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This thesis titled
Inequality in the Appalachian Region:
Impact of Place, Education, and Gender on Income Disparity
by
STACI R. VAUGHAN
has been approved for
the Department of Sociology and Anthropology
and the College of Arts and Sciences by
Cynthia Anderson
Professor of Sociology
Robert Frank
Dean, College of Arts and Sciences
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ABSTRACT
VAUGHAN, STACI R., M.A., August 2015, Sociology
Inequality in the Appalachian Region: Impact of Place, Education, and Gender on Income
Disparity
Director of Thesis: Cynthia Anderson
This research uses county-level data to examine the impact of place on income
inequality in the Appalachian region. Previous research on theories of spatial inequality
suggest geographical isolation is a significant predictor of life chances. The research
questions ask if county-level income decreases due to a county being Appalachian and
rural and what effect does the female-headed household rate and education rate have on
income. The dependent variable is income and independent variables include
unemployment, education, female-headed household rates, the Appalachian region, and
rurality. Specific hypotheses are: (1) Appalachian counties will have lower county-level
incomes than non-Appalachian counties. (2) Counties with high female-headed
household rates will have lower incomes. (3) Education rates will be higher in non-
Appalachian counties in comparison to Appalachian counties. Linear regression models
were performed via SPSS with data primarily from the Census and American Community
Survey. Central findings show unemployment, education, female-headed households, the
Appalachian region, and rurality predict county-level income at 71.3 percent. It remains
that this region needs continued attention and funding from the state and government
level.
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ACKNOWLEDGMENTS
Mom, Grandma, and Papaw: Thank you, from the bottom of my heart, for
everything you have done for me. I am thankful for your continued support to chase my
dreams and I will forever be thankful for that. I love you all more than you could ever
know.
Jordan: Thank you for your unconditional love and for being my best friend.
Even in the toughest of times you always provide me with support, encouragement, and
love. I look forward to being your rock as you endure graduate school. No matter what
we may encounter in the future, I know we will persevere and enjoy our beautiful life
together.
My friends: Thank you all for your continued support throughout our friendship.
Stephanie, thank you for always being one phone call away. Even when I am in tears you
always know the right thing to say to get me in good spirits. Katlyn, I honestly do not
know how I would have survived graduate school without you. You always provide
support, whether it is through conversation, movie nights, or bringing me lunch. I am
forever grateful for that! I am really going to miss having you right next door. Clara,
thank you for being so caring and a wonderful friend. Without you, I would not have able
to experience an amazing side of Athens and Ohio University. I am excited to see where
our friendship takes us next! Neel, thank you for your optimism. No matter what situation
we are in you are always hopeful and a joy to be around. I am looking forward to visiting
you and your beautiful family in Tennessee!
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My committee: Thank you for being my academic family. I have grown so much
through your courses and mentorship. Dr. Thorne, thank you for your support, especially
during my first year in graduate school. I am grateful to have worked under you as a
graduate assistant. Also, I am thankful for your advice on teaching and for your detailed
edits to my writing. Dr. Henderson, I will be forever thankful for all of the support you
have given me in these two years. I appreciate all of the advice you provided about
classes, teaching, my thesis and especially life. I am very thankful for all of the time you
took working with me and your caring support outside of the office. Dr. Anderson, I am
extremely grateful for your support and encouragement these last two years. You have
helped me tremendously throughout the ups and downs of graduate school and especially
with my thesis. Looking back, my ideas on what I wanted to research have developed
vastly from taking your class my first semester here, presenting at the Southern
Sociological Society conference in Charlotte, to analyzing the results from my data set. I
am very thankful for your time and help with me on this thesis – I am so proud of it!
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LIST OF TABLES
Page
Table 1: Descriptive Statistics Variables for all Counties…………...………..…………56
Table 2: Descriptive Statistics While Controlling for Appalachia………………………57
Table 3: Pearson Correlation for Variables……………………………………………...58
Table 4: Linear Regression Analysis for Income Variable…………...……………….…59
Table 5: Linear Regression Analysis with Appalachia and Rurality Model………….....60
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LIST OF FIGURES
Page
Figure 1: The Appalachian and non-Appalachian Counties….……………….…………61
Figure 2: Map of Counties in the Appalachian Region………………………………….62
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CHAPTER ONE: INTRODUCTION
1.1 Problem Statement
This research is about the effect of geographic place within the Appalachian
region on income. The impact of place will be examined through a literature review, the
collection of data, and an analysis of the findings. Other variables that will examine
inequality in the Appalachian region include education and gender and how they may
influence income. This is an important problem because the Appalachian region is
geographically isolated and those living in the region are disadvantaged in terms of
income and education (Pollard 2003; Moore 2005).
Previous literature suggests that the Appalachian region is not only geographically
isolated but impoverished and economically underdeveloped (Thorne, Tickamyer, and
Thorne 2005). The Appalachian region is a symbol of poverty with Appalachian poverty
being higher in comparison to the nation (Thorne et al. 2005). Also, rural Appalachia is
lagging behind rural America in terms of economic prosperity (Thorne et al. 2005; Moore
2005). Scholars find that significant inequalities exist in educational attainment and
income for women (Latimer and Oberhauser 2005). In 2000, 18 percent of Appalachians,
25 years of age and older, held a four-year degree compared to 25 percent for the nation.
(Gebremariam, Gerbremedhim, and Schaeffer 2011). Education is directly related to
occupational opportunities (Torpey and Watson 2014). For women in the Appalachian
region, lower levels of education may result in disproportionate employment in lower-
paying, service sector jobs such as support services (Latimer and Oberhauser 2005).
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What is missing in the literature is a recent analysis of the data to compare counties with
the 13 states that contain Appalachian counties.
The purpose of my research is to illuminate significant differences across
Appalachian counties and better understand the effect of rural place on income. I use
descriptive statistics, correlations, and linear regression models to analyze county-level
data. I examine factors of rurality in the Appalachian counties across 13 states, and also
levels of educational attainment, percentage of female-headed households and
unemployment rates. By providing linear regression models, county-level income can be
predicted from specified variables that existing literature has found to be significant. My
research will contribute to the literature on spatial inequality in Appalachian by updating
data and measures for the region and offering policy recommendations based on findings.
1.2 The Appalachian Region
The Appalachian region is made up of 428 counties spread across 13 states. First
designated as a nationally important region by President Kennedy in 1963, the
Appalachian Regional Commission was formed from the 1964 President’s Appalachian
Regional Commission report (Appalachia Executive Summary 2015). The Appalachian
Regional Commission (ARC) is a government agency that advocates for economic
development in the Appalachian region (Appalachia Executive Summary 2015).
While Appalachian counties are diverse in terms of population, income, and
geographical place, the region is generally seen as impoverished with a low
socioeconomic status (Thorne, Tickamyer, and Thorne 2005). Moore (2005) explains that
the rural Appalachian region is lagging behind rural America because of geographic
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isolation and economic stagnation. Almost half of people in the Appalachian region live
in rural areas (Gohl 2014). Workers in rural areas earn less, on average, than workers in
urban centers. In 2005, rural workers earned 72 cents for every dollar earned within an
urban area (Litcher and Graefe 2011). Due to recent national financial crises, there are
increased rates of unemployment across the nation (Litcher and Graefe 2011). Over the
past decade, jobs were lost in rural areas, especially those jobs with high wages such us
extractive jobs (Litcher and Graefe 2011).
Despite federal and state government aid to rid the Appalachian area of economic
distress, the region remains consistently poor and underdeveloped (Latimer 2000). The
region continues to be important to the United States government as federal and state
initiatives seek to alleviate economic distress. In 2011, President Obama created the
White House Rural Council so that rural regions can be economically prosperous (Gohl
2014; White House Rural Council 2011). One of the initiatives of this council, Made in
Rural America, seeks to increase economic opportunity in rural Appalachia by expanding
the region’s exports and increasing employment (Gohl 2014).
From a spatial inequality perspective, it is important to acknowledge that although
Appalachian counties as a whole are often described as poor and disadvantaged, there are
some that are economically strong (Pollard 2003). Appalachian counties that contain
large urban centers are likely to be doing better than the national average in terms of
income (Pollard 2003). One example is Allegheny County. This is an Appalachian county
home to the city of Pittsburgh. Allegheny County’s estimated median household income
was $46,215 in 2009, which is significantly higher than that of Philadelphia County,
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Pennsylvania, a non-Appalachian county, where the estimated median household income
was $37,045 in 2009 (City Data 2012; City Data 2013). The discrepancy in income
across geographic place points to the importance of understanding spatial inequality
when looking at the Appalachian region. Economic development, income, and measures
of inequality spread unevenly across areas. Research must address the impact of place,
such as rurality, on outcomes (Anderson and Weng 2011). My next section introduces
theories of spatial inequality and their role in understanding income in the Appalachian
region.
1.3 Sociological Perspectives
Economic and spatial differences between rural and urban areas within
Appalachia are the largest factor contributing to underdevelopment for the region
(Latimer and Oberhauser 2005). On average, urban areas contain more workers with
stronger skills and higher education levels than do rural areas (Gibbs 2002; Appalachia
Executive Summary 2015). Rural areas have suffered from significant education
inequalities due to poverty (Litcher and Graefe 2011).
The Appalachian region has experienced an uneven development of the
institutionalized state of cultural capital (Litcher and Graefe 2011) which is defined as an
asset or resource in the form of academic qualifications (Bourdieu 1986). Education plays
an important role in determining one’s life chances, and as Bourdieu articulated, it
influences one’s cultural capital (1986). Hong and Wernet’s (2007) research found that
most working poor had less than a high school degree, compared to the working non-
poor. These findings support Bourdieu’s theory of cultural capital since most working
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poor have not completed their high school degree (Hong and Wernet’s 2007). In terms of
county-level income, a large working-poor population would have a negative impact.
Education also plays an important role in county-level economic development in terms of
providing role models and support systems for children (Ali and Saunders 2006).
The 2015 Executive Summary for Appalachia recognizes improvement in the
high school graduation rates across Appalachian counties (nearly equal to that of the
nation), but notes a significant gap between Appalachian and non-Appalachian college
completion rates (Appalachia Executive Summary 2015). As previously stated,
Appalachian’s had a high school completion rate four percent less than non-Appalachians
(Gebremariam et al. 2011). In 2008-2012, 28.5 percent of the U.S. population (age 25
years and over) had bachelors’ degrees versus 21.3 percent for the Appalachian region
(Appalachia Executive Summary 2015). This information suggests that the rural
Appalachian region has improved for education, but it is still imperative that researchers
continue to track the trends for the Appalachian region.
In addition to education, gender is a key factor contributing to income status
(Latimer and Oberhauser 2005). Overall, women in the U.S. earn less than men (Latimer
2000). The wage gap is greater in rural areas because rural regions have higher rates of
low-wage work in comparison to the United States (Anderson and Weng 2011). Because
households require two stable incomes, female-headed households and households with
single women tend to be disadvantaged in the labor market (Latimer and Oberhauser
2005; Anderson and Weng 2011). Also, female-headed households with children in rural
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areas have the highest rate of low-wage work than the overall U.S. rate (Anderson and
Weng 2011).
1.4 Overview of Thesis
Chapter 2 grounds the research in scholarly literature on spatial inequality while
highlighting aspects of rural labor markets and cultural capital. The literature review
highlights important aspects of rural Appalachia, gender, and education. Chapter 3
provides a detailed outline of the research methodology, including the sample, measures
of key variables, and analysis technique. Chapter 4 presents the results. Chapter 5
discusses the significant findings in terms of theory, policy, and future research.
My research questions ask if county-level income decreases due to a county being
Appalachian and rural and what effect does the unemployment rate, education rate, and
female-headed household rate have on income in Appalachian and non-Appalachian
counties.
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CHAPTER TWO: LITERATURE REVIEW
This research focuses on the effects geographic place in the Appalachian region.
County-level income is examined alongside key variables such as gender and education.
This chapter will provide a theoretical perspective for spatial inequality with attention to
rural labor markets and cultural capital. Next, is a detailed look at the literature on the
rural Appalachian region, female-headed households, and education alongside the
theoretical framework.
Social inequality has long-been a focus of sociological research (Lobao 2007).
The role of space is very significant in understanding inequalities among various
geographical locations (Lobao 2007). Spatial inequality recognizes the impact of where
one lives and works as a key component to their access of resources (Lobao 2007).
Researchers concerned with economic well-being recognize an uneven development
across space and how resources are unevenly distributed (Lobao 2007).
Spatial inequality calls attention to issues of benefits and disadvantages across
space, such as rural spaces or urban spaces (Slack 2010). Space is more largely being
recognized as a key component to understanding variations in inequality (Slack 2010).
Spatial inequality is the overall theory used because rural labor markets and cultural
capital are influenced by geographical location (Lobao 2014). Spatial inequality
perspectives analyze and question differences that effect people and places due to
location. I will draw from literature on rural labor markets to understand the significance
of the Appalachian region and incorporate theories of cultural capital to develop the role
of gender, female-headed household and education.
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Most research on sociological inequalities is aspatial, meaning that spatial
theories are absent (Lobao 2014). The significance of geographical difference, or place,
on income inequality is underdeveloped (Lobao 2014). County-level income can be
altered by changes within rural labor markets and cultural capital (Campbell 2011).
Often, further economic inequalities arise from jobs being lost in some areas, while other
places (such as metro regions) are gaining (Slack 2014). The USDA (2015) commonly
uses ‘metro’ to represent urban areas and ‘nonmetro’ to represent rural areas. Similarly, I
will use research on metro and nonmetro areas to represent rural and urban places.
An example of spatial inequality can be viewed between two Ohio counties –
Geauga County and Meigs County. In 2010, the census reported the median household
income for Geauga (a non-Appalachian county) as $69,214 with an unemployment rate
of 7.5 percent. Meigs County (Appalachian) had a reported median household income for
2010 by the census as $34,978 and an unemployment rate of 14.9 percent. These two
Ohio counties are separated by less than 230 miles but are vastly different in terms of
opportunities and life chances. Spatial inequality can address these differences alongside
perspectives of rural labor markets and the Fordist period.
The Fordist period is an important concept to incorporate to better understand the
changes in rural labor markets. This era represents a stage of manufacturing growth from
1945 to the early 1970s (Lobao 2014). The rural Appalachian region ushered in labor
unions that held a vested interest for the workers and their economic needs (Lobao 2014;
Slack 2014). In the Fordist era, rural areas in the U.S. were industrialized, meaning there
18. 18
was employment in the manufacturing sector (Lobao 2014). Industrialization provided
the region with raw materials and a stable labor force (Slack 2014).
Rural areas in Appalachia that were coal dependent faced persistent poverty
(Duncan and Lamborghini 1994) due to structural changes in the labor markets that
altered employment in the region (Slack 2014). With the decline of the Fodist period,
which resulted in manufacturing, extractive, and agricultural jobs leaving the area, the
economic development within the rural region weakened (Litcher and Graefe 2011;
Lobao 2014). The weakening of the Fordist era lead to changes for workers in rural areas
due to a shift in the employment sector from extractive jobs, such as mining and oil, to
the service industry (Litcher and Graefe 2011; Lobao 2014). The labor markets within
rural areas encountered lower employment quality when the service sector replaced
manufacturing jobs (Slack 2014). Labor forces encountered lower wages and lost the
majority of their bargaining power with the decline of manufacturing and rise of
globalization (Litcher and Graefe 2011; Lobao 2014).
This is an unfortunate consequence of the end of the Fordist period since labor
markets in rural areas were weakened (Lobao 2014). In the post-Fordist era, globalization
“moved risk away from corporations and toward workers” weakening the stability of
employment in rural spaces (Lobao 2014:548). The rural Appalachian region is detached
from the arrival of information and communication technologies from globalization
(Lobao 2014). These shifting labor markets in rural America have kept employees in
rural areas earning less money than those in urban areas (Gibbs 2002). In order for an
area to thrive there needs to be stable, well-paying jobs. Approximately half a million
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jobs were lost in Appalachia due to the disappearance of most mining, coal and steel jobs.
Some towns such as Youngstown, Ohio experienced a loss of employment up to 40
percent (Moore 2005). Outcomes of a weakened local economy are the decline in the
well-being of the region and increased out-migration of young adults (Litcher and Graefe
2011).
Next, cultural capital is discussed as part of the theoretical framework for spatial
inequalities because it is a disparity that arises from location. Bourdieu (1986) discusses
and defines three forms of cultural capital – the embodied state, the objectified state, and
the institutionalized state. For purposes of this research, the institutionalized state of
cultural capital will be used. Cultural capital may be gained from the social class one is
born into and academic success (Bourdieu 1986). This state of cultural capital identifies
various education levels as having value (Bourdieu 1986). Rural Appalachians are likely
to encounter less economic returns to their cultural capital due to the location in which
they work and live, their gender and marital status, and finally their educational
attainment (Billings and Tickamyer 1993; Tickamyer and Duncan 1990; Smith and
Glauber 2013). Section 2.4 of this chapter will further discuss the literature on cultural
capital and education.
2.1 Rural Appalachia
Inequalities throughout the rural Appalachian region have been a focus for some
researchers and policy makers. In 1964, Lyndon B. Johnson led the Appalachian
Regional Commission so a report could be developed on Appalachia following President
Kennedy’s death (PARC 1964). The President’s Appalachian Regional Commission
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(PARC) prepared a comprehensive action program to economically develop the
Appalachian region (PARC 1964). In 1960, the per capita income for Appalachia was 65
percent of the national average (PARC 1964). One Appalachian county in Kentucky had
the lowest per capita annual income of $840 in the Appalachian region, with the highest
in Appalachia amounting to $1,600 (PARC 1964). The average national per capita
income at the time was $1,900 annually (PARC 1964). The PARC report (1964)
suggested that a lack of urbanization and deficits in education were contributing factors
that hindered Appalachian development. This report also stated that those living in
Appalachia had no desire to abandon their homes, and that this was a significant loss for
the Appalachian region and nation (PARC 1964). Spatial inequalities are clear from the
PARC (1964) report since the lack of urbanization is noted.
To address economic challenges that rural America is facing, President Obama
established the White House Rural Council to focus on providing economic opportunity
in Appalachia and across rural America (Gohl 2014). In 2014, the Obama administration
held a “Made in Rural America” export forum hosted by the Appalachian Regional
Commission (ARC) (USDA 2014). The “Made in Rural America” initiative now focuses
on improving economic growth in rural regions with the rise of globalization (Gohl
2014).
Economic growth has been lacking in rural America in general and the
Appalachian region in particular (Latimer and Oberhauser 2005). Economic inequalities
are spatially and socially different in Appalachia compared to other regions in the
country. Due to Appalachia’s geographical isolation and poor labor market, this region
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has not prospered in the 20th
century (Moore 1994). Specifically, rural Appalachia is
lagging behind rural America, and urban Appalachia is doing poorer than urban America
(Moore 2005).
In Appalachia, the availability and quality of jobs is worse than the rest of the
nation and average wages are 10 percent lower (Foster 2003). Appalachia’s poverty rate
is higher in comparison to the rest of the country (Thorne et al. 2005). Also, rural areas
contain a disproportionate share of the nation’s population living in poverty (Tickamyer
and Duncan 1990). Research shows that poverty in Appalachia is an issue of rural
poverty (Billings and Blee 2002). Rural communities’ poverty can be linked to rural
isolation, limited opportunity structure, unstable employment, and poor mobility
(Tickamyer and Duncan 1990, Billings and Tickamyer 1993). The central Appalachian
region, which is overwhelmingly rural, has historically had the deepest poverty within the
region (Thorne et al. 2005), highlighting the relation between rurality and poverty.
In addition to existing in a rural area, neighboring counties may alter the
performance of a county’s economic condition (Gebremariam et al. 2011). This is
because surrounding counties’ labor markets can impact each other (Gebremariam et al
2011). Isserman and Rephann (1995) found that a well-maintained highway system
aided in lowering transportation costs, brought higher profits into the area, expanded
businesses, and generated more income and employment expansion in the local economy.
This shows how areas experiencing spatial isolation in rural locations, along with weak
labor markets, may need adequate transportation in order to be economically stable in
rural labor markets.
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Varying ruralness can account for differing opportunities and stratification within
the labor market (Duncan and Lamborghini 1994). Metro counties in Appalachia are
more economically stable compared to areas with a higher rurality (Moore 2005). In
2000, nine out of ten economically stable counties in Appalachia were metro (Pollard
2003). Also, nonmetro workers lack employment stability in comparison to metro
counterparts (Jensen, Hsu, and Schachter 1999).
2.2 Gender and the Female-Headed Household
In the Appalachian region, gender has been a contributing factor in socio-
economic inequality (Latimer and Oberhauser 2005). Research has shown that women
are likely to earn less than their male counterparts, especially within rural Appalachia
(Latimer and Oberhauser 2005). In comparison to women living in metro areas, nonmetro
women are more disadvantaged in their employment, and also more likely to be become
unemployed (Jensen et al. 1999). Along with other factors that can influence living in
poverty (being female, unmarried, and less educated) the unemployed also tend to be
younger and have only one income within the family (Hong and Wernet 2007). Also,
female-headed households with young children are more vulnerable to living in poverty
(Thorne et al. 2005).
Variables correlated with unemployment are being female, non-white, unmarried,
and less educated (Jensen et al. 1999). Also, the ability to become adequately re-
employed is reduced for women (Jensen et al. 1999). In respect to women living in
Appalachia, Latimer (2000) found that with other variables held constant (age, education
level, etc.), Appalachian women earned 23 percent less in income. Therefore, being a
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female in Appalachia poses greater disadvantages than females living in non-Appalachian
regions in relation to income. Other studies have also shown that women living in metro
areas have greater return on their education than women living in non-metro areas
(McLaughlin and Perman 1991).
As women enter the workforce, many are still responsible for household labor and
childcare (Tickamyer and Duncan 1990). Tickamyer and Tickamyer’s (1988) research
found gender-specific poverty rates based on female-headed households. As previously
stated, woman are more likely to experience poverty in the Appalachian region than in
the non-Appalachian region. Unpaid labor for women, such as an expanded workload to
include at-home duties, promotes poverty (Tickamyer and Duncan 1990; Tickamyer and
Tickamyer 1988). In 1980 in the United States, 40.3 percent of female-headed
households with children were living in poverty (Tickamyer and Tickamyer 1988). In
contrast, in Kentucky (a rural, Appalachian state) this statistic increases to 46.0 percent
(Tickamyer and Tickamyer 1988). This percentage increases to 51.9 percent for the
central region of Appalachia (an area of Appalachia with high rurality) (Tickamyer and
Tickamyer 1988). This shows how, during a single time period, percentages for female-
headed households living in poverty can vary by geographical location.
It is also important to note that less-skilled women are doing comparatively better
than their similarly-skilled male counterparts (Blank and Shierholz 2006). One possibility
for why men with low skill sets are earning less than low skill women (Welch 2000) may
be due to “technological shifts that caused losses to less-skilled men” (Blank and
Shierholz 2006:7). For women, these technological shifts have brought relative economic
24. 24
improvements in comparison to their male counterparts (Welch 2000; Blank and
Shierholz 2006). This is particularly significant in areas with geographical isolation
where the rural labor markets face unstable employment. Even though less-skilled
women are doing better than less-skilled men, many forms of inequality for women and
location remain (Blank and Shierholz 2006; Smith and Glauber 2013). The effects of
spatial isolation for women cause vulnerability for female workers during economic
change, leading them to greater economic disadvantages in rural communities
(Tickamyer and Duncan 1990). Therefore, combining factors of gender, marital status,
and children along with rural spaces, Appalachian spatial isolation can have detrimental
outcomes in respect to one’s employment and income.
2.3 Education as Cultural Capital
The Appalachian region has experienced an uneven development of cultural
capital (such as education) and economic resources (Latimer and Oberhauser 2005).
Bourdieu (1977) describes the institutionalized state of cultural capital as one’s skills and
level of education. Appalachia is a region of low-income populations, low education
rates, and low-occupational statuses (Gebremariam et al. 2011). Also, educational
attainment is lower in Appalachia than the rest of the country (Gebremariam et al. 2011).
The rate for Appalachian’s with a four year college degree was 18 percent, and the
nation’s rate was 25 percent (Gebremariam et al. 2011).
Lack of urbanization meant deficits in education in 1960 (PARC 1964). More
work skills for women tend to lead to growing returns on education and greater
experience, except for metro women who earn greater returns to their education than non-
25. 25
metro women (McLaughlin and Perman 1991; Blank and Shierholz 2006). Higher
education for women can lead to higher incomes and increased employment, especially in
comparison to those with a lower education (who are less likely to find stable
employment) (Blank and Schierholz 2006). Education can also lead to faster wage
growth, but gains to education are dependent on place (Blank and Shierholz 2006; Smith
and Glauber 2013). As stated previously, the returns to education for women are greater
in metro areas (Smith and Glauber 2013). Historically, non-metro areas reported a lower
education rate than metro areas. However, in 2007 both areas (non-metro and metro) had
a high school completion rate of 71 percent (Smith and Glauber 2013).
Appalachian schools, like the rest of the nation’s schools, teach students values
and aspirations (DeYoung 1995). Duncan and Lamborghini (1994) found that many
residents of Appalachia viewed enrollment in college as the first step towards a career.
Schools in Appalachia play an important role in influencing young adults towards a
higher education (DeYoung 1995). Since education is viewed as a form of cultural
capital, and possibly a route out of poverty, having access to college (either financially or
by location) is important for rural areas to increase the opportunity for residents to obtain
a career (Obermiller and Howe 2004). Also, Duncan and Lamborghini’s (1994) work
found cultural capital plays a significant role in getting a job interview, since jobs are
scarce and may only be available to who the employer knows.
In DeYoung’s (1995) research, one West Virginia county was analyzed. West
Virginia differs from the other 12 states containing Appalachian counties because all
counties in West Virginia are Appalachian. With the county being studied in DeYoung’s
26. 26
1995 research, the largest employer was the school board (and this was also the case for
surrounding counties). At the time, rural schools educated 28 percent of the United
States’ children (DeYoung 1995). With many areas of the region lacking stable
employment, it is important that local public schools play a role in transitioning students
from rural to metropolitan employment in Appalachia, so that the region’s graduates can
have financial security upon graduation (DeYoung 1995). The primary aim for the school
district in DeYoung’s (1995) research was to develop students’ human resources for the
post-secondary opportunities (that lie elsewhere after high school). This displays the role
that the institutionalized state of cultural capital can increasing one’s life chances.
Where poverty is highest, local governments are unable to fund adequate
education services (Nord 1997). This may be due to high poverty rates in a county that
does not allow additional funds to bolster educational resources. Since an important
aspect of the American Dream is education, it is important for children to have pathways
to education, and role models to positively influence them. Ali and Saunders’ (2006)
research noted that where there are fewer adults with a degree, there are fewer children
exposed to educational role models. Since parts of Appalachia lack personal experience
with college (and the application process), youth may experience troubles with admission
(Ali and Saunders 2006). However, students with high self-efficacy had strong
expectations to attend college (Ali and Saunders 2006).
Education may be difficult to aspire to when young adults are growing up, or have
grown up, in persistent poverty (Nord 1997). Poverty can inhibit one’s expectations of
receiving higher education (Nord 1997). With aspirations hindered, the effects of rural
27. 27
poor, social isolation can lower the expectations for escaping poverty (Bourdieu 1977).
Another important aspect for higher education in the rural area is family. Family plays a
very important role in one’s life and family support may not continue if the young person
moves outside of the rural region – where post-secondary education and employment are
(DeYoung 1995). Even if remaining in a rural area means receiving a lower pay,
residents often choose to stay with their family (DeYoung 1995; Jensen et al. 1999). This
undermines employment opportunities because these prospects are often located outside
of the region. Being less educated is a drawback that can lead to becoming unemployed
and can make adequate employment harder to achieve once again (Jensen et al. 1999;
Hong and Wernet 2007). Family is an important aspect of spatial inequalities in that
strong family ties often keep residents from migrating to educational or employment
opportunities.
2.4 Research Questions
This chapter highlights the role of space, labor markets, education and gender on
income inequality. Based on the literature review, I hypothesize that Appalachian
counties will have lower county-level incomes than non-Appalachian counties within the
data set. This prediction is due to the Appalachian region being more geographically
isolated than non-Appalachian areas (Moore 1994). Second, I predict across county types
that counties with high female-headed household rates will have lower county-level
incomes compared to counties with lower female-headed household rates, due to a
possible single income. Also, the female-headed household variable is defined as having
at least one child under the age of 18 in the home. The final hypothesis predicts that
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education rates will be higher in non-Appalachian counties. This hypothesis was formed
due to the Appalachian region having less educational attainment in comparison to the
nation (Gebremariam et al. 2011) and an uneven development of cultural capital (Latimer
and Oberhauser 2005).
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CHAPTER THREE: METHODS
3.1 Sample
The area of study includes county-level data for the 13 states that are home to the
Appalachian Region. States include: Alabama, Georgia, Kentucky, Maryland,
Mississippi, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee,
Virginia, and West Virginia. Within these states, there are 1099 counties (including
Virginia’s eight independent cities which are not located in a county). For purposes of
this research, independent cities are considered counties in the data, since the Census and
American Community Survey recognize each as a county in terms of data. Appalachian
counties were identified using a classification scheme developed by the Appalachian
Regional Commission (2010). A list of all counties, with designations for Appalachian
counties, can be found in Appendix 1.
One source of data was the American Community Survey (five-year estimate,
2006-2010). The American Community Survey provided the median household income
per county, high school education completion rates, and college education completion
rates. A second source of data was the U.S. Census. Census data from 2010 are analyzed
since this was the most recent year census data were collected. Census data from 2010
were collected for rurality, unemployment, and female-headed households. Data were
matched to counties using FIPS codes.
As noted earlier, the Appalachian region spans state boundaries. Figure 1
provides a visual illustration of the 13 states and counties. Of the 1099 counties in the 13
states encompassing the Appalachian region, 428 are designated Appalachian. Counties
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in black represent Appalachian counties. The 671 counties in white are non-Appalachian
counties. Figure 2 provides a more detailed outline of the 428 Appalachian counties. This
additional figure is important because it provides the county-outlines and county-level
names for the Appalachian region. County-level names for the entire data set can be
found in the beginning of the Appendix.
3.2 Measures
The dependent variable, income, was obtained from the 2010 Census five-year
estimate, 2006-2010. This variable was measured by the median household income in the
past 12 months (in 2010 inflation-adjusted dollars). Income was logged due to having
larger data values in comparison to the other variables. Logging a variable allows the data
to represent a normal distribution, where a one percent increase represents a one-unit
increase.
Independent variables include Appalachian county, rurality, unemployment,
education, and female headed household. The first independent variable, Appalachian
county, was created using the Appalachian Region Commission’s definition. A “0” was
assigned to all non-Appalachian counties and a “1” was assigned to Appalachian
counties.
The second independent variable is rurality. The Census Bureau defines rural as
all territory outside of urban areas (USDA 2013). Sociologically, rurality is a concept
used to describe the spatial separation of areas (Bell and Osti 2010). Rurality is measured
using the 2013 Rural-Urban Continuum Code. This code was obtained from the United
States Department of Agriculture Economic Research Service, which defines the 2013
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Rural-Urban Continuum Codes as, “a classification scheme that distinguishes
metropolitan (metro) counties by the population size of their metro area, and
nonmetropolitan (nonmetro) counties by degree of urbanization and adjacency to a metro
area or areas” (USDA 2013; Documentation, para 1). From the 2013 Rural-Urban
Continuum Code definitions, a code from one to nine is assigned. Among metro counties,
a code of 1 is assigned to counties in a metro area of 1 million population or more, 2 is
assigned to counties in metro areas of 250,000 to 1 million population, and a 3 for
counties in metro areas of fewer than 250,000 population. For nonmetro counties, a 4
represents an urban population of 20,000 or more, adjacent to a metro area; a 5 is for an
urban population of 20,000 or more, not adjacent to a metro area; a 6 is for an urban
population of 2,500 to 19,999, adjacent to a metro area; a 7 is for an urban population of
2,500 to 19,999, not adjacent to a metro area; an 8 is for those counties completely rural
or less than 2,500 urban population, adjacent to a metro area; and a 9 for counties that are
completely rural or less than 2,500 urban population, not adjacent to a metro area. When
a histogram was completed for rurality, along with a normal curve, a significant drop-off
for a score of five was identified. With this information, rurality was re-coded. Counties
that previously received a score of one to five were re-coded as a ‘0’ for ‘not rural’. Also,
counties with codes from six to nine were re-coded as ‘1’ for rural.
The third independent variable measures unemployment using the 2010 US
Census. The Census defines unemployed as civilians 16 years and older who “(1) were
neither “at work” nor “with a job but not at work” during the reference week, and (2)
were actively looking for work during the last 4 weeks, and (3) were available to start a
32. 32
job” (United States Census Bureau, 2010; Definitions, para 3). The unemployment rate
represents the number of unemployed people as a percentage of the civilian labor force.
The fourth independent variable is education. For purposes of this research,
education was broken down into levels of completion with a specific focus on the
percentage of county population with only a high school degree. Data were retrieved
from the American Community Survey, 2006-2010. A high school education is defined
as the percent of persons who have only obtained a high school degree. College education
was defined as the percent of persons with at least a four-year degree.
The fifth independent variable is female-headed household information. Data for
this variable were collected from the U.S. Census (2010). The Census defines a female-
headed household as one with no husband present with children living in the home, who
are under 18 years of age.
Finally, an interaction variable was created to better understand the relationship
between the Appalachian region and rurality in the linear regression models. This
variable was created in SPSS by combining the Appalachian variable with the newest
measure for rurality to analyze the unique effect of a county being Appalachian and rural.
3.3 Descriptive Statistics
The analyses began by running descriptive statistics on all variables (income,
unemployment, college education, high school education, female-headed households,
rurality, and Appalachia) to find any missing values, the mean, median, standard
deviation, minimum, and maximum. No missing values were found for the 1,099
counties. Then, histograms for all variables were run along with a normal curve and no
33. 33
outliers were identified in the data. Next, the same descriptive statistics were run
performed while controlling for Appalachia. It was important to control for Appalachia
so that differences could be identified between Appalachian and non-Appalachian
counties.
Table 1 shows the descriptive statistics for each variable. Of the 1,099 counties,
9.4 percent is the minimum percentage of persons with only a high school degree, and
54.6 percent is the maximum (N = 1099). The high school degree mean for all counties is
36.8 (SD = 7.0). Also, the minimum percentage for persons with a college education is
3.7 percent, with a maximum of 71.0 percent. The college education mean for all counties
is 17.8 (SD = 9.2). The minimum value for female-headed households is 2.5 percent, with
a maximum of 17.7 percent (N = 1099).
Table 2 provides the descriptive statistics while controlling for the Appalachian
region. This table also displays the income variable, without the log, so the statistics can
be viewed in dollar amounts. The mean income for non-Appalachian counties (N = 671)
is $45,114.53 (log = 4.6). The mean income for Appalachian counties (N = 428) is
$38,803.79 (log = 4.6). Table 2 provides specific data to answer the research questions
since variables can be viewed for the Appalachian and non-Appalachian counties. The
high school degree mean for Appalachian counties is 39.0 percent; non-Appalachian
counties have a lower mean of 35.4 percent. It is not surprising that non-Appalachian
counties have a lower mean for high school completion rates, because this variable was
operationalized to represent only a high school degree. Therefore, Appalachian counties
have higher rates of high school-only completion. The college education mean for non-
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Appalachian counties is 19.3 percent and the mean for Appalachian counties is 15.3
percent. The female-headed household mean for non-Appalachian counties is 7.9 percent
and the mean for Appalachian counties is 6.0 percent. This suggests that there is a higher
percentage of female-headed households in non-Appalachian counties.
The Pearson correlation was run for all variables to determine significant
correlations. This correlation was run for the data on all counties (Appalachian and non-
Appalachian) to gain a complete look at how each variable correlates (and if
significantly) with another. The goal afterwards was to use a linear regression analysis for
the income variable and incorporate the interaction term (combining Appalachia and
rurality) into the final model. Table 3 displays the Pearson correlation for the dependent
and independent variables (N = 1099). All variables’ correlations are significant at the
0.01 level (two-tailed). Income and unemployment correlate at -0.6. Income and high
school have a correlation of -0.4, while income and college correlate at 0.7. Income and
female-headed households correlate at -0.3 and income correlates with the interaction
term (Appalachia and rurality) at -0.4. Unemployment and high school have a correlation
of 0.3 and unemployment correlates with college at -0.5. Unemployment correlates with
female-headed households at 0.4 and with the interaction term at 0.2. High school and
college correlate at -0.7. High school and female-headed households correlate at -0.2,
while college and female-headed households correlate at -0.1. High school and the
interaction term correlate at 0.2. College correlates with the interaction term at -0.3.
Female-headed households and the interaction term have a correlation of -0.3. Knowing
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that all correlations are significant allows for linear regression analyses to further explore
the research questions.
3.4 Analyses of Linear Regressions
To answer my research questions, SPSS was used to run regression models.
Regression models work best to determine the relationship between the dependent
variable (income) and the independent variables. My goal through the use of regressions
was to draw generalizations from my large sample size to analyze rates and patterns for
the counties. This analysis is the best way to answer my research questions because
regressions show predictability, whereas correlations only show the measure of
association between variables.
The main method of analyses for the study were regressions with the dependent
variable income. Each regression had four models that controlled for various independent
variables. The regression provided unstandardized coefficients, such as B and the
standard error; standard coefficients such as Beta; and also ‘t’ with the significance of
each variable. An interaction term was created for Appalachia and rurality to find the
relationship between the two, in respect to income.
Table 4 presents the summary of the linear regression analysis for income (N =
1099) by using four models. Model 1 shows the R Square (0.4) for unemployment with
the log of income as the constant dependent variable, meaning that 40 percent of the
variance is explained by unemployment (for income, with a standard error of 0.001).
Model 2 shows that 62.3 percent of the variance is explained by unemployment, high
school, and college. When adding female-headed household, as displayed in model 3,
36. 36
62.7 percent of the variance for the dependent variable is explained, meaning the female-
headed household variable predicts income an additional 0.4 percent. Finally, in model 4,
the interaction term is added (Appalachia and rurality) and the R square increases to 0.67,
meaning that 67 percent of the variance for income is explained by the independent
variables.
While Table 4 was able to aid in answering the research questions, Table 5
provides insight for the variables Appalachia and rurality separately without being
combined in the interaction term. In model 3 on Table 5, Appalachia and rurality are
entered before the interaction term to see the extent to which these variables predict
income. The table presents findings for the R square from model 1 (unemployment),
model 2 (high school education, college, and female-headed household added), model 3
(Appalachia and rurality added), and finally model 4 with the interaction term. In order,
the R square in percentages increases from 40 percent to 62.7 percent to 71.3 percent, and
71.3 percent. The increase from model 2 to model 3 on Table 5 is important because all
variables up to model 2 (unemployment, high school education, college, and female-
headed household) were expected to have a significant impact on the county’s median
household income. The goal of this research was to see the increase, or decrease, in
predictors of income when a county is Appalachian. The findings show an 8.6 percent
increase from model 2 to model 3, meaning that Appalachia and rurality have an impact
on county-level income.
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CHAPTER FOUR: DISCUSSION
The purpose of my research is to explore the economic impacts across space,
namely the Appalachian and non-Appalachian region in 13 states. Data were gathered
from the U.S. Census Bureau, American Community Survey, and the Appalachian
Regional Commission. Descriptive statistics, correlations, and linear regression analyses
were completed using SPSS with a dependent variable of county-level income. Based on
the literature presented in Chapter 2, I hypothesized that income would be lower in
Appalachian counties, high female-headed household rates would cause lower county-
level income, and that educational attainment would be higher in non-Appalachian
counties. The purpose of this chapter is to discuss the results and significant findings
from Chapter 3 alongside the hypotheses.
4.1 Hypothesis One
The first hypothesis predicted that income would be lower in rural Appalachian
counties (Moore 2005). Table 2 displays the results for the descriptive statistics while
controlling for Appalachian counties. Appalachian counties have a mean income of
$37,803.79 while non-Appalachian counties have a mean income of $45,114.53, thus
accepting the first part of hypothesis one – Appalachian counties have a lower income
than non-Appalachian counties.
Appalachia’s geographical isolation has led to the region being unable to prosper
with the rest of the nation (Moore 1994). Rurality was found to be a significant indicator
for income. 27.2 percent of the variance was explained by rurality in Appalachian
counties, therefore, rurality is a significant indicator for income. Also, rurality and
38. 38
income are highly correlated. This is significant because it reaffirmed the existing
literature and allowed this research to move forward with linear regression models.
Other differences are found between Appalachian and non-Appalachian counties
in Table 2. Non-Appalachian counties have a minimum county-level income $730 higher
than the Appalachian counties minimum. This is likely due the Appalachian region being
geographically isolated (Moore 1994). Also, there is a $27,969 difference between the
maximum earning Appalachian and non-Appalachian county, with the non-Appalachian
county earning more. Finally, descriptive statistics for rurality, in Table 2, suggest that
non-Appalachia is less rural than Appalachia. This supports the research because
Appalachia is more rural than non-Appalachia, and factors of rurality have impacted the
regions income. These findings support the first hypothesis. The following two
hypotheses consider the independent variables of female-headed households and
education.
4.2 Hypothesis Two
Next, I consider the impact of female-headed households on income. The second
hypothesis predicted that counties with high rates of female-households would have
lower incomes compared to counties with low female-headed household rates. The
literature suggests that female-headed household rates are a significant indicator of
county-level income (Latimer and Oberhauser 2005). Previous research on rural
Appalachia has shown that women are likely to earn less than their male counterparts
(Latimer and Oberhauser 2005).
39. 39
Being female has found to be a penalty in terms of income, as well as not being
married (Jensen et al. 1999). Because women are often in low paying jobs, female-headed
households increase the likelihood of poverty (Thorne et al. 2005). In a 1988 study, 40.3
percent of female-headed households in the United States were living in poverty, the rate
was significantly higher for female-headed households in Appalachian Kentucky at 51.9
percent (Tickamyer and Tickamyer 1988).
The descriptive statistics in Table 2 show that the mean percentage of female-
headed households for counties in Appalachia is 6.0 percent, with a minimum of 2.5
percent and a maximum of 13.1 percent. For non-Appalachia counties, the mean
increases to 7.9 percent with a minimum of 2.8 percent and a maximum of 17.7 percent.
This supports findings from the literature that higher rates of female-headed households
are likely to be found in urban areas (Tickamyer and Duncan 1990), possibly due to less
spatial isolation from employment.
While the variable for female-headed households is highly correlated with the
income variable, Table 4 shows that when the female-headed household variable is added
into the multiple regression the variance slightly increases. This is a very small change in
predicting income. In comparison, the unemployment rate, high school degree rates, and
college degree rates are greater predictors. The unemployment rate predicts county-level
income at 40.0 percent. This is not surprising due to how highly the unemployment
variable and income variable correlate, and there is vast literature on how employment
trends impact the local and national economy.
40. 40
It was expected that the female-headed household rate would have a greater
impact on predicting county-level median household income. Since female-headed
households did not greatly affect income, this does not support the second hypothesis as
well as the dominant literature that areas with high female-headed household rates would
impact county-level income. While surprising, this could be due to the other independent
variables that were already controlled for in model 3 on Table 4. As shown,
unemployment, high school degree, college degree, and female-headed households
predict income at 62.7 percent. This percentage is significant and it may be that female-
headed households are not adding to the prediction for income, but, the prediction rate
remains high with the previous variables. However, this does not suggest that the female-
headed household variable is insignificant.
While the findings for the second hypothesis contradict the literature, it is possible
that the operationalization of female-headed households as a variable from the Census
Bureau is problematic. Female-headed household was defined as having no husband
present and children living in the home who are under 18 years of age. This definition
only specifies that there is no husband present, therefore, it is possible that a female-
headed household under these conditions may have a fiancé or other partner living in the
home earning an income. Also, this definition does not include other factors that may be
likely in an impoverished area, such as other relatives living in the home who also have
an income and children living at home who are 18 or older.
41. 41
4.3 Hypothesis Three
Appalachia has experienced low education rates in comparison to the rest of the
country, and is described as having low cultural capital (Latimer and Oberhauser 2005).
In 2000, 81 percent of people residing outside of Appalachia over 25 years old had a high
school degree, while Appalachia was at 77 percent (Gebremariam et al. 2011). Also, the
percentages for four year college degrees for non-Appalachia and Appalachia counties
were 25 percent and 18 percent respectively.
To better understand the role of education in Appalachian counties the final
hypothesis projected that education rates would be higher in non-Appalachian counties,
in comparison to Appalachian counties that are rural. Table 2 shows that the mean high
school completion rate for Appalachian counties is 39.0 percent. It is 35.4 percent for
non-Appalachian counties. College degree rates, on the other hand, are higher in non-
Appalachian counties (15.3 percent compared to 19.3 percent).
As predicted, non-Appalachian counties had higher levels of education, with the
college degree rate mean 4 percentage points higher than the mean for Appalachian
counties. It is not surprising that college rates are higher in the non-Appalachian region
given that these counties are less impoverished and often have greater access to
educational resources. It may be that high school completion rates are higher in
Appalachian counties because the variable was defined as only a high school degree –
with no further education. Therefore, as predicted, non-Appalachian counties had high
college education rates – leading to lower (only) high school education rates. These
findings show support for the second hypothesis.
42. 42
Other interesting findings for education on Table 2 show a minimum high school
degree rate for Appalachia of 20.5 percent and non-Appalachia of 9.4 percent. This is
important because it shows that in the county with the minimum for Appalachia has 20.5
percent of the population with only a high school degree, which is significantly higher
than the minimum non-Appalachian county (9.4 percent). Also, the county maximum for
Appalachia and non-Appalachia is 54.6 percent and 51.0 percent, respectively. This
means that these counties have a lower college completion rate due to the definition of
the high school variable. The county minimum college degree rate for Appalachia is 3.7
percent and for non-Appalachia it is 4.3 percent. Again, this shows that educational
attainment is higher in non-Appalachian counties. The county maximum for Appalachia
and non-Appalachia is 49.7 percent and 71.0 percent, respectively for at least a four year
college degree. With this example, the county maximum for non-Appalachia is almost 24
percent higher in terms of a four year college degree than the maximum county for the
Appalachian region.
4.4 Discussion of Models
This research focused on identifying the factors that influence county-level
income based upon Appalachia and rurality. A new variable (the interaction term) was
created to better understand the effects of a county being both rural and Appalachian. The
term, Appalachia.Rurality, combined the independent variables Appalachia and rurality.
The interaction term significantly correlates with income showing that factors of
Appalachia and rurality are important for the regression models. Next, findings from the
43. 43
linear regression on Table 5 are discussed, highlighting key points about the interaction
term.
Two models, as shown in Table 5, display significant findings relating to the goal
of this research. Model 2 consists of the independent variables unemployment, high
school education, college, and female-headed household. Model 3 includes the same
variables but adds in Appalachia and rurality. The prediction increases from model 2 to
model 3; 62.7 percent to 71.3 percent respectively. The greater predictability of model 3
demonstrates that Appalachia and rurality have a significant impact on county-level
income.
Factors of unemployment, education, and female-headed households predict 62.7
percent of the income (Table 4). This supports findings from the literature review that
high unemployment rates negatively impact a county’s income (Tickamyer and Duncan
1990), that education affects one’s earnings (Latimer and Oberhauser 2005), and that
female-headed households are a significant variable for income levels (Latimer and
Oberhauser 2005). Also, the interaction term (combining Appalachia and rurality)
explains 67.0 percent of the variance for income. Adding the interaction term improves
the predictive power of the linear regression model.
Before the interaction term is added in Table 5, model 3, Appalachia and rurality
are separately added in the model. Significant findings were the result of the predictive
power of the model which increased 8.6 percent. This means that when Appalachia and
rurality are added to the regression model, separately, and before the interaction term, the
total predictive power for Table 5 rises to 71.3 percent. Then, in next model (model four),
44. 44
the interaction term is added and the prediction does not increase. This is significant
because it shows that the two variables, Appalachia and rurality, have a stronger
predictive force in predicting income than the interaction term.
One explanation for the 71.3 percent in model three and model four is that the
interaction term is identical to the variables (Appalachia and rurality) when they are
separate. Another likely explanation is that the residuals from the regression for the
interaction term should be made into a new variable. Even without the extraction of the
residuals for the interaction term on Table 5, this table provided a stronger overall
prediction than Table 4 – an increase of 8.6 percentage points.
This study examined data for 1,099 Appalachian and non-Appalachian counties in
terms of rurality, income, unemployment, education, and female-headed households. Two
of the three hypotheses were supported. My research shows that the Appalachian
continues to face challenges and exists as “a region apart – geographically and
statistically” (PARC 1964;xv). The following chapter will conclude my analysis by
discussing strengths and limitations of this study and policy implications.
45. 45
CHAPTER FIVE: CONCLUSION
5.1 Theory of Spatial Inequality
Spatial inequality was highlighted in the findings on Table 2 for the education
variable. Rural areas having reduced access to higher education, both financially and
spatially, perpetuates the inequality of the area (Slack 2010). Also, even if one is able to
develop their institutionalized state of cultural capital, returns on this investment are
lower in rural areas when compared to urban spaces (Slack 2010). Disadvantages appear
in employment too. Nonmetro workers receive lower wages for similar work
(MchLaughlin and Perman 1991). These examples highlight the difficulties one may
experience in purchasing secure transportation to get to school and work, along with the
cost of gas, updated plates, and maintenance.
Also, rural areas are lacking stable employment due to 1) the end of the Fordist
period, and 2) a college degree that may not directly translate into a career post-
graduation. Rural labor markets lack stable employment, high wages, and a diverse
economy (Tickamyer and Duncan 1990). Slack (2010) found that those who are working
and poor are more likely to be residing in nonmetro areas than metro areas, thus
highlighting the insufficient labor market.
In terms of cultural capital, my findings show that one’s institutionalized state of
cultural capital is hindered because Appalachian counties have a college degree rate that
is 4 percentage points less than the non-Appalachian counties. Rural Appalachians are
likely to gain less cultural capital than people residing in an urban area due to spatial
inequalities (Smith and Glauber 2013). Research finds that, “the benefits to education are
46. 46
unequally distributed” showing that nonmetro regions are at a disadvantage (Smith and
Glauber 2013:1391). Another factor that impact one’s cultural capital while living in
rural Appalachia is their marital status, which may indicate a single household income
(Billings and Tickamyer 1993; Smith and Glauber 2013). All of these aspects alongside
the theories of spatial inequality, rural labor markets, and cultural capital help to explain
factors that impact county-level income in the area of study.
The theory of spatial inequality was used to recognize differences among the rural
Appalachian region and non-Appalachian region within the 13 states for this study. These
variations were analyzed through the descriptive statistics and linear regression models.
Also, this theory aided in explaining rural labor markets, since these markets are
influenced by space as stated in the literature review. Spatial inequality and rural labor
markets also helped explain the effects of the female-headed household rate when
comparing the Appalachian counties to the non-Appalachian counties. Also, females in
the Appalachian region have less cultural capital due to their lessened returns to
education (McLaughlin and Perman 1991; Blank and Shierholz 2006; Smith and Glauber
2013).
5.2 Strengths and Limitations
Findings from my research support previous literature – education levels, female-
headed household rates (along with unemployment and education), rurality, and the
Appalachian region are significant factors in predicting county-level income. My
research contributes to the literature by providing 2010 Census and American
Community Survey data for 1,099 counties. One strength of the study is that quantitative
47. 47
methods were used to analyze data reported from the U.S Census Bureau and the
American Community Survey. Another strength of this research is the relevance of the
topic. In 2014, President Obama echoed remarks of President Johnson from 1964 stating
that the Appalachian region remains disadvantaged and is a concern for our county (Gohl
2014).
As shown in the literature review, my findings, and recent reports in the news,
poverty in Appalachian remains a large concern not only for researchers, but also for the
public. The Ohio Poverty Report of January 2015 used the latest data from the American
Community Survey to show that 17.6 percent of the 32 Appalachian Ohio counties were
poor, whereas the rest of Ohio averaged 15.4 percent (Ohio Development Services
Agency 2015). Athens County in Ohio had one of the highest poverty rates, along with
Jackson, Pike, Scioto, and Adams (Ohio Development Services Agency 2015). All of
which are Appalachian counties (Ohio Development Services Agency 2015).
There are several limitations of this research. First, the two sources of data and the
time period in which they are reported may be problematic. The Census produces data
every 10 years, whereas the American Community Survey collects data every year and
provides multiple yearly estimates. I used both, hoping the data reflected a point in time.
For example, I collected the unemployment variable from the 2010 Census, along with
female-headed household percentages. For my education variables (high school degree
and college degree), I collected data from the American Community Survey, based off a
five-year estimate from 2006-2010. This may be problematic since the Census data was
from a one-year period and the American Community survey was from a five-year
48. 48
estimate. In later research, I would evaluate data only from the American Community
Survey (ACS) since it provides a larger range of variables with the option to select exact
time periods.
Second, the variable used for a high school degree lacked construct validity,
meaning that the inferences made for this term did not accurately represent the intended
measure. High school degree was operationalized from the Census to merely include
peoples with only this degree. With this definition, I was not able to find those within a
county with at least a high school degree, which may show a more representative
measure for the third hypothesis on education. The high school degree variable differed
in this aspect from the college degree because the college variable contained those with at
least a four-year degree. As discussed in Chapter 4, the findings that Appalachian
counties have higher rates of high school completion could be due to a poorly defined
variable.
Finally, the female-headed household variable also lacked construct validity. The
definition of this variable was not inclusive of other factors that may influence income in
the home, such as children 18 years of age and older, friends other relatives living in the
home, and unmarried partners. Therefore, the data for this variable may not have been
precise enough to account for all possible income for the household.
5.3 Future Research and Policy Implications
Future research should include the addition of important variables and regions.
First, I would create a new variable from the residuals on the interaction term in the linear
regression. This is likely to lead to significant variance in the prediction of income.
49. 49
Second, I would expand this study by using the economic classification system for each
Appalachian county – distressed, at-risk, competitive, or attainment. By adding further
descriptive variables to each Appalachian county, I could analyze the differences in
income, rurality, female-headed households, and educational attainment among them.
Also, I would compare data from the Northern, Central, and Southern Appalachia
regions and the nation. Anderson and Weng (2011) completed a similar study with these
regions in Appalachia, along with regions across the nation (Anderson and Weng 2011).
They found that low-wage jobs were particularly hard to escape in rural areas due to
problems of transportation and access to good jobs (Anderson and Weng 2011). Also,
Anderson and Weng’s (2011) research points to various industry types, race and
ethnicity, and age variables that I would expand in later research. However, Anderson
and Weng’s (2011) research is dated and would benefit from the addition of recent data
from the ACS.
Lastly, future research could add the distance one drives to work from a rural,
Appalachian area. This is an important variable since this research, along with the
literature, finds that location is extremely important to income and employment, partly
due to spatial isolation (Litcher and Graefe 2011). Rural areas often lack transportation,
child care, and social services due to their location (Anderson and Weng 2011). Finally,
higher rates of low-wage workers are found in rural areas due to spatial inequalities
(Anderson and Weng 2011).
Researchers must look at counties across regions in order to better understand the
impact of spatial inequalities. I support the research of Gebremariam et al (2011) which
50. 50
states cooperation between counties in various regions may be necessary for economic
success. Spatial inequalities do not stop at county boarders. Differences in well-paying
employment and other resources may slightly change from county to county, but spatial
inequality does not stop at county boarders.
The Appalachian area continues to be lower than the non-Appalachian region in
terms of economic prosperity and education. While reports from the Appalachian
Regional Commission suggest that the region has progressed, they also show that
economic challenges remain as a result of a loss of manufacturing jobs (Appalachia
Executive Summary 2015). Recent policy suggestions from Lobao (2014) state that social
researchers need to ask questions about this diverse region and continue exploring the
effects of spatial inequality. This region needs continued attention and funding from the
state and federal government agencies.
In conjunction with prior policy recommendations (Gebremariam et al. 2011;
Lobao 2014), I would endorse funding to develop the transportation systems in rural
Appalachian regions. This would improve the effects of spatial isolation in the region by
offering those living in rural areas an opportunity to pursue urban employment. Also, I
recommend federal funding for the region to develop a stronger infrastructure which
would encourage outside industries to locate in the region thus increasing employment
opportunities. While a transportation system would help residents find employment in the
metropolitan areas, it is important that those living in the rural Appalachian region have
industries in the labor market where they reside.
51. 51
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56. 56
Table 1. Descriptive Statistics of Variables for all Counties (N = 1099)
Minimum Maximum Mean Std. Deviation
Log of Income 4.3 5.0 4.6 0.1
Appalachia 0.0 1.0 - -
Rurality 0.0 1.0 - -
Unemployment Rate
(%)
4.4% 21.9% 10.6 2.6
High School Degree
Only (%)
9.4% 54.6% 36.8 7.0
Minimum 4-year
College Degree (%)
3.7% 71.0% 17.8 9.2
Female-Headed
Household Rate (%)
2.5% 17.7% 7.2 2.3
Appalachia Rurality
(interaction term)
0.0 1.0 - -
57. 57
Table 2. Descriptive Statistics while Controlling for Appalachia
Appalachia Non-Appalachia
Variable Mean Minimum Maximum Mean Minimum Maximum
Income $37,803.79 $19,351.00 $87,605.00 $45,114.53 $20,081.00 $115,574.00
Log of Income 4.6 4.3 5.0 4.6 4.3 5.1
Unemployment Rate (%) 10.7 5.3% 20.4% 10.5% 4.4% 21.9%
High School Degree Only
(%)
39.1 20.5% 54.6% 35.4% 9.4% 51.0%
Minimum 4-yearCollege
Degree (%)
15.3 3.7% 49.7% 19.3% 4.3% 71.0%
Female-Headed Household
Rate (%)
6.0 2.5% 13.1% 7.9% 2.8% 17.7%
Rurality - 0.00 1.00 - 0.00 1.00
Note: Appalachia N = 428, Non-Appalachia N = 671.
58. 58
Table 3. Pearson Correlation for Variables
Log of Income Unemployment High School College
Female Headed
Household
Unemployment
Rate -0.6**
High School
Degree
(only)
-0.4**
.03**
College Degree
(at least 4 years)
0.7**
-0.5**
-0.7**
Female-Headed
Household Rate -0.3**
0.4**
-0.2**
-0.1**
Appalachia
Rurality
(interaction
term)
-0.4**
0.2**
0.2**
-0.2**
-0.2**
Note: **. Correlation is significant at the 0.01 level (2-tailed).
59. 59
Table 4. Summary of Linear Regression Analysis for Income Variable
Model 1 Model 2 Model 3 Model 4
Variable B B B B
Unemployment Rate
-0.029***
(0.001)
-0.015***
(0.001)
-0.014***
(0.001)
-0.001***
(0.001)
High School Degree
(only) 0.004***
(0.000)
0.003***
(0.001)
0.003***
(0.000)
College Degree
(at least 4 years) 0.009***
(0.000)
0.009***
(0.000)
0.008***
(0.000)
Female-Headed
Household Rate -0.004**
(0.001)
-0.010***
(0.001)
Appalachia Rurality
(interaction term) -0.070***
(0.006)
R2
0.400 0.623 0.627 0.670
Note: Numbers in parentheses are the standard error; **p < .01, ***p < .00.; N = 1099.
60. 60
Table 5. Linear Regression Analysis with Appalachia and Rurality Model
Model 1 Model 2 Model 3 Model 4
Variable B B B B
Unemployment Rate -0.029***
(0.001)
-0.014**
(0.001)
-0.009**
(0.001)
-0.009**
(0.001)
High School Degree (only) 0.003** 0.003*** 0.003***
(0.001) (0.000) (0.000)
College Degree
(at least 4 years)
0.009*** 0.007*** 0.007***
(0.000) (0.000) (0.000)
Female-Headed Household Rate -0.004** -0.013** -0.013**
(0.001) (0.001) (0.001)
Appalachia -0.063* -0.067
(0.005) (0.006)
Rurality -0.054* -0.057
(0.005) (0.006)
Appalachia Rurality 0.008
(interaction term) (0.008)
R2
0.400 0.627 0.713 0.713
Note: *p < .05. **p < .01. ***p < .00; N = 1099.
61. 61
Figure 1. The Appalachian and non-Appalachian Counties; Staci Vaughan 2015
62. 62
Figure 2. Map of Counties in the Appalachian Region; ARC 2010
63. 63
APPENDIX: LIST OF STATES AND COUNTIES IN THE DATA SET
*Note – ‘0’ represents non-Appalachian counties and a ‘1’ represents Appalachian
counties.
State County Appalachian
Alabama Autauga County 0
Baldwin County 0
Barbour County 0
Bibb County 1
Blount County 1
Bullock County 0
Butler County 0
Calhoun County 1
Chambers County 1
Cherokee County 1
Chilton County 1
Choctaw County 0
Clarke County 0
Clay County 1
Cleburne County 1
Coffee County 0
Colbert County 1
Conecuh County 0
Coosa County 1
Covington County 0
Crenshaw County 0
Cullman County 1
Dale County 0
Dallas County 0
DeKalb County 1
Elmore County 1
Escambia County 0
Etowah County 1
Fayette County 1
Franklin County 1
Geneva County 0
Greene County 0
Hale County 1
Henry County 0
64. 64
Houston County 0
Jackson County 1
Jefferson County 1
Lamar County 1
Lauderdale County 1
Lawrence County 1
Lee County 0
Limestone County 1
Lowndes County 0
Macon County 1
Madison County 1
Marengo County 0
Marion County 1
Marshall County 1
Mobile County 0
Monroe County 0
Montgomery County 0
Morgan County 1
Perry County 0
Pickens County 1
Pike County 0
Randolph County 1
Russell County 0
St. Clair County 1
Shelby County 1
Sumter County 0
Talladega County 1
Tallapoosa County 1
Tuscaloosa County 1
Walker County 1
Washington County 0
Wilcox County 0
Winston County 1
Georgia Appling County 0
Atkinson County 0
Bacon County 0
Baker County 0
Baldwin County 0
Banks County 1
Barrow County 1
Bartow County 1
65. 65
Ben Hill County 0
Berrien County 0
Bibb County 0
Bleckley County 0
Brantley County 0
Brooks County 0
Bryan County 0
Bulloch County 0
Burke County 0
Butts County 0
Calhoun County 0
Camden County 0
Candler County 0
Carroll County 1
Catoosa County 1
Charlton County 0
Chatham County 0
Chattahoochee County 0
Chattooga County 1
Cherokee County 1
Clarke County 0
Clay County 0
Clayton County 0
Clinch County 0
Cobb County 0
Coffee County 0
Colquitt County 0
Columbia County 0
Cook County 0
Coweta County 0
Crawford County 0
Crisp County 0
Dade County 1
Dawson County 1
Decatur County 0
DeKalb County 0
Dodge County 0
Dooly County 0
Dougherty County 0
Douglas County 1
Early County 0
66. 66
Echols County 0
Effingham County 0
Elbert County 1
Emanuel County 0
Evans County 0
Fannin County 1
Fayette County 0
Floyd County 1
Forsyth County 1
Franklin County 1
Fulton County 0
Gilmer County 1
Glascock County 0
Glynn County 0
Gordon County 1
Grady County 0
Greene County 0
Gwinnett County 1
Habersham County 1
Hall County 1
Hancock County 0
Haralson County 1
Harris County 0
Hart County 1
Heard County 1
Henry County 0
Houston County 0
Irwin County 0
Jackson County 1
Jasper County 0
Jeff Davis County 0
Jefferson County 0
Jenkins County 0
Johnson County 0
Jones County 0
Lamar County 0
Lanier County 0
Laurens County 0
Lee County 0
Liberty County 0
Lincoln County 0
67. 67
Long County 0
Lowndes County 0
Lumpkin County 1
McDuffie County 0
McIntosh County 0
Macon County 0
Madison County 1
Marion County 0
Meriwether County 0
Miller County 0
Mitchell County 0
Monroe County 0
Montgomery County 0
Morgan County 0
Murray County 1
Muscogee County 0
Newton County 0
Oconee County 0
Oglethorpe County 0
Paulding County 1
Peach County 0
Pickens County 1
Pierce County 0
Pike County 0
Polk County 1
Pulaski County 0
Putnam County 0
Quitman County 0
Rabun County 1
Randolph County 0
Richmond County 0
Rockdale County 0
Schley County 0
Screven County 0
Seminole County 0
Spalding County 0
Stephens County 1
Stewart County 0
Sumter County 0
Talbot County 0
Taliaferro County 0
68. 68
Tattnall County 0
Taylor County 0
Telfair County 0
Terrell County 0
Thomas County 0
Tift County 0
Toombs County 0
Towns County 1
Treutlen County 0
Troup County 0
Turner County 0
Twiggs County 0
Union County 1
Upson County 0
Walker County 1
Walton County 0
Ware County 0
Warren County 0
Washington County 0
Wayne County 0
Webster County 0
Wheeler County 0
White County 1
Whitfield County 1
Wilcox County 0
Wilkes County 0
Wilkinson County 0
Worth County 0
Kentucky Adair County 1
Allen County 0
Anderson County 0
Ballard County 0
Barren County 0
Bath County 1
Bell County 1
Boone County 0
Bourbon County 0
Boyd County 1
Boyle County 0
Bracken County 0
Breathitt County 1
69. 69
Breckinridge County 0
Bullitt County 0
Butler County 0
Caldwell County 0
Calloway County 0
Campbell County 0
Carlisle County 0
Carroll County 0
Carter County 1
Casey County 1
Christian County 0
Clark County 1
Clay County 1
Clinton County 1
Crittenden County 0
Cumberland County 1
Daviess County 0
Edmonson County 1
Elliott County 1
Estill County 1
Fayette County 0
Fleming County 1
Floyd County 1
Franklin County 0
Fulton County 0
Gallatin County 0
Garrard County 1
Grant County 0
Graves County 0
Grayson County 0
Green County 1
Greenup County 1
Hancock County 0
Hardin County 0
Harlan County 1
Harrison County 0
Hart County 1
Henderson County 0
Henry County 0
Hickman County 0
Hopkins County 0
70. 70
Jackson County 1
Jefferson County 0
Jessamine County 0
Johnson County 1
Kenton County 0
Knott County 1
Knox County 1
Larue County 0
Laurel County 1
Lawrence County 1
Lee County 1
Leslie County 1
Letcher County 1
Lewis County 1
Lincoln County 1
Livingston County 0
Logan County 0
Lyon County 0
McCracken County 0
McCreary County 1
McLean County 0
Madison County 1
Magoffin County 1
Marion County 0
Marshall County 0
Martin County 1
Mason County 0
Meade County 0
Menifee County 1
Mercer County 0
Metcalfe County 1
Monroe County 1
Montgomery County 1
Morgan County 1
Muhlenberg County 0
Nelson County 0
Nicholas County 1
Ohio County 0
Oldham County 0
Owen County 0
Owsley County 1
71. 71
Pendleton County 0
Perry County 1
Pike County 1
Powell County 1
Pulaski County 1
Robertson County 1
Rockcastle County 1
Rowan County 1
Russell County 1
Scott County 0
Shelby County 0
Simpson County 0
Spencer County 0
Taylor County 0
Todd County 0
Trigg County 0
Trimble County 0
Union County 0
Warren County 0
Washington County 0
Wayne County 1
Webster County 0
Whitley County 1
Wolfe County 1
Woodford County 0
Maryland Allegany County 1
Anne Arundel County 0
Baltimore County 0
Calvert County 0
Caroline County 0
Carroll County 0
Cecil County 0
Charles County 0
Dorchester County 0
Frederick County 0
Garrett County 1
Harford County 0
Howard County 0
Kent County 0
Montgomery County 0
Prince George's County 0
72. 72
Queen Anne's County 0
St. Mary's County 0
Somerset County 0
Talbot County 0
Washington County 1
Wicomico County 0
Worcester County 0
Baltimore city 0
Mississippi Adams County 0
Alcorn County 1
Amite County 0
Attala County 0
Benton County 1
Bolivar County 0
Calhoun County 1
Carroll County 0
Chickasaw County 1
Choctaw County 1
Claiborne County 0
Clarke County 0
Clay County 1
Coahoma County 0
Copiah County 0
Covington County 0
DeSoto County 0
Forrest County 0
Franklin County 0
George County 0
Greene County 0
Grenada County 0
Hancock County 0
Harrison County 0
Hinds County 0
Holmes County 0
Humphreys County 0
Issaquena County 0
Itawamba County 1
Jackson County 0
Jasper County 0
Jefferson County 0
Jefferson Davis County 0
73. 73
Jones County 0
Kemper County 1
Lafayette County 0
Lamar County 0
Lauderdale County 0
Lawrence County 0
Leake County 0
Lee County 1
Leflore County 0
Lincoln County 0
Lowndes County 1
Madison County 0
Marion County 0
Marshall County 1
Monroe County 1
Montgomery County 1
Neshoba County 0
Newton County 0
Noxubee County 1
Oktibbeha County 1
Panola County 1
Pearl River County 0
Perry County 0
Pike County 0
Pontotoc County 1
Prentiss County 1
Quitman County 0
Rankin County 0
Scott County 0
Sharkey County 0
Simpson County 0
Smith County 0
Stone County 0
Sunflower County 0
Tallahatchie County 0
Tate County 0
Tippah County 1
Tishomingo County 1
Tunica County 0
Union County 1
Walthall County 0
74. 74
Warren County 0
Washington County 0
Wayne County 0
Webster County 1
Wilkinson County 0
Winston County 1
Yalobusha County 1
Yazoo County 0
New York Albany County 0
Allegany County 1
Bronx County 0
Broome County 1
Cattaraugus County 1
Cayuga County 0
Chautauqua County 1
Chemung County 1
Chenango County 1
Clinton County 0
Columbia County 0
Cortland County 1
Delaware County 1
Dutchess County 0
Erie County 0
Essex County 0
Franklin County 0
Fulton County 0
Genesee County 0
Greene County 0
Hamilton County 0
Herkimer County 0
Jefferson County 0
Kings County 0
Lewis County 0
Livingston County 0
Madison County 0
Monroe County 0
Montgomery County 0
Nassau County 0
New York County 0
Niagara County 0
Oneida County 0
75. 75
Onondaga County 0
Ontario County 0
Orange County 0
Orleans County 0
Oswego County 0
Otsego County 1
Putnam County 0
Queens County 0
Rensselaer County 0
Richmond County 0
Rockland County 0
St. Lawrence County 0
Saratoga County 0
Schenectady County 0
Schoharie County 1
Schuyler County 1
Seneca County 0
Steuben County 1
Suffolk County 0
Sullivan County 0
Tioga County 1
Tompkins County 1
Ulster County 0
Warren County 0
Washington County 0
Wayne County 0
Westchester County 0
Wyoming County 0
Yates County 0
North
Carolina Alamance County 0
Alexander County 1
Alleghany County 1
Anson County 0
Ashe County 1
Avery County 1
Beaufort County 0
Bertie County 0
Bladen County 0
Brunswick County 0
Buncombe County 1
76. 76
Burke County 1
Cabarrus County 0
Caldwell County 1
Camden County 0
Carteret County 0
Caswell County 0
Catawba County 0
Chatham County 0
Cherokee County 1
Chowan County 0
Clay County 1
Cleveland County 0
Columbus County 0
Craven County 0
Cumberland County 0
Currituck County 0
Dare County 0
Davidson County 0
Davie County 1
Duplin County 0
Durham County 0
Edgecombe County 0
Forsyth County 1
Franklin County 0
Gaston County 0
Gates County 0
Graham County 1
Granville County 0
Greene County 0
Guilford County 0
Halifax County 0
Harnett County 0
Haywood County 1
Henderson County 1
Hertford County 0
Hoke County 0
Hyde County 0
Iredell County 0
Jackson County 1
Johnston County 0
Jones County 0
77. 77
Lee County 0
Lenoir County 0
Lincoln County 0
McDowell County 1
Macon County 1
Madison County 1
Martin County 0
Mecklenburg County 0
Mitchell County 1
Montgomery County 0
Moore County 0
Nash County 0
New Hanover County 0
Northampton County 0
Onslow County 0
Orange County 0
Pamlico County 0
Pasquotank County 0
Pender County 0
Perquimans County 0
Person County 0
Pitt County 0
Polk County 1
Randolph County 0
Richmond County 0
Robeson County 0
Rockingham County 0
Rowan County 0
Rutherford County 1
Sampson County 0
Scotland County 0
Stanly County 0
Stokes County 1
Surry County 1
Swain County 1
Transylvania County 1
Tyrrell County 0
Union County 0
Vance County 0
Wake County 0
Warren County 0
78. 78
Washington County 0
Watauga County 1
Wayne County 0
Wilkes County 1
Wilson County 0
Yadkin County 1
Yancey County 1
Ohio Adams County 1
Allen County 0
Ashland County 0
Ashtabula County 1
Athens County 1
Auglaize County 0
Belmont County 1
Brown County 1
Butler County 0
Carroll County 1
Champaign County 0
Clark County 0
Clermont County 1
Clinton County 0
Columbiana County 1
Coshocton County 1
Crawford County 0
Cuyahoga County 0
Darke County 0
Defiance County 0
Delaware County 0
Erie County 0
Fairfield County 0
Fayette County 0
Franklin County 0
Fulton County 0
Gallia County 1
Geauga County 0
Greene County 0
Guernsey County 1
Hamilton County 0
Hancock County 0
Hardin County 0
Harrison County 1
79. 79
Henry County 0
Highland County 1
Hocking County 1
Holmes County 1
Huron County 0
Jackson County 1
Jefferson County 1
Knox County 0
Lake County 0
Lawrence County 1
Licking County 0
Logan County 0
Lorain County 0
Lucas County 0
Madison County 0
Mahoning County 1
Marion County 0
Medina County 0
Meigs County 1
Mercer County 0
Miami County 0
Monroe County 1
Montgomery County 0
Morgan County 1
Morrow County 0
Muskingum County 1
Noble County 1
Ottawa County 0
Paulding County 0
Perry County 1
Pickaway County 0
Pike County 1
Portage County 0
Preble County 0
Putnam County 0
Richland County 0
Ross County 1
Sandusky County 0
Scioto County 1
Seneca County 0
Shelby County 0
