no growth or lost population in the 1990s. Foreign immigration was the
major source of growth in the U.S. population, accounting for nearly 20% of the
national non-metro growth in the1990s. While about 83% of all residents and nearly
90% of immigrants lived in urban areas in 2003, the immigration into rural and
agricultural areas may be more socially significant than these broad data might
suggest.
Immigration is important to farming, meat packing, and textiles; and immigrant
professionals, e.g., physicians, also play an increasingly important role in many rural
areas. Much of labor-intensive agriculture is located in the South and in
geographically large western counties classified by the census as metropolitan areas.
Crop production, fruit and vegetable farming, and meat packing industries are reliant¹⁸
on hired farm workers. Hispanics comprised 42% of hired farm workers in 2002.
Some are new immigrants from central Mexico and non-Spanish speaking Indians
of southern Mexico and Guatemala. In the Upper Midwest, Mexicans and Mexican-
Americans from the Rio Grande Valley along with a few middle-class Cubans and
Puerto Ricans and other Latin Americans also reside. The majority of Hispanic
immigrants in the Upper Midwest arrived to work in the region’s new and expanding

¹⁶USDA Economic Research Service calculation from Census of Population data, U.S.
Bureau of the Census.
¹⁷John Cromartie, “Non-metro Migration Drops in the West and Among College Graduates,”
USDA-ERS, Rural Conditions and Trends, 11(2), December 2000.
¹⁸Jack Runyan, “Farm Labor: Demographic Characteristics of Hired Farmworkers,” USDA-
ERS Briefing Room, 2003, [http://www.ers.usda.gov/Briefing/FarmLabor/Demographics/].

swine and turkey processing plants. In the Lower Midwest, many immigrants took
jobs in the meat packing plants.¹⁹
The Declining Opportunity Structure of Remote
Rural Areas of the Great Plains
Population loss throughout much of the rural and farming dependent areas of the
Great Plains region has been persistent and continual for over 50 years. Out-
migration of young residents and lower fertility rates of those who remain have led
not only to population loss, but also to the increased proportion of the aging in the
population remaining. When low population densities are added to this demographic
mix, a picture of a slowly declining region emerges (see Table 2). The average
population in the remote counties in all of the states but Montana had negative
growth rates between 1990 and 2000 (Table 2). All the states but Montana had²⁰
growth rates below the national average of approximately 13%. Not only did North
Dakota’s remote counties have the lowest growth rate among the 7 states, the state
average was negative as well. Population densities in the region, as measured by
population per square mile, average less than a tenth of the average non-metro county
average of slightly over 36 persons per square mile.

¹⁹For an overview of rural population trends, see [http://www.ers.usda.gov/Briefing/
Population].
²⁰ A recent analysis of rural population growth rates showed that rates varied greatly across
counties and across decades. Interestingly, the research showed that only about 20% of the
variance in population growth could not be attributed to state- or national-level variables,
leaving nearly 80% of the variance that must be explained by variables that vary across or
within counties. See Tzu-Ling Huang, P. Orazem, and D. Wohlgemuth, “Rural Population
Growth, 1950-1990: The Roles of Human Capital Industry, Structure, and Government
Policy,” American Journal of Agricultural Economics 84, no. 3 (2002), pp. 615-627.

Table 2. Great Plains Population
Average Average Ave r a ge Average Average
Re mo t e Re mo t e St at e Re mo t e St at e
County County Population County Population
Population, Population Cha n ge , Population Density
2000 (1)Change,1990-2000Density(population

1990-2000(%)(2)(populationper sq.mi)

(%)per sq.mi)(3) (4)
K a nsas 3,173 -5.10 8.5 4.0 32.9
Montana 6,120 2.6 12.9 2.3 6.2
Nebraska 2,825 -6.1 8.4 2.7 22.3
Nor t h 3,768 -13.0 -1.2 3.5 9.3
Dakota
Oklahoma 4,281-10.09.74.050.3
South 4,125 -0.4 8.5 3.2 9.9
Dakota
T e xas 3,554 -1.0 22.8 2.8 79.6
^Sources^:^C^eⁿ^s^u^s^{2000, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^S^D^A^-^E^con^o^mⁱ^{c R}^e^s^earch^S^e^rvⁱ^c^e^;^B^u^reau^of
^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.
^(1)R^em^{ote cou}ⁿ^ties^{are def}ⁱⁿ^e^{d as}^th^os^{e w}^ith^couⁿ^t^y^popu^lation^den^s^ities^of^{6 or f}^e^w^e^{r pers}^on^s^per
^sq^ua^r^e^mⁱ^l^e^aⁿ^d^oⁿ^t^h^e^fa^r^eⁿ^d^o^f^a^r^u^r^a^l^-^ur^b^a^{n c}^o^ntⁱ^nuum^sc^a^l^e^aⁿ^d^a^sc^a^l^e^m^e^a^s^urⁱ^{ng t}^h^e^d^e^gr^e^e^o^f
^u^r^banⁱⁿ^f^l^u^eⁿ^{ce on}^{a ru}^{ral area. (See A}^ppen^d^ix^A⁾^.
^(2)A^v^e^rag^e^U^.^S^.^Popu^l^a^tⁱ^oⁿ^C^h^an^g^e^{, 1990-}^{2000 i}^s^13.1%
^(3)A^v^e^rag^e^U^.^S^.ⁿ^oⁿ^-^m^e^t^r^o-^couⁿ^t^y^popu^l^a^tⁱ^oⁿ^den^sⁱ^t^y^{(2000) i}^s^{36.3 pers}^on^s^{per s}^q^u^a^{re m}ⁱ^l^e
⁽⁴⁾^Iⁿ^clu^d^{es a state’}^{s m}^e^tr^o^p^o^litan^ar^eas.
Persons aged 65 and over comprise about 12% of the total U.S. population. For
the remote counties of the Great Plains region, this age group makes up 16-21% of
the counties’ population (see Table 3). A proportionately higher elderly population
produces significant challenges for rural areas, perhaps most important is providing
health care services. The migration from rural areas also displays distinctive patterns.
The number of non-metro counties with decreasing population rose from 600 from
1990-1995 to 855 in 1999 suggesting that there may be growing momentum for
population loss.²¹ Much of this more recent increase in rural out-migration (1997-
1999) occurred among college graduates, with those moving out in numbers nearly
equal to those moving in for the first time since the early 1990s.²² Whether this
pattern also characterized the remote counties is not clear. Average state high school

²¹Ibid., p.29.
²²See John Cromartie, “Non-Metro Migration Drops in the West and Among College
Graduates,” USDA-ERS, Rural Conditions and Trends, 11(2), December 2000; Daniel
Lichter et al., “Migration and the Loss of Human Resources in Rural Areas,” in Investing
in People: The Human Capital Needs of Rural America, ed. L. J. Beaulieu and D. Mulkey
(Boulder: Westview Press, 1995).

graduation rates in the Great Plains states, with the exception of Texas, exceed the
national average. For remote rural counties, the percentage of high school graduates
is somewhat lower in North Dakota, South Dakota, and Texas than either their state
or national averages. For those with bachelors degrees or higher, the Great Plains
states are slightly below national averages while their remote counties are
substantially below both averages.
Table 3. Great Plains Age and Education Structure, 2000
^A^v^erage^A^v^erage^A^v^erage^A^v^erage^A^v^erage^A^v^{erage S}^t^at^e
^Rem^o^te^Sta^t^e^Rem^o^te^Sta^t^e^Rem^o^te^Bach^el^o^r^s
^C^ounty^P^opula^tion^C^ounty^Hⁱ^g^h^-^High^-^C^ounty^de^g^r^e^e^or^hig^h^e^r
^P^opula^tion^{65 a}ⁿ^d^Sc^hool^Sc^hool^Bach^el^o^r^s^(%⁾
^{65 a}ⁿ^d^olde^r⁽^%⁾^g^r^a^dua^te^s^,²⁵^g^r^a^dua^te^s^,^de^g^r^e^e^or
^olde^r⁽^%⁾^a^{nd olde}^r⁽^%⁾^{25 a}ⁿ^d^hig^h^e^r
^olde^r⁽^%⁾^(%⁾
^K^a^ns^a^s^21.2^13.3^84.1^86.0^17.0^25.8
^Montaⁿ^a^16.4^13.4^83.1^87.2^17.9^24.4
^N^e^br^a^s^k^a^20.1^13.6^86.7^86.6^16.0^23.7
^No^rth^21.3^14.7^77.3^83.9^14.5^22.0
^Dako^ta
^O^k^la^hom^a^20.0^13.2^80.8^80.6^17.5^20.3
^South^17.6^14.3^78.2^84.6^14.4^21.5
^Dako^ta
^T^e^x^a^s^16.9⁹^.9^69.6^75.7^15.5^23.2
^Unite^d^12.4^80.4^24.4
^S^t^at^es
^Sources^:^C^eⁿ^s^u^s^{2000, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^{, U}^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^rce.
Based on per capita income levels, many of the poorest counties in the United²³
States are in very rural, farming-dependent counties. Only one county among the
poorest 50 counties is a metropolitan county. Eleven of the 20 poorest counties in
the United States are located in remote counties in Nebraska, and North and South

²³Based on U.S. Bureau of the Census measures of poverty, farm-dependent counties (see
definition, page 35), while still having relatively high rates of poverty (an average of 15.7%
in 1999), saw the largest decline in county poverty rates of the various ERS county
economic classifications from 1989-1999, probably due to relatively larger farm payments
and relatively strong national economy. The Census measures the poverty rate by
establishing poverty thresholds, i.e., the dollar amounts used to determine poverty status.
Each person or family is assigned one out of 48 possible poverty thresholds. Thresholds
vary according to size of the family and ages of the household members. The same
thresholds are used throughout the United States and updated annually for inflation using
the Consumer Price Index for all urban consumers.

Dakota (see Table 4). Other high-poverty counties are located in the South and
Appalachia and in areas where there is a high proportion of racial minorities.²⁴
Table 4. Poor Great Plains County Rankings (1999)
County/State Rank Rur a l - Ur ban Ur ban
(1) ContinuumInfluencePer Capita Income
Code (2)Code (3)(4)(5)
Loup, Nebraska199$4,896
McPherson, 2 9 9 $ 6,940
Nebraska
Keya Paha,599$9,993
Nebraska
Ziebach, South699$10,390
Dakota
Arthur, Nebraska799$10,655
Todd, South1099$10,920
Dakota
Sioux, North1199$11,023
Dakota
Sioux, Nebraska 1299$11,147
Shannon, South1378$11,351
Dakota
Blaine, Nebraska1699$11,576
Slope, North1999$12,097
Dakota
^Source:^Bu^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^;^U^S^D^A^Econ^omⁱ^c^R^e^s^earch^S^e^rvⁱ^c^e
⁽¹⁾^R^aⁿ^kⁱ^s^am^on^g^t^h^{e 3,141 cou}ⁿ^tⁱ^e^sⁱⁿ^t^h^{e N}^a^tⁱ^oⁿ^,^wⁱ^t^h^{1 bei}ⁿ^g^t^h^{e cou}ⁿ^t^y^wⁱ^t^h^t^h^{e l}^o^w^e^s^t^{per capi}^t^a
ⁱⁿ^co^m^e^.
^{(2) (3) See A}^ppen^d^ix^A^f^o^{r a dis}^c^u^s^sⁱ^on^of^th^es^{e ER}^{S s}^cales
^{(4) U}^.^S^.^{per capi}^t^aⁱⁿ^com^e^{, 1999=$28,543}
^{(5) N}^oⁿ^-^m^e^t^r^{o per capi}^t^aⁱⁿ^com^e^{, 2000=$19,850}
Average poverty and unemployment rates are higher in rural areas than in urban
areas. The non-metro poverty rate declined from a high of 17.1 in 1993 to a record
low of 13.4 in 2000. By 2001, the non-metro poverty rate had increased to14.2%
while for metro areas the rate was 11.1%.²⁵ The remote rural counties of the Great
Plains often had higher rates of poverty than non-metro areas as a whole and

²⁴Dean Joliffe, “Non-Metro Poverty: Assessing the Effects of the 1990s,” Amber Waves,
USDA-ERS, June 2003.
²⁵Ib i d .

substantially lower median household incomes (see Table 5). It should be noted,
however, that North Dakota, South Dakota, Oklahoma, and Montana are also home
to significant Native American populations, who rank among the poorest in the
nation, with unemployment rates often exceeding 50% on some reservations.
Table 5. Remote County Household Income, Poverty, and
Unemployment Rates, 2000-2001
^Me^diaⁿ^Me^diaⁿ^A^v^erage^A^v^erage^A^v^erage^A^v^{erage S}^t^at^e
^Rem^o^te^Sta^t^e^Rem^o^te^Sta^t^e^R^e^m^o^te^C^ounty^Uⁿ^e^m^ploy^m^e^nt
^C^ounty^H^ous^e^hold^C^ounty^Po^v^e^r^t^y^Uⁿ^e^m^ploy^m^e^nt^{Rate (%}⁾
^H^ous^e^hold^In^co^m^e^Po^v^e^r^t^y^{Rate (%}⁾^{Rate (%}⁾⁽²⁾
^In^co^m^e^Rate(%⁾⁽²⁾
⁽¹⁾
^K^a^ns^a^s^$^32,856^$^40,624^11.3⁹^.9^2.6⁴^.3
^Montaⁿ^a^$^29,426^$^33,024^17.4^14.6⁴^.8^4.6
^N^e^br^a^s^k^a^$^29,241^$^39,250^13.9⁹^.7^2.5³^.1
^No^rth^$^29,169^$^34,604^15.0^11.9³^.4^2.8
^Dako^ta
^O^k^la^hom^a^$^30,889^$^33,400^13.7^14.7²^.6^3.8
^South^$^28,010^$^35,282^22.3^13.2⁵^.1^3.3
^Dako^ta
^T^e^x^a^s^$^29,569^$^39,927^18.4^15.4³^.6^4.9
^Unite^d^$^41,944^13.4⁴^.8%
^S^t^at^es
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^bor,^Bu^reau
^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^earch^S^e^rv^{ice; U.S. Departm}^eⁿ^t^of^C^o^m^m^e^{rce, B}^u^reau^of
^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.
^{(1) U.S. av}^erag^{e, 12.4%}
^{(2) T}^h^es^{e dat}^a^{are f}^o^{r 2001}
Human Capital Issues in Remote Rural Areas
Human capital refers generally to the level of education and training of a defined
group (e.g., population or labor force) and is important because of the direct²⁶
relationship between educational attainment and earnings. The demand for workers
with at least some postsecondary education has been increasing in recent decades and²⁷
is projected to rise at an above average rate in coming years. Compared to metro
areas, rural areas are chronically short of human capital.²⁸ As Table 3 shows, remote

²⁶See CRS Report 95-1081, Education Matters: Earnings by Highest Year of Schooling
Completed.
²⁷See CRS Report 97-764, The Skill (Education) Distribution of Jobs: How Is It Changing?
²⁸Lief Jenson and D. McLaughlin, “Human Capital and Non-metropolitan Poverty,” in L.
J. Beaulieu and D. Mulkey (eds.), Investing in People: The Human Capital Needs of Rural
(continued...)

counties in the Great Plains have many fewer residents with a bachelors degree than
the nation as a whole. While the national average for a bachelors degree or higher
is 24.4%, the metropolitan rate is higher still at 29.1%. For remote rural counties in
the Great Plains, the average is about 16%, about the same as the U.S. average for all
non-metro counties in 2000. High-school graduation rates in non-metro areas have
improved over the decade. Data from the Census of Population, calculated by
USDA’s Economic Research Service, showed that in 2000, only 23.2% of persons

25 and over had less than a high school education, down from 31.2% in 1990.

It would be misleading to attribute the economic problems of remote rural
economies to a comparatively low-skilled population alone. As Table 5 shows, the
unemployment rates in remote counties, with the exception of South Dakota, are
actually lower than the national average. These relatively low unemployment rates
suggest that rural workers may suffer more from low-wage employment and
underemployment than they do from unemployment. This in part, reflects the types
of industries that dominate rural areas: peripheral manufacturing, extractive
industries, and low-wage service sector jobs.²⁹ Even highly skilled rural workers
however, earn lower wages than their urban counterparts.³⁰ In 2000, the percentage
of non-metro adults 25 and older with a high school diploma was higher in non-metro
areas than in metro areas (35.5% vs. 26.9%), although only 15.5 percent of non-metro
adults 25 and older held bachelors degrees compared to 26.6 of metro residents 25
and older.³¹ According to data from the Rural Sociological Society Task Force on
Rural Poverty, at every level of education, average earnings and income are lower in
non-metro than in metro areas.³²
The low reported unemployment rates could also suggest that, due to limited
opportunities, rural workers have dropped out or never entered the workforce and are,

²⁸(...continued)
America (Boulder, CO: Westview Press, 1995).
²⁹Earnings, income, rates of poverty, education and training, etc., are factors both of the
characteristics of the labor force and of an area’s industrial structure. They can be analyzed
separately, but the economic characteristics of an area result from both the organization of
labor supply and the economic structure of labor demand within a region.
³⁰Some research suggests that this poses a paradox in regard to public policies aimed at
raising the level of human capital in rural areas. A times series analysis of rural areas
showed that rural population growth was affected most by improvements in human capital
stock over time. Because urban returns to education appear to be higher than those of rural
areas, increasing the rural human capital stock actually decreased the working-age
population, largely because more educated labor moved elsewhere. See Tzu-Ling Huang,
P. Orazem, and D. Wohlgemuth, “Rural Population Growth, 1950-1990: The Roles of
Human Capital Industry, Structure, and Government Policy,” American Journal of
Agricultural Economics 84, no. 3 (2002), pp. 615-627.
³¹Robert Gibbs, “Rural Labor and Education: Rural Education,” USDA-ERS Briefing Room,

2003. See [http://www.ers.usda.gov/Briefing/LaborandEducation].

³²Data were based on Bureau of Economic Analysis personal income data adjusted for
inflation with the implicit price deflator for personal consumption expenses. See Rural
Sociological Society Task Force on Persistent Rural Poverty, Persistent Rural Poverty in
Rural America (Boulder, CO: Westview Press, 1993).

accordingly, not officially counted as unemployed. This is undoubtedly true, but
such bias in measuring unemployment is likely equally true for metropolitan areas
as well, even though the composition of available employment differs. The non-
farm employment change in remote counties shown in Table 6 gives a picture of a
slow growth area relative to the state as a whole. This could be discouraging to
would-be workers choosing areas to which they might migrate. There is also
evidence to suggest that some rural labor market groups, such as underemployed
workers and discouraged workers, respond less to business cycle movements.
Therefore, an expansion may be less likely to benefit these individuals in rural areas
than in urban areas.³³ Evidence, however, is lacking that unemployment counts in
rural areas are any less or any more accurate than those for metro areas.
One substantive implication seems clear: rural people may suffer less from
unemployment than from myriad forms of underemployment, e.g., working less than
full time.³⁴ While average rates of high school graduation increased in rural areas
over the 1990s, earnings per job did not. The inflation adjusted rural-urban earnings
gap (as opposed to total income) was over 30% greater in 1995 than it was in 1977.³⁵
There are also other possible reasons for this gap including the lower likelihood of
non-metro workers moving out of low wage jobs than central city residents, greater
involuntary part-time work among non-metro workers, higher proportion of non-
metro workers in minimum wage jobs (12% vs. 7%), and higher rates of
underemployment and unemployment among women compared to metro areas.³⁶
Some recent research also suggests that an increased demand for unskilled, largely
Hispanic labor may have contributed to lower wages for skilled workers (largely men
with high school education) in some rural areas. Results from this research indicated

³³See Jill L. Findies and L. Jensen, “Employment Opportunities in Rural Areas: Implications
for Poverty in a Changing Environment,” American Journal of Agricultural Economics 80
(1998), pp. 1000-1007.
³⁴Ib i d .
³⁵ Douglas Rhoades and Mitch Renkow, “Explaining Rural-Urban Earnings Differentials in
the United States,” paper presented at the Annual Meeting of the American Agricultural
Economics Association, Salt Lake City, Utah, 1998, American Journal of Agricultural
Economics 80 (5), p. 1172. A study of rural and urban North Carolina counties also showed
that rural areas had both lower rates of return to schooling and a greater sensitivity of
earnings to local labor market conditions than urban counties, although national
macroeconomic trends had the dominant impact on both metro and non-metro counties. See
Mark Renkow, “Rural versus Urban Growth: Why Do Rural Counties Lag Behind?” Center
for Regional Development, North Carolina State University, 1995 ([http://www.ces.ncsu.
edu/resources/economics/crdnews/]).
³⁶See Jill L. Findies and L. Jensen, “Employment Opportunities in Rural Areas: Implications
for Poverty in a Changing Environment,” American Journal of Agricultural Economics 80
(1998), pp. 1000-1007; Jill L. Findeis, “Gender Differences in Human Capital in Rural
America,” in Lionel Beaulieu and David Mulkey (eds.), Investing in People: The Human
Capital Needs of Rural America (Boulder, CO: Westview Press, 1995); Tim Parker and
Leslie Whitener, “Minimum Wage Legislation: Rural Workers Will Benefit More than
Urban Workers from Increase in Minimum Wage,” USDA-ERS, Rural Conditions and
Trends 8 (1), 1997.

that increased labor demand favored skilled and professional workers overall but
favored unskilled workers in some rural industries, e.g., meatpacking.³⁷
Real non-metro per capita income (in 1996 dollars) — as opposed to earnings
alone — increased 2.4% between 1995-96 compared to 2.1% in metro areas. The
ratio of non-metro to metro income improved from 71.2% in 1995 to 71.4% in
1996.³⁸ Table 6 shows that the average wage per non-farm job in remote counties
is substantially lower than the average for their respective state’s as a whole and that
remote county per capita income change was substantially lower than for their
respective states with the exception of North Dakota. Moreover, while per capita
income grew by over 21% from 1990-2000 in the United States as a whole, per capita
income grew in Great Plains remote counties on average by only about 5%.
Table 6. Remote County Employment Structure
^Re^m^o^te^State^Re^m^o^te^A^v^erag^e^A^v^erag^{e R}^e^m^o^te^A^v^erag^{e State P}^e^r
^Co^unt^y^A^v^erag^e^Co^unt^y^State^Co^unty^P^e^r^Ca^p^ita^Cap^{ita I}ⁿ^co^m^e
^A^v^erag^e^Wa^g^e^pe^r^P^r^iv^{ate Non}^-^Prⁱ^v^a^t^e^In^com^e^C^h^an^g^e^,^C^h^an^g^e^{, 1990-}²⁰⁰⁰
^Wa^g^e^pe^r^no^n-Fa^r^m^Far^m^Noⁿ^-^Farm^1990-^{2000 (%}⁾^(%⁾
^no^n-Fa^r^m^{Job, 2000}^Em^pl^oy^m^eⁿ^t^Em^pl^oy^m^eⁿ^t
^{Job, 2000}^Cha^nge^,^Cha^nge^,
^1990-¹⁹⁹⁹^1990-¹⁹⁹⁹
^(%⁾^(%⁾
^K^aⁿ^s^as^$^19,094^$^29,360^31.2^18.4^-^4.6^14.3
^Mon^t^an^a^$^20,111^$^24,274^19.1^30.0⁴^.6^10.2
^N^e^bras^k^a^$^17,842^$^27,692^17.5^25.0^-^11.1^16.0
^N^o^rt^h^D^a^k^o^t^a^$^20,777^$^24,683^22.8^27.3^22.7^18.2
^O^k^l^a^h^o^m^a^$^20,928^$^26,988^12.7^24.5⁰^.7^10.8
^S^o^u^t^h^D^a^k^o^t^a^$^19,194^$^24,802^32.0^37.2^17.1^21.4
^T^e^x^a^s^$^22,540^$^34,941^26.2^32.4⁶^.0^20.7
^Uⁿⁱ^t^e^{d S}^t^at^es^$^35,323^18.4^21.3
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^bor^S^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^,
^Econ^om^{ic R}^e^s^earch^Serv^{ice; U.S. Departm}^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.
Although low-wage employment is not unique to rural areas, it does make up
a significant portion of all rural jobs. In a detailed examination of low-wage rural
employment, USDA’s Economic Research Service identified 465 counties they

³⁷ Constance Newman, Impacts of Hispanic Population Growth on Rural Wages, USDA.
AER-826, September 2003.
³⁸Linda M. Ghelfi, “Rural Per Capita Income Grows Slightly More than Urban,” USDA-
ERS, Rural Conditions and Trends 9 (2), 1997.

defined as low-wage counties.³⁹ A county was defined as low-wage if it fell into the
top quintile of rural counties ranked by the share of wage and salary workers in low-
wage industries. While federally-defined poverty, unemployment, and population
growth rates in low-wage counties did not differ significantly from other rural
counties, low-wage counties were characterized by a different mix of jobs. Industries
that tend to pay well on average are less likely to be located in low-wage counties and
jobs pay less on average than similar jobs elsewhere. With less diverse economies
to begin with, employers in low-wage counties also have little competition in setting
prevailing wage rates. Unsurprisingly, low-wage counties have relatively small
numbers of workers and a larger proportion of older, less educated workers. These
factors, coupled with out-migration of younger, higher skilled workers, make it
difficult to attract the more technologically advanced production sectors that might
improve both the wage structure and the level of work skills.
The northern Great Plains region has the largest cluster of low-wage counties.
Nearly half are located in North and South Dakota and Nebraska. Low-wages and
small per capita income growth characterize both low-wage counties and remote
counties as defined here. Counties remote from metro centers and with small pools
of skilled workers are not positioned to attract employers who need access to
suppliers and customers. The absence of skilled workers is also reflected in the lack
of economic diversity in low-wage counties. Nearly half of the 465 low-wage
counties analyzed by ERS are also classified in the county typology in Appendix A,
Table 7 as “farming-dependent.” Most of these are also located in the northern Great
Plains region. This is not to imply that farm-employment per se is the source of the
low-wages. Rather, farm-dependent counties may be low-wage because they are
remote, have low population densities, and/or have been unable to diversify their
economies outside the traditional agriculture economy. Few low-wage counties are
dependent on manufacturing which generally pays higher wages in rural counties
compared to other rural employment options.⁴⁰ Residents of remote counties without
diverse economies simply have very limited opportunities to improve education and
skill levels beyond that required for rudimentary, entry level jobs. The primary
economic base of remote rural areas — farming/ranching, mining, forestry, and oil
and gas extraction — is also prone to boom-bust cycles that not only make earning
a living difficult, but may also help explain the frequent out-migration.
Six of the 10 industries with the largest share of employment in low-wage
counties were classified as low-wage industries. The 10 largest low-wage industries
all have greater shares of employment in low-wage counties than in other non-metro
counties. Lower wages also exist within each industry so that the same job pays less

³⁹In the study’s sample, at least 41% of all workers in these 465 counties were employed in
industries paying average wages that would not lift a full-time, full-year worker above the
weighted-average poverty threshold for a family of four ($15,569 in 1995). Average wages
were calculated for each three-digit Standard Industrial Code (SIC) industry in each county
rather than assuming a single average for each industry. See Robert Gibbs and J. B.
Cromartie, “Low-wage Counties Face Locational Disadvantages,” USDA-ERS, Rural
Conditions and Trends 11 (2), 2000.
⁴⁰The low-wage counties that are dependent on manufacturing tend to be located in the rural
South. Similarly, all mining-dependent low-wage counties are in the West.

in a low-wage county than in other non-metro counties. For example, medical
doctors and health care personnel in low-wage counties earn on average 28% less
than comparable workers in rural medical clinics elsewhere. Appendix B presents
data on the top 25 industries in rural counties and compares low-wage counties with
other rural counties.⁴¹
While many remote counties are poor and low-wage, the dynamics that drive
these two processes are not necessarily the same. Of the 465 counties that ERS
identified as low-wage, only about one-third were persistently poor and only about
one-third of the persistently poor counties were low-wage.⁴² This relation suggests
that the underlying economic and social conditions associated with significant
poverty and low-wages may differ. If low-wage earnings employment were the sole
source of poverty, one would expect low-wage counties to have higher poverty rates,
on average, than non low-wage counties. Conversely, one might expect persistently
poor counties to be mostly low-wage counties. While it is substantially true that the
economies of rural America were historically grounded in a manufacturing and
agricultural base that produces large numbers of low-wage jobs, changes over the last
50 years have resulted in fewer agricultural jobs and more in services. Still, these
jobs pay lower wages on average than similar sectoral employment in urban areas.
In the Great Plains region, remote, low-density counties are low-wage counties.
The Great Plains region and, to some extent, the greater Midwestern region, also
depends heavily on large-scale, capital-intensive agricultural production. Thirty of
the 242 remote rural counties are in the leading 100 U.S. counties in total acreage in

⁴¹Some observers point to cost-of-living differences between rural and urban areas to
account for earnings differentials or to account for price differences when comparing
standards of living between geographic areas. Conceptual and measurement issues in
developing indexes that might estimate geographic cost-of-living are significant. Commonly
accepted measures such as Bureau of Labor Statistics Family Budget Studies, the American
Chamber of Commerce Researchers Association Cost of Living Index, and the Bureau of
Labor Statistics city Consumer Price Indexes generally focus on metropolitan areas. Rural
areas, in particular, are excluded from these calculated measures. See Laura A. Blanciforti
and Edit Kranner, “Estimating County Cost of Living Indexes: The Issue of Urban versus
Rural,” Research Paper 9718, West Virginia Regional Research Institute, 1997. For an
analysis of the conceptual and measurement complexities of cost-of-living indexes, see
National Academy of Sciences, At What Price: Conceptualizing and Measuring Cost-of-
Living and Price Indexes (Washington, D.C.: National Academy Press, 2002). One study
of rural and urban residents in Wisconsin concluded that metro and non-metro households
spent about the same on such essentials as food, clothing, transportation, utilities, and
medical care (although in the National Academy of Sciences study cited above, constructing
an index for medical care proved more difficult than any other component of the Consumer
Price Index). Non-metro residents lived on less income largely because they were
disproportionately elderly and had higher concentrations of households with paid-up
mortgages than metro areas. See Linda M. Ghelfi, “About That Lower Cost of Living in
Non-Metro Areas,” USDA-ERS, Rural Development Perspectives, October 1988.
⁴²“Persistently poor” counties are defined by ERS as those counties with 20% or more of
their population with poverty level incomes in each of four years 1960, 1970, 1980, and

1990.

cropland.⁴³ Policies that have largely targeted farm household income have not
produced an economic reversal in this region. The evidence of demographic and
socioeconomic trends in the Great Plains suggests to many observers that a
continuing reliance on commodity agriculture to the exclusion of other, better paying
employment, may be a formula for continuing population out-migration, fewer
service and retail centers, and declining living standards for many rural households
in the non-farming sector. With significant changes occurring as well in the structure
of agriculture leading to further concentration in production, non-farm employment,
and closer ties between population centers and rural areas, more attention to regional
solutions may hold greater promise for the Great Plains in the intermediate term than
the traditional state or county-based solutions alone.
Federal Funding in the Great Plains
This section relies on 1995 Bureau of the Census data generated by USDA’s
Economic Research Service.⁴⁴ The data focused on funding for 750 federal programs
that were traceable to the county level. These programs accounted for 88% of all
federal funds including funding to individuals, to businesses, and to public entities.
Based on an examination of all federal funds received in FY1995, the Great Plains
received more federal funds, per capita, than the country as a whole. (The Great
Plains region as defined in the ERS report, in addition to the 7 states discussed in this
report, also includes parts of eastern Wyoming, Colorado, New Mexico and western
Minnesota). Per capita funds were 10% higher in the region ($5,447) than in the
Nation as a whole ($4,973). Most of these funds were direct payments to individuals,
e.g., Social Security and disability, farm subsidies, salaries, wages, and procurement
contracts. Compared to the Nation as a whole, the region gets relatively more
funding from such programs as agriculture and natural resource payments, defense
and space, and community resource programs. While retirement and disability
payments in the Great Plains account for slightly less than the Nation as a whole
(32% vs. 34%), these payments are significant to local economies because of the
relatively high percentages of elderly and disabled in the non-metro population (See
Table 4 above and Appendices C-I)).
Non-metro counties rather than metro counties accounted for the greater levels
of federal funding for the Great Plains as compared to the national average. Federal
funding to non-metro counties in the Great Plains was 19% more per capita than for
non-metro counties nationally and was 8% more per capita for Great Plains’ metro
counties compared to all metro counties. This difference in non-metro funding is
explained by the relatively high level of payments to individuals in farm-dependent
counties. Over half (277) of the 477 Great Plains counties were farm-dependent. The
per capita annual government payments to these farm-dependent counties was
$6,196. The 26 ERS-defined government-dependent counties and the 62 persistent

⁴³U.S. Census of Agriculture, Ranking of States and Counties, vol. 2, part 2, 1997.
⁴⁴ See Rick Reeder, F. Bagi, and S. Calhoun, “Which Federal Programs Are Most Important
for the Great Plains?” USDA-ERS, Rural Development Perspectives, vol. 13. no. 1, June

1998.

poverty counties received $6,462 and $5,886 per capita, respectively.⁴⁵ The relatively
few non-metro counties receiving low federal payments per capita tended to be (1)
near or adjacent to metro counties, (2) specialize in mining, and (3) have little
farming or were involved mostly in ranching operations.
Aside from retirement, disability, wages, salaries, and contracts, non-metro areas
of the Great Plains are distinguished in their receipt of “other” direct payments,
which include farm payments. Nationally, “other” direct payments to non-metro
counties account for only 2% of federal funds and direct loans only about 4% of
federal funds. For Great Plains non-metro counties, “other” direct payments account
for 10% of funds and direct loans account for 7% of federal receipts. Farm-
dependent counties in the Great Plains receive 17% of their total federal funds from
non-farm “other” direct payments, and 12% from direct loans (farm and non-farm).
Federal funding in the Great Plains region may raise questions about the
effectiveness of certain rural development strategies or human capital improvements
in the region. The region receives higher per capita funding than the nation as a
whole for elementary and secondary education, for higher education, and for
agricultural and natural resource programs. Much of this latter funding goes to farm-
dependent counties which are also disproportionately remote counties. Yet,
socioeconomic data indicates that, despite these programs, there has not been a strong
positive effect in improving the social welfare for most of the residents of these
counties (See Appendices C-I). There are a number of possible explanations. In
other rural economies outside the Great Plains, the agricultural sector may be
important, but it is situated in an overall economy that is more diverse, thus the
agricultural income and employment multiplier effects tend to be diluted in the local
economy. In the Great Plains region, direct federal payments for agriculture and
community resources are significant within the local economy, even more so in
remote counties.⁴⁶ Yet, this funding, as an income transfer, appears to have a
generally weak effect in building local economic development capacity. Given that
farm payments in the late 1990s provided as much as 60% of farm household
incomes in some areas of the United States, one can clearly understand their
importance to certain families. Because the populations are so small in remote Great
Plains counties, however, a few farm households receiving disproportionately higher
direct farm payments may give a very misleading impression about per capita income
and earnings for the county as a whole. The fact that farming-dependency,
remoteness, and poverty are intertwined within a particular county may suggest a
distinctive confluence of socioeconomic forces that limit the development potential
of these areas.
Federal payments make up a relatively significant share of personal income in
the Great Plains. There are also few employment alternatives in the private sector to

⁴⁵See footnote 9, above. The ERS county classification defines “government-dependent”
counties as those where at least 25% of total county income comes from government. See
Appendix A for ERS county profiles.
⁴⁶Community resource funding supports economic development, community facilities,
environmental protection, housing, and transportation. Compared with non-metro counties
nationally, non-metro Great Plains counties receive 32% more in community resources..

replace the exodus of jobs from agriculture and mining. Non-metro counties in
general and remote counties of the Great Plains in particular have relatively high
proportions of elderly and disabled residents, making the federal funds they receive
significantly more important to rural residents and communities. Changes in
Medicare, federal transportation policies, or agricultural/natural resource programs
may, in the absence of employment alternatives, may have a significantly greater
effect in the Great Plains than in other regions.
Nationally, the counties with the highest job growth in the 1990s were service-
based and government based counties. From 1995-1998 these counties grew at a
rate of approximately 1.2%. Mining-dependent counties grew at about 0.7% and
manufacturing-dependent counties grew at approximately 0.5% during that period.
Job growth was about 0.25% in farm-dependent counties. While the number of farm-
dependent counties has decreased nationally from over 60% of non-metro counties
in 1950 to about 15% in 2000, most of the counties that remain dependent on
agriculture are in the Great Plains region, along with Minnesota and Iowa.
Policy Options for Remote Rural Areas
of the Great Plains
Current strategies for rural economic development in the Great Plains are not
substantially different from the strategies used in other rural regions. Given the
distinctiveness of the problems facing remote rural areas in the region, the
effectiveness of current government policies is an important question. It might be
impractical to suggest that the tremendous diversity characterizing rural America also
implies that separate federal policies should be created for each distinctive rural area.
The range of existing federal loans and grants designed to create or support small
businesses, improve rural infrastructure, and address the most pressing needs of low-
income populations in housing, sewerage, and water have made important
contributions to rural residents. One issue that Congress may ultimately face is
whether such programs will provide needed economic stimulus in an
internationalized economy where U.S. rural areas not only compete with each other,
but increasingly with foreign countries, whose labor costs, land, and various
regulatory structures may give them advantages.
Virtually any state can point to numerous small-scale, public and private sector
rural economic development initiatives achieving notable successes within their
borders. New jobs have been created, existing jobs retained, worker skills upgraded,
new infrastructure built, and small rural communities revitalized based on a mix of
agriculture, small-businesses, public service employment, and entrepreneurial
activity. A larger number of remote rural communities, however, either are only
holding their own or losing ground in terms of quality of life that the area’s existing
economic structure can provide. While their socioeconomic characteristics and
histories are quite different, they appear to pose some seemingly intractable
development problems — intractable in the sense that the current set of federal rural
policies do not seem to create the necessary local capacities to effect a significant
change in social well-being. The remote rural areas of the Great Plains reveal these
challenging development problems, perhaps to a greater extent than any other region

of the United States. Manufacturers find remoteness a significant barrier to
relocating facilities; the growth and demand for business and professional services
is greatest in urban areas; climate and landscape in the Great Plains offer little to
encourage growth of tourism-related development. The result is very few well-paid
jobs to replace lost employment in traditional sectors.
One overarching strategy that seems to be emerging is to consider rural
economic development in regional terms. New regional development alliances are
appearing throughout the United States (discussed below). One reason they may be
doing so now is because the long-standing patterns of poverty and economic distress
that characterize the Northern Great Plains, as well as other relatively impoverished
and socially isolated areas, have not responded as successfully to the existing range
of economic development programs as have other rural areas. Second, agricultural
production, perhaps more so than in other regions, is central to the economies of
remote Great Plains counties. But the summary of research presented here suggests
that unless new initiatives in integrating agriculture and rural development strategies
are successful, even the long-standing importance of that sector may not reverse the
trends now shaping the Great Plains region. Finally, the remoteness of so much of
the Great Plains is a significant factor in frustrating even the most optimistic
development strategies.
A Continuing Role for Agriculture?
It appears that the agriculture-dependent counties of the Great Plains region
generally and remote counties in particular may have stark choices before them.
These areas may remain in agriculture because non-agricultural development
strategies have not been as successful in the Great Plains region as they have been in
other parts of the United States. This is so even though, on a per capita basis, the
counties of the Great Plains receive a relatively high level of federal funds.
Population in this area is declining because modern agriculture does not provide
high-wage jobs and there are few choices for non-farm work. What non-farm work
is available is generally low-skilled and/or pays less than comparable work in urban
areas.
Single-sector dominated economies are highly vulnerable to external shocks,
e.g., global price declines for raw agricultural commodities, cheaper sourcing sites
for timber and minerals. Even in local economies where tourism is important, shifts
in vacation destinations can damage businesses and related tax revenues. More
economically diverse areas weather macro-economic shocks better. With most U.S.
farm households heavily dependent on off-farm income, those in rural areas without
diversified economies are at an increasing disadvantage.⁴⁷ This disadvantage is
especially severe for farm-dependent counties. If one assumes that agriculture will
remain a significant economic sector in the Great Plains for the foreseeable future,
successful rural development strategies may need to consider the extent to which
agriculture can impede or promote more diverse rural economies. Rather than

⁴⁷Only approximately 13% of farm households receive more than 80% of their household
income from farming. See Ashock Mishra and M. Morehart, “Farm Families’ Savings:
Findings from the Arms Survey,” Agricultural Outlook, April 2002.

focusing largely on policies that aim at bulk production of agricultural commodities,
policymakers may begin to evaluate non-traditional ways in which agriculture might
contribute to healthy rural economies. In many areas of the country, it may be the
case that a healthy non-farm rural economy will become the most effective means of
maintaining communities and the future sustainability of the farm population. Rural
development researchers and others observe that because less than 2% of rural
residents identify farming as their primary occupation, efforts to stimulate economic
development through agriculture may not directly address the large majority of the
population who have few if any substantial ties to agriculture. The Great Plains
might arguably be one area of the United States where a greater emphasis on non-
farm policies might have the effect of integrating agriculture in ways that enhance the
overall regional economy.⁴⁸
As discussed above, farming-dependent counties are disproportionately
represented among remote and low-wage/income counties. Few development
strategies that focus on the non-farm sector have had pronounced positive effects in
the Great Plains. Other variables limit the economic development opportunities of
the region. First, the remoteness of the region makes it unlikely that advanced
manufacturing facilities will locate there. Many high-technology manufacturing
enterprises increasingly choose to locate near suppliers or their customer base. They
also rely on business and professional services that are almost non-existent in remote
rural areas. Second, there are fewer natural environmental amenities such as are
found in the Mountain West or in many retirement destination areas. The Plains may
hold their own beauty to many, but the climate may not be conducive to strategies
that rely on attracting and retaining high-paying manufacturing firms. With estimates
as high as 300,000 U.S. communities vying for 15,000 firms reportedly seeking to
relocate, remote areas of the Great Plains face a serious challenge.
Structural Changes in Agriculture. Changes in agriculture have led some
analysts to suggest new policy considerations for the Great Plains.⁴⁹ Long-standing
trends toward fewer, larger, and more specialized commercial farms and ranches in
the United States (horizontal integration) are well documented. Not only have these
trends been observed for many years, recent data suggest they may be accelerating
as pressures increase from global competitors and as new agricultural technologies
continue to reinforce the substitution of capital for labor. Some researchers have
argued that current trends are leading to a farm structure where 10,000 acre corn⁵⁰
farms may soon become the economically efficient size unit for that commodity.
Rapid and increasing consolidation and coordination and deepening vertical
integration in agriculture are indicators of a more fundamental restructuring occurring
in the global food and fiber system today. A growing share of commodity producers,

⁴⁸Edwin S. Mills, “The Location of Economic Activity in Rural and Non-Metropolitan
United States,” in E.N. Castle (ed.), The Changing American Countryside, (Lawrence:
University of Kansas Press, 1995), pp. 103-133.
⁴⁹Michael D. Boehlje, Steven L. Hofing, and R. Christopher Schroeder, Farming in the 21^st
Century, Staff Paper # 99-9, Department of Economics, Purdue University, 1999.
⁵⁰National Corn Growers Association, Changes in the Evolution of Corn Belt Agriculture,
February 2002.

mostly within animal production currently, are joining supply chains.⁵¹ A supply
chain is a tightly organized production system formed by agribusiness firms that, in
its most coordinated form, could potentially link each step of food production from
proprietary genetic material to the grocery shelf. Broiler production is the exemplar
of this trend. Approximately 40 firms now contract to produce 97% of all broilers.
These trends are appearing increasingly in pork production and are beginning in cash
grains.
A distinguishing characteristic of supply chains is their reliance on contractual
agreements, licenses, joint ventures, integrated ownership, and other business
arrangements with different segments of the agro-food system. These alliances with
producers may permit contracting firms to by-pass more traditional commodity
markets. To better insulate themselves from price volatility and dwindling markets,
many commodity producers are abandoning their independent operations and
adopting contract commodity production and marketing arrangements with
agribusiness firms. According to the USDA’s Economic Research Service, about
35% of the total value of U.S. agricultural production in 1998 was produced under
some form of contractual arrangement.⁵² Over half of large family farms are
involved in some form of contracting and these farms accounted for over 66% of the
total value of commodities under contract.⁵³ Over 90% of the total value of contract
production was in 10 commodity groups: soybeans, corn, fruit, vegetable, nursery,
cotton, cattle, hogs, poultry, and dairy.
The growth of supply chains has implications for remote Great Plains counties
because of their potential for creating geographically specific production sectors in
agriculture that some observers have characterized as a hub, spoke, and wedge
cluster.⁵⁴ For example, a livestock-processing plant located at a hub is built near
livestock-feeding operations. These feeding operations are supplied by mills drawing
their grain and oilseed through transportation and communication spokes connecting
crop production “wedges” in the periphery. Few clusters may be needed to supply
the demand. Many farming areas that might wish to become a “hub” may not be able
to assemble the necessary capital and managerial services to do so. The
characteristics of remote rural counties of the Great Plains might make the region
compatible with large-scale animal operations. On the other hand, it is possible that
only a relatively few hubs will be economically feasible under supply chain
arrangements. Other countries, e.g., Canada, may also become increasingly
competitive as supply hubs. Some industry observers believe that under a supply
chain arrangement, for example, 50 or fewer pork producers and 12 state-of-the-art

⁵¹Mark Drabenstott, “Rural America in a New Century,” Main Street Economist, Federal
Reserve Bank of Kansas City, October 1999.
⁵² USDA-ERS, Agricultural Resource Management Study, 1998.
⁵³Ib i d .
⁵⁴Mark Drabenstott and L. G. Meeker, “Consolidation in U.S. Agriculture: The New Rural
Landscape and Public Policy,” Economic Review, Kansas City Federal Reserve, October

1999.

packing plants could, in the near future, supply the entire U.S. pork market.⁵⁵
Integrated ownership of a supply hub could also displace resources from traditional
farms and rural areas.⁵⁶
The trends toward supply chains and integrated agro-food chains may pose
problems for remote, farm-dependent areas in the Great Plains. A different kind of
agriculture, however, one that is not oriented exclusively to the production of bulk
commodities, may have some potential in revitalizing the Great Plains. A recent
workshop on integrating agriculture into rural development strategies pointed to
many new agricultural ventures that have been successful.⁵⁷ They tend to be based
on small-scale entrepreneurship, new marketing strategies, and the needs of rural
people and consumers. New opportunities in value-added production may also offer
remote counties in the Great Plains a way to build production agriculture into new
economic development strategies.⁵⁸ Given the role that the land-grant system plays
in the “treadmill of production,” Congress may also consider ways of making
publicly funded agricultural research more responsive to the needs of new
agricultural enterprises, e.g., non-traditional crops, alternative production systems,
marketing strategies for value-added agricultural development. With the aging of
existing farm owner/operators, new opportunities for beginning farmers may offer
other ways to revitalize the relation between agriculture and rural economic
development.⁵⁹ This is not to suggest that large-scale agriculture will cease to have
a significant role in the Great Plains. The Great Plains may actually offer new
competitive advantages for this sector through innovations in environmental control
and management technologies directed toward the agricultural sector.
It has long been a central statement of hope and optimism that support for
agriculture would translate into strong, sustainable rural communities. When
agriculture dominated the rural economy in the early 20^th century, this was, in large
part, true. But, with the exception of some areas of the United States, agriculture
plays a relatively small role in most rural economies now. Modernizing agriculture
has traditionally meant improving production; and improving production has been
defined almost exclusively as increasing output per unit. Supported by the land-grant
university system, research into ever-increasing production efficiency has also been

⁵⁵G. Benjamin, “Industrialization in Hog Production: Implications for Midwest Agriculture,”
Economic Perspectives, Federal Reserve Bank of Chicago, 1997.
⁵⁶Opposition to these industrialization trends is also widespread because concentration and
consolidation in the agro-food industry continues to be regarded as a significant threat to the
survival of small family farms. See, for example, William Heffernan, Consolidation in the
Food and Agriculture System, Report to the National Farmers Union, February 1999.
⁵⁷See Agriculture as a Tool for Rural Development: Workshop Proceedings, Henry A.
Wallance Center for Agricultural and Environmental Policy, April 2003.
⁵⁸Nontraditional crops, new agricultural production techniques, small-scale processing
facilities, and bio-fuel plants may offer rural areas new ways of integrating agriculture into
local economies. See CRS Report RL31598, Value-Added Agricultural Enterprises in Rural
Development Strategies.
⁵⁹Some programs do exist. A portion of Farm Security Agency loans are earmarked each
year for beginning farmers.

associated with ever increasing scales of production. Larger and larger farms and
ranches capable of taking advantage of scale efficiencies were often seen as a
necessary correlate to technologically driven agriculture. That model has been
captured most succinctly in Willard Cochrane’s analogy of the “treadmill of
production.”⁶⁰ Output-enhancing research benefits consumers in lowering the price
of food, but it can be argued it does so at the expense of the producer who must adopt
the newest output-enhancing research on ever-shrinking profit margins.
Overcoming Remoteness: An Interstate Skyway System
While other rural areas in the United States may be at some distance from urban
areas or even sizeable population centers, the Great Plains region has few population
centers and very few large cities. The Northern Great Plains Regional Authority is
working on a regional transportation plan that will integrate new telecommunications
technology and rail, bus, truck, maritime, and air transportation.⁶¹ Certain
innovations occurring in air transport may also hold new possibilities for the region
in mitigating the impact of remoteness.
In 2001, Congress authorized the Commission on the Future of the U.S.
Aerospace Industry (P.L.106-398).⁶² The Commission’s final report was issued in
November, 2002. The report envisions an integrated 21st Century transportation
system based on a common infrastructure of communications, navigation, and
surveillance systems. The report proposes an “interstate skyway system” — like the
Eisenhower highway program of the 1950s and 1960s — using broadband digital
communications, precision surveillance and navigation, and high-resolution weather
forecasts. Such a system could link small, remote areas within a larger region and
thus make them more appealing areas for economic development.
The Commission report reviewed data that suggest the hub-and-spoke system
characterizing the existing passenger airline system may become obsolete as it

⁶⁰The “treadmill effect” refers to technology and its influence on agricultural production. In
the quest for a safe, plentiful, and inexpensive food supply, land grant universities and a
public support system promote this as a public good. Advancements in technology create
the “treadmill effect” for agricultural producers by continuously requiring the systematic
adoption of new technology in order to remain competitive. In turn, this systematic adoption
of technology either reduces or holds prices down for farmers while it increases their cost
of production. Producers who fail to adopt new technology lose their competitive advantage.
Producers who adopt new technology are often rewarded with even lower prices and a
narrower profit margin. See Willard Cochrane, The Development of American Agriculture:
A Historical Analysis, 2nd ed., (Minneapolis: University of Minnesota Press, 1993).
⁶¹Overview of Transportation Infrastructure and Services in the Northern Great Plains,
report prepared for the Northern Great Plains Regional Authority by the Northeast-Midwest
Institute, 2000.
⁶²The Commission on the Future of the United States Aerospace Industry was established
by Section 1092 of the Floyd D. Spence National Defense Authorization Act of 2002. The
Commission was formed to study the future of the United States aerospace industry in the
global economy, particularly in relationship to United States national security; and to assess
the future importance of the domestic aerospace industry for the economic and national
security of the United States.

becomes increasingly congested. In its stead, the Commission recommended the
further investigation of a Small Aircraft Transport System (SATS), essentially an air-
taxi system. Such a system could link small, remote areas within a larger region, and,
with capacity for regional travel, some of the disadvantages of remote locations
might be mitigated. Whether this innovation could make the Great Plains more
attractive to manufacturers is unknown.
The SATS concept is based on a new generation of affordable small aircraft
supported by an airborne “internet.” Each would operate within a system of small
airports serving thousands of suburban, rural and remote communities. The SATS
concept makes greater use of small aircraft for personal and business transportation.
SATS should be able to do this by increasing the supply of smaller aircraft for
“flight-on-demand” and for use in “point-to-point” direct travel between smaller
aviation facilities (such as regional airports, general aviation and other landing
facilities including heliports).
The SATS architecture would incorporate an advanced, on-board weather data
collection system for any landing facility in the United States. SATS would use
Internet communications technologies for travel planning and scheduling. SATS
research is intended to create the possibility of using landing facilities that would not
require control towers or radar surveillance. The SATS architecture would be created
to operate within the National Airspace System (NAS), but in a more automated
manner among the 5,000 or so existing public-use landing facilities. With a total of
over 18,000 of these smaller landing facilities serving vast numbers of communities
in the United States, ultimately, all of these facilities could employ SATS operating
capabilities.
National Aeronautical and Space Administration (NASA) investments in
technologies have led to the emergence of a new generation of small aircraft. These
new aircraft would possess near-all-weather operating capabilities and would be
compatible with the modernization of the National Airspace System. The new aircraft
would incorporate state-of-the-art advancements in avionics, airframes, engines, and
advanced pilot training technologies.
Regional Approaches to Rural Economic Development
Introduction. Regional economic development alliances are enjoying a
resurgence of interest in many parts of the United States. While the concept of such
alliances is not new, its application to rural areas has been minimal. Proponents of
regional approaches share the view that the historic pattern of community-based
economic development no longer addresses the complexity of rural issues that may
characterize a larger geography. The fiscal crises in many states are also creating
pressures on many rural communities to seek new solutions to providing essential
community services through pooling resources. Largely the creation of state and
regional development entities and metropolitan planning organizations, these new
regional organizations have adopted two general categories of strategies. First,
strategies based on the types of regions involved, i.e., regional organizations that
attempt to address common problems arising between urban and rural areas or that
better balance urban and rural needs as these areas overlap. A second development
category is based on the types of projects in which regions are involved, e.g., building

or revitalizing rural cultures, developing broadband capacity, preserving natural
resources, enhancing transportation infrastructure.⁶³
Congress has had a long history of support for regional authorities such as the
Tennessee Valley Authority (TVA) and the Appalachian Regional Commission
(ARC). Both the TVA and the ARC have continued to support economic
development and social change in their respective regions. A substantial body of
literature now exists on the impact of these regional authorities. While there
continue to be differences in opinion about the development successes of these
authorities, an empirical assessment of ARC’s impact over 26 years in the region’s
391 counties, concluded that the programs did produce significant growth. Using a
methodology based on paired communities, the authors concluded that growth was
significantly faster in the 391 Appalachian counties than it was in the control
counties. This also held true for Central Appalachia, the poorest sub-region in the
ARC. Another reported result was improved local planning in ARC counties
compared to the control counties.⁶⁴
More recently, Congress has authorized new regional approaches to common
concerns by establishing the Denali Commission (1998), the Delta Regional
Authority (2000) and, most recently, the Northern Great Plains Regional Authority
(2002). Legislation for three other regional bodies was also introduced in the108^th
Congress: (1) a bill to establish a Southwest Border Authority to promote economic
development in the border regions of Arizona, California, New Mexico and Texas
(S. 458/H.R. 1071); (2) a bill to create a regional authority in the Southeast (H.R.
141), The Southeast Crescent Authority (SECA). The SECA would assist
economically distressed communities in Alabama, Georgia, Florida, Mississippi,
North Carolina, South Carolina, and Virginia; and (3) a bill to create the Delta Black
Belt Regional Authority (H.R. 678). The bill to create the Southwest Border
Authority was referred to the Committee on Environment and Public Works in
February 2003. The bill to establish the SECA was referred in January, 2003 to the
Subcommittee on Economic Development, Public Buildings and Emergency
Management of the House Transportation and Infrastructure Committee and in
February, 2003 to the Subcommittee on Domestic and International Monetary Policy,
Trade, and Technology of the House Financial Services Committee. In June, 2003,
the Subcommittee on Economic Development, Public Buildings and Emergency
Management forwarded the measure to its Full Committee. The bill to create the
Delta Black Belt Regional Authority was referred to the Subcommittee on Domestic
and International Monetary Policy, Trade, and Technology on March 10, 2003.
The Northern Great Plains Regional Authority (NGPRA). The
NGPRA is a newly created federal-state-provincial partnership that includes Iowa,

⁶³For a selective overview of five case studies of regional development organizations, see
Multi-Region Economic Development Strategies Guide: Case Studies in Multi-Region
Cooperation to Promote Economic Development, National Association of Regional
Councils, 2000.
⁶⁴Andrew Isserman and T. Rephann, “The Economic Effects of the Appalachian Regional
Commission: An Empirical Assessment of 26 Years of Regional Development Planning,”
Journal of the American Planning Association 61 (3), summer 1995.

Minnesota, Nebraska, North and South Dakota, and the Provinces of Manitoba and
Saskatchewan. In 1994, Congress passed the Northern Great Plains Rural
Development Act (P.L.103-318). The following year, the Northern Great Plains
Rural Development Commission was established. In 1997, the Commission issued
its regional development report to Congress and the Commission was sunset. Later
that year, NGP, Inc. was established to implement the Commission’s
recommendations. Discussions with the region’s congressional delegation led to a
plan to create a regional development authority similar to the one Congress created
for the Delta Authority. The Farm Security and Rural Investment Act of 2002
(P.L.107-171, Section 6028) established the NGPRA to implement the Commission’s
plan and authorized $30 million to be appropriated each year (2002-2007) to support
the Authority’s programs. No funding, however, was appropriated for the Authority
in FY2002 or FY2003. For FY2004, the Authority was provided $1.5 million in
funding by the Consolidated Appropriations Act of 2004 (P.L.108-199).
At the local level, the NGPRA intends to rely on the existing network of the
Economic Development Administration’s (EDA) designated economic development
districts to coordinate efforts within a multi-county area. These EDA districts,
known as local development districts (LDDs), are regional entities with extensive
experience in assisting small municipalities and counties improve basic infrastructure
and help stimulate economic growth. They also serve as the delivery mechanism for
a variety of other federal and state programs, such as aging, economic development,
emergency management, small business development, telecommunications,
transportation and workforce development programs.
NGPRA Economic Development Strategies. The NGPRA has identified
four areas for their strategic planning: (1) Agriculture and Natural Resources, (2)
Economic and Policy Analysis, (3) Information Technology, and (4) Leadership
Capacity Development. Given the central role of agriculture in the regional
economy, the Authority is integrating into its planning: shifts in consumer demand
toward organic foods; a recognition of the shift to supply-chains in production
(discussed above) and the corresponding need to develop identity preserved
commodities; and the emerging importance of non-food commodities, i.e., bio-based
industrial commodities. A central objective is to turn the Great Plains into an
internationally recognized center for biomass research and use. These agricultural
plans are also grounded more broadly in transforming the transportation systems of
the regions, developing local and regional leadership capacity, and expanding the
availability and use of information technologies within the region.
Legislation in the 108^th Congress
As with past congresses, Members of the 108^th Congress have introduced a wide
range of bills that would have direct implications for rural areas. Legislation
addressing health care, business development, Medicare, community development
organizations, telecommunications, transportation, conservation, and Native
American issues, among others, either target rural areas specifically or are open to all
political jurisdictions. In addition to these initiatives, funds for rural development
programs authorized by the 2002 farm bill (P.L.107-171) also provide loan and grant
support specifically to rural areas for water and waste water facilities, value-added

agricultural development, telemedicine, rural business development, alternative
energy, Native Americans, and rural housing.
Two bills introduced in the 108^th Congress (H.R. 2194 and S. 602) and
discussed below, specifically target areas that have suffered significant population
out-migration over the past 20 years. While not designating remote rural areas per
se, the bills’ provisions may be of particular interest to remote areas. Approximately
one-third of the 242 remote counties had the population losses in the last decade
alone that qualify for assistance authorized in these two bills (see also Figure 1
above). A third bill (H.R. 137) targets rural job creation and labor training from a
regional basis. Supporters say that regional approaches to rural development may
hold particular promise through the increased recognition of the significant ties that
exist between urban/suburban areas and their outlying rural areas.
New Homestead Act (H.R. 2194 and S. 602). These identical bills
provide financial assistance and incentives designed to stem population out-migration
from rural areas. The qualifying criterion is that an individual live in or relocate to
a county that is (1) outside a metropolitan statistical area and (2) has suffered a 10%
or greater population out-migration over the previous 20 years. Modeling itself on
the original Homestead Act of 1862, the bill would provide financial incentives to
both individuals and businesses. Provisions include:
I. New Homestead Opportunities
!Student loan repayments: Authorizes the Secretary of Education to pay up to
a total of $10,000 over five years to any person who (1) completes either an
associate or bachelor degree and (2) resides in a qualifying county and (3) is
employed in a qualifying county. These provisions would potentially have the
effect of stemming the loss of the most educated ;
!Tax incentives for new home buyers: Provides $5,000 tax credit for the home
purchases of individuals who locate in qualified areas for five years (or 10%
of purchase price, whichever is lower);
!Tax deductions: Protects home values by allowing losses in home value to be
deducted from federal income taxes;
!Individual Homestead Accounts: Creates tax-favored accounts to help build
savings and increase access to credit. Individuals can contribute a maximum
of $2,500 per year for up to five years and there is a government-matching
contribution of 25-100% depending on income. Tax and penalty-free
distributions can be made after five years for small business loans, education
expenses, first-time home purchases, and un-reimbursed medical expenses.
Accounts can grow tax-free and all funds are available for withdrawal upon
retirement.
II. New Incentives for Main Street Businesses
!Creates Rural Investment Tax Credits to target investments in high
out-migration counties. States receive $1 million of these credits per eligible
county. Credits are allocated to businesses that move to or expand to a
qualifying county. Businesses use these credits to offset the cost of newly

constructed or existing buildings. Over a 10-year period, businesses can use
these credits to reduce their taxes by as much as 80% of their total investment.
!Authorizes Micro-enterprise Tax Credits to aid small businesses (5 or fewer
employees) in high out-migration counties. Micro-enterprises can use the tax
credits to reduce their taxes by 30-percent of their qualifying new investment
(limited to $25,000 lifetime). For equipment purchases tied to Rural
Investment Tax Credit projects, businesses would be able to accelerate the
equipment’s depreciation.
III. New Homestead Venture Capital Fund
!Establishes a $3 billion venture capital fund to invest in businesses in high
out-migration counties. The fund would guarantee up to 40% of private
investments in existing business and start-ups, and up to 60% of such
investments in manufacturing or high-technology ventures;
!The fund can take equity positions and extend credit to other approved
entities;
!Federal government would invest $200 million per year for 10 years; states
and private investors would be required to provide yearly matching funds of
$50 million each (or $1 for each $4 of federal funds).
New Homestead Economic Opportunity Act (H.R. 1686). This bill is
almost identical to H.R. 2194 and S. 602. It includes the same titles and authorizes
the same provisions with some slight modification (e.g., the student loan repayment
maximum is $3000 per year rather than $2000). As with the New Homestead Act,
this bill also makes living and working in a county with a 10% population out-
migration over the previous 20 years the qualifying criterion for assistance.
Rural America Job Assistance and Creation Act (H.R. 137). This bill
is also aimed at improving the opportunities available to areas where population out-
migration is significant.
!Expands the Work Opportunity Tax Credit within designated “development
zones” where population has declined, where job growth is low, and where
poverty is high;
!Provides grants to business consortia for developing the work skills of
regional workers. The training is directed toward the development of skills
that are benchmarked to advanced industry practices;
!Provides grants for business “incubators” for newly established small and
medium-sized businesses.
Status of Legislation. In June 2003, H.R. 2194 was referred to the House
Agriculture Subcommittee on Conservation, Credit, Rural Development and
Research and S. 602 was read twice and referred to the Committee on Finance. H.R.

1686 was also referred to the House Agriculture Subcommittee on Conservation,

Credit, Rural Development and Research. H.R. 137 was referred to the House
Financial Services Committee Subcommittee on Domestic and International
Monetary Policy, Trade, and Technology in February, 2003.

Conclusion
Some might argue that what is occurring in the remote counties of the Great
Plains region today is the inevitable logic of technological progress, the decline of
older industries, and the existence of more attractive opportunities in urban areas.
Moreover, it represents a long-standing cycle of economic ups and downs for the
region. But an argument can also be made that the output-enhancing technologies
of public agriculture research were never neutral. From this perspective, it is
necessary to review real, tangible costs as well as gains. There is ample evidence that
relocation decisions are not uni-dimensional: People do not relocate simply to
increase income; opportunities to increase household income are weighed against
competing desires and interests. The desire to live in a rural community where one’s
family has long resided are understandable decisions made with conscious trade-offs.
People do leave areas, however, when there are very few choices for gaining a
livelihood. Data discussed above indicates that the Great Plains remains
disproportionately farm-dependent, that it is suffering a substantial population out-
migration, that traditional rural economic development strategies have not been
notably successful in the region, and that the Great Plains relies heavily on various
forms of federal payments. While such payments also go to other areas in the United
States, they are now central to the well-being of many residents of the Great Plains.
Yet, the form of payments, i.e., income supports, may not have the same long-term
impact as capital investment funds. In the absence of successful efforts to reverse the
decline, the result of these various trend lines appears somewhat pessimistic.
Historical evidence reveals how the changing organization of industrial
production produces clear winners and clear losers. The rise of the textile industry
in 18^th century Britain depopulated rural areas in the course of two generations,
displaced skilled craftsmen, and forever altered the social and spatial histories of that
country. The long trend-line of a shrinking farm sector in the United States is not
news. It has happened in every region of the country. But, the conditions in the
remote counties of the Great Plains are different in degree if not kind and may require
different responses. The slow decline of agricultural employment has not been
accompanied by significant opportunities in other areas. One observer testified
before Congress, “the farm and ranch communities of the nation’s heartland are in
the midst of an opportunity crisis.”⁶⁵
Some analysts and observers would hold that, in the absence of evidence that
public intervention was a necessary correction to otherwise well-functioning markets,
what is occurring in the Great Plains, while disruptive, may be inevitable. From that
perspective, the question asked above of “Why invest in rural America?” will have
been definitively answered by market logic.⁶⁶ Not doing anything but allowing
existing trends to continue unabated, may, in effect, be a public policy. The

⁶⁵Chuck Hassebrook, testimony on rural development before the Senate Agriculture,
Nutrition, and Forestry Committee, 107^th Congress, 2^nd session, August 2, 2001.
⁶⁶This is not to imply that economic criteria are the only or even the most important basis
for making economic development decisions. While market forces remain the dominant
means of allocating resources and wealth in the United States, they have never been the sole
means of making policy decisions.

consequences of such policy a decision may not have been adequately assessed,
however. The great difficulty is determining what the realistic options are from a
public policy perspective. Initial congressional efforts to create new incentives to
reverse regional population out-migration would predictably be welcomed by Great
Plains communities. In their view, reversing population out-migration may be the
first order of business.⁶⁷
It can be argued that the Great Plains is not remote because it is economically
undeveloped; it is economically undeveloped because it is remote and remains
largely dependent on a single dominant but declining economic sector. New
initiatives in regional transportation and developments in broadband
telecommunications may offer important if partial solutions to some of the problems
of remoteness. At their height in the 1960s and 1970s, however, U.S. regional
policies to address rural-urban disparities were still relatively modest efforts.⁶⁸
Current congressional efforts to expand on regional solutions in other geographic
areas may produce outcomes that the Great Plains Regional Authority can adapt to
their own circumstances. But unless the areas become more attractive for people to
live and work, such interventions may produce only modest changes.⁶⁹ Market
changes and the deepening of economic internationalization may direct precisely that
outcome. Yet, policymakers, rural researchers, and rural observers have yet to fully
understand how spatial and socioeconomic environments have interacted to produce
the existing development patterns in the Great Plains and retarded alternative
patterns.⁷⁰ Remoteness is not the only variable in these interactions, but it may serve

⁶⁷ Michael Lind, “The new Continental Divide,” The Atlantic Monthly, January-February

2003.

⁶⁸In contrast, Europe has embraced relatively ambitious regional programs. This regional
emphasis in Europe may reflect more pronounced disparities between urban areas and rural
regions there compared to the United States. Most recently, one can see this policy
difference in the EU’s Common Agricultural Policy reforms, where rural development is one
of the three central pillars of agricultural reform.
⁶⁹ Some regional development analysts have argued that making areas attractive to the
“creative classes” is a necessary ingredient for successful economic development in the
future. Conventional economic development models may no longer suffice. Focusing more
on why certain cities are declining and others thriving, these observers cite the importance
of making adaptations in local cultures to attract and retain creative class employees.
Business have begun doing this, but civic leaders have generally not grasped that what is
true for corporations may also be true for cities and regions. See Richard Florida, The Rise
of the Creative Class (New York: Basic Books, 2002). In contrast, other analysts have
argued that the statistical evidence for the role of the “creative class” is far less convincing.
See Steve Malanga, “The Curse of the Creative Class,” City Journal, vol. 14 (1), winter

2004.

⁷⁰It is also the case that what rural researchers often think is effective might be otherwise.
In a late 1980s study of 548 non-metro counties, researchers for the National Governors’
Association were surprised to learn that 13 variables widely thought to be important factors
in differentiating communities that grew from those that did not (e.g., change in
employment, federal spending on development, county population, adjacency to a metro
area) could only explain about 17% of the growth that actually occurred. See Sandra S.
Batie, DeWitt John, and Kim Norris, A Brighter Future for Rural America? Strategies for
(continued...)

as a proxy for a multi-dimensional set of characteristics that exerts a powerful
influence on the possibilities available to the Great Plains region. Rural development
programs that are place-specific, i.e., that take existing social and economic
development programs and modify them to address the particular circumstances of
specific rural areas, could have value to the Great Plains region and other distinctive
rural areas.

⁷⁰(...continued)
Communities and States (Washington, D.C.: National Governors’ Association, 1988).

Appendix A. Measuring Rurality
Rural development researchers have pointed out the importance of developing⁷¹
more analytically sound rural taxonomies for public policy. Probably the most
widely cited rural typologies were developed by USDA’s Economic Research Service
These typologies are based on a county’s general economic specialization and its
policy type (Tables 7 and 8). While they have been very useful for breaking down
the great diversity of rural areas into more manageable units, they may not be as
useful for targeting rural development policies as typologies that are comprised of
multi-dimensional scales. Linking a particular set of rural development policies to
varied rural conditions would be aided by the development of a rural taxonomy
permitting the delineation of one group of rural places from another based on a set
of particular characteristics of the rural places. Remote rural areas have
characteristics that are different from, for example, rural areas that are within closer
commuting distance to a city. They do not differ from other rural areas simply in
terms of their remoteness, although this is a significant characteristic. Rather,
remoteness seems to be a central identifier encompassing multidimensional attributes
of these areas, for example, significant population loss, low-wages, above average
poverty, distinctive demographic characteristics, single-sector economies and/or
high-unemployment. Particular combinations of socioeconomic characteristics could
be helpful in identifying particular policy regimes to address the particular needs of
these areas.⁷²

⁷¹See David Freshwater, “What Can Social Scientists Contribute to the Challenges of Rural
Economic Development?” Journal of Agricultural and Applied Economics 32 (2), August

2000.

⁷²Ibid., p.348.

Table 7. USDA Classification of Non-Metro Counties
by Economic Type
Economic Type (1)DefinitionNumber of Counties
(1989 data)
Farming-dependentGreater than or equal to556⁷³
20% of total labor and
proprietors’ income from
agriculture
Manufacturing-dependentGreater than or equal to506
30% of total income from
manufacturing
Mining-dependentGreater than or equal to146
20% of total income from
mining
Government-dependentGreater than or equal to244
25% of total income from
government
Service-dependent50% or more of total323
income from service sector
employment (2)
^Source:^C^ook,^P^e^ggy^J^.^an^{d K}^a^ren^L^.^Mi^zer.^T^h^{e Revi}^s^e^{d ERS C}^ount^{y T}^y^pol^ogy^{. USDA}^-^E^R^S^,
^N^o^v^e^m^b^{er. 1994.}
^{(1) Econ}^omⁱ^c^an^{d pol}ⁱ^c^y^t^y^pes^can^an^{d do ov}^erl^a^p
^{(2) T}^h^{e s}^e^rv^{ice s}^{ector en}^com^p^as^s^e^s^a^wⁱ^{de v}^a^riety^of^em^ploy^m^eⁿ^t^an^{d in}^clu^d^es^s^u^ch^areas^as^areas^as
^{retail, bu}^sⁱⁿ^e^s^s^an^{d prof}^es^sⁱ^on^{al s}^e^rv^ices^{, edu}^cation^,^fⁱⁿ^aⁿ^{ce, in}^s^u^ran^{ce, an}^{d real es}^tate.

⁷³The data here are quite old. This table provides a general distributional picture that may
still be valid, although the counties falling into each category have likely changed. For
example, in 1999, ERS reported that there were 312 farming-dependent counties, a decline
of 44% since 1989.

Table 8. USDA Classification of Non-Metro Counties
by Policy Type
Policy Type (1)DefinitionNumber of Counties
(1989 data)
Transfer-dependent25% or more of personal381
income from
Federal/State/local transfer
payments (weighted
average)
Retirement-destinationPopulation aged 60 and190
older increased 15% or
more during 1980-1990
Persistent Poverty20% or more of county535
population in each of four
years: 1960, 1970, 1980,
1990 with poverty-level
income
Commuting 40% or more of county’s381
workers commuting
outside their county of
residence in 1990
Federal Lands30% of county’s land area270
federally owned in 1987
^Source:^C^ook^{, Peg}^g^y^{J. an}^{d K}^a^ren^L^.^Mi^zer.^T^h^{e Revi}^s^e^{d ERS C}^ount^{y T}^y^pol^ogy^{. USDA}^-^E^R^S^,
^N^o^v^e^m^b^{er. 1994.}
^{(1) Econ}^omⁱ^c^an^{d pol}ⁱ^c^y^t^y^pes^can^an^{d do ov}^erl^a^p.
Researchers at the USDA’s Economic Research Service also developed two
widely used, unidimensional scales that divide the 3,141 counties, county
equivalents, and cities into nine codes. The first (Table 9) classifies urban counties
by size and non-metro counties by their degree of urbanization and proximity to a
metro area. The scale permits analysis of trends in non-metro areas that may be
related to population density and the influences from the metro area. “Adjacent”
non-metro counties are physically adjacent to one or more of the Office of
Management and Budget’s (OMB) Metropolitan Statistical Areas (MSA) and have
at least 2% of the employed labor force in the non-metro county commuting to cental
metro counties. Non-metro counties that do not meet these criteria are classified as
“not adjacent.”

Table 9. Rural-Urban Continuum Codes
Code Description
Metropolitan Counties
1Counties in metro areas of 1 million population or more
2Counties in metro areas of 250,000 to 1 million population
3Counties in metro areas of fewer than 250,000 population
Non-Metropolitan Counties
4Urban population of 20,000 or more, adjacent to a metro area
5Urban population of 20,000 or more, not adjacent to a metro area

6Urban population of 2,500 to 19,999, adjacent to a metro area.

7Urban population of 2,500 to 19,999, not adjacent to a metro area
8Completely rural or less than 2,500 urban population, adjacent to a metro
area.
9Completely rural or less than 2,500 urban population, not adjacent to a metro
area
^Source:^USDA^Econ^om^{ic R}^e^s^earch^Serv^ice.
Table 10 presents a second scale based on the evidence that an area’s
geographic context has a significant effect on its development. It is somewhat
discouraging for rural development researchers to acknowledge that over the past 20
years, most successful rural areas became so through some urban-based influence.⁷⁴
The Urban Influence Codes in Table 10 recognize this empirical reality and classify
counties both by size and by access to larger economies. Small rural economies with
access to centers of trade, finance, and communication fare much better socially and
economically than remote counties. While the Internet may make some physical
access less important in the future, those rural areas with access to dynamic
population centers are more likely than remote rural areas to create and maintain
diverse and successful economies.

⁷⁴ Some researchers regard the city as the essential engine of development. See Jane Jacobs,
The Economy of Cities (1969) and Cities and the Wealth of Nations (1984).

Table 10. Urban Influence Codes
Code Description
Metropolitan Counties

1Large - in a metro area of 1 million or more population.

2Small - in a metro area of fewer than1 million population.

Non-Metropolitan Counties
3Adjacent to a large metro area and contains a city of at least 10,000
population.
4Adjacent to a large metro area and does not contain a city of at least 10,000
population.
5Adjacent to a small metro area and contains a city of at least 10,000
population.
6Adjacent to a small metro area and does not contain a city of at least 10,000
population.

7Not adjacent to a metro area and contains a city of at least 10,000 population.

8Not adjacent to a metro area and contains a town of at least 2,500-9,999
population.
9Not adjacent to a metro area and does not contain a city of at least 2,500
population.
^Source:^USDA^Econ^om^{ic R}^e^s^earch^Serv^ice.
The Urban Influence Codes are based on the official OMB metro status as
announced in June, 1993, and rely on population and commuting data from the 1990
Census of Population. Non-metro counties are considered adjacent if they abut a
metro area and have at least 2% of employed persons commuting to work in a core
county of the metropolitan area.
There are 836 metro counties, of which 311 are part of large metro areas and
525 are part of small metro areas. There are 2,305 non-metro counties, 186 adjacent
to large metro areas and 63 that contain their own city. Another 815 non-metro
counties are adjacent to small metro areas, of which 188 have their own city. Of the
1,304 non-metro counties that are not adjacent to a metro area, 234 have their own
city, 555 have a town, and 515 are rural. Not all metro areas are completely
surrounded by adjacent counties. Some counties abutting metro areas do not meet
the 2% commuting requirement to considered “adjacent.” Some of the urban
influence groups are concentrated in particular census divisions. The most
concentrated are the rural non-adjacent counties: 41% are in the West North Central
Division of the United States which includes Nebraska, South Dakota, North Dakota,
and Montana.

Appendix B. Top 25 Industries in Low-Wage Rural Counties
Low-Wage CountiesOther Rural Counties
RankStandard Industrial ClassificationShare ofAnnual EarningsRankShare of JobsAnnual
Jobsper Job(%)Earnings per
(%)Job
1Elementary and secondary schools10.6$20,23017.5$22,487
2Eating/drinking places7.3$6,99726.6$7,788
3Grocery stores4.1$10,67143.4$12,047
4Nursing and personal care3.9$12,01552.4$13,981
5Government offices3.5$14,06272.0$18,572
ⁱ^{ki/CRS-RL32372}6 Hospitals 3.4 $19,917 3 3.9 $24,161
^g/w
^s^.or7Hotels and motels2.2$9,87891.6$12,584
^le^ak
8Mens/boys clothing2.1$12,714250.7$14,705
^://wiki
^h^ttp9 Banks 2.0 $22,291 12 1.3 $23,091
10 Am usement/recreation 1.5 $12,611 14 1.1 $13,498
11Gas stations1.5$10,674171.0$11,907
12Trucking and courier1.4$21,067101.6$24,714
13 Meatpacking 1.4 $15,817 11 1.4 $19,986
14Department stores1.3$11,35262.0$12,216
15Public safety1.0$20,289131.3$27,359
16Solid waste management0.9$24,682440.5$28,274
17 Sawmills 0.9 $18,725 22 0.7 $24,311

18U.S. Postal Service0.9$26,783280.6$30,625

Low-Wage CountiesOther Rural Counties
RankStandard Industrial ClassificationShare ofAnnual EarningsRankShare of JobsAnnual
Jobsper Job(%)Earnings per
(%)Job
19Medical offices/clinics0.9$30,364151.1$42,290
20Farm wholesaling0.9$15,044640.3$18,758
21Car dealers0.9$23,171180.9$27,269
22Family services0.9$13,499240.7$15,386
23Home health care0.8$16,458400.6$16,678
24Nondurable wholesaling0.8$19,581310.6$21,533
ⁱ^{ki/CRS-RL32372}25Highway construction0.8$20,963290.6$21,147^Source:^{1995 Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^cal^dat^a^{prepared by}^U^S^D^A^Econ^omⁱ^c^R^e^s^earch^S^e^rvⁱ^{ce (G}ⁱ^bbs^an^{d C}^r^om^artⁱ^e^at^f^ootⁿ^o^t^e^{39, abov}^e).
^g/w^No^te:^In^du^s^t^ries^w^ith^av^erag^{e earn}ⁱⁿ^g^{per j}^{ob in}^low^-^w^a^g^e^couⁿ^ties^below^th^{e f}^o^u^r^-^p^ers^oⁿ^pov^erty^th^res^h^{old are in}^bold.

^s^.or
^le^ak
^://wiki
^h^ttp

The following tables in Appendices C-I present socioeconomic data on the 242
remote rural counties in seven states of the Great Plains region. Two criteria were
used to select the counties: (1) a county population density of 6 or fewer persons per
square mile and (2) a Rural-Urban Continuum Code of 6-9 and a Urban Influence
Code of 6-9. Only a few of these counties have codes less than 8, making them
among the most rural counties in the United States. For a description of these two
scales, see Appendix A above.
These county codes are based on the 1990 Census data on worker commuting
and the 1993 classification of OMB Metropolitan Statistical Areas (MSA). New
Urban Influence Codes and new Rural-Urban Continuum Codes based on the 2000
Census are not expected to be available until mid-2004. The development of
updated codes requires commuting data (journey-to-work) from the U.S. Census and
the new updated OMB Metropolitan Statistical Areas.

Appendix C. Remote Kansas Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Ka^nsa^s^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾²⁰⁰⁰^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^{Barber 9}⁹⁵³⁰⁷⁽¹⁰⁾^5.2^$33,407^10.1³^.3^$19,725
^C^h^as^{e 9}⁹³⁰³⁰⁰³^.9^$32,656^8.6⁴^.3^$17,386
^C^l^ark⁹⁹²³⁹⁰⁽¹⁾³^.2^$33,857^12.7¹^.7^$21,111
^C^o^m^aⁿ^c^h^e⁹⁹¹⁹⁶⁷⁽¹⁵⁾^2.9^$29,415^10.2¹^.5^$15,604
^Decatu^r⁹⁹³⁴⁷²⁽¹⁴⁾^4.5^$30,257^11.6²^.3^$15,502
^El^k⁹⁹³²⁶¹²⁵^.1^$27,267^13.8⁴^.9^$16,702
^G^o^v^e⁹⁹³⁰⁶⁸⁵⁵^.1^$33,510^10.3¹^.6^$18,690
^G^r^ah^am⁸⁶²⁹⁴⁶⁽¹⁷⁾^3.0^$31,286^11.5²^.3^$18,616
ⁱ^{ki/CRS-RL32372}^G^r^eel^ey⁹⁹¹⁵³⁴^(13.5)^3.9^$34,605^11.6³^.6^$19,158
^g/w^Ham^ilton⁹⁹²⁶⁷⁰^11.8²^.4^$32,033^11.5¹^.8^$20,354
^s^.or^H^odg^em^an⁹⁹²⁰⁸⁵^(4.2)²^.5^$35,994^11.5²^.7^$18,900
^le^ak
^Jew^e^l^l⁹⁹³⁷⁹¹^(10.8)^4.7^$30,537^11.6¹^.7^$16,557
^://wiki^K^earn^y⁹⁹⁴⁵³¹^12.5⁴^.6^$40,149^11.7⁴^.5^$20,742
^h^ttp^Kⁱ^ow^a⁹⁹³²⁷⁸^(10.4)^5.1^$31,576^10.8²^.1^$18,275
^L^aⁿ^e⁹⁹²¹⁵⁵^(9.3)³^.3^$36,047^8.2³^.9^$20,761
^Lⁱⁿ^c^olⁿ⁹⁹³⁵⁷⁸^(2.1)⁵^.1^$30,893^9.7²^.9^$16,288
^L^o^g^aⁿ⁹⁹³⁰⁴⁶^(1.1)²^.9^$32,131^7.3²^.5^$19,534
^Meade⁹⁹⁴⁶³¹^9.0⁴^.3^$36,761^9.3²^.1^$21,862
^Mort^on⁹⁹³⁴⁹⁶^0.5⁴^.8^$37,232^10.5²^.4^$26,057
^N^e^s^s⁹⁹³⁴⁵⁴^(14.4)^3.8^$32,340^8.7¹^.8^$19,575
^O^s^born^e⁹⁹⁴⁴⁵²^(8.5)⁵^.5^$29,145^10.4³^.1^$16,730
^R^a^w^lⁱⁿ^s⁹⁹²⁹⁶⁶^(12.9)^3.2^$32,105^12.5²^.5^$17,961
^R^u^s^h⁹⁹³⁵⁵¹^(7.6)⁵^.3^$31,268^9.7²^.3^$21,042
^S^h^eri^d^an⁹⁹²⁸¹³^(7.6)³^.4^$33,547^15.7¹^.7^$21,394
^S^mⁱ^t^h⁹⁹⁴⁵³⁶^(10.7)^5.7^$28,486^10.7¹^.9^$17,458
^S^t^an^t^oⁿ⁹⁹²⁴⁰⁶^3.1³^.4^$40,172^14.9²^.1^$20,759

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Ka^nsa^s^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾²⁰⁰⁰^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^T^r^eg^o⁹⁹³³¹⁹^(10.2)^4.2^$29,677^12.3²^.2^$17,719
^Wal^l^ace⁹⁹¹⁷⁴⁹^(4.0)²^.0^$33,000^16.1³^.0^$17,236
^Wi^chⁱ^t^a⁹⁹²⁵³¹^(8.2)³^.8^$33,462^14.8³^.2^$22,029
^C^o^uⁿ^t^{y A}^v^erage³¹⁷³^(5.1)⁴^.0^$32,856^11.3²^.6^$19,094
^Kan^s^as^{2.69 m}^illion⁸^.5^32.9^$40,624^9.9⁴^.3^$29,360
^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323
Table 2
^{Per capita incom}^e
ⁱ^{ki/CRS-RL32372}^Hi^gh^-S^ch^ool^{Private non-farm}^em^p^l^oym^en^{t ch}^an^ge,^{Per capita incom}^e^ch^an^{ge, 1990-2000}^ch^an^ge,^1980-2000
^g/w^P^o^pula^t^io^{n 6}⁵^grad^u^a^tes^,²⁵^B^a^chelors^degree^1990-1999⁽^N^egat^iv^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^s^.or^Kan^s^as^C^o^uⁿ^tⁱ^e^s^P^op^u^l^ati^oⁿ^,²⁰⁰⁰^a^{nd o}^l^der^a^{nd o}^l^{der (}^%⁾^{or h}ⁱ^gh^{er, (%}⁾ⁿ^u^m^b^{ers in}^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^le^ak^{Barber 5307}^21.5^85.8^21.0²^.4^0.2⁽^8.8)
^://wiki^C^h^as^{e 3030}^18.7^87.1^19.6⁽^42.5)^26.6^20.6
^h^ttp^C^l^ark²³⁹⁰^21.8^87.4^22.1^27.4⁽^14.9)^12.4
^C^o^m^aⁿ^c^h^e¹⁹⁶⁷^25.8^91.3^15.1³^.8^(20.5)^22.3
^Decatu^r³⁴⁷²^26.2^86.4^15.4⁽^16.2)^(3.0)^10.5
^El^k³²⁶¹^25.3^80.0^10.6⁵^.2^9.2^17.5
^G^o^v^e³⁰⁶⁸^22.7^84.5^18.4^28.3⁽^23.6)^47.8
^G^r^ah^am²⁹⁴⁶^23.7^83.6^17.4^23.0^16.7^53.2
^G^r^eel^ey¹⁵³⁴^17.7^83.7^17.4⁽^0.9)^(19.9)^13.4
^Ham^ilton²⁶⁷⁰^18.4^76.7^17.4^73.1⁽^10.6)^48.8
^H^odg^em^an²⁰⁸⁵^19.0^86.9^19.7^128.3⁽^3.0)^63.6
^Jew^e^l^l³⁷⁹¹^25.9^87.6^13.8^22.1⁽^13.7)^38.7
^K^earn^y⁴⁵³¹^11.1^75.8^15.0^58.7⁽^29.2)^34.3
^Kⁱ^ow^a³²⁷⁸^21.3^85.2^18.9^26.7⁽^1.1)^32.9
^L^aⁿ^e²¹⁵⁵^20.5^88.5^18.5^27.0⁽^4.6)^15.1
^Lⁱⁿ^c^olⁿ³⁵⁷⁸^23.5^85.0^17.4^97.1⁽^6.8)^3.7

^{Per capita incom}^e
^{Private non-farm}^{Per capita incom}^e^ch^an^ge,
^Hi^gh^-S^ch^ool^em^p^l^oym^en^{t ch}^an^ge,^ch^an^{ge, 1990-2000}^1980-2000
^P^o^pula^t^io^{n 6}⁵^grad^u^a^tes^,²⁵^B^a^chelors^degree^1990-1999⁽^N^egat^iv^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^Kan^s^as^C^o^uⁿ^tⁱ^e^s^P^op^u^l^ati^oⁿ^,²⁰⁰⁰^a^{nd o}^l^der^a^{nd o}^l^{der (}^%⁾^{or h}ⁱ^gh^{er, (%}⁾ⁿ^u^m^b^{ers in}^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^L^o^g^aⁿ³⁰⁴⁶^20.7^86.7^17.5^14.7⁽^9.3)^(5.0)
^Meade⁴⁶³¹^17.9^80.3^19.6^56.4⁶^.9^36.0
^Mort^on³⁴⁹⁶^13.9^81.9^16.6^72.4^15.2^29.2
^N^e^s^s³⁴⁵⁴^24.2^84.4^17.9^36.6⁽^2.4)^18.5
^O^s^born^e⁴⁴⁵²^25.7^84.8^15.5⁷^.6^(9.5)^17.9
^R^a^w^lⁱⁿ^s²⁹⁶⁶^25.6^84.7^15.9^19.2⁽^3.1)^35.9
^R^u^s^h³⁵⁵¹^25.3^82.8^16.4^31.6⁰^.4^(0.3)
^S^h^eri^d^an²⁸¹³^20.3^87.8^15.9^28.6^14.2^64.3
^S^mⁱ^t^h⁴⁵³⁶^27.9^84.6^16.7⁶^.7^7.7^32.6
^S^t^an^t^oⁿ²⁴⁰⁶^13.0^78.0^16.9^52.7⁽^20.2)^62.3
ⁱ^{ki/CRS-RL32372}^T^r^eg^o³³¹⁹^24.0^84.3^14.0^15.7⁽^8.6)^7.3
^g/w^Wal^l^ace¹⁷⁴⁹^18.1^84.0^17.2^41.8²^.0^22.6
^s^.or^Wi^chⁱ^t^a²⁵³¹^16.0^77.7^15.5^56.2⁽^28.5)^60.2
^le^ak
^://wiki^C^o^uⁿ^t^{y A}^v^erage³¹⁷³^21.2^84.1^17.0^31.2⁽^4.6)^27.8
^h^ttp^Kan^s^as²^.⁶⁹^m^illioⁿ¹³^.³86.025.818.414.330.5
United States^281.4^12.4^80.4^24.4^18.4^21.3^65.4
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^ear^ch^Serv^{ice; U.S. Departm}^eⁿ^t
^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.

Appendix D. Remote Montana Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Mo^ntaⁿ^a^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾²⁰⁰⁰^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b²⁰⁰⁰
^Beav^erh^ead⁷⁸⁹²⁰²^9.2¹^.5^$28,962^17.1³^.3^$21,025
^Bi^g^h^o^rn⁶⁶¹²⁶⁷¹^11.8²^.3^$27,684^29.2^16.8^$24,234
^Bl^aiⁿ^e⁹⁹⁷⁰⁰⁹^4.2¹^.6^$25,247^28.1⁵^.6^$20,516
^Broadw^at^er⁹⁹⁴³⁸⁵^32.2²^.8^$32,689^10.8⁴^.3^$23,852
^C^a^rbon⁸⁶⁹⁵⁵²^18.2³^.9^$32,139^11.6⁴^.6^$17,971
^C^a^rt^er⁹⁹¹³⁶⁰^(9.5)⁰^.5^$26,312^18.1²^.3^$14,572
^C^h^ou^t^eau⁸⁶⁵⁹⁷⁰^9.5¹^.4^$29,150^20.5³^.1^$16,823
^C^u^s^t^er⁷⁸¹¹⁶⁹⁶^0.0³^.1^$30,000^15.1³^.7^$21,695
ⁱ^{ki/CRS-RL32372}^D^aⁿⁱ^el^s⁹⁹²⁰¹⁷^(11.0)^1.6^$27,306^16.9²^.8^$20,597
^g/w^D^a^w^s^on⁷⁸⁹⁰⁵⁹^(4.7)⁴^$31,393^14.9²^.7^$19,602
^s^.or^F^a^l^l^oⁿ⁹⁹²⁸³⁷^(8.6)¹^.9^$29,944^12.5²^.6^$22,622
^le^ak^F^e^rg^u^s⁷⁸¹¹⁸⁹³^(1.6)²^.8^$30,409^15.4⁵^.8^$20,657
^://wiki^G^a^rfⁱ^e^l^d⁹⁹¹²⁷⁹^(19.5)^0.3^$25,917^21.5²^.2^$16,007
^h^ttp^G^l^aci^er⁷⁸¹³²⁴⁷^9.3⁴^$27,921^27.3^11.1^$22,496
^G^o^l^d^en^V^a^l^l^e^y⁸⁶¹⁰⁴²^14.3⁰^.8^$27,308^25.8⁴^.7^$17,226
^G^r^anⁱ^t^e⁹⁹²⁸³⁰^11.1¹^.5^$27,813^16.8⁷^.7^$19,266
^Jef^f^e^rs^on⁷⁷¹⁰⁰⁴⁹^26.6⁴^.8^$41,506^9.0⁴^.4^$25,616
^Ju^di^t^h^Basⁱⁿ⁸⁶²³²⁹^2.1¹^.2^$29,241^21.1³^.7^$17,933
^Lⁱ^bert^y⁹⁹²¹⁵⁸^(6.0)¹^.6^$30,284^20.3²^.9^$19,513
^Lⁱⁿ^c^olⁿ⁷⁸¹⁸⁸³⁷^7.8⁴^.8^$26,754^19.2^11.3^$22,503
^Madi^s^oⁿ⁹⁹⁶⁸⁵¹^14.4¹^.7^$30,233^12.1³^.4^$19,597
^McC^oⁿ^e⁹⁹¹⁹⁷⁷^(13.1)^0.9^$29,718^16.8²^.3^$19,585
^Meag^h^e^r⁸⁶¹⁹³²^6.2⁰^.8^$29,375^18.9⁵^.9^$17,876
^Miⁿ^e^ral⁹⁹³⁸⁸⁴^17.2²^.7^$27,143^15.8⁸^.2^$19,074
^Mu^s^s^e^l^S^h^el^l⁸⁶⁴⁴⁹⁷^9.5²^.2^$25,527^19.9⁶^.6^$17,639
^Park⁷⁸¹⁵⁶⁹⁴^8.1⁵^.5^$31,739^11.4⁴^.7^$19,412

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Mo^ntaⁿ^a^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾²⁰⁰⁰^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b²⁰⁰⁰
^Pet^r^ol^eu^m⁹⁹⁴⁹³^(5.0)⁰^.3^$24,107^23.2²^.4^$16,212
^P^h^illips⁹⁹⁴⁶⁰¹⁽^10.9)¹^$28,702^18.3⁴^.4^$18,769
^Pon^d^era⁷⁸⁶⁴²⁴^0.1⁴^$30,464^18.8⁴^.2^$20,180
^Pow^d^{er R}ⁱ^v^e^r⁹⁹¹⁸⁵⁸^(11.1)^0.6^$28,398^12.9¹^.9^$15,200
^Pow^e^l^l⁷⁸⁷¹⁸⁰^8.5²^.8^$30,625^12.6⁴^.8^$23,862
^Prai^ri^e⁹⁹¹¹⁹⁹^(13.3)^0.8^$25,451^17.2⁴^.6^$16,765
^Rⁱ^ch^l^aⁿ^d⁷⁸⁹⁶⁶⁷^(9.8)⁵^.1^$32,110^12.2⁴^.9^$21,219
^R^oos^ev^el^t⁷⁸¹⁰⁶²⁰^(3.4)⁴^.7^$24,834^32.4⁷^.2^$19,971
^R^o^s^e^bu^d⁷⁸⁹³⁸³^(10.7)^2.1^$35,898^22.4⁷^.1^$29,318
^S^aⁿ^d^ers⁹⁹¹⁰²²⁷^18.0³^.1^$26,852^17.2⁸^.3^$19,929
^S^h^eri^d^an⁹⁹⁴¹⁰⁵^(13.3)^2.8^$29,518^14.7³^.2^$18,185
ⁱ^{ki/CRS-RL32372}^{Still W}^a^ter⁸⁶⁸¹⁹⁵^25.4³^.6^$39,205^9.8³^.1^$37,366
^g/w^S^w^eet^G^r^as^s⁹⁹³⁶⁰⁹^14.4¹^.7^$32,422^11.4²^.6^$18,244
^s^.or^T^e^t^oⁿ⁸⁶⁶⁴⁴⁵^2.8²^.8^$30,197^16.6³^.5^$19,512
^le^ak
^T^ool^e⁷⁸⁵²⁶⁷^4.4²^.6^$30,169^12.9²^.7^$21,916
^://wiki^T^r^eas^u^r^e⁸⁶⁸⁶¹^(1.5)⁰^.9^$29,830^14.7³^.2^$17,393
^h^ttp^V^a^l^l^e^y⁷⁸⁷⁶⁷⁵^(6.8)¹^.7^$30,979^13.5³^.5^$19,986
^Wh^eat^l^aⁿ^d⁹⁹²²⁵⁹^0.6¹^.6^$24,492^20.4³^.5^$16,953
^Wi^bau^x⁹⁹¹⁰⁶⁸^(10.3)^0.2^$28,224^15.3²^.6^$16,109
^C^o^uⁿ^t^{y A}^v^erage⁶¹²⁰^2.6²^.3^$29,426^17.4⁴^.8^$20,111
^Mon^t^an^a⁹⁰²¹⁹⁵^12.9⁶^.2^$33,024^14.6⁴^.6^$24,274
^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323

Table 2
^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,^p^a^ren^t^h^eses)
^M^oⁿ^t^an^{a C}^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)
^Beav^erh^ead⁹²⁰²^13.6^89.3^26.4^39.5⁸^.0^27.5
^Bi^g^H^o^rn¹²⁶⁷¹^8.6^76.4^14.3^22.9³^.8^(14.1)
^Bl^aiⁿ^e⁷⁰⁰⁹^12.9^78.7^17.4³^.3^(5.6)^166.3
^Broadw^at^er⁴³⁸⁵^16.4^85.2^15.0^46.9⁶^.4^22.2
^C^a^rbon⁹⁵⁵²^16.8^88.1^23.3^43.2⁷^.9^20.5
^C^a^rt^er¹³⁶⁰^17.9^83.3^13.6⁽^15.9)^7.3^16.1
^C^h^ou^t^eau⁵⁹⁷⁰^17.5^87.1^20.5^26.4⁽^32.6)^15.1
^C^u^s^t^er¹¹⁶⁹⁶^17.1^84.9^18.8^21.8⁴^.1^(0.4)
^D^aⁿⁱ^el^s²⁰¹⁷^23.5^85.3^14.1^36.5^37.3^63.4
^D^a^w^s^on⁹⁰⁵⁹^17.7^82.7^15.1⁶^.6^11.3⁽^2.4)
ⁱ^{ki/CRS-RL32372}^F^a^l^l^oⁿ²⁸³⁷^17.9^85.7^14.4^33.3^13.3⁽^7.8)
^g/w^F^e^rg^u^s¹¹⁸⁹³^19.9^86.3^19.1^18.2⁵^.7^15.1
^s^.or^G^a^rfⁱ^e^l^d¹²⁷⁹^19.3^84.7^16.8⁽^6.8)^11.8^23.5
^le^ak
^G^l^aci^er¹³²⁴⁷^9.2^78.6^16.5⁹^.2^6.1⁽^21.2)
^://wiki^G^o^l^d^en^V^a^l^l^e^y¹⁰⁴²^16.5^70.5^16.2^43.2⁽^0.5)^19.2
^h^ttp^G^r^anⁱ^t^e²⁸³⁰^15.9^87.8^22.1^18.4⁽^0.8)^8.4
^Jef^f^e^rs^on¹⁰⁰⁴⁹^10.3^90.2^27.7^83.5^10.9^29.6
^Ju^di^t^h^Basⁱⁿ²³²⁹^17.2^87.6^23.6⁽^33.0)^(16.1)^26.4
^Lⁱ^bert^y²¹⁵⁸^19.7^75.0^17.6^59.8⁽^26.2)^0.7
^Lⁱⁿ^c^olⁿ¹⁸⁸³⁷^15.2^80.2^13.7⁽^3.9)^0.9^12.7
^Madi^s^oⁿ⁶⁸⁵¹^17.2^89.8^25.5⁷^.2^11.7^21.2
^McC^oⁿ^e¹⁹⁷⁷^18.9^86.1^16.4⁽^7.4)^22.2^22.5
^Meag^h^e^r¹⁹³²^18.2^83.4^18.7^10.1²^.2^34.5
^Miⁿ^e^ral³⁸⁸⁴^14.2^83.2^12.3³^.7^0.5³^.9
^Mu^s^s^e^l^s^h^e^l^l⁴⁴⁹⁷^17.5^82.6^16.7⁽^8.0)^(7.6)⁽^26.2)
^Park¹⁵⁶⁹⁴^14.9^87.6^23.1^28.2^15.4⁶^.9
^Pet^r^ol^eu^m⁴⁹³^17.0^82.9^17.4⁽^15.4)^(0.1)^117.2
^P^h^illips⁴⁶⁰¹^17.6^82.4^17.1⁽^7.2)^(2.6)^20.8

^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,^p^a^ren^t^h^eses)
^M^oⁿ^t^an^{a C}^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)
^Pon^d^era⁶⁴²⁴^3.0^81.6^19.8^10.6⁽^4.9)^16.9
^Pow^d^{er R}ⁱ^v^e^r¹⁸⁵⁸^18.5^83.4^16.0^31.8¹^.9^(8.3)
^Pow^e^l^l⁷¹⁸⁰^14.0^81.9^13.1^16.6⁵^.6^13.4
^Prai^ri^e¹¹⁹⁹^24.1^78.8^14.8^116.7^14.1^26.1
^Rⁱ^ch^l^aⁿ^d⁹⁶⁶⁷^15.6^83.5^17.2⁹^.4^16.5⁶^.8
^R^oos^ev^el^t¹⁰⁶²⁰^11.6^80.6^15.6⁽^3.2)^24.7^17.3
^R^o^s^e^bu^d⁹³⁸³^8.9^84.4^17.6⁽^8.6)^6.6^28.4
^S^aⁿ^d^ers¹⁰²²⁷^16.9^81.2^15.5^27.1⁴^.5^12.5
^S^h^eri^d^an⁴¹⁰⁵^23.6^81.2^18.4⁶^.1^28.5^34.5
^Stillw^ater⁸¹⁹⁵^14.5^87.⁵^17.8⁷^.0^29.4^31.8
^S^w^eet^G^r^as^s³⁶⁰⁹^17.6^88.9^23.6^29.6¹^.0^6.3
ⁱ^{ki/CRS-RL32372}^T^e^t^oⁿ⁶⁴⁴⁵^16.6^83.4^20.8^34.7⁽^11.7)^15.7
^g/w^T^ool^e⁵²⁶⁷^15.9^81.0^16.8^20.0⁽^11.3)^(3.0)
^s^.or^T^r^eas^u^r^e⁸⁶¹^16.7^86.3^18.2⁽^6.7)^(16.0)^(20.9)
^le^ak
^V^a^l^l^e^y⁷⁶⁷⁵^19.0^83.9^15.7^10.4^24.2^37.7
^://wiki^Wh^eat^l^aⁿ^d²²⁵⁹^19.3^69.0^13.5^14.7⁽^14.6)^(6.7)
^h^ttp^Wi^bau^x¹⁰⁶⁸^21.5^76.8^16.0^80.2^13.9⁷^.8
^C^o^uⁿ^t^{y A}^v^erage⁹⁰²¹⁹⁵^16.4^83.1^17.9^19.1⁴^.6^18.6
^Mon^t^an^a⁹⁰²¹⁹⁵^13.4^87.2^24.4^30.0^10.2^17.9
^Un^{ited States}^{281.4 m}^illion^12.⁴^80.4^24.4^18.4^21.3^65.4
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^e^a^rch^S^e^rvⁱ^ce;^U.S^.^Depart^m^eⁿ^t
^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s.

Appendix E. Remote Nebraska Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Ne^br^a^s^k^a^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^A^r^t^h^u^r⁹⁹⁴⁴⁴^(3.9)⁰^.6^$27,375^13.8³^.4^$13,194
^Banⁿ^e^r⁹⁹⁸¹⁹^(3.9)¹^.1^$31,399^13.6¹^.7^$18,604
^Bl^aiⁿ^e⁹⁹⁵⁸³^(13.6)^0.9^$25,278^19.4¹^.6^$19,878
^Boy^d⁹⁹²⁴³⁸^(14.0)^5.2^$26,075^15.2³^.8^$16,518
^Browⁿ⁹⁹³⁵²⁵^(3.6)³^$28,356^11.1³^.4^$19,007
^C^h^as^e⁹⁹⁴⁰⁶⁸^(7.1)⁴^.9^$32,551^9.6²^.2^$19,666
^C^h^erry⁷⁸⁶¹⁴⁸^(2.5)¹^.1^$29,268^12.3¹^.8^$17,457
^C^u^s^t^er⁷⁸¹¹⁷⁹³^(3.9)⁴^.8^$30,677^12.4¹^.9^$20,363
ⁱ^{ki/CRS-RL32372}^D^e^u^e^l⁹⁹²⁰⁹⁸^(6.2)⁵^.1^$32,981^9.1²^.8^$18,206
^g/w^D^uⁿ^d^y⁹⁹²²⁹²^(11.2)^2.8^$27,010^13.6²^.0^$20,528
^s^.or^F^r^on^tⁱ^e^r⁹⁹³⁰⁹⁹^(0.1)³^.2^$33,038^12.2²^.1^$19,218
^le^ak^G^a^rden⁹⁹²²⁹³^(6.8)¹^.4^$26,458^14.8³^.1^$20,618
^://wiki^G^a^rfⁱ^e^l^d⁹⁹¹⁹⁰²^(11.2)^3.8^$27,407^12.6¹^.8^$16,320
^h^ttp^G^o^s^p^er⁹⁹²¹⁴³^11.2⁴^.2^$36,827^7.9²^.2^$17,688
^G^r^an^t⁹⁹⁷⁴⁷^(2.9)¹^$34,821^9.7¹^.7^$15,951
^G^r^eel^ey⁹⁹²⁷¹⁴^(9.7)⁵^.3^$28,375^14.6³^.0^$17,299
^H^a^y^e^s⁹⁹¹⁰⁶⁸^(12.6)^1.7^$26,667^18.4²^.4^$18,342
^Hⁱ^t^c^h^c^ock⁹⁹³¹¹¹^(17.0)^5.3^$28,287^14.9³^.1^$18,657
^H^o^l^t⁷⁸¹¹⁵⁵¹^(8.3)⁵^.2^$30,738^13.0³^.0^$18,439
^H^ook^er⁹⁹⁷⁸³^(1.3)¹^.1^$27,868^6.9³^.1^$14,879
^K^e^y^a^Pah^a⁹⁹⁹⁸³^(4.5)¹^.3^$24,911^26.9¹^.3^$21,236
^Kⁱ^m^b^al^l⁶⁶⁴⁰⁸⁹^(0.5)⁴^.3^$30,586^11.1²^.2^$18,881
^L^o^g^aⁿ⁹⁹⁷⁷⁴^(11.8)^1.5^$33,125^10.5²^.3^$16,025
^L^o^u^p⁹⁹⁷¹²^4.2¹^.2^$26,250^17.7¹^.9^$15,521
^McPh^ers^oⁿ⁹⁹⁵³³^(2.4)⁰^.6^$25,750^16.2¹^.0^$13,703
^Morri^l^l⁹⁹⁵⁴⁴⁰^0.3³^.8^$30,235^14.7²^.8^$18,879

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Ne^br^a^s^k^a^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^Perkⁱⁿ^s⁹⁹³²⁰⁰^(5.0)³^.8^$34,205^13.6¹^.9^$20,938
^R^o^ck⁹⁹¹⁷⁵⁶^(13.0)²^$25,795^0.0³^.8^$16,753
^S^h^eri^d^an⁹⁹⁶¹⁹⁸^(8.2)²^.8^$29,484^13.2²^.7^$16,713
^Sⁱ^ou^x⁹⁹¹⁴⁷⁵^(4.8)⁰^.7^$29,851^15.4¹^.3^$14,792
^T^h^om^as⁹⁹⁷²⁹^(14.3)^1.2^$27,292^14.3⁵^.7^$17,865
^Wh^eel^er⁹⁹⁸⁸⁶^(6.5)¹^.6^$26,771^20.9²^.9^$18,795
^C^o^uⁿ^t^{y A}^v^erage²⁸²⁵^(6.1)²^.7^$29,241^13.4²^.5^$17,842
^Nebras^k^a^{1.4 m}^illion⁸^.4^22.3^$39,250^9.7³^.1^$27,692
^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323
ⁱ^{ki/CRS-RL32372}Table 2
^g/w^{Per cap}ⁱ^t^aⁱⁿ^com^e
^s^.or^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^ch^an^{ge, 1980-}²⁰⁰⁰
^le^ak^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^N^e^b^r^as^k^a^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^://wiki^A^r^t^h^u^r⁴⁴⁴^16.4^89.5^15.7⁽^7.0)^(36.8)^(42.4)
^h^ttp^Banⁿ^e^r⁸¹⁹^16.0^94.2^19.6^N^A⁽^11.7)^(56.8)
^Bl^aiⁿ^e⁵⁸³^16.8^93.4^12.3^N^A⁽^43.7)^(12.2)
^Boy^d²⁴³⁸^24.3^83.0^12.8^33.1⁽^13.0)^(12.9)
^Browⁿ³⁵²⁵^22.5^83.3^17.2^25.6⁽^6.8)^16.3
^C^h^as^e⁴⁰⁶⁸^21.1^86.4^16.6^26.7⁹^.5^58.0
^C^h^erry⁶¹⁴⁸^17.3^85.3^19.4^48.0¹^.5^6.3
^C^u^s^t^er¹¹⁷⁹³^21.1^87.5^16.1²^.8^7.6^56.6
^D^e^u^e^l²⁰⁹⁸^22.9^85.3^17.4^30.8⁽^6.7)^(15.3)
^D^uⁿ^d^y²²⁹²^22.4^82.4^16.7^30.1⁴^.5^80.3
^F^r^on^tⁱ^e^r³⁰⁹⁹^16.9^88.3^17.9^33.2¹^.7^57.1
^G^a^rden²²⁹³^24.0^85.2^14.2⁽^0.5)^(2.9)⁽^16.4)
^G^a^rfⁱ^e^l^d¹⁹⁰²^24.8^81.1^13.4¹^.2^24.5^65.8
^G^o^s^p^er²¹⁴³^20.8^88.9^17.6⁷^.1^(9.0)^89.7

^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰
^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^N^e^b^r^as^k^a^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^G^r^an^t⁷⁴⁷^13.7^90.3^24.7^46.8⁽^19.0)^(27.1)
^G^r^eel^ey²⁷¹⁴^23.2^83.2^13.5^20.0⁽^7.4)^94.3
^H^a^y^e^s¹⁰⁶⁸^19.9^89.1^11.6⁽^5.1)^(37.1)^50.0
^Hⁱ^t^c^h^c^ock³¹¹¹^22.3^85.6^13.8⁽^15.5)^(8.8)^12.8
^H^o^l^t¹¹⁵⁵¹^19.8^84.5^14.5^19.5⁴^.7^73.1
^H^ook^er⁷⁸³^26.9^89.7^15.7^47.9⁽^25.3)^(12.3)
^K^e^y^a^Pah^a⁹⁸³^20.7^82.2^15.7⁽^21.2)^(17.2)^38.5
^Kⁱ^m^b^al^l⁴⁰⁸⁹^21.0^84.6^13.5^38.9⁰^.7^(6.8)
^L^o^g^aⁿ⁷⁷⁴^17.6^90.8^10.5^28.9⁽^10.6)^(9.3)
^L^o^u^p⁷¹²^19.5^91.8^13.3⁽^36.8)^(47.6)^(49.3)
^McPh^ers^oⁿ⁵³³^18.2^88.6^22.2⁽^36.4)^(40.3)^(27.5)
ⁱ^{ki/CRS-RL32372}^Morri^l^l⁵⁴⁴⁰^17.0^79.4^14.3^25.8⁽^13.4)^(22.1)
^g/w^Perkⁱⁿ^s³²⁰⁰^19.3^87.1^17.6^50.7⁽^7.1)^(0.7)
^s^.or^R^o^ck¹⁷⁵⁶^22.3^87.4^12.2^38.5⁽^18.8)^41.2
^le^ak
^S^h^eri^d^an⁶¹⁹⁸^21.7^86.1^17.2^18.6⁷^.2^6.7
^://wiki^Sⁱ^ou^x¹⁴⁷⁵^16.2^86.4^21.5⁽^82.1)^(36.6)^(50.2)
^h^ttp^T^h^om^as⁷²⁹^20.3^83.7^17.2^186.4^10.2⁽^15.8)
^Wh^eel^er⁸⁸⁶^16.8^90.8^14.9⁽^31.5)^(7.8)^140.3
^C^o^uⁿ^t^{y A}^v^erage²⁶⁵⁹^20.1^86.7^16.0^17.5⁽^11.1)^15.9
^Neb^r^ask^a¹^.⁴^m^illioⁿ¹³^.⁶⁸⁶^.⁶²³^.⁷²⁵^.⁰¹⁶^.⁰⁴²^.⁶
^Un^{ited States}^{281.4 m}^illion^12.⁴^80.4^24.4^18.4^21.3^65.4
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^e^a^rch^S^e^rvⁱ^ce;^U.S^.^Depart^m^eⁿ^t
^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.

Appendix F. Remote North Dakota Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^No^r^t^{h Da}^k^o^ta^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inc^o^m^e^($^),²⁰⁰⁰^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^A^d^am^s⁹⁹²⁵⁹³^(18.3)^3.2^$29,079^10.4²^.0^$19,407
^Ben^s^on⁹⁹⁶⁹⁶⁴^(3.3)⁵^.2^$26,668^29.1⁷^.5^$21,613
^B^illin^g^s⁹⁹⁸⁸⁸^(19.9)^1.⁰^$32,667^12.8³^.9^$16,890
^B^o^ttin^eau⁷⁸⁷¹⁴⁹^(10.8)^4.⁸^$29,853^10.7³^.1^$19,113
^Bow^m^an⁹⁹³²⁴²^(9.8)³^.1^$31,906^8.2¹^.9^$18,126
^Bu^rk^e⁹⁹²²⁴²^(25.3)^2.7^$25,330^15.4²^.5^$21,444
^C^a^v^a^lⁱ^e^r⁹⁹⁴⁸³¹^(20.3)^4.1^$31,868^11.5²^.9^$20,209
^Dⁱ^ck^ey⁹⁹⁵⁷⁵⁷^(5.7)⁵^.4^$29,231^14.8²^.2^$19,293
ⁱ^{ki/CRS-RL32372}^Dⁱ^vⁱ^de⁹⁹²²⁸³^(21.2)^2.3^$30,089^14.6¹^.9^$15,699
^g/w^D^uⁿⁿ⁹⁹³⁶⁰⁰^(10.1)^2.0^$30,015^17.5³^.6^$20,235
^s^.or^Eddy⁹⁹²⁷⁵⁷^(6.6)⁴^.7^$28,642^9.7⁴^.8^$18,615
^le^ak^Em^m^oⁿ^s⁸⁶⁴³³¹^(10.3)^3.2^$26,119^20.1⁴^.6^$18,149
^://wiki^G^o^l^d^en^V^a^l^l^e^y⁹⁹¹⁹²⁴^(8.7)²^.1^$29,967^15.3²^.1^$16,948
^h^ttp^G^r^an^t⁸⁶²⁸⁴¹^(19.9)^2.1^$23,165^20.3²^.7^$16,760
^G^rⁱ^g^g^s⁹⁹²⁷⁵⁴^(16.6)^4.7^$29,572^10.1¹^.7^$20,207
^H^e^t^tⁱⁿ^g^e^r⁹⁹²⁷¹⁵^(21.2)^3.0^$29,209^14.8²^.2^$18,839
^Kⁱ^dder⁹⁹²⁷⁵³^(17.4)^2.5^$25,389^19.8⁵^.3^$17,760
^L^a^Mou^r^e⁹⁹⁴⁷⁰¹^(12.7)^4.7^$29,707^14.7²^.9^$18,000
^L^o^g^aⁿ⁹⁹²³⁰⁸^(18.9)^2.9^$27,986^15.1²^.2^$16,140
^McH^eⁿ^r^y⁹⁹⁵⁹⁸⁷^(8.3)³^.5^$27,274^15.8⁵^.0^$19,036
^McIn^t^o^s^h⁹⁹³³⁹⁰^(15.7)^4.1^$26,389^15.4²^.2^$16,826
^McK^eⁿ^zⁱ^e⁹⁹⁵⁷³⁷^(10.1)^2.3^$29,342^17.2²^.6^$22,896
^McL^ean⁸⁶⁹³¹¹^(11.0)^5.0^$32,337^13.5⁵^.9^$25,880
^Mouⁿ^t^rai^l⁹⁹⁶⁶³¹^(5.6)³^.8^$27,098^19.3⁴^.7^$20,791
^N^e^l^s^on⁸⁶³⁷¹⁵^(15.8)^4.5^$28,892^10.3⁴^.0^$17,154
^O^lⁱ^v^er⁸⁶²⁰⁶⁵^(13.3)^3.3^$36,650^14.9⁴^.9^$42,407

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^No^r^t^{h Da}^k^o^ta^Co^unti^e^s^Co^nti^nuum^Co^de^Inf^l^ue^nc^e^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^{ers in}^p^a^ren^t^h^eses)^De^nsi^t^y^(po^p^/^s^q.^mⁱ^.⁾^Inc^o^m^e^($^),²⁰⁰⁰^Pov^ert^{y R}^a^t^e^(%⁾^Uⁿ^em^p^l^oym^en^t^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^Pi^erce⁷⁸⁴⁶⁷⁵^(7.5)⁵^.0^$26,524^12.5³^.3^$18,035
^R^eⁿ^v^ille⁹⁹²⁶¹⁰^(17.4)^3.⁶^$30,746¹¹^1.9^$19,179
^S^a^rg^en^t⁹⁹⁴³⁶⁶^(4.0)⁵^.3^$37,213^8.2²^.8^$33,929
^S^h^eri^d^an⁹⁹¹⁷¹⁰^(20.4)^2.2^$24,450²¹^6.2^$18,693
^Sⁱ^ou^x⁹⁹⁴⁰⁴⁴^7.5³^.4^$22,483^39.2⁵^.4^$24,520
^S^l^ope⁹⁹⁷⁶⁷^(15.4)^0.7^$24,667^16.9²^.2^$10,375
^S^t^eel^e⁸⁶²²⁵⁸^(6.7)³^.4^$35,757^7.1¹^.2^$22,101
^T^o^wⁿ^er⁹⁹²⁸⁷⁶^(20.7)^3.5^$32,740^8.9²^.7^$19,638
^Wel^l^s⁹⁹⁵¹⁰²^(13.0)^4.6^$31,894^13.5³^.4^$17,796
^C^o^uⁿ^t^{y A}^v^erage³⁷⁶⁸^(13.0)^3.5^$29,169¹⁵^3.4^$20,077
ⁱ^{ki/CRS-RL32372}^N^o^rt^h^D^a^k^o^t^a⁶⁴²²⁰⁰^(1.2)⁹^.3^$34,604^11.9²^.8^$24,683
^g/w^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323
^s^.or
^le^ak
Table 2
^://wiki^{Per cap}ⁱ^t^aⁱⁿ^com^e
^h^ttp^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^ch^an^{ge, 1980-}²⁰⁰⁰
^No^r^t^{h Da}^k^o^ta^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^A^d^am^s²⁵⁹³^24.1^83.1^16.6⁽^4.5)^24.1^48.9
^Ben^s^on⁶⁹⁶⁴^13.5^73.8^10.9^18.6⁽^10.6)^47.0
^B^illin^g^s⁸⁸⁸¹³^.⁵⁷⁷^.⁸¹⁸^.⁸⁶⁶^.³²³^.⁹⁽²^.⁸⁾
^B^o^ttin^eau⁷¹⁴⁹^21.3^81.3^14.9^21.5^15.2^75.9
^Bow^m^an³²⁴²^21.8^82.2^17.9⁹^.4^13.7^37.1
^Bu^rk^e²²⁴²^25.1^78.8^12.0⁽^29.5)^22.4^87.3
^C^a^v^a^lⁱ^e^r⁴⁸³¹^2.9^78.8^13.1^27.4^54.2^107.6
^Dⁱ^ck^ey⁵⁷⁵⁷^21.3^79.6^16.6^28.6^15.4^87.6
^Dⁱ^vⁱ^de²²⁸³^29.5^80.4^13.3⁸^.1^32.3^57.7
^D^uⁿⁿ³⁶⁰⁰^17.4^77.5^16.3^48.5^32.1^13.6

^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰
^No^r^t^{h Da}^k^o^ta^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^Po^pul^a^tⁱ^o^{n 6}⁵^a^{nd o}^l^de^r^grad^u^a^t^e^s^,^{25 an}^d^old^{er (}^%⁾^Bach^{elors d}^e^{gree or}^hⁱ^gh^{er, (}^%⁾^em^p^l^oym^en^t^ch^an^ge,^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)⁽^N^egat^iv^{e n}^u^m^b^{ers in}^p^a^ren^t^h^eses)
^Eddy²⁷⁵⁷^24.7^75.5^15.9⁽^14.1)^(10.9)^51.4
^Em^m^oⁿ^s⁴³³¹^25.6^65.9^12.3^79.8^45.6^102.2
^G^o^l^d^en^V^a^l^l^e^y¹⁹²⁴^21.3^87.4^19.8⁴^.2^(3.6)⁷^.9
^G^r^an^t²⁸⁴¹^24.7^73.4^11.2^31.2^57.8^117.1
^G^rⁱ^g^g^s²⁷⁵⁴^25.7^78.7^15.7^69.5⁸^.1^125.3
^H^e^t^tⁱⁿ^g^e^r²⁷¹⁵^25.2^74.8^14.4⁽^10.6)^65.4^210.8
^Kⁱ^dder²⁷⁵³^24.0^72.0^11.0⁽^9.2)^24.5^204.0
^L^a^Mou^r^e⁴⁷⁰¹^23.4^75.3^13.9^31.1^18.0^196.9
^L^o^g^aⁿ²³⁰⁸^27.0^66.0^12.9⁽^5.8)^25.6^213.4
^McH^eⁿ^r^y⁵⁹⁸⁷^21.8^76.9^13.2²^.0^1.1^46.5
^McIn^t^o^s^h³³⁹⁰^34.2^59.3⁹^.9^14.8^40.9^134.2
ⁱ^{ki/CRS-RL32372}^McK^eⁿ^zⁱ^e⁵⁷³⁷^15.7^79.1^15.7²^.2^23.7^13.4
^g/w^McL^ean⁹³¹¹^20.4^79.0^15.1^15.0⁷^.9^51.7
^s^.or^Mouⁿ^t^rai^l⁶⁶³¹^17.7^77.9^15.6^18.1^18.8^52.4
^le^ak
^N^e^l^s^on³⁷¹⁵^27.4^81.4^17.5²^.2^(13.8)^76.9
^://wiki^O^lⁱ^v^er²⁰⁶⁵^14.2^79.9^12.0⁽^8.9)^36.4^57.7
^h^ttp^Pi^erce⁴⁶⁷⁵^24.1^76.7^14.7^19.0⁽^0.3)^75.2
^R^eⁿ^v^ille²⁶¹⁰^22.0^84.1^16.1^34.7^23.2^126.6
^S^a^rg^en^t⁴³⁶⁶^16.9^81.1^12.7^26.0^25.5^128.2
^S^h^eri^d^an¹⁷¹⁰^26.6^67.8⁹^.7^83.0⁸^.6^62.1
^Sⁱ^ou^x⁴⁰⁴⁴^5.6^78.5^11.2^207.2^22.9^33.7
^S^l^ope⁷⁶⁷^17.9^82.5^16.0⁽^18.5)^117.2^334.4
^S^t^eel^e²²⁵⁸^19.6^86.1^19.8^20.1⁽^4.9)^230.7
^T^o^wⁿ^er²⁸⁷⁶^23.3^81.9^16.1⁽^5.7)^36.7^107.2
^Wel^l^s⁵¹⁰²^26.0^72.6^13.7^16.2⁽^4.2)^60.3
^C^o^uⁿ^t^{y A}^v^erage³⁷⁶⁸^21.3^77.3^14.5^22.8^22.7^96.6
^N^o^rt^h^D^a^k^o^t^a⁶⁴²²⁰⁰^14.7^83.9^22.0^27.3^18.2^46.1
^Un^{ited States}^{281.4 m}^illion^12.⁴^80.4^24.4^18.4^21.3^65.4
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^e^a^rch^S^e^rvⁱ^ce;^U.S^.^Depart^m^eⁿ^t
^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.

Appendix G. Remote Oklahoma Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^Ok^l^a^ho^m^a^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inc^o^m^e^($^),²⁰⁰⁰^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^Beav^er⁹⁹⁵⁸⁵⁷^(2.80)^3.3^$36,715^11.7²^.7^$23,288
^Cⁱ^m^a^rron⁹⁹³¹⁴⁸^(4.60)^1.8^$30,626^16.6²^.3^$18,257
^D^e^w^e^y⁹⁹⁴⁷⁴³^(14.60)^5.5^$28,172¹⁵^2.7^$19,928
^Ellis⁹⁹⁴⁰⁷⁵^(9.40)^3.⁷^$27,951^12.5³^.1^$19,845
^G^r^an^t⁹⁶⁵¹⁴⁴^(9.60)^5.7^$28,977^13.7²^.7^$23,796
^H^a^rper⁸⁶³⁵⁶²^(12.30)^3.9^$33,705^10.2²^.8^$20,529
^R^o^g^e^{r Mills}⁹⁹³⁴³⁶^(17.10)^3.6^$30,078^16.3¹^.9^$20,855
^C^o^uⁿ^t^{y A}^v^erage⁴²⁸¹^(10.06)^3.93^$30,889^13.71^2.6^$20,928
ⁱ^{ki/CRS-RL32372}^Ok^lah^o^m^a^{3.4 m}^illion⁹^.7^50.3^$33,400^14.7³^.8^$26,988
^g/w^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323
^s^.or
^le^ak
Table 2
^://wiki^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^{Per cap}ⁱ^t^aⁱⁿ^com^e
^h^ttp^Po^pul^a^tⁱ^o^{n 6}⁵^Hⁱ^gh^-^S^ch^ool^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^Prⁱ^v^a^t^e^no^n-f^a^r^m^em^p^l^oym^en^t^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^O^k^lah^o^m^a^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^Beav^er⁵⁸⁵⁷^16.9^81.2^17.6⁽^0.6)^4.4⁽^5.7)
^Cⁱ^m^a^rron³¹⁴⁸^18.6^76.6^17.7^34.7⁽^12.4)^45.9
^D^e^w^e^y⁴⁷⁴³^21.0^79.8^16.6⁽^16.1)^(3.5)^15.0
^Ellis⁴⁰⁷⁵^22.0^81.2^19.²^44.7⁽^3.2)^(1.8)
^G^r^an^t⁵¹⁴⁴^21.4^85.7^16.2⁽^17.9)^(10.0)^11.1
^H^a^rper³⁵⁶²^21.7^82.1^19.2¹^.2^11.2^48.1
^R^o^g^e^{r Mills}³⁴³⁶^18.7^79.³^15.8^43.2^18.4^48.1
^C^o^uⁿ^t^{y A}^v^erage⁴²⁸¹^20.0^80.8^17.5^12.7⁰^.7^23.0
^Ok^lah^o^m^a³^.⁴^m^illioⁿ¹³^.²⁸⁰^.⁶²⁰^.³²⁴^.⁵¹⁰^.⁸¹⁸^.¹
^Un^{ited States}^{281.4 m}^illion^12.⁴^80.4^24.4^18.4^21.3^65.4
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^e^C^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^e^a^rch^S^e^rvⁱ^ce;^U.S^.^Depart^m^eⁿ^t
^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.

Appendix H. Remote South Dakota Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d
^So^{uth Da}^k^o^ta^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^(%⁾^R^a^t^e^{, 2001 (}^%⁾^j^o^b^,²⁰⁰⁰
^A^u^rora⁹⁹³⁰⁵⁸^(2.5)⁴^.4^$29,783^11.4²^.4^$18,915
^Benⁿ^e^t^t⁹⁹³⁰⁷⁴^11.5²^.7^$25,313^39.2⁵^.9^$20,293
^Bu^f^f^a^l^o⁹⁹²⁰³²^15.5³^.7^$12,692^56.9⁸^.4^$24,432
^Bu^t^t^e⁷⁸⁹⁰⁹⁴^14.9³^.5^$29,040^12.8⁴^.0^$20,001
^C^a^m^pbel^l⁹⁹¹⁷⁸²^(9.3)²^.7^$28,793^14.1⁶^.8^$16,942
^C^l^ark⁹⁹⁴¹⁴³^(5.9)⁴^.6^$30,208^14.8⁷^.3^$17,581
^C^o^rs^on⁹⁹⁴¹⁸¹^(0.3)¹^.7^$20,654^41.0⁸^.3^$18,520
^C^u^s^t^er⁸⁶⁷²⁷⁵^17.7⁴^$36,303^9.4³^.8^$20,588
ⁱ^{ki/CRS-RL32372}^D^e^w^e^y⁹⁹⁵⁹⁷²^8.1²^.4^$23,272^33.6^16.1^$22,473
^g/w^Edm^uⁿ^d^s⁹⁹⁴³⁶⁷^(0.3)³^.8^$32,205^13.8²^.2^$18,802
^s^.or^F^a^l^l^Rⁱ^v^e^r⁷⁸⁷⁴⁵³^1.4⁴^.2^$29,631^13.6⁴^.1^$20,871
^le^ak^F^a^u^l^k⁹⁹²⁶⁴⁰^(3.8)²^.7^$30,237^18.1²^.7^$17,841
^://wiki^G^r^eg^ory⁹⁹⁴⁷⁹²^(10.6)^5.3^$22,732^20.1³^.7^$17,887
^h^ttp^H^aak^on⁹⁹²¹⁹⁶^(16.3)^1.4^$29,894^13.9²^.6^$19,336
^H^aⁿ^d⁹⁹³⁷⁴¹^(12.4)³^$32,377^9.2²^.2^$17,860
^H^aⁿ^s^on⁹⁹³¹⁵⁹^4.8⁰^.6^$33,049^16.6²^.3^$21,867
^H^y^de⁹⁹¹⁶⁷¹^(1.5)²^$31,103^12.3²^.6^$19,347
^Jack^s^oⁿ⁹⁹²⁹³⁰^4.2¹^.5^$23,945^36.5⁷^.0^$18,736
^Jerau^l^d⁹⁹²²⁹⁵^(5.4)⁴^.6^$30,690^20.6²^.4^$18,159
^Jon^e^s⁹⁹¹¹⁹³^(9.9)¹^.4^$30,288^15.8¹^.5^$17,633
^L^y^m^aⁿ⁹⁹³⁸⁹⁵^7.1²^.2^$28,509^24.3⁴^.9^$17,230
^Mars^h^a^l^l⁹⁹⁴⁵⁷⁶^(5.5)⁵^.8^$30,567^13.9⁷^.3^$19,547
^McPh^ers^oⁿ⁹⁹²⁹⁰⁴^(10.0)^2.8^$22,380^22.6²^.3^$15,392
^Mel^l^e^t^t^e⁹⁹²⁰⁸³^(2.5)¹^.6^$23,219^35.8⁶^.5^$16,274
^Miⁿ^e^r⁹⁹²⁸⁸⁴^(11.9)^5.7^$29,519^11.8⁵^.8^$18,433
^Perkⁱⁿ^s⁹⁹³³⁶³^(14.5)^1.4^$27,750^16.9²^.7^$17,556
^Pot^t^e^r⁹⁹²⁶⁹³^(15.6)^3.7^$30,086^12.6³^.6^$17,291
^S^aⁿ^born⁹⁹²⁶⁷⁵^(5.6)⁵^$33,375^14.9³^.4^$15,970

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d
^So^{uth Da}^k^o^ta^Co^unti^e^s^Co^nti^nuum^Co^de^Inf^l^ue^nc^e^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^{ers in}^p^a^ren^t^h^eses)^De^nsi^t^y^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^Pov^ert^{y R}^a^t^e^(%⁾^Uⁿ^em^p^l^oym^en^t^R^a^t^e^{, 2001 (}^%⁾^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm^j^o^b^,²⁰⁰⁰
^S^h^anⁿ^oⁿ⁷⁸¹²⁴⁶⁶^25.9⁴^.7^$20,916^52.3^12.6^$25,710
^S^pⁱⁿ^k⁷⁸⁷⁴⁵⁴^(6.6)⁵^.3^$31,717^12.8³^.6^$19,878
^S^t^an^l^e^y⁹⁹²⁷⁷²^13.0¹^.7^$41,170^8.7²^.8^$20,458
^S^u^l^l^y⁹⁹¹⁵⁵⁶^(2.1)¹^.6^$32,500^12.1²^.4^$18,265
^T^odd⁹⁹⁹⁰⁵⁰^8.5⁶^$20,035^48.3⁸^.3^$21,262
^T^rⁱ^p^p⁷⁸⁶⁴³⁰^(7.1)⁴^.3^$28,333^19.9³^.1^$18,847
^Zⁱ^ebach⁹⁹²⁵¹⁹^13.5¹^.1^$18,062^49.9^14.4^$21,593
^C^o^uⁿ^t^{y A}^v^erage⁴¹²⁵^(0.4)³^.2^$28,010^22.3⁵^.1^$19,194
^S^o^u^t^h^D^a^k^o^t^a⁷⁵⁴⁸⁴⁴^8.5⁹^.9^$35,282^13.2³^.3^$24,802
^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323
ⁱ^{ki/CRS-RL32372}
^g/w
^s^.orTable 2
^le^ak
^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^{ge, 1980-}²⁰⁰⁰
^://wiki^So^{uth Da}^k^o^ta^Po^pul^a^tⁱ^o^{n 6}⁵^Hⁱ^gh^-^S^ch^ool^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^Prⁱ^v^a^t^e^no^n-f^a^r^m^em^p^l^oym^en^t^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^h^ttp^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^A^u^rora³⁰⁵⁸^21.6^79.5^12.7^15.3^16.4^96.6
^Benⁿ^e^t^t³⁰⁷⁴^11.1^71.3^12.7^42.3⁰^.9^33.4
^Bu^f^f^a^l^o²⁰³²^6.5^63.9⁵^.4^(24.0)^0.4^51.8
^Bu^t^t^e⁹⁰⁹⁴^15.2^79.8^12.2^25.0⁸^.9^5.4
^C^a^m^pbel^l¹⁷⁸²^22.1^79.2^14.8^32.5^57.8^96.0
^C^l^ark⁴¹⁴³^22.2^76.6^11.4^22.0^20.2^73.6
^C^o^rs^on⁴¹⁸¹^10.5^76.0^11.3⁰^.4^36.3^58.2
^C^u^s^t^er⁷²⁷⁵^16.0^88.9^24.4⁰^.2^3.1^11.7
^D^e^w^e^y⁵⁹⁷²^8.3^77.4^12.2^121.0^25.9^44.0
^Edm^uⁿ^d^s⁴³⁶⁷^22.2^73.6^15.5^35.4^26.5^127.3
^F^a^l^l^Rⁱ^v^e^r⁷⁴⁵³^22.5^82.5^19.2^54.1^19.6⁸^.7
^F^a^u^l^k²⁶⁴⁰^22.9^73.7^13.1^40.3^27.0^103.5
^G^r^eg^ory⁴⁷⁹²^24.8^77.7^12.0^13.5^14.8^51.0

^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰
^So^{uth Da}^k^o^ta^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^Po^pul^a^tⁱ^o^{n 6}⁵^a^{nd o}^l^de^r^grad^u^a^t^e^s^,^{25 an}^d^old^{er (}^%⁾^Bach^{elors d}^e^{gree or}^hⁱ^gh^{er, (}^%⁾^em^p^l^oym^en^t^ch^an^ge,^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)⁽^N^egat^iv^{e n}^u^m^b^{ers in}^p^a^ren^t^h^eses)
^H^aak^on²¹⁹⁶^18.0^86.3^15.4^20.5^22.7^87.7
^H^aⁿ^d³⁷⁴¹^24.2^80.1^15.6⁷^.0^12.2^58.5
^H^aⁿ^s^on³¹⁵⁹^14.9^75.1^14.0^20.1^34.6^107.0
^H^y^de¹⁶⁷¹^22.3^80.5^16.0^61.2⁷^.7^69.0
^Jack^s^oⁿ²⁹³⁰^11.6^82.7^16.2^30.4⁽^0.7)^58.9
^Jerau^l^d²²⁹⁵^25.6^79.6^12.3^15.7^11.2^108.9
^Jon^e^s¹¹⁹³^18.2^86.2^17.8^32.9⁽^0.7)^35.5
^L^y^m^aⁿ³⁸⁹⁵^13.6^81.1^15.9^46.1⁴^.1^91.9
^Mars^h^a^l^l⁴⁵⁷⁶^21.3^75.6^16.2^49.0^13.0^96.9
^McPh^ers^oⁿ²⁹⁰⁴^29.6^58.8^10.7⁽^8.5)^14.2^65.8
^Mel^l^e^t^t^e²⁰⁸³^13.2^78.1^16.6^126.5⁽^5.6)^19.5
ⁱ^{ki/CRS-RL32372}^Miⁿ^e^r²⁸⁸⁴^23.9^79.6^13.5⁽^9.0)^16.0^92.2
^g/w^Perkⁱⁿ^s³³⁶³^23.7^80.3^14.6⁽^7.6)^5.7^46.4
^s^.or^Pot^t^e^r²⁶⁹³^25.0^80.8^16.2^65.2^59.9^127.3
^le^ak
^S^aⁿ^born²⁶⁷⁵^19.5^82.7^14.8^26.8^30.4^110.3
^://wiki^S^h^anⁿ^oⁿ¹²⁴⁶⁶^4.8^70.0^12.1^53.8^34.3^51.8
^h^ttp^S^pⁱⁿ^k⁷⁴⁵⁴^18.9^81.4^14.4⁹^.0^14.0^96.8
^S^t^an^l^e^y²⁷⁷²^11.0^87.7^22.1^70.9^35.9^47.1
^S^u^l^l^y¹⁵⁵⁶^17.4^84.9^16.4⁸^.1^13.5^197.5
^T^odd⁹⁰⁵⁰^5.8^74.1^12.1^50.1^32.8^30.0
^T^rⁱ^p^p⁶⁴³⁰^19.7^80.2^13.5^25.7⁴^.2^43.5
^Zⁱ^ebach²⁵¹⁹^7.5^71.4^12.0^49.0⁽^19.6)^(15.8)
^C^o^uⁿ^t^{y A}^v^erage⁴¹²⁵^17.6^78.2^14.4^32.0^17.1^68.2
^S^o^u^t^h^D^a^k^o^t^a⁷⁵⁴⁸⁴⁴^14.3^84.6^21.5^37.2^21.4^52.6
^Un^{ited States}^{281.4 m}^illion^12.⁴^80.4^24.4^18.4^21.3^65.4
^Sources^:^U^.^S^.^D^e^part^m^eⁿ^t^of^C^o^m^m^e^{rce, Bu}^reau^of^t^h^{e C}^eⁿ^s^u^s^;^U^.^S^.^D^e^part^m^eⁿ^t^of^L^a^{bor, Bu}^reau^of^L^a^{bor S}^t^atⁱ^s^tⁱ^c^s^;^U^S^D^A^{, Econ}^omⁱ^c^R^e^s^ear^ch^Serv^{ice; U.S.}^Departm^eⁿ^t
^of^C^o^m^m^e^{rce, Bu}^reau^of^Econ^omⁱ^c^Aⁿ^al^y^sⁱ^s^.

Appendix I. Remote Texas Counties
Table 1
^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^T^e^xa^{s Co}^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^A^r^m^s^t^r^on^g⁸⁶²^,148^6.3²^.2^$39,194^10.6¹^.3^$25,776
^Bay^l^or⁷⁸^4,093^(6.7)⁵^.0^$24,627^16.1⁴^.2^$19,654
^Borden⁹⁹⁷²⁹^(8.8)⁰^.9^$29,205^14.0²^.0^$25,395
^Brew^s^t^er⁷⁸^8,866^2.5¹^.4^$27,386^18.2²^.2^$21,549
^Bri^s^coe⁹⁹¹^,790^(9.2)²^.2^$29,917^16.0²^.7^$18,747
^C^o^ch^ran⁷⁸³^,730^(14.8)^5.6^$27,525^27.0⁶^.5^$21,645
^C^o^k^e⁸⁶^3,864^12.9³^.8^$29,085^13.0¹^.9^$21,997
^C^o^l^lⁱⁿ^g^s^w^o^rt^h⁹⁹³^,206^(10.3)^3.9^$25,437^18.7¹^.1^$21,802
ⁱ^{ki/CRS-RL32372}^C^oⁿ^c^h^o⁸⁶^3,966^(15.3)^3.1^$25,446^18.4⁵^.0^$26,876
^g/w^C^o^ttle⁹⁹^1,904^30.3²^.5^$31,312^11.9¹^.9^$19,761
^s^.or^C^r^an^e⁶⁶³^,996^(14.1)^5.9^$32,194^13.4⁶^.0^$31,329
^le^ak^C^r^ock^e^t^t⁷⁷^4,099^0.5¹^.5^$29,355^19.4²^.6^$21,252
^://wiki^C^u^l^b^ers^oⁿ⁷⁸²^,975^(12.7)^0.9^$25,882^25.1⁷^.6^$18,935
^h^ttp^D^a^l^l^a^m⁷⁸⁶^,222^13.9³^.6^$27,946^14.1²^.2^$24,966
^Dⁱ^ck^en^s⁹⁹²^,962^7.4²^.8^$25,898^17.4³^.1^$22,250
^D^oⁿ^l^ey⁹⁹^3,828^3.6⁴^.0^$29,006^15.9²^.6^$18,789
^Edw^a^rds⁹⁹²^,162^(4.6)¹^.1^$25,298^31.6⁴^.6^$21,062
^Fⁱ^s^h^er⁹⁸^4,344^(10.3)^5.4^$27,659^17.5³^.2^$21,151
^F^o^ard⁹⁹¹^,622^(9.6)²^.5^$25,812^14.3²^.8^$16,897
^G^a^rza⁶⁶⁴^,872^(5.3)⁵^.7^$27,206^22.3²^.5^$22,592
^G^l^as^s^c^ock⁸⁶¹^,406^(2.8)¹^.6^$35,655^14.7³^.0^$22,661
^H^a^l^l⁹⁹^3,782^(3.1)⁴^.3^$23,016^26.3⁴^.2^$17,922
^H^a^rt^l^e^y⁷⁸⁵^,537^52.4²^.5^$46,327^6.6¹^.2^$22,852
^H^e^m^p^hⁱ^l^l⁹⁹^3,351^(9.9)⁴^.1^$35,456^12.6¹^.7^$26,630
^H^u^ds^pet^h⁸⁶^3,344^14.7⁰^.6^$21,045^35.8⁴^.3^$24,227
^Iri^oⁿ⁸⁶¹^,771^8.7¹^.5^$37,500^8.4²^.3^$28,254
^Jef^f^D^a^vⁱ^s⁹⁹²^,207^13.4⁰^.9^$32,212^15.0¹^.8^$21,340
^Ji^m^H^o^g^g⁶⁶^5,281^3.4⁴^.5^$25,833^25.9⁴^.5^$20,361

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^T^e^xa^{s Co}^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^K^eⁿ^e^dy⁹⁹⁴¹⁴^(10.0)^0.3^$25,000^15.3¹^.8^$19,983
^K^eⁿ^t⁹⁹⁸⁵⁹^(15.0)^1.1^$30,433^10.4²^.0^$20,354
^Kⁱ^m^b^l^e⁷⁸^4,468^8.4³^.3^$29,396^18.8¹^.7^$18,812
^Kⁱⁿ^g⁷⁸³⁵⁶^0.6⁰^.4^$35,625^20.7³^.8^$31,445
^K^eⁿⁿ^e^y⁹⁹³^,379^8.3²^.3^$28,320^24.0⁶^.3^$22,267
^Kⁿ^ox⁹⁹^4,253^(12.1)^5.7^$20,665^22.9³^.3^$21,693
^L^a^S^a^l^l^e⁶⁶^5,866^11.6³^.5^$21,857^29.8⁶^.2^$23,579
^Lⁱ^ps^com^b⁹⁹^3,057^(2.7)³^.4^$31,964^16.7²^.0^$25,457
^L^o^vⁱⁿ^g⁹⁹⁶⁷^(37.4)^0.2^$40,000^0.0⁷^.9^$36,569
^Martⁱⁿ⁶⁵^4,746^(4.2)⁵^.4^$31,836^18.7⁴^.1^$25,665
^Mas^oⁿ⁹⁹³^,738^9.2³^.7^$30,921^13.2¹^.6^$19,578
ⁱ^{ki/CRS-RL32372}^McMu^l^l^eⁿ⁹⁹⁸⁵¹^4.2⁰^.7^$32,500^20.7³^.1^$31,205
^g/w^Men^a^rd⁸⁶^2,360^4.8²^.5^$24,762^25.8⁴^.0^$18,031
^s^.or^Mot^l^e^y⁹⁹¹^,426^(6.9)¹^.5^$28,348^19.4¹^.7^$18,846
^le^ak
^O^l^dh^am⁸⁶^2,185^(4.1)¹^.5^$33,713^19.8¹^.4^$22,073
^://wiki^Pecos⁷⁸^16,809^14.5³^.1^$28,033^20.4⁵^.0^$22,994
^h^ttp^Presⁱ^dⁱ^o⁷⁸^7,304^10.0¹^.7^$19,860^36.4^23.5^$21,236
^R^eag^an⁶⁶^3,326^(26.3)^3.8^$33,231^11.8³^.0^$24,434
^R^eal⁹⁹^3,047^26.3³^.4^$25,118^21.2³^.8^$15,165
^R^eev^es⁷⁷^13,137^(17.1)^6.0^$23,306^28.9⁶^.8^$18,204
^R^obert^s⁹⁹⁸⁸⁷^(13.5)^1.1^$44,792^7.2¹^.5^$20,430
^S^aⁿ^S^a^ba⁷⁸^6,186^14.5⁴^.8^$30,104^16.6²^.9^$20,451
^S^c^h^l^ei^ch^er⁸⁶^2,935^(1.8)²^.3^$29,746^21.5²^.3^$21,094
^S^h^ack^l^e^f^o^rd⁸⁶^3,302^(0.4)³^.6^$30,479^13.6²^.2^$23,488
^S^h^erm^aⁿ⁹⁹³^,186^11.5³^.1^$33,179^16.8¹^.5^$22,448
^S^t^erlⁱⁿ^g⁸⁶¹^,393^(3.1)¹^.6^$35,129^19.3³^.8^$21,282
^S^t^on^ew^al^l⁹⁹¹^,693^(15.9)^2.2^$27,935^18.0⁴^.7^$21,153
^S^u^t^t^oⁿ⁷⁸⁴^,077^(1.4)²^.8^$34,385^25.2²^.8^$24,289
^T^e^rrel^l⁹⁹¹^,081^(23.3)^0.6^$24,219^13.5³^.0^$23,783
^T^h^rock^m^o^rt^on⁹⁹^1,850^(1.6)²^.1^$28,277^19.9²^.1^$18,746

^P^o^p^u^l^at^ion^C^h^an^ge,
^Rur^a^l^-^Ur^baⁿ^Ur^baⁿ^1990-^{2000 (}^%⁾^Po^pul^a^tⁱ^oⁿ^Me^di^aⁿ^Ho^use^h^o^l^d^A^v^{erage w}^{age p}^{er n}^oⁿ^-^f^a^rm
^Co^nti^nuum^Inf^l^ue^nc^e⁽^N^egat^iv^{e n}^u^m^b^{ers in}^De^nsi^t^y^Pov^ert^{y R}^a^t^e^Uⁿ^em^p^l^oym^en^t
^T^e^xa^{s Co}^unti^e^s^Co^de^C^o^d^e^P^o^p^u^l^at^ion^,²⁰⁰⁰^p^a^ren^t^h^eses)^(po^p^/^s^q.^mⁱ^.⁾^Inco^m^e^($⁾^{, 2000}^(%⁾^R^a^t^e^{, 2001 (}^%⁾^jo^b,²⁰⁰⁰
^U^p^t^oⁿ⁸⁶³^,404^(23.5)^3.6^$28,977^4.1^$28,407
^C^o^uⁿ^t^{y A}^v^erage^3,554^(1.1)²^.8^$29,569^18.4³^.6^$22,540
^T^e^x^a^s^{20.8 m}^illioⁿ^22.8^79.6^$39,927^15.4⁴^.9^$34,941
^Un^{ited States}^{281.4 m}^illion^13.1^79.6^$41,994^12.4⁴^.8^$35,323
Table 2
^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰
^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^Tex^a^s^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
ⁱ^{ki/CRS-RL32372}^A^r^m^s^t^r^on^g²¹⁴⁸^19.2^82.4^20.5^N^A⁽^5.8)^(19.2)
^g/w^Bay^l^or⁴⁰⁹³^24.1^70.1^12.1^40.7⁽^3.6)^(5.5)
^s^.or^Borden⁷²⁹^16.3^83.9^21.4^N^A⁽^39.4)^(52.3)
^le^ak^Brew^s^t^er⁸⁸⁶⁶^14.6^78.6^27.7^28.8^28.3^21.5
^://wiki^Bri^s^coe¹⁷⁹⁰^19.3^74.8^17.5⁽^7.9)^(6.0)^43.4
^h^ttp^C^o^ch^ran³⁷³⁰^14.4^62.7^10.2⁹^.6^3.8^123.3
^C^o^k^e³⁸⁶⁴^24.1^74.2^14.7^67.8²^.6^5.3
^C^o^l^lⁱⁿ^g^s^w^o^rt^h³²⁰⁶^22.0^71.3^15.3⁽^3.8)^2.1^73.0
^C^oⁿ^c^h^o³⁹⁶⁶^13.8^59.3^14.1^89.9^15.9³^.6
^C^o^ttle¹⁹⁰⁴^25.6^66.1^15.³^49.2⁽^23.1)^44.5
^C^r^an^e³⁹⁹⁶^10.9^68.7^12.8⁽^27.5)^7.5⁽^12.8)
^C^r^ock^e^t^t⁴⁰⁹⁹^12.9^62.1^10.4^26.8⁽^10.4)^(1.3)
^C^u^l^b^ers^oⁿ²⁹⁷⁵^11.2^56.1^13.9^31.6^21.3⁽^8.2)
^D^a^l^l^a^m⁶²²²^10.3^65.0⁹^.6^38.9^24.1^88.5
^Dⁱ^ck^en^s²⁹⁶²^19.0^70.6⁸^.4^26.0⁽^5.8)^17.8
^D^oⁿ^l^ey³⁸²⁸^21.7^78.2^15.8⁽^17.6)^(8.4)^16.8
^Edw^a^rds²¹⁶²^16.2^67.1^17.3⁽^31.7)^3.1⁽^0.1)
^Fⁱ^s^h^er⁴³⁴⁴^22.7^73.3^12.4^15.7⁽^4.0)^14.3
^F^o^ard¹⁶²²^23.1^70.0^10.5⁽^6.2)^(3.4)^28.5
^G^a^rza⁴⁸⁷²^14.1^70.1^10.0⁽^2.6)^22.8^25.8

^{Per cap}ⁱ^t^aⁱⁿ^com^e
^Hⁱ^gh^-^S^ch^ool^Prⁱ^v^a^t^e^no^n-f^a^r^m^{Per cap}ⁱ^t^aⁱⁿ^com^e^ch^an^ge,^1990-²⁰⁰⁰⁽^N^egat^iv^{e n}^u^m^b^ers^ch^an^{ge, 1980-}²⁰⁰⁰
^Po^pul^a^tⁱ^o^{n 6}⁵^grad^u^a^t^e^s^,^{25 an}^d^Bach^{elors d}^e^{gree or}^em^p^l^oym^en^t^ch^an^ge,⁽^N^egat^iv^{e n}^u^m^b^{ers in}
^Tex^a^s^C^o^uⁿ^t^ies^P^op^u^l^at^ion^,²⁰⁰⁰^a^{nd o}^l^de^r^old^{er (}^%⁾^hⁱ^gh^{er, (}^%⁾^1990-¹⁹⁹⁹ⁱⁿ^p^a^ren^t^h^eses)^p^a^ren^t^h^eses)
^G^l^as^s^c^ock¹⁴⁰⁶^9.0^69.9^18.7^117.2⁽^20.3)^74.5
^H^a^l^l³⁷⁸²^21.5^61.7⁹^.1^81.9⁽^11.5)^(31.8)
^H^a^rt^l^e^y⁵⁵³⁷^11.9^77.3^17.6^158.8⁽^8.2)^48.4
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