Economic Diversity in Appalachia

Statistics, Strategies, and Guides for Action

February 2014

Prepared for the Appalachian Regional Commission
ECONOMIC DIVERSITY IN APPALACHIA

CONTENTS

List of Tables...................................................................................................................... iii

List of Figures..................................................................................................................... iii

About this Report .............................................................................................................. iv

Executive Summary ............................................................................................................ v

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

Research on Diversity and Regional Development............................................................. 3

Economic Diversity and Regional Stability ..................................................................... 3

Economic Diversity and Regional Growth ...................................................................... 4

Industrial Specializations and Regional Growth and Stability ........................................ 4

Summary ........................................................................................................................ 5

Appalachian diversity: A Statistical portrait ....................................................................... 6

Diversity is What, How, and Why................................................................................... 6

Diversity is also Where ................................................................................................... 6

Measuring Diversity ....................................................................................................... 7

Applying the Measures................................................................................................... 8

Economic Diversity Across the U.S. and Appalachia ...................................................... 9

Urbanization and Specialization...................................................................................13

Diversity and Economic Outcomes...............................................................................18

Diversity and Geography..............................................................................................20

Illustrative Examples of Diversity in Context................................................................23

Diversity and Development Strategy in Appalachia .........................................................25

Case Study Selection ....................................................................................................25

Case Study Findings......................................................................................................26

Common Themes and Trends ......................................................................................38

Mutliple avenues for achieving diversification ............................................................47

Lessons for Development practice ...................................................................................49

Growth Occurs through Specialization.........................................................................49

Diversification Strategy as Risk Assessment and Opportunity Capture .......................50

Regionalism Supports Diversification...........................................................................50

Diversification is Supporting the Fundamentals ..........................................................51

Diversification Needs Information ...............................................................................52

References........................................................................................................................53

Appendix: Data and methods...........................................................................................55

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ECONOMIC DIVERSITY IN APPALACHIA

Base Data and Methods for Calculating Economic Diversity........................................55

Measuring Industry-based Economic Diversity ............................................................56

Measuring Function-based Economic diversity............................................................56

Measuring Occupation-based Economic Diversity.......................................................59

Measuring Knowledge-based Economic Diversity .......................................................59

Geographic aggregations..............................................................................................61

Presentation of Metrics................................................................................................62

LIST OF TABLES

Table 1: Economic Diversity by Region, 2012 ..................................................................... 9

Table 2: Economic Diversity by County Character, 2012 ..................................................13

Table 3: Economic Diversity by Region, 2012 ...................................................................16

Table 4: Economic Diversity by County Character, 2012 ..................................................16

Table 5: Percent of Appalachian Counties with Particular Functional Specializations, 

2012 ..................................................................................................................................18

Table 6: Diversity by County Economic Status, Appalachia, 2012 ....................................19

Table 7: Industrial Diversity and Employment Growth.....................................................20

Table 8: Ratio of County to Commuting Shed Economic Diversity by Region, 2012 ........21

Table 9: Illustrative Examples—Appalachian Economic Diversity and Outcomes, 2012..23

Table A1: Functional Categories with Selected Industry Examples..................................58

Table A2: Illustrative Descriptions of Knowledge Clusters ...............................................60

LIST OF FIGURES

Figure 1: U.S. Industrial Diversity, 2012............................................................................10

Figure 2: Appalachian Sub-regions ...................................................................................11

Figure 3: Distribution of Diversity, by Appalachian Region ..............................................12

Figure 4: Distribution of Diversity, by Appalachian County Character .............................14

Figure 5: Rural-Urban County Types in Appalachia ..........................................................15

Figure 6: Distribution of diversity, by Appalachian region (Index benchmarked to county 

character)..........................................................................................................................17

Figure 7: Distribution of diversity, by Appalachian County Character (Index benchmarked 

to county character) .........................................................................................................17

Figure 8: Functional Specializations in Appalachian Commuter Sheds ............................22

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ABOUT THIS REPORT

This report is one of four documents prepared as part of an extensive analysis of 

economic diversity for the Appalachian Regional Commission by the University of Illinois 

at Urbana-Champaign’s Regional Economics Applications Laboratory and the Center for 

Regional Economic Competitiveness, with assistance from the RUPRI Center for Rural 

Entrepreneurship and EntreWorks Consulting. Project leaders were Edward Feser, Ken 

Poole, Mark White, and Geoffrey Hewings, with Troy Mix serving as Project Manager.

Authors of this report were Edward Feser, Troy Mix, Mark White, and Ken Poole. Deb 

Markley and Erik Pages contributed to the case studies. William Cook and Erol Yildirim

were primarily responsible for building the web tool.

In addition to this report, the documents include a guide to the web tool (A 

Practitioner's Guide for Planning and Analysis with the Appalachian Economic Diversity 

Web Tool), a detailed synopsis of case studies (Case Studies in Economic Diversification 

in Appalachia), and a technical report detailing methods, data, and additional findings (A 

Statistical Portrait of Economic Diversity in Appalachia). All reports associated with the 

project can be found here: 

http://economicdiversityinappalachia.creconline.org/Report/. 

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ECONOMIC DIVERSITY IN APPALACHIA

EXECUTIVE SUMMARY

Diversification is a worthy local economic development objective. Other things equal, 

diverse economies tend to be more stable because they are less dependent on single 

industries or firms. The broader mix of economic activities in a place means that decline 

in one sector may sometimes be offset by growth in another. At the same time, diversity 

does not guarantee faster growth, higher incomes, or more widely shared prosperity. 

Sometimes a community may appear economically diverse because a major economic 

growth engine—a large manufacturing plant, a mine, a military base—has closed, 

reducing the level of specialization. Dependence on one or a few sectors yields positive 

economic outcomes when those sectors are growing. Dependence is a source of 

vulnerability when key sectors find their competitive position threatened.

Diversification is not a valuable strategy because there is a simple link between diversity 

and economic outcomes. There is not. Instead, pursuing diversity as a goal helps 

economic development practitioners and community stakeholders better detect and 

understand economic opportunities and threats. Understanding why a community has a 

high or low level of diversity; comparing different types of economic diversity, such as 

industrial or occupational; benchmarking a community’s diversity against appropriate 

peers and investigating the causes of observed differences; juxtaposing the diversity of a 

particular community against the diversity of the broader economic region of which it’s 

a part; and digging deeper to connect diversity levels to competitive opportunities and 

threats that industries, occupations, and firms face are all valuable forms of economic 

intelligence gathering that can inform a comprehensive development strategy.

This report, commissioned by the Appalachian Regional Commission, does three things. 

First, it offers a quantitative portrait of economic diversity trends in Appalachian 

counties and sub-regions, benchmarked to U.S. trends. The portraits draws on four 

complementary indexes of diversity: industrial, functional, occupational, and 

knowledge, with the first two based on the mix of industries in a place and the latter 

two based on the mix of occupations. Second, the report summarizes diversity trends, 

economic development practices, and diversification strategies in ten Appalachian 

counties. The ten cases offer insights into economic development practitioners’ 

understanding of what economic diversity means for their communities; describe how a 

diverse or non-diverse local economic structure can aid or thwart economic 

development planning efforts; and identify particular diversification approaches that are 

meeting with success in the Appalachian region. Third, the report offers general lessons 

about what diversity means for economic development practice.

An especially important accompaniment to the report is a website—

economicdiversityinappalachia.creconline.org—which supplies data and maps along 

with interactive tools for exploring diversity trends in Appalachia and the U.S. The site 

enables an economic development practitioner to compare his or her county’s level of 

diversity against a range of comparison options, including any U.S. county or selection of 

counties, counties identified as most similar to the developer’s county through

economic and demographic profiles, and counties of similar levels of urbanization. The

site also joins diversity indexes with basic industry and occupational mix information to 

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ECONOMIC DIVERSITY IN APPALACHIA

support analyses that “look behind” the level of diversity to understand its origins in the 

specific characteristics of industries and labor force. Tips for using the information and 

tools—A Practitioner's Guide for Planning and Analysis with the Appalachian Economic 

Diversity Web Tool—is available on the website.

Also accompanying the report are two additional documents. The first—A Statistical 

Portrait of Economic Diversity in Appalachia—provides additional details and analysis of 

the diversity metrics. The second—Case Studies in Economic Diversification in 

Appalachia—provides extensive narratives of the research conducted in the ten case

study counties.

The analysis contained in this report offers several lessons for local economic 

development practice. First, a competitive regional economy, and one that is also 

diverse in comparison to other regional economies of similar levels of development and 

scale, is likely to be comprised of multiple competitive specializations. A diversification 

goal should not be simply to somehow encourage the emergence and expansion of a 

generically diverse mix of economic activity, but rather to support the competitiveness 

and growth of a number of specializations or clusters that can serve as the multi-legged 

foundation for the local economy. Put differently, a good diversification strategy is a 

matter of implementing many successful specialization strategies simultaneously.

Second, the local economic developer should seek to fully assess and understand the 

“risk” associated with the existing economic base of his or her locality. A highly 

specialized economy may face comparatively little risk of significant decline over a 

foreseeable future if robust demand for its goods and services is certain. Alternatively, a 

diverse economic base can be under threat if multiple industries face significant 

disruption. An important role for the local economic developer is to fully understand the 

competitive factors underpinning the economic base and use this knowledge to 

anticipate possible disruptions that might be countered through development 

strategies.

Third, the developer should scan for economic opportunities—whether through 

business expansion, entrepreneurship, or attraction strategies, or other economic 

development initiatives—that might be nurtured through appropriate public sector 

actions. Regional economic diversification is not akin to financial portfolio 

diversification; a region cannot choose to actively divest itself of a particular segment of 

its economy (although it can allow a segment to founder or languish). Instead, it can 

shift its economic mix primarily by encouraging new industries and activities. In this 

sense, diversification strategies build on fundamental principles of economic 

development more broadly.

Fourth, the pursuit of an appropriate level of regionalism in economic development is a 

good strategy because local and regional diversity are rarely independent of one 

another. The understanding of a community’s particular economic role within its larger 

labor market area and region can help to clarify the necessity and potential content of 

regional arrangements. In order to compensate for local gaps in factors such as 

workforce skills or infrastructure, individual communities might seek to highlight their 

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ties to nearby communities; business recruitment or cluster strategies may be more 

successful if they highlight the region’s resources, not just those of individual 

communities; local economic development goals may be advanced more rapidly by 

partnering on major infrastructure projects; and so on. The larger region may offer 

significant avenues for diversification that are well beyond the reach of a smaller 

locality.

Fifth, overall, the best diversification strategy is a sound, well-balanced economic 

development strategy. Communities that successfully implement diversification 

strategies share several common traits. They develop their strategies on a solid 

foundation of analysis and research. They think and operate regionally so as to 

maximize the resources and assets available to them. Their development professionals 

work across silos to create broader networks and coalitions and to leverage networks 

and expertise. They put the right leaders and staff in place to ensure effective 

implementation. And they have a process in place for developing and implementing 

their strategies and for incorporating new leaders. Economic diversity is a legitimate 

economic aspiration and goal, but like all economic development goals, it is only 

accomplished if area leaders and stakeholders thoughtfully and effectively implement 

their economic development strategies.

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INTRODUCTION

Many rural Appalachian areas have long depended on a few dominant industries, such 

as manufacturing, mining, and forestry. Considerable research suggests that this 

dependence is closely tied to negative economic outcomes (Bradshaw, 1992; 

Freudenburg and Wilson, 2002; Stedman, Patriquin, and Parkins, 2011). Likewise, 

Appalachia’s urban regions also rely on a few industries. For example, large, verticallyintegrated steel producers propelled the economies of Youngstown, Ohio and 

Pittsburgh, Pennsylvania for many decades. As U.S. steel manufacturing declined, the 

regions faced many difficulties in adapting to an increasingly service-oriented economy 

due to the steel industry’s institutionalized labor practices and corporate structures 

(Hoerr, 1988; Safford, 2009). 

Prompted in part by Appalachia’s legacy of low economic diversity and the lack of 

practical guidance available for pursuing diversification strategies, the Appalachian 

Regional Commission (ARC) sponsored research aimed at better understanding 

economic diversity in Appalachia and identifying common diversification strategies. 

Begun in November 2012, this research consisted of three major components: 1) the 

assembly of a quantitative portrait of economic diversity in Appalachia and the United

States; 2) the compilation and assessment—through focused case studies—of views of 

economic diversity and common regional development strategies related to 

diversification in the Appalachian region; and 3) the development of guides for local 

economic development practitioners in the use of the data and strategy findings.

This report summarizes those research findings, with a focus on extracting lessons that 

offer the most potential for informing local and regional economic development efforts 

across Appalachia.1 Accompanying the report is a website—

economicdiversityinappalachia.creconline.org—which provides interactive data, maps 

and other analytical tools for exploring diversity trends in Appalachia and the U.S.2

Following a synopsis of previous research on diversity and regional development, the 

report outlines and applies several approaches to defining economic diversity. Each 

diversity definition yields an index that provides a different and complementary lens for 

viewing and understanding the economic challenges and opportunities facing local 

communities in Appalachia. The definitions recognize different features of local 

economies, including their industrial make-up, the major roles or “economic functions” 

they play in their larger regional contexts, and the characteristics of their workforces. 

Applying the definitions to varying geographic scales—county versus multi-county 

1 Detailed findings and methods from the data analysis and case study components of 

this project can be found in two additional technical reports: A Statistical Portrait of 

Economic Diversity in Appalachia and Case Studies in Economic Diversification in 

Appalachia. Both reports are available at 

http://economicdiversityinappalachia.creconline.org/Report/. 

2 A manual for development professionals—A Practitioner's Guide for Planning and 

Analysis with the Appalachian Economic Diversity Web Tool—is also available on the 

website.

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commuting shed—lends additional insight into a locality’s level of diversity and 

implications for growth and development.

The empirical analysis is followed by summaries of diversity trends, economic 

development practices, and diversification strategies (if underway) in ten Appalachian 

counties. The ten counties selected for case study analysis are either highly 

concentrated in a particularly specialty (i.e., not diverse) or have pursued tangible 

strategies to increase their level of economic diversity. The aim of the case study work 

was to generate insights into economic development practitioners’ own understanding 

of what economic diversity means for their communities; to better understand how a 

diverse or non-diverse local economic structure can aid or thwart economic 

development planning efforts; and to discover what diversification approaches are 

meeting with success in the region. 

The report concludes with a discussion of several general lessons for economic 

development practice. Diversity is certainly a complicated phenomenon; it is not 

unambiguously associated with strong economic performance. Indeed, in many regions, 

an increasing level of diversity may be associated with the decline of previously 

successful industrial specializations or clusters. A place may become more diverse 

because its longstanding economic engine is sputtering. The pattern of diversity and 

growth in regions unfolds in complex ways over long periods of time and through a 

continuous process of economic structural transition and evolution, the net effect of 

ongoing new business locations, new business formations, firm expansions and 

contractions, and firm re-locations and closures. In this context, targeting economic 

diversity as a narrow economic development goal is less useful than using it as one of a 

suite of metrics informing regular economic development strategizing and planning.

Systematic investigation into why a community either lacks economic diversity or has 

become diverse over time can uncover useful insights, such as: the level of cyclical or 

structural risk the community faces given its existing economic mix; the importance of 

the community’s economic linkages to larger regional, national, and global economies; 

opportunities for growth in emerging industries or industries that align with local 

workforce strengths; and possible investments, policies, or initiatives that can assist a 

structural transition from a vulnerable set of industries. Recognizing that diversity is not 

simply a matter of how many industries are present in place, but also the characteristics 

of those industries, their linkages to the wider economy, and the skills and knowledge 

characteristics of the workers they employ, offers a much richer picture of economic 

strengths and vulnerabilities. Likewise, understanding that a locality’s diversity depends 

not only on its own economic mix but the mix of its larger region, can reveal previously 

unknown opportunities for grown and development through the leveraging of regional 

assets and creation of innovative inter-jurisdictional partnerships.

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RESEARCH ON DIVERSITY AND REGIONAL DEVELOPMENT

Regional development practitioners often cite economic diversification as a desirable 

goal for the purported benefits it can provide in reducing exposure to economic 

downturns and opening up potential avenues for economic growth. Considerable 

scholarship has accompanied this practical focus, with much of the attention on devising 

and evaluating methods for measuring regional economic diversity and testing the 

relationships among economic diversity and regional growth and stability (Attaran, 

1986; Conroy, 1975; Dissart, 2003; Frenken, Van Oort, and Verburg, 2007; Jackson, 

1984; Mack, Grubesic, and Kessler, 2007; McLaughlin, 1930). Existing research is 

organized around investigating three major claims: 1) that economic diversity enhances 

the stability of regional employment levels; 2) that increased economic diversity results 

in increased employment growth rates; and 3) that regions’ particular industry 

specializations play a significant role in determining regional employment stability.

ECONOMIC DIVERSITY AND REGIONAL STABILITY

Scholars and practitioners have long advanced the common sense notion that economic 

diversity can reduce the impact of economic shocks on a region’s employment. Dissart 

(2003) referred to economic diversity as an “averaging process: the greater the variety 

of industries in a region, and the more dispersed the regional employment among these 

industries, the less likely a region is to suffer severe...economic decline” (p. 424). Put 

differently, diverse regions are expected to be more stable since “their fortunes are not 

tied to the fortunes of a few industries” (Chinitz, 1961, p. 281). 

There is considerable, though not unequivocal, empirical evidence of a positive 

relationship between regional economic diversity and stability. In a review of the 

economic diversity literature since 1930, Dissart (2003) reported that a majority of some 

40 studies have found a positive relationship between the diversity and stability of 

regional economies, and that larger economies tend to be both more diverse and more 

stable than smaller economies. Malizia and Ke (1993) found a relationship between 

increased U.S. metropolitan area diversity and both less unemployment and more 

employment stability. Conroy (1975) also reported a significant, positive relationship 

between metropolitan area diversity and economic stability. In a recent study of 

employment in regions of the Netherlands, Frenken et al. (2007) found a negative 

relationship between the diversity of regional employment across major industry 

sectors and the growth of unemployment in those regions.

In one example of a contrary study, Attaran (1986) found no relationship between 

economic diversity and employment stability across U.S. states. While Hammond and 

Thompson (2004) reported a negative relationship between economic diversity and 

employment volatility, they present other findings that question the wisdom of pursuing 

simplistic diversification policies in order to seek stability. In particular, they found that 

increased local spending on education and increased educational attainment had a 

significant, positive impact on employment volatility, likely due in part to the increased 

mobility that tends to accompany a more educated workforce (Hammond and 

Thompson, 2004, pp. 537-539). That finding highlights a potential tradeoff between 

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policies that seek economic stability and policies that seek to improve long-term growth 

rates in ways that can exacerbate regional employment volatility, such as investments to 

improve education.

ECONOMIC DIVERSITY AND REGIONAL GROWTH 

Presumably, three connections could exist between economic diversity and growth. 

First, the presence of more industries in a region could create opportunity for growth by 

providing for support services (e.g., accounting and legal services) that increase the 

incentive for firms to locate and expand in a region. Second, a greater variety of subindustries within a larger industry may offer growth opportunities by multiplying the 

number of possible productive linkages among existing and prospective new firms. For 

example, a community dominated by the production of an agricultural commodity may 

enjoy immediate employment growth as the economy diversifies through the addition 

of industries aimed at processing those commodities. Later, the increased regional 

income associated with the addition of that processing activity might result in increased 

employment in local-serving industries such as retail trade and personal services 

(Watkins, 1963). Third, a diversity of industries in a region may increase growth through 

innovation by improving “opportunities to interact, copy, modify, and recombine ideas, 

practices and technologies across industries” (Frenken et al., 2007, p. 687). 

The empirical evidence evaluating the relationship between diversity and growth is 

more mixed than it is for the relationship between diversity and stability. In the words of

(Dissart (2003)), “the evidence regarding the relationship between economic diversity 

and employment growth is less conclusive [and]...research on the relationship between 

economic diversity and income levels and growth yields contradictory results” (p. 434). 

For example, Wagner and Deller (1998) found a positive relationship between economic 

diversity and growth in per capita incomes, while Attaran (1986) found a negative 

relationship between those variables. Refining the notion of the type of diversity that is 

important for economic growth, Frenken et al. (2007) reported a significant positive 

relationship between the diversity of employment by industry within major economic 

sectors and the rate of employment growth.

INDUSTRIAL SPECIALIZATIONS AND REGIONAL GROWTH AND STABILITY

While economic diversity is often measured in an industry-blind manner, many scholars

stress the differential impacts of certain specializations. Employment in durable goods 

manufacturing—an industry thought to suffer from similar cyclical downturns—has 

been studied for its relationship to economic stability and considerable attention has 

been paid to the negative economic impacts of specialization in some extraction-based 

industries.

For example, Malizia and Ke (1993) and Hammond and Thompson (2004) found that 

employment in mining reduces economic stability. Freudenburg and Wilson (2002)

reviewed approximately 300 studies on this topic, finding that about half reported 

negative economic impacts from mining employment, with the remainder finding 

mostly mixed or neutral impacts. Where positive impacts were found, they tended to 

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relate mining employment to income growth, not regional employment growth. In 

related work, Auty (2000) reported that, since the 1960s, developing resource-abundant 

countries have experienced slower growth than have relatively resource-poor countries. 

Papyrakis and Gerlagh (2007) tested this relationship in a developed country context 

and found that resource-abundant states in the United States experienced slower 

growth than less endowed states.

Scholarship on the development of staple-based economies has sought to explain the 

stunted growth that often accompanies resource-based industries (Watkins, 1977). This 

literature advances the “staple trap” model for explaining the vicious economic cycle 

that keeps resource-dependent regions from diversifying. One iteration of this model 

specifies that, in regions with significant natural resources, industrialization is delayed 

since significant profits can be enjoyed through resource extraction; less urbanization 

occurs; a less skilled workforce results; and government intervention is called upon to 

create jobs and protect industries—reducing the competitiveness of extractive 

industries in the process (Auty, 2000, 2001). 

SUMMARY

Academic research is mixed on the precise nature of the relationships between 

economic diversity, stability, and growth in employment and incomes in localities and 

regions. In general, more diverse places tend to be more stable; however, they are not 

always fast growing. On the one hand, the variation in findings across studies is due to 

differences in time periods and locations under study, as well as differences in research 

designs and methods. On the other hand, the lack of consensus in the research 

literature is more fundamental: the growth and development implications of the level of 

economic diversity at any given point in a region’s economic trajectory are influenced by 

many factors, including the specific industry mix, trade and other linkages to other 

regions, the characteristics of the labor force, the broader regional context, and the 

legacies that existing industries bring to local civic culture and leadership.

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ECONOMIC DIVERSITY IN APPALACHIA

APPALACHIAN DIVERSITY: A STATISTICAL PORTRAIT

Traditionally, a diverse local or regional economy is viewed as one which has a varied 

mix of industries and the absence of dominance of any one industry in terms of 

employment or income. Researchers have devised a variety of metrics—from simple 

univariate indices to more complex indicators developed from analogies to portfolio 

theory—to capture this commonsense perspective and study the relationships between 

diversity, growth, and stability. However, from the perspective of the economic 

development professional who is charged with identifying, advocating, and 

implementing strategies and programs to support local job and wealth creation, what 

“economic diversity” means is considerably more complex. 

DIVERSITY IS WHAT, HOW, AND WHY

In fact, economic diversity is best understood as a multidimensional concept: as a varied 

mix in what a place makes (its private sector firms and other employers); as a varied mix 

in what a place does (the skills and capabilities of its workforce); and as a varied mix in 

the reasons there is demand for—and supply of—the goods and services that a 

community’s employers and workers produce. Goods and services are what a local 

economy produces; the nature of the human capital in a place shapes how a local 

economy is able to produce; and the sources of demand and reasons for supply of goods 

and services reveal why a local economy is able to compete in the global marketplace.

Some places are a better fit for certain economic activities than other places. This notion 

of comparative advantage has long been part of economists’ toolbox for explaining 

regional differences and the essential lessons can contribute to understanding regional 

economic diversity. The advantage of a place for particular industries might come from 

the presence of certain natural resources, the existence of a workforce with the 

requisite skills to perform a particular activity, or the presence of a finance and business 

support services network that has long catered to the needs of a particular industry 

sector. In addition to benefitting incumbent firms, existing industry specializations may 

grow as those advantages attract new, related firms to the region. Approaching 

economic diversity from the point of view of development practice often means 

investigating the factors that make a region attractive or unattractive to particular 

industries and then analyzing the demand, workforce, technology, and locational 

characteristics of its economic specializations.

DIVERSITY IS ALSO WHERE

Geography influences the diversity of a place in two key senses. First, in an increasingly 

global economy, the fate of local industries is tied to the fates of distant industries, 

consumers, and markets. The geography of the economic linkages of a region with other 

regions may either increase or decrease its effective economic diversity, and 

concomitantly alter the risk of economic decline or possibilities for opportunity capture

and growth. Other things equal, industries that serve few and/or highly volatile markets, 

or trade with few and/or vulnerable partners, reduce economic diversity and increase 

the risk of decline.

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ECONOMIC DIVERSITY IN APPALACHIA

Second, a specific locality’s diversity is contingent on both the economic base of its 

immediate jurisdiction and the economic base of the larger functional economic area of 

which it is a part. A given jurisdiction can be nominally non-diverse in its own industry 

base but be tightly linked to a region with a highly diverse industry base. Likewise, a 

jurisdiction with a diverse local economic mix may still face considerable risk if it is tied 

to a broader region that depends on one or a few industries, markets, suppliers, skill 

sets, or technologies.

MEASURING DIVERSITY

Ideally, a measure of local diversity will take into account the number and distribution of 

different kinds of economic activities present because more economically diverse places 

have a larger variety of activities, other things equal. At the same time, the measure will 

be flexible enough to be applied to a range of variables that capture the different kinds 

of diversity described above.

One such indicator takes the shares of each type of activity in the local economy, 

multiplies them by the logarithms of their inverses, and sums up the values.3 The 

mathematical details are less important than the result: the measure yields a higher 

value for places with a broader and more even mix of economic activities, and it 

registers as zero in the hypothetical case of a location with only a single type of 

economic activity. The value will be quite low for a community with just a few industries 

that account for most economic activity. Conversely, the value will be high for a 

community with more balanced employment across many industries.

Altering the ways economic activity is defined creates a suite of indexes reflecting the 

multiple dimensions of economic diversity described above. The following are four key 

ways to define economic activity:

1) As employment in different individual industries in the North American 

Industrial Classification System (NAICS). This produces a measure of 

industry-based economic diversity (which the report refers to as industrial 

diversity).

2) As employment in eleven broad groups of industries, with the groups 

reflecting different functions (or roles) local economies play in their larger 

regions or the national economy. Examples of such roles are when a place 

serves as a center for health services delivery (e.g., the location of a 

regional hospital), as a higher education center (e.g., a college town), as a 

center for technology-intensive manufacturing (e.g., Silicon Valley), or as a 

government center (e.g., a state capital city). The result is a measure of 

function-based economic diversity (or functional diversity).

3 This is called an entropy measure of diversity. A more detailed description of the 

methods and findings summarized in this section is provided in the companion technical 

report Statistical Portrait of Economic Diversity in Appalachia. 

7 

 
ECONOMIC DIVERSITY IN APPALACHIA

3) As employment by occupation. This produces a measure of occupationbased economic diversity (or occupational diversity).

4) As employment in twelve broad groups of occupations, with the groups 

reflecting the different types and levels of knowledge required for success 

in various professions. This produces a measure of knowledge-based 

economic diversity (or knowledge diversity).

Calculating the four basic metrics for counties as well as the multi-county regional 

economies in which they sit offers a rich picture of local diversity that can inform 

economic development practice, particularly when benchmarked against appropriate 

comparison counties and regions and supplemented with additional data on the specific 

industries and occupations present (and absent) in the place. The notion of appropriate 

comparison is important. Very large places—urban counties and metropolitan areas—

clearly will be more diverse than very small places, simply because the former can 

support a much larger variety of economic activities. Accordingly, the analysis below 

describes Appalachian diversity for urban versus rural places.

APPLYING THE MEASURES 

Measures of industrial, functional, occupational, and knowledge diversity were 

calculated for U.S. counties and a variety of other geographies using detailed 

employment data for 1999, 2009, and 2012.4 Industrial diversity is measured for the 

1,000+ six-digit NAICS industries. Occupational diversity is measured for 96 occupational 

groups defined in the 2000 Standard Occupational Classification (SOC) system.5

Functional and knowledge diversity are calculated by first grouping industries and 

occupations into relevant clusters and then recalculating the measures. In the case of 

functional diversity, the 1,000+ NAICS industries were grouped into the eleven clusters 

reported in Table A1, with each cluster representing a distinct and broad type of 

economic activity that tends to concentrate in particular locations and which represents 

a major economic function or role (manufacturing center, government center, etc.). In 

the case of knowledge diversity, the 96 occupations were grouped into the twelve 

clusters reported in Table A2, with each cluster constituting occupations that require 

the same types of knowledge of the workers that hold them.

Counties are the basic units of analysis, with county diversity indexes reported as 

standardized scores or qualitative rankings based on the relationship of individual 

measures to average diversity levels. To standardize the diversity measures, each 

county’s raw diversity score was divided by the average score across all U.S. counties, 

resulting in a standardized scale where 1.0 represents the average and values greater 

4 Sources of the employment data are county-level estimates prepared by Economic 

Modeling Specialists International (EMSI) for 2009 and 2012, and County Business 

Patterns data for 1999 that were suppression-adjusted following the methodology in 

Isserman and Westervelt (2006).

5 Estimates of employment by occupation in each county were developed by assuming 

that national average industry staffing patterns (as revealed in the Bureau of Labor 

Statistics’ National Staffing Patterns Matrix) hold in each county.

8 

 
ECONOMIC DIVERSITY IN APPALACHIA

than 1.0 represent above average diversity. Diversity scores were then classified into 

one of six categories ranging from “Very Low” to “Very High,” according to nearness to 

the mean diversity score and the overall distribution of diversity values. For example, a 

diversity score located more than two standard deviations above the mean value is a 

“Very High” level of diversity while a score less than one standard deviation below the 

mean is a “Below Average” score.

Diversity indexes were generated for all U.S. counties; for the Appalachian region as a 

whole; for five sub-regions designated by the Appalachian Regional Commission (see 

Figure 2; and for U.S. Census Regions. Diversity levels are compared for counties of four 

types—Urban, Mixed Urban, Mixed Rural, or Rural—defined by Isserman (2005). The 

Isserman urban/rural typology (see Figure 5) takes into account the population density 

of a county and the relative size of urban and rural areas within it. Each county’s unique 

region was identified using 2006-2010 commuting data from the American Community 

Survey; for a given county, nearby counties are defined as part of its labor market area if 

they are either senders or receivers of at least five percent of the core county’s 

workforce.

ECONOMIC DIVERSITY ACROSS THE U.S. AND APPALACHIA

In 2012, industrial diversity in U.S. counties ranged from a minimum in Chattahoochee 

County, GA—where the measure was only 23 percent of the average level—to a 

maximum in Orange County, CA—where the measure was nearly 25 percent over the 

average value. Between those extremes, more than half of all U.S. counties have a level 

of industrial diversity no more than ten percent above or below the national average. 

Although counties with above average industrial diversity are more common than those 

with below average diversity, there are some extremely low diversity counties in the 

country.

Table 1 summarizes industrial, functional, occupational, and knowledge diversity rates 

by U.S. Census region and Appalachian sub-region. There are minor regional differences 

in economic diversity of all types. On average, counties in the U.S. Northeast tend to be 

Table 1: Economic Diversity by Region, 2012

Region Counties

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

United States 3,142 0.23 1.00 0.16 1.00 0.68 1.00 0.77 1.00 

 Midwest 1,055 0.41 0.99 0.37 0.99 0.68 0.99 0.77 1.00 

 Northeast 217 0.79 1.11 0.80 1.10 0.95 1.02 0.94 1.02 

 South 1,423 0.23 0.99 0.29 0.99 0.76 1.00 0.82 1.00 

 West 447 0.26 0.99 0.16 0.98 0.77 1.00 0.83 1.00 

ARC counties 420 0.74 1.02 0.62 1.03 0.89 1.01 0.90 1.01 

 Northern 86 0.79 1.08 0.78 1.08 0.95 1.02 0.95 1.02 

 North Central 63 0.77 0.98 0.67 1.03 0.95 1.01 0.95 1.01 

 Central 82 0.74 0.95 0.74 0.99 0.89 0.99 0.95 1.00 

 South Central 85 0.75 1.03 0.62 1.05 0.95 1.01 0.90 1.01 

 Southern 104 0.82 1.03 0.71 1.03 0.94 1.01 0.94 1.00 

Index benchmarked to overall U.S. average

Knowledge

 

Industrial Functional Occupational

9 
ECONOMIC DIVERSITY IN APPALACHIA

more diverse, reflecting the higher overall urban density of that region of the country. 

The least diverse counties in the U.S. tend to be found in the nation’s highly agricultural 

and rural mid-section, the Great Plains, central Appalachia, and selected mountain and 

southwestern states (see Figure 1).

The 420 counties in Appalachia as defined by the Appalachian Regional Commission are, 

on average, slightly more diverse than the U.S. average. However, there are important 

sub-regional differences. Figure 2 maps the sub-regions and Figure 3 plots the 

distribution of each type of diversity by sub-region, showing maximum and minimum 

values and the range of values in the middle 50 percent of the distribution. The wider 

the box for a given sub-region in Figure 3, the greater is the range of diversity across 

those counties that are 25 percent above and below the national average (the middle 

half of counties). Looking at the top panel in the figure, most counties in the Northern 

and Southern Appalachian sub-regions have above average levels of industrial diversity, 

while most counties in the Central sub-region have below average levels of industrial 

diversity.

Several findings are evident from a scan of the four panels in Figure 3. First, diversity 

levels vary much more for industrial and functional diversity than for occupational and 

knowledge diversity. In Appalachia, occupational and knowledge diversity tend to be 

very similar and are generally near the national average. At the same time, there is 

some variation, with the Central sub-region posting the lowest levels of occupational 

and knowledge diversity, consistent with its below average industrial and functional 

diversity. Second, Appalachia generally fares well in its level of functional diversity when 

Figure 1: U.S. Industrial Diversity, 2012

10
ECONOMIC DIVERSITY IN APPALACHIA

compared against the U.S. average benchmark. That is because Appalachia is relatively 

more manufacturing-intensive than many U.S. counties, particularly those in the 

sparsely populated national mid-section, and fewer counties in Appalachia are deeply 

specialized in particular functions like government services, health services, tourism, 

and the like. Third, some counties in Appalachia post very low levels of industrial and 

functional diversity, even if the region as a whole is broadly more diverse than the U.S. 

average. Such counties are usually heavily dependent on extraction activities—examples 

are Boone, Calhoun, and Doddridge counties in West Virginia—and economic 

development planning to counter the potential negatives associated with reliance on 

just one or a few sectors is particularly critical.

Figure 2: Appalachian Sub-regions

11
ECONOMIC DIVERSITY IN APPALACHIA

Figure 3: Distribution of Diversity, by Appalachian Region

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

Industrial diversity Functional diversity Occupational diversity Knowledge diversity

Highest county

Lowest county

75th percentile

25th percentile

Middle 

half of 

counties

Legend

1.0 = At U.S. Average

12
ECONOMIC DIVERSITY IN APPALACHIA

URBANIZATION AND SPECIALIZATION

Very broadly, the more urban the county, the more economically diverse it tends to be. 

Table 2 and Figure 4 summarize diversity levels for counties of four types in Appalachia: 

urban, mixed urban, mixed rural, and rural. Figure 5 maps county types in the region. 

Industrial and occupational diversity decline the more rural the county. Functional 

diversity tends to be higher in mixed urban and mixed rural counties. Mixed urban/rural 

counties are usually either the suburban counties of metropolitan regions or they are 

home to the modestly urbanized centers of sparsely populated areas. Hence, they are 

neither highly specialized in a few urban functions—such as advanced business 

services—as can be common in the core counties of metropolitan areas, nor do they 

tend to be dependent on agricultural or extractive industries, as is typical of many highly 

rural counties.

These patterns suggest that a more appropriate benchmark for indexing diversity is the 

national average for counties of similar levels of urbanization or “character” (urban 

counties base-lined to the U.S. urban average, mixed urban counties base-lined to the 

U.S. to mixed urban average, and so on). Tables 3 and 4 and Figures 6 and 7 summarize 

diversity rates and distributions with the indexes for each county benchmarked in this 

manner. The general picture is one of variation across Appalachia, with higher diversity 

in the more industrialized Northern and South Central sub-regions and lower diversity in 

the Central and North Central sub-regions. Overall, once one controls for the large 

number of rural and mixed rural counties in Appalachia, most of Appalachia is more 

diverse than the national average, regardless of diversity type (industrial, functional, 

occupational, knowledge) or sub-region. This is clearest in Figure 6, at least with respect 

to industrial and functional diversity. For example, roughly three-quarters of rural 

counties in Appalachia are more industrially and functionally diverse than their rural 

counterparts nationwide.

 Certainly regional comparative economic advantages make certain locations more

suitable for activities linked to particular economic functions. For example, the presence 

of significant mineral resources and forest stands make some regions more suitable as a 

location for natural resources-based processing industries. While significant 

employment in natural resources industries is not synonymous with a lack of functional 

or industrial diversity, related factors such as the topography associated with mineral 

Table 2: Economic Diversity by County Character, 2012

Region Counties

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

United States 3,142 0.23 1.00 0.16 1.00 0.68 1.00 0.77 1.00 

ARC counties 420 0.74 1.02 0.62 1.03 0.89 1.01 0.90 1.01 

 Urban 5 1.14 1.17 0.90 0.99 1.02 1.03 1.00 1.02 

 Mixed Urban 29 0.95 1.13 0.89 1.06 0.95 1.02 0.98 1.01 

 Mixed Rural 146 0.89 1.07 0.81 1.08 0.94 1.02 0.90 1.01 

 Rural 240 0.74 0.97 0.62 1.01 0.89 1.00 0.94 1.01 

Note: County character defined using U.S. Census 2010 data.

Industrial Functional Occupational Knowledge

Index benchmarked to overall U.S. average

 

13
ECONOMIC DIVERSITY IN APPALACHIA

resource deposits and the large land areas that agricultural and forestry activities 

consume may inhibit attracting and sustaining a diverse set of industries or functions in 

such places.

In fact, economic mix of the least diverse regions in Appalachia tend be oriented toward 

agricultural and resource extraction activities. Table 5 summarizes functional 

Figure 4: Distribution of Diversity, by Appalachian County Character

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

Industrial diversity Functional diversity Occupational diversity Knowledge diversity

Highest county

Lowest county

75th percentile

25th percentile

Middle 

half of 

counties

Legend

1.0 = At U.S. Average

14
ECONOMIC DIVERSITY IN APPALACHIA

specializations in the region.6 Just over 40 percent of Appalachian counties are 

specialized in agriculture and resource extraction; about one-third are specialized in 

capital-intensive manufacturing. The least common specializations in Appalachia are 

knowledge-intensive business services and corporate management and administration.

The agriculture and resource extraction complex of industries is a specialization in 

nearly three-quarters (72 percent) of the 82 counties of the comparatively low diversity 

Central sub-region, with capital-intensive manufacturing and healthcare the next most 

prevalent—but yet relatively uncommon—specializations. In the highest diversity 

Northern sub-region, the mix of specializations is much broader, with agriculture and 

resource extraction, capital-intensive manufacturing, healthcare, and higher education 

all relatively common specializations. Likewise, multiple specializations are relatively 

typical in the Southern, South Central, and North Central sub-regions.

6 Details on the identification of functional specializations by county are available in A 

Statistical Portrait of Economic Diversity in Appalachia, available at 

http://economicdiversityinappalachia.creconline.org/Report/. 

Figure 5: Rural-Urban County Types in Appalachia

15

 
ECONOMIC DIVERSITY IN APPALACHIA

Table 3: Economic Diversity by Region, 2012

Table 4: Economic Diversity by County Character, 2012

Region Counties

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

United States 3,142 0.24 1.00 0.17 1.00 0.68 1.00 0.77 1.00 

 Midwest 1,055 0.44 1.00 0.39 1.00 0.68 1.00 0.77 1.00 

 Northeast 217 0.85 1.05 0.74 1.06 0.97 1.01 0.94 1.02 

 South 1,423 0.24 0.99 0.31 1.00 0.75 1.00 0.82 1.00 

 West 447 0.28 0.99 0.17 0.98 0.79 0.99 0.83 1.00 

ARC counties 420 0.79 1.02 0.67 1.03 0.90 1.01 0.90 1.01 

 Northern 86 0.84 1.05 0.86 1.05 0.97 1.01 0.95 1.02 

 North Central 63 0.82 1.00 0.67 1.04 0.97 1.01 0.95 1.01 

 Central 82 0.79 0.99 0.71 1.01 0.90 1.00 0.95 1.00 

 South Central 85 0.80 1.03 0.79 1.04 0.95 1.01 0.90 1.01 

 Southern 104 0.83 1.03 0.71 1.02 0.93 1.01 0.94 1.00 

Knowledge

 

Industrial Functional Occupational

Index benchmarked U.S. urban, mixed urban, mixed rural, & rural averages

Region Counties

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

Lowest 

county

Average 

county

United States 3,142 0.24 1.00 0.17 1.00 0.68 1.00 0.77 1.00 

ARC counties 420 0.79 1.02 0.67 1.03 0.90 1.01 0.90 1.01 

 Urban 5 0.99 1.02 0.95 1.01 0.99 1.00 1.00 1.02 

 Mixed Urban 29 0.84 1.00 0.82 1.00 0.94 1.00 0.98 1.01 

 Mixed Rural 146 0.83 1.01 0.73 1.01 0.93 1.00 0.90 1.01 

 Rural 240 0.79 1.03 0.67 1.05 0.90 1.01 0.94 1.01 

Note: County character defined using U.S. Census 2010 data.

Industrial Functional Occupational Knowledge

Index benchmarked U.S. urban, mixed urban, mixed rural, & rural averages

 

16
ECONOMIC DIVERSITY IN APPALACHIA

Figure 6: Distribution of diversity, by Appalachian region (Index benchmarked to 

county character) 

Figure 7: Distribution of diversity, by Appalachian County Character (Index 

benchmarked to county character) 

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

Industrial diversity Functional diversity

Highest county

Lowest county

75th percentile

25th percentile

Middle 

half of 

counties

Legend

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

Industrial diversity Functional diversity

Highest county

Lowest county

75th percentile

25th percentile

Middle 

half of 

counties

Legend

17
ECONOMIC DIVERSITY IN APPALACHIA

DIVERSITY AND ECONOMIC OUTCOMES

The Appalachian Regional Commission categorizes counties into one of five categories 

based on their relative economic status: distressed, at-risk, transitional, competitive, or 

attainment.7 Economically distressed counties in Appalachia have the lowest levels of 

industrial diversity among the five county types (see Table 6). Yet their level of 

functional diversity is relatively high in comparison to more prosperous counties. One 

interpretation of this paradox is that the relatively even spread of employment across 

eleven broad functional economic categories in distressed counties reflects the inability 

of such places to nurture competitive specializations. In counties that demonstrate 

more robust economic outcomes, the characteristics that contribute to their economic 

vitality may make them attractive for the growth of linked industries and industry 

clusters; the result is often higher industrial diversity given the presence of more 

industries but lower functional diversity given the presence of several specializations 

among those industries. This suggests that economic diversity more broadly is not the 

absence of specializations, but the presence of multiple, competitive specializations or 

clusters.

7 See 

http://www.arc.gov/appalachian_region/CountyEconomicStatusandDistressedAreasinA

ppalachia.asp for details on the Appalachian Regional Commission’s economic status 

designations

Table 5: Percent of Appalachian Counties with Particular Functional Specializations, 

2012

Functional specialization Total Northern

North 

Central Central

South 

Central Southern

Number of counties 420 86 63 82 85 104

Agriculture & resource extraction 40.2 27.9 61.9 72.0 31.8 19.2

Capital-intensive manufacturing 32.9 34.9 4.8 14.6 42.4 54.8

Healthcare 5.7 9.3 6.4 6.1 5.9 1.9

Higher education 5.0 8.1 3.2 2.4 7.1 3.9

Engineering-intensive manufacturing 4.1 4.7 4.8 1.2 5.9 3.9

Government 3.8 4.7 11.1 3.7 0.0 1.9

Distributive services 3.6 4.7 3.2 0.0 1.2 7.7

Finance, insurance & real estate 1.9 1.2 1.6 0.0 0.0 5.8

Media, entertainment & recreation 1.9 3.5 3.2 0.0 3.5 0.0

Knowledge-intensive business services 0.7 0.0 0.0 0.0 2.4 1.0

Corporate management & administration 0.2 1.2 0.0 0.0 0.0 0.0

Appalachian Sub-region

 

18

 
ECONOMIC DIVERSITY IN APPALACHIA

The occupational mix of Appalachian counties also tends to be dominated by skilled and 

semi-skilled labor, whereas competitive and attainment counties have a greater 

proportion of higher skilled service workers such as medical science and health 

professionals. It is also the case that business establishments in Appalachia’s distressed 

and at-risk counties tend to be larger than establishments in more prosperous counties. 

The dominance of a few large businesses in a region may make the place more 

vulnerable to periodic economic downturns while also reducing the supply of 

entrepreneurs.8

There is little evidence that diversity can be linked to economic growth. In fact, some 

regions have experienced the opposite situation: diversity associated with economic 

decline. Table 7 compares employment and industrial diversity change for the periods 

1999-2009 and 2009-2012, for the U.S. as a whole and Appalachia. Counties are grouped 

into four categories, including those which: experienced a significant increase in 

industrial diversity (i.e., changes in diversity more than one standard deviation above 

average); a modest increase in diversity; a modest decrease in diversity; a significant 

decrease in diversity (i.e., a reduction in diversity more than one standard deviation 

below average). 

Where significant changes to diversity did occur, decreases in diversity tended to be 

associated with significant increases in employment levels, while increases in diversity 

tended to be associated with either decreases in employment levels or relatively smaller 

employment increases. For example, the 31 counties in Appalachia that experienced a 

significant decrease in industrial diversity between 2009 and 2012 had an average 

increase in employment of 5.7 percent. Over the same time period, the 27 Appalachian 

counties that experienced a significant increase in industrial diversity had an average 

8 Detailed statistics on counties’ occupational mix and establishment size trends are 

available in A Statistical Portrait of Economic Diversity in Appalachia, available at 

http://economicdiversityinappalachia.creconline.org/Report/. 

Table 6: Diversity by County Economic Status, Appalachia, 2012

19

 
ECONOMIC DIVERSITY IN APPALACHIA

decrease in employment of 3 percent. In the case of changes between 1999 and 2009, 

counties that experienced increases in diversity averaged significantly smaller rates of 

employment increase than did counties that experienced decreases in diversity.9

DIVERSITY AND GEOGRAPHY

For most counties, their economic base depends on the economic activity that takes 

place in nearby jurisdictions. Workers commute to job centers in adjacent counties, 

households shop and purchase services outside the county, and firms draw workers 

from neighboring communities. The average Appalachian county loses more workers via 

out-commuting than it gains through in-commuting. Comparing own-county industrybased diversity against commuting shed diversity indicates that more diverse counties 

attract workers from surrounding counties. In all regions of Appalachia, the average 

ratios of county industrial and functional diversity to regional (commuting shed) 

industrial and functional diversity are below 1.0 (see Table 8). Regions, by virtue of their 

larger scale, are generally more diverse. Any particular county’s diversity must be 

viewed in the context of its labor market area.

Figure 8 maps the functional specializations of county commuting sheds across the 

Appalachian states. While an individual county’s functional specialization only accounts 

for employment within that county, the functional specialization of a county’s 

9 One of the more notable recent examples of diversification and decline occurred in 

Clinton County, Ohio, where DHL closed a major distribution hub that employed 7,000 

people. The loss contributed to one of the largest county-level increases in industrial 

diversity recorded between 2009 and 2012. Within Appalachia, a large increase in 

diversity occurred in Whitfield County, Georgia where the carpet manufacturing 

industry continued to shed jobs, reducing the specialization and leaving local serving 

industries with no replacement for the region’s most significant exporting industry. 

Table 7: Industrial Diversity and Employment Growth

Change in industrial 

diversity

Number of 

counties

Percent 

change in 

employment

Percent 

change in 

diversity

Number of 

counties

Percent 

change in 

employment

Percent 

change in 

diversity

Significant increase (diversification)

U.S. counties 162 43.0 108.0 219 -0.8 3.0

ARC counties 21 16.4 96.3 27 -3.0 2.7

Modest increase (diversification)

U.S. counties 1,460 33.7 13.7 944 1.5 0.5

ARC counties 174 19.4 12.5 131 1.6 0.5

Modest decrease (specialization)

U.S. counties 1,432 46.0 -10.8 1,725 2.8 -0.7

ARC counties 225 25.4 -9.8 239 2.3 -0.8

Significant decrease (specialization)

U.S. counties 80 315.5 -43.7 254 8.7 -3.5

ARC counties 8 88.2 -36.7 31 5.7 -3.2

 

1999 - 2009 2009 - 2012

20

 
ECONOMIC DIVERSITY IN APPALACHIA

commuting shed accounts for employment in all those counties identified as that 

county’s major commuting partners. Neighboring counties are much more likely to 

share the same commuting shed functional specialization than they are to share the 

same functional specialization for employment within their own boundaries. For 

example, Pennsylvania State University and other colleges and universities in central 

Pennsylvania create an identifiable hub of higher education activity at the commuting 

shed level that tends to be obscured when functional specializations are investigated on 

a county-by-county basis. Similarly, the prevalence of finance, insurance and real estate 

and knowledge-intensive business services specializations demonstrate the often farflung impacts of cities—such as Atlanta, Birmingham, New York City, and Philadelphia—

on their surrounding regions. Similarly, the significant number of corporate 

management and administration specializations in southwestern Ohio and northern 

Kentucky demonstrate the impact of Cincinnati’s industry mix on the region. The 

commuting shed perspective on functional specializations also suggests that many 

counties in Appalachia rely on economic activities that occur outside ARC’s official 

regional boundary. For example, New York City is part of the commuting shed for 

Monroe County in northeast Pennsylvania. The knowledge-intensive business services 

specializations emerging from Atlanta in northwestern Georgia seem to demonstrate a 

similar phenomenon of significant activity crossing the ARC boundary.

Table 8: Ratio of County to Commuting Shed Economic Diversity by Region, 2012

Region Counties

Lowest 

county

Average 

county

Lowest 

county

Average 

county

United States 3,142 0.21 0.90 0.19 0.91 

 Midwest 1,055 0.39 0.90 0.33 0.91 

 Northeast 217 0.68 0.95 0.68 0.97 

 South 1,423 0.21 0.89 0.25 0.90 

 West 447 0.26 0.92 0.19 0.93 

ARC counties 428 0.68 0.90 0.58 0.92 

 Northern 86 0.68 0.93 0.71 0.94 

 North Central 63 0.76 0.89 0.58 0.91 

 Central 83 0.68 0.87 0.63 0.87 

 South Central 92 0.74 0.90 0.67 0.92 

 Southern 104 0.72 0.91 0.65 0.93 

 

Industrial Functional

21
ECONOMIC DIVERSITY IN APPALACHIA

Figure 8: Functional Specializations in Appalachian Commuter Sheds

22
ECONOMIC DIVERSITY IN APPALACHIA

ILLUSTRATIVE EXAMPLES OF DIVERSITY IN CONTEXT

Brief explanations of the examples listed in Table 9 shed further light on the nature of 

economic diversity across a sample of Appalachian counties10. Located in metropolitan 

Atlanta, Cherokee County, Georgia has a high level of industrial diversity and smaller 

than average establishment sizes. Reflecting a common pattern in many rural and 

exurban counties, the local school system represents the county’s largest employer with 

many small establishments in retail trade and finance, insurance and real estate sectors. 

Cherokee depends heavily on the surrounding region for economic opportunities, with 

more than 40,000 resident workers commuting outside the county for work. Garrett 

County, Maryland—another county with a high level of industrial diversity—has above 

average levels of employment in agriculture and resource extraction, capital-intensive 

manufacturing, and recreation-related industries tied to the nearby Pittsburgh and 

Washington metropolitan areas. As with Cherokee, Garrett has a diverse set of smallerthan-average sized establishments that contribute to its industrial diversity.

Montour County, Pennsylvania is a competitive county with below average industrial 

diversity. There is a large concentration of employment connected to the corporate 

headquarters of a large, regional medical center, and a large proportion of residents’ 

earnings are attributable to relatively high wage corporate and healthcare sector jobs. In 

10 Extra jobs represent the difference between actual employment in a functional 

category and expected employment in a functional category if this category accounted 

for the same percentage of county employment as it did for U.S. employment. See the 

Appendix for more details on extra jobs and the assignment of functional 

specializations.

Table 9: Illustrative Examples—Appalachian Economic Diversity and Outcomes, 2012

23

 
ECONOMIC DIVERSITY IN APPALACHIA

Martin County, Kentucky—a distressed county—jobs associated with coal mining and a 

federal penitentiary accounts for approximately one quarter of total employment.

Both Centre County, Pennsylvania and McDowell County, West Virginia have low levels 

of industrial diversity, larger than average establishment sizes, and an identifiable, 

dominant industry. Centre is home to the main campus of Pennsylvania State University, 

which accounts for more than 24,000 employees and is a significant regional 

employment draw. McDowell contains significant employment in the mining industry 

that pays higher wages than many other jobs in the county. This concentration of 

earnings in McDowell likely contributes to the county’s distressed status, with those not

employed in mining earning relatively low wages. In Centre, on the other hand, earnings 

are distributed relatively more evenly and the county enjoys transitional status.

24
ECONOMIC DIVERSITY IN APPALACHIA

DIVERSITY AND DEVELOPMENT STRATEGY IN APPALACHIA

Mines and mills dominated the economies of many Appalachian communities for years. 

The dependence that many communities had on these dominant industries and 

employers left many communities highly vulnerable to economic shocks. When those 

industries declined, communities had few options for charting a new economic 

trajectory. In light of this, many communities now identify a more diversified economy 

as a key economic development goal. In doing so, they hope to better position 

themselves to either mitigate economic risk or leverage new economic growth 

opportunities. Much like investors seeking a diversified portfolio, communities feel 

more secure when they can rely on a variety of employment and wealth generators. 

However, demonstrating a clear connection between increased diversity and specific 

diversity strategies can prove difficult. To understand this issue in a more in-depth

manner, the project team sought to identify the key features of strategies in diverse 

communities, or communities that have experienced increases in diversity and 

economic growth. This research included site visits and telephone interviews with key 

stakeholders in an effort to develop case studies about ten counties located in different 

areas of the Appalachian region. This section summarizes the findings from the case

study research.

11 First, the report provides a brief review of the process used to select

the ten case studies. Then, the report summarizes the findings from that research as 

well as the common themes and trends that emerged from the case studies. 

CASE STUDY SELECTION

The project team began by using measures of diversity, growth, and economic distress, 

as defined earlier in this report, to identify a short list of potential case study counties. 

As part of the effort to narrow the candidates, researchers examined the economies of 

these counties and conducted preliminary research about the economic development 

policies that different counties employed. This provided insights about whether the 

county or its broader region had identified economic diversity as an issue, or if the 

county or region were involved in initiatives to strengthen and diversify the county or 

region’s economic base. The subsequent selection criteria considered the following 

factors: 

• Whether the county or its surrounding region had a current economic 

development strategy; 

• Whether the county leaders were engaged in some kind of meaningful 

economic development efforts; 

• Whether the county was participating in current multi-county, regional 

initiatives. 

11 A more detailed description of the case studies, and the methods used to select the 

cases, can be found in the companion technical report Case Studies in Economic 

Diversification. 

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ECONOMIC DIVERSITY IN APPALACHIA

Based on this preliminary research and consultation with ARC staff, the research team

selected ten counties for case studies. It is important to note that these case studies are 

not necessarily examples of best practices; some case studies feature counties with very 

high levels of economic diversity while others have very low diversity. The ultimate goal 

was to select a set of case studies that spoke to a wide range of diversity-related issues. 

The project team conducted the case study research through a series of site visits and 

phone interviews conducted in April and May of 2013. During the course of these case 

studies, the project team spoke with numerous stakeholders including local economic 

development and planning organizations, educational institutions, community groups, 

county and local government, and representatives from the business community. These

stakeholders provided information about their community’s economic development 

activities, and the role that economic diversity goals played in motivating those 

activities.

CASE STUDY FINDINGS

The ten case study counties evidence a number of diversity-related issues. As noted 

above, the research team sought counties that had diverse economies, were lacking 

diverse economies, or had a recent change in diversity. The team also sought to include 

cases from both urban and rural counties throughout the ARC region. Additionally, the 

case studies represented a mix of functional specializations, so the selected counties 

had economies driven by different industries, including manufacturing, education, or 

extractive industries, as well as some places with no explicit dominant specialization. 

Listed below are the selected case study counties: 

• Tioga County, NY

• Lycoming County, PA

• Garrett County, MD

• Upshur County, WV

• Knott County, KY

• Washington County, VA

• Rutherford County, NC

• Pickens County, SC

• Lauderdale County, AL

• Oktibbeha County, MS

The case analyses focus primarily upon how each community approaches the issue of 

diversity in economic development strategy and policy, as well as the actions taken to 

achieve greater economic diversification. The cases also highlight many key issues or 

lessons learned from each county’s individual experience.

TIOGA COUNTY, NEW YORK

Tioga County, NY is a rural county in New York State’s Southern Tier. Its largest 

community is Owego, NY, but given that it is situated between Binghamton, Ithaca, and 

Elmira, it largely serves as a bedroom community to those relatively larger metro areas. 

The county has a long history of relying heavily on single, large employers beginning 

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ECONOMIC DIVERSITY IN APPALACHIA

with Endicott Johnson, then IBM, and now Lockheed Martin. This dependence has 

created both great opportunity and great risk. Lockheed Martin recently employed as 

many as 4,000 employees in relatively high-paying jobs, but lost nearly 1,600 jobs due to 

the loss of the Marine One Presidential Helicopter contract. These job losses highlighted 

the extent to which the county relies upon this one employer, and in a sense shows the 

county’s crisis of diversity. 

The future of Lockheed Martin’s facility lies with decisions being made by Congress and 

Lockheed Martin’s corporate leadership. Shrinking defense budgets have forced 

Lockheed Martin to make a number of strategic corporate site location decisions in 

recent years. Local leaders have very limited influence on these external factors. In 

essence, the locus of control over activities that could significantly alter the region’s 

economic trajectory is found outside of the county and outside of the region. 

In spite of these forces, local policymakers and practitioners have identified local 

strategies to advance Tioga County’s diversification and economic development goals. 

Many of these efforts rely on practitioners operating in a collaborative manner. Tioga is 

a small county and lacks the resources to pursue extensive business recruitment and 

attraction—activities that might not necessarily be appropriate for a small, rural county 

anyway. Instead, county economic development stakeholders focus on strengthening 

relationships both locally and regionally. Continuous efforts are made to build cohesion 

among local service providers (e.g., Tioga County Department of Economic Development 

and Planning, the Tioga County Industrial Development Agency, the local Chambers of 

Commerce, or the Small Business Development Center at nearby Binghamton University 

in Broome County) so that businesses receive seamless delivery of services. Building 

these connections has also helped to support entrepreneurial efforts. For instance, 

potential entrepreneurs can receive mentoring from SCORE (Service Corps of Retired 

Executives) representatives.

Local leaders have also made efforts to leverage broader regional connections. These 

opportunities have come through regional information sharing with stakeholders in 

Broome County (Binghamton) to the east and Chemung County (Elmira) to the west. As 

a result, local officials feel that they have avoided wasteful beggar-thy-neighbor 

activities by not providing incentives to companies that are staying in the region, but 

just changing their address. Tioga County has further applied a regional lens by looking 

at opportunities available in neighboring Pennsylvania. While New York State currently 

bans the fractured drilling (“fracking”) for natural gas, Tioga County is home to about a 

dozen support companies that are involved in Marcellus Shale drilling in Pennsylvania

counties immediately to the south. Tioga has also embraced its role as a bedroom 

community and has invested in making itself an appealing place for workers in 

neighboring counties. 

The challenge with many of these more locally-based diversification efforts is that they 

tend to represent approaches that are far more incremental in nature than the 

problems they are meant to address. Ten or twenty new entrepreneurs per year, for 

instance, cannot outweigh the loss of 1,600 good paying manufacturing jobs at 

Lockheed Martin. This conundrum is a real issue facing many places like Tioga County 

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that rely heavily on one large employer or industry, as those economic pillars cannot be 

easily replaced.

LYCOMING COUNTY, PENNSYLVANIA

Lycoming County is home to 117,000 residents, with Williamsport being its largest 

community. Its economic base has traditionally focused around activities such as 

lumber production and manufacturing. More recently Lycoming County and 

Williamsport, PA have become a major activity center for the natural gas industry. Direct 

jobs in natural gas have grown from 140 in 2009 to over 1,800 in 2012.12 This growth 

has been evident not only in industries that directly support natural gas, such as 

trucking, but also more indirectly in industries such as retail and accommodations. 

Consequently, the creation of a new regional economic specialization (natural gas) has 

helped to diversify the existing economic base.

Lycoming County took advantage of the Marcellus Shale boom not only because of its 

proximity to this natural resource, but also because it made investments in several 

ready-to-use industrial sites that positioned the county for opportunities as they arose. 

Recognizing the transformative potential of the natural gas industry, area stakeholders 

wanted to ensure that they were adequately prepared. Once it became clear that these 

activities were going to significantly accelerate the regional economy, this planning 

began in earnest. As part of this preparation, approximately 10 area stakeholders went 

to Fort Worth, TX to see how that community had been affected by rapid natural gas 

development. They sought to understand not only how the gas boom would affect 

economic development, but also the area’s schools, public services, health care 

providers, and infrastructure among other areas. In short, they wanted to learn from the 

Fort Worth experience to minimize the development’s unintended consequences.

The natural gas boom helped Lycoming County grow, even during the recession. Yet as 

noted above, county leaders continue to emphasize the importance of promoting 

economic diversity and not becoming over-reliant on natural gas.13 There is a clearly 

stated desire to ensure that Williamsport remains more than just a gas town. The 

Williamsport/Lycoming Chamber of Commerce has emphasized business retention and 

expansion efforts as a foundation for maintaining and improving the county’s economic 

diversity. As a result, the Chamber invests significant time into meeting with non-gas

companies to ensure that their needs are being met, and investments in those firms are 

not crowded out by the current interest in natural gas. Even though natural gas has 

been vitally important to growth, other industries are critical for the region’s overall 

stability. 

The natural gas boom is fostering a stronger interest in broadening the industrial base, 

and other efforts are also underway to ensure that the region leverages that growth 

12 Data provided by Economic Modeling Specialists International 

(www.economicmodeling.com) 

13 http://www.williamsport.org/pdf/PlanofAction.pdf (Page 3)

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while limiting its over-reliance on natural gas as an economic engine. Local leaders also 

have made efforts to diversify the area workforce’s skill base. Pennsylvania College of 

Technology (Penn College) has made great efforts to prepare the area workforce for the 

gas boom, but the College has also done this with diversity in mind. Since 2009, Penn 

College has trained approximately 10,000 people for natural gas-related activities. The 

College has used customized, non-degree programs extensively to provide much of this 

training. When Penn College sought to invest in new degree programs, they 

intentionally avoided creating programs that were specifically dedicated to natural gas. 

Based on a regional workforce needs analysis, they instead sought to develop programs 

that were not only relevant to the natural gas industry, but also had broader 

applicability to the workforce needs of other industries. As a result, they put resources 

into developing new programs in mechatronics (which also supports area 

manufacturers) and emergency management (which also supports public services and 

utilities).

GARRETT COUNTY, MARYLAND

Garrett County is the State of Maryland’s western most county and is known for being 

home to Deep Creek Lake, a prominent Mid-Atlantic resort area. The Garrett County 

experience represents a strong example of how a community can respond to a diversity 

crisis. In 1996, Bausch and Lomb closed its glasses factory in Oakland (the county’s 

largest community) and relocated 600 jobs to San Antonio, TX. The closure’s economic 

shock to this relatively rural county served as the impetus for leaders to initiate a 

strategic planning process that successfully focused efforts on creating a stronger, more 

diverse economy.

The loss of Bausch and Lomb created a crisis atmosphere that spurred community 

action. The community responded with an honest assessment of the county’s existing 

strengths, weaknesses, opportunities and threats (SWOT), and that discussion led to the 

development of a short, concise strategic plan focused on leveraging investments in the 

county’s existing assets and infrastructure for the purpose of growing and diversifying 

the Garrett County economy. Since its adoption, the plan has been routinely updated

with significant input and consensus from five key Garrett County organizations, 

including the Garrett County Economic Development Department, Garrett County 

Development Corporation, Garrett County Chamber of Commerce, Garrett County 

Community Action Committee, and Garrett College.

Over the past 20 years, the county has grown and diversified by becoming a destination 

for second-home buyers and tourists. The area has been quite effective at leveraging its 

proximity to the Washington, DC metro area to attract weekend visitors, vacation-home 

buyers, and retirees. In addition to connecting to these regional sources of demand, the 

county—through its economic development planning process—has consistently sought 

to understand the issues facing companies in multiple sectors such as manufacturing, 

retail, real estate, tourism, and agriculture and national resources. By regularly speaking 

with firms in these sectors, county stakeholders have crafted strategies to overcome 

impediments to business growth. The community has also worked to improve the 

environment for entrepreneurs by expanding the county’s broadband infrastructure

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through the efforts of the One Maryland Broadband Network and support from the 

Appalachian Regional Commission. The County has embarked on several workforce 

development initiatives led by the President of Garrett College. One of these initiatives 

has been the Garrett Promise, which provides Garrett County high school graduates and 

GED completers with scholarships with full tuition payment in their graduation year.

It is important to note that designing strategies to achieve growth and diversification 

means little if those initiatives are not effectively implemented. Garrett County 

exemplifies the benefits of an ongoing planning process. Garrett County’s current 

Economic Development Plan—updated in 201314—is a broad-based and detailed plan 

that has wide community support. First and foremost, systematically updating the plan 

through an engagement process designed to build leadership consensus ensures more 

effective implementation. As part of this effort, leaders identify and agree on ways to 

measure the strategies’ outcomes as a way to demonstrate progress, and provide 

accountability. By revisiting their strategy every few years, Garrett County leaders also 

seek to ensure that the plan continues to meet community needs and responds to 

changing economic conditions. Ultimately, this proactive “plan and do” process results 

in an economic development approach that is more proactive and less ad hoc.

UPSHUR COUNTY, WEST VIRGINIA

Upshur County, WV is a small, rural county located in the Alleghany Mountain foothills, 

with its largest community being Buckhannon, WV. Upshur County’s economy relies 

heavily on natural resources as the county possesses a wide array of resources ranging 

from lumber to coal and natural gas deposits. These activities, combined with a longstanding manufacturing base, have been the county’s historical economic drivers. Given 

this history, the county has consistently maintained diversification as an economic 

development goal. In those efforts, the county focuses on regionally-oriented economic 

development efforts in three key sectors— agriculture, hardwood products, and 

tourism. 

The county currently participates in a regional Rural Jobs and Innovation Accelerator 

grant (sponsored by multiple Federal development agencies) that focuses on promoting 

local foods and growing its agriculture sector. This grant provides support for creating 

and strengthening food value chains between area firms and others throughout the 

state. For instance, one current local effort—led by a local “agripreneur”—helps local 

organic growers produce for homes and restaurants and creates a community kitchen 

for food processing. The region is also seeking to grow its forest products industry 

through a regional Hardwood Alliance Zone established to capture value-added timber 

product activities. The region has abundant natural hardwood resources as well as the 

local sawmills and other services needed to grow the region’s value-added production.

This regional collaboration focuses on marketing and investing in the infrastructure 

needed to attract value-added hardwood companies. Regional leaders have also sought 

14 http://www.garrettcounty.org/resources/economic-development/pdf/GCED-2013-

Strategic-Plan-Refresh-Process.pdf

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to promote and grow the tourism sector. Three counties—Randolph, Lewis, and 

Upshur—recognized that each county alone does not provide sufficient activities for

tourists, but the counties together can bundle their events and attractions to appeal to 

overnight visitors. As a result, leaders are creating a tourist corridor and marketing the 

activities as “33 Things To Do Along Route 33.” 

This spirit of collaboration has emerged within Upshur County itself. Significant private 

sector leadership, particularly in the town of Buckhannon, has advanced

“CreateBuckhannon”—a locally driven effort designed to upgrade the county seat’s 

amenities. Local volunteers with no formal organizational structure are managing the 

effort. The group conducts a regular weekly lunch meeting open to all to identify 

projects and manage their implementation. Through this venue, the community secured 

a USDA grant to build the farmers’ market, a grant to create a downtown park, provided 

new raised beds at the senior center, restored a Civil War-era home, and built bike and 

walking trails throughout town. By making the community a more appealing place to 

live and work, local leaders hope to create an environment more conducive to 

entrepreneurs and skilled workers. The work of this informal group has brought energy 

to community residents and created a positive image for those outside the community. 

KNOTT COUNTY, KENTUCKY

Located in the eastern Kentucky coalfields, Knott County is a distressed rural county. It is 

currently home to 16,124 residents and has a long trend of out-migration and

population decline. Mining is the county’s single largest employing sector and 

employment in this sector has declined from almost 1,500 jobs to just under 900 jobs 

between 2009 and 2012. The mining job losses contribute greatly to an unemployment 

rate that, at 15.9 percent,

15 remains almost twice the state and U.S. rate. This 

dependence on mining has led to a local economy that lacks diversity. This is an issue 

identified by the region, and within Knott County efforts to address this issue are based 

around using the county’s assets to promote activities like tourism. 

Several large initiatives form the foundation of the County’s tourism development 

efforts. One key piece of this effort was the creation of the Kentucky School of Craft

which was funded in part by the State of Kentucky’s Community Development Initiative 

(CDI) and operated as part of Hazard Community and Technical College (HCTC). The idea 

behind the school was to offer residential opportunities for people to train to become 

master craftsmen in Appalachian crafts. The strategy also included the establishment of 

the Kentucky Appalachian Artisan Center and incubator on Hindman’s Main Street as a 

potential retail outlet for artisanal work. The school was launched with CDI support but, 

for a number of reasons—including staff turnover, lack of housing opportunities for

students, and limited demand for long-term residential programs—it has not yet had 

the expected economic impact. In spite of this, the CDI process helped local residents 

witness the power of collaboration and engagement among local citizens.

15 U.S. Bureau of Labor Statistics Local Area Unemployment Statistics, June 2013.

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One of the outgrowths of the CDI experience was local leadership focused on adventure 

tourism. Knott County’s relative isolation does not bode well for the recruitment of 

outside industry, but the county does have entrepreneurial spirit in the region’s

“hollows” and the emphasis on tourism is designed to capitalize on that spirit. While 

the School of Craft combined with the focus on heritage tourism was aided by the 

state’s CDI, energy for adventure tourism came from county leadership, public and 

private. In 2006, county leaders made a priority of creating an adventure tourism park. 

As a legacy of the county’s dependence on natural resource extraction, there are 

thousands of acres of reclaimed strip mine and forest lands uniquely suited to a range of 

outdoor recreational activities. In 2007, Mine Made Paradise Park opened – a 

partnership between a prominent regional coal company, local residents and the 

county. The park covers over 43,000 acres and has 100 miles of trails for off-road biking 

and additional horseback riding trails and stables. 

The tourism-focused efforts in Knott County offer the potential for diversifying the 

economy, or at the very least replacing some of the jobs lost in the coal industry. The 

School of Craft effort has experienced some difficulty because it has not been closely 

linked to other tourism activities. The grassroots adventure tourism effort has helped to 

increase in the number of visitors (a key metric for tourism activities). Furthermore, this 

success has helped to support local efforts to expand facilities and trails at the park. But 

much like the School of Craft effort, the adventure sports park has much more room to 

grow. As it currently operates, the park is relatively self-contained with park visitors 

camping on the grounds rather than staying in lodging off-site. This limits the potential 

impact the park might otherwise have on the broader county and regional economies. 

As the county’s tourism efforts continue to grow, they are more likely to create greater 

diversification, as more visitors will likely create greater opportunities in other activities 

such as lodging, restaurants and retail. A broader regional effort designed to create an 

adventure and heritage tourism brand and destination would further increase the 

economic impacts.

WASHINGTON COUNTY AND BRISTOL CITY, VIRGINIA

Washington County, VA and the independent city of Bristol, VA are located along the 

Interstate 81 corridor in southwest Virginia. Bristol is on the Virginia-Tennessee Border 

which runs down the city’s Main Street. Washington County has not been overly 

dependent on a single industry, although it has historically been a manufacturing 

dependent area. Development efforts are seeking to capture a number of different 

opportunities, and several locational factors such as the interstate corridor, several 

downtown areas, and access to multiple cultural and outdoor amenities have largely 

shaped these economic development strategies. As a result, the area’s economic 

development strategies have three relatively distinct focus levels—County, City, and 

region. 

Led by the county commission and the county economic developer, Washington County 

has focused its economic development activities on commercial and industrial 

development along the I-81 corridor. The County seeks to attract not only projects 

related to manufacturing and distribution, but they are also looking for ‘destination’ 

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retail like Bass Pro Shop or Cabela’s. To date these efforts have been largely successful; 

development at one of two major interchanges along I-81 is almost complete and 

significant new development is planned for the second. 

The economic development focus within the area’s two largest cities—Bristol and 

Abingdon—differs significantly from the county, and instead focuses on downtown 

revitalization, supporting small business owners and tourism. There is a growing 

recognition, particularly in Bristol, that entrepreneurial and small business support 

services are needed to help local businesses actually succeed in their main street 

locations. Bristol also has an active Main Street program—Believe in Bristol—driven 

primarily by private sector leadership. Private sector leaders frequently play a catalytic

role in advancing Bristol’s tourism development and main street projects. This has been 

most evident through the construction of the Birthplace of Country Music museum

where support for the construction of this museum came from the private sector and 

not local government.

Complementing these local efforts, multi-county tourism efforts have helped to achieve 

important successes including the completion of the Birthplace of Country Music

museum. Furthermore, Washington County has benefited from the Crooked Road, a 

southwest Virginia music heritage trail that bisects the county. Other multi-county 

efforts benefiting Washington County include ‘Round the Mountain’—southwest 

Virginia’s artisan network. These types of regional efforts allow communities to connect

the economic value generated from existing assets with potential external demand. To 

further support these efforts, Heartwood opened in 2010 as an artisan center located 

along I-81. Heartwood, funded with ARC and state tobacco trust funds, serves as the 

region’s gateway to heritage and cultural tourism. 

The three elements of the area’s economic development— commercial and industrial 

development, main street development (in both Bristol and Abingdon) and tourism—

serve as the basis for developing a more diverse regional economy. However, the efforts 

have resulted from independent action that may require much greater participation and 

a more cohesive shared vision to be successful in the future. Despite the inherent 

difficulties in getting very different sets of actors to collaborate in the region, aligning

the industrial development, Main Street, and tourism strategies through a regionally 

cohesive vision could help ensure more impactful economic outcomes. 

RUTHERFORD COUNTY, NORTH CAROLINA

Located in North Carolina on its South Carolina border, Rutherford County lies in the 

midst of the area between Asheville, Charlotte, and Greenville-Spartanburg. A 

distressed county, Rutherford has lost employment over the previous decade and has an 

unemployment rate of 12.7 percent that remains well above average compared to both 

the U.S. and North Carolina.16 Rutherford County traditionally relied on manufacturing 

to provide local jobs. This area included a number of textile mill towns like 

16 U.S. Bureau of Labor Statistics Local Area Unemployment Statistics, June 2013.

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Rutherfordton, Forest City and Spindale, but the jobs they offered disappeared to China 

during the first decade of the 2000s, well before the Great Recession of 2008-2009. 

Even so, manufacturing remains an important part of the county’s economy. Yet, the 

county’s leaders understand that they must find alternative sources for local 

employment. For instance, the county has attracted several data centers—most notably 

Facebook. The county also has a growing in-migrating retiree population, particularly 

around Lake Lure and Chimney Rock in the western part of the county. 

Given the once dominance of textiles in the mill towns of Rutherford County, it is no 

surprise that county leaders consider economic diversification as an important goal in 

the region’s Comprehensive Economic Development Strategy (CEDS). The Isothermal 

Planning and Development Commission, in particular, provides leadership for 

Rutherford and its neighboring counties17 in designing and implementing strategies that 

encourage new industry development. As a result of the CEDS process, regional leaders 

are now implementing several key initiatives to transform the regional economy from its 

traditional reliance on textile manufacturing to prepare for other industries. For 

instance, the county is working to upgrade several older, obsolete industrial buildings. 

The Region C Workforce Development Board (representing Rutherford, McDowell, 

Cleveland, and Polk counties) is partnering with two neighboring workforce boards on a 

regional literacy initiative and is promoting greater usage of worker certifications (e.g., 

ACT WorkKeys) among employers and workers, sponsoring job shadowing programs, 

and facilitating efforts to communicate employer skills requirements to students and 

other potential workers. As part of this effort, the Rutherford County EDC is also 

seeking to better connect area firms to available services such as those offered by the

SBTDC, Industrial Extension Service, Community College, or local utilities. 

Rutherford County also seeks to diversity its sources of wealth as well as its employment 

base. For example, the county has support retirement attraction efforts that leverage 

several natural assets (e.g., the Blue Ridge Mountains, Lake Lure). These new retiree 

residents bring new money into the community through the spending of their 

retirement savings, Medicare payments and/or Social Security income. This spending 

supports other economic activities such as construction, local retail, and recreational 

facilities like golf courses or equestrian facilities. Further efforts are being made to grow 

Rutherford County’s attractiveness as a retirement destination by improving health care 

services, broadband access and housing options. 

PICKENS COUNTY, SOUTH CAROLINA

Pickens County is located in Upstate South Carolina and is part of the GreenvilleSpartanburg MSA. Local economic developers often refer to three different parts of 

Pickens County: 1) a western section focused on the city of Clemson and Clemson 

University—South Carolina’s land grant university, 2) a central section that remains very 

rural and focused on agriculture, and 3) an eastern section that serves as a bedroom 

17 The service area of the Isothermal Planning and Development Commission includes 

Rutherford County, as well as Cleveland, McDowell and Polk counties.

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community to Greenville, SC. Historically, the Pickens County economy relied on cotton 

and textile manufacturing to drive the county’s economy. More recently, Clemson 

University and urban sprawl form Greenville-Spartanburg have become much more 

important economic drivers. 

Competitiveness issues, more so than diversification, motivates Pickens County 

economic development efforts. The county’s economic development efforts focus on 

growing industry clusters such as automotive, plastics and metal working, and medical 

devices and pharmaceuticals. To support this cluster development, Pickens County—

under the auspices of the county government and Alliance Pickens (the county’s 

public/private economic development arm)—pursues an economic development 

strategy focused largely on attracting new firms, primarily manufacturers. Pickens 

County’s primary selling points have been its lower labor and business costs, but it has 

also sought to leverage its proximity to the automotive manufacturing cluster located

around Greenville/Spartanburg. Like many other communities in the southern 

Appalachians, Pickens County also tries to capitalize on its natural beauty to attract 

tourists and retirees. Complementing the county’s business attraction efforts, local 

economic developers describe the high-end residential and resort communities as 

assets that appeal to visiting corporate executives.

Pickens County also clearly benefits from the presence of Clemson University and its

economic ripple effects. For instance, not only is Clemson University a major employer 

in the county, but also faculty, staff and student spending represents significant drivers 

for the county’s retail activity and real estate market. However, the collaboration 

between the county and Clemson University currently appears relatively limited, 

providing many opportunities to more fully leverage economic spin-offs from the 

university. 

Similarly, Pickens County benefits from its proximity to the nearby Greenville job center 

and the region’s automotive manufacturing cluster. The spillover effects of being 

located in Upstate South Carolina near the BMW automotive cluster has helped spur 

growth in a broad array of related industries (e.g., construction, retail, etc.). This has 

widened the county’s economic base so that it is not completely reliant on the 

university or the region’s legacy textile mills for jobs and economic activity. However, 

greater, and more formal, interaction with regional business leaders and the university 

are essential foundations for enhancing the region’s economic diversity.

LAUDERDALE COUNTY, ALABAMA

Located in northwest Alabama, Lauderdale County is home to over 92,000 residents. 

More than 40 percent of these residents live in the city of Florence, which along with 

Muscle Shoals in Colbert County form the core of the two-county Florence-Muscle 

Shoals MSA. Also located in Lauderdale County, the University of North Alabama plays 

an important role in supporting the county’s workforce and entrepreneurial 

development efforts. The broader Shoals region has a long historical connection with 

the Tennessee Valley Authority (TVA). The Shoals region, Lauderdale County In 

particular, have long served as one of Alabama’s leading manufacturing centers. For 

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many years, Tennessee River shipping access and low-cost TVA-provided power have 

provided the region with the competitive advantage required to attract and retain 

several major manufacturing facilities. However, like many parts of the traditional “Rust 

Belt,” the local economy suffered major setbacks when Northwest Alabama 

manufacturers were so hard hit in the 1980s. This experience laid the foundation for an 

economic development strategy focused on broadening and diversifying the region’s 

base.

Concerns about economic diversity do not explicitly drive local economic development 

strategies, but those efforts tend to be framed and implemented within a broader 

regional context. For instance, the Regional leaders established the Shoals Economic 

Development Authority (SEDA) in 1986 to serve as the region’s primary business 

recruitment arm. SEDA became Alabama’s first multi-county industrial recruitment 

entity. While many of the resulting investments are made outside of Lauderdale County, 

these successful new business locations create much needed employment opportunities 

for county residents. Since 2007 the region has also operated an independent Shoals 

Industrial Development Committee. The Shoals Industrial Development Committee is 

composed of public and private sector leaders and oversees a large “deal-closing” fund

for prospective economic development projects. The Northwest Alabama Council of 

Local Governments (NACOLG)—the area’s Local Development District—is another 

regional development organization that represents the public sector in this broader 

region. NACOLG manages a number of federal and state-backed business loan 

programs. The collaboration between and within these regional organizations facilitates 

more effective economic development practice throughout the region, as it facilitates 

scale and coordination of economic development activities.

While manufacturing remains an important part of the regional economy, efforts are 

underway to leverage many of Lauderdale County’s broader regional assets in order to 

develop other industry sectors. For instance, Florence has emerged as a major regional

retail center, attracting numerous shoppers from Northern Alabama, Mississippi and 

Tennessee. Florence is also a regional medical hub and is home to ECM Hospital system, 

one of the region’s largest employers with more than 1,200 employees. Tourism is 

another area of emphasis. Spurred by organizations like the Lauderdale County Tourism 

and Downtown Florence United, Lauderdale County leverages many of its broader 

regional assets to support its tourism industry. For instance, the region’s place on the 

Robert Trent Jones Golf Trail makes the region a golfing destination more so than any 

single golf course would. Similarly, there are efforts underway to capitalize on the 

Muscle Shoals region’s musical heritage. While the area hosts many local events and 

festivals (e.g., the W.C. Handy Music Festival), there are also efforts to develop these 

assets in a broader regional strategy. The Americana Music Triangle is a proposal to link 

five states (AL, AR, LA, MS, and TN) along a tourist trail that introduces visitors to key 

spots in the development of American roots music. Along with Nashville and New 

Orleans, the Shoals would be a key stop within the Triangle. Once again, marketing

these tourism assets in a unified and strategic manner is more likely to draw tourists 

than promoting multiple attractions individually. Moreover, by leveraging these broader 

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regional assets Lauderdale County is better positioned to capture some of the 

opportunities that these assets create.

OKTIBBEHA COUNTY, MISSISSIPPI

Located in East Central Mississippi, Oktibbeha County is home to nearly 48,000 residents 

many of who live in the City of Starkville. It is also home to Mississippi State University 

(MSU)—Mississippi’s land grant university—and its 20,000 students. MSU serves as the 

county’s largest economic engine, but the dominance of the university also means that 

Oktibbeha County lacks economic diversity. The county’s leaders have sought to create 

greater balance by focusing the county’s economic growth on two fronts. The county is 

leveraging the university to develop complementary economic activity while also 

successfully implementing more traditional industrial recruitment and retention 

activities to attract new activity to locate in the county. 

Local economic development leaders acknowledge that MSU’s recent growth has driven 

Oktibbeha County’s economy. While new employment and population growth has 

strained the on-campus infrastructure and tightened affordable housing options around 

Starkville, this growth has also expanded markets for local businesses. Local initiatives, 

such as those sponsored by Main Street Starkville, have smartly sought to capitalize on 

these new opportunities to diversify the economic base. 

However, the town and gown relationship is key for both the university’s long-term 

growth and the community’s ability to leverage that asset. This relationship often 

depends on a more proactive engagement by MSU senior leadership, a relationship has 

not always been a high priority for the university. Under current leadership, MSU and 

Starkville are enjoying a renaissance in their partnership. For instance, community 

leaders are pro-actively seeking to link the community to many of the MSU-related 

activities like football games. In branding Starkville as “Mississippi’s College Town”, Local 

leaders hope to turn Starkville into a destination for football fans or parents of MSU 

students. Success in these efforts should create more opportunities to grow other 

activities such as tourism and retail.

Continued efforts to attract visitors to Starkville are also a top priority. Starkville and 

MSU are currently collaborating to develop a hotel and convention center that should 

help attract more visitors to the community. Besides making Starkville a destination, the 

community’s leadership also hopes to better connect the community with the campus. 

Consequently, the Starkville Main Street program promotes local shops and community 

districts—like the popular Cotton District—to the campus community (faculty, staff and 

students) so that they are more likely to see these areas as attractive destinations for 

shopping and nightlife. 

The other major plank of Oktibbeha County’s economic development strategies focuses 

around more traditional business attraction and retention efforts. However, these 

economic development activities are being undertaken in an increasingly regional 

context. Oktibbeha County is part of a larger region known as Mississippi’s Golden 

Triangle, which also encompasses Clay and Lowndes Counties. Several economic 

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development organizations serve these three counties, with the newest being the 

Golden Triangle Development LINK (GTDL). GTDL is the region’s primary business 

attraction and marketing agency. Key economic development partners are now working 

to attract new industries—particularly manufacturers—to the broader Golden Triangle 

region as opposed to focusing on their individual county. These initiatives further link 

Oktibbeha County to the wider regional economy by connecting the intellectual assets 

and talent based at MSU to emerging manufacturing centers in both Clay and Lowndes 

County. 

COMMON THEMES AND TRENDS

The 10 case studies profiled in this report offer both lessons and cautionary tales. This 

section seeks to identify the common themes and findings that emerged from these 

case studies. The goal is not only to improve practitioners’ understanding of diversity as 

a motivating factor in economic development, but also to give those same practitioners 

insights that might help as they develop their own economic diversification strategies. 

DIVERSITY IS MORE OFTEN A GOAL THAN AN ARTICULATED STRATEGY

Many communities identify a more diverse economy as a goal or a value, but diversity is 

less likely to motivate individual economic development strategies. As a consequence, 

diversity does not tend to drive strategy development in the same way as a concept like 

competitiveness. Competitiveness-driven strategies are not necessarily mutually 

exclusive with regional efforts to diversify, but they do lead to different sets of priorities 

and types of strategies. A focus on competitiveness can often lead places to select 

specific clusters around which to focus energy and resources. This, in turn, may limit 

their activities to only those that support those clusters, foregoing potential 

opportunities in other non-core activities. 

Diversification does not necessarily need to be seen as a goal in itself to be an important 

concept in framing decisions about economic development strategy. Instead, explicit 

consideration of diversification within a strategic planning process may help reveal

different kinds of strategies and also lead to the implementation of programs or policies 

that ultimately help diversify the community’s economic base. As a result, 

diversification as a priority might encourage leaders to consider developing a broader 

array of skills or focusing on finding new sources of wealth rather than just emphasizing 

job creation. Thinking carefully about diversity can be helpful for many places, even as 

leaders consider other goals.

Where diversity does drive thinking, it is usually the result of crisis (e.g., the loss of a 

major employer or major industry) or the recognition that a crisis might be imminent 

because the community is overly reliant on a single industry or employer. The 

justification for seeking economic diversity often centers on mitigating risk and/or 

capturing opportunity. For those places that rely on a single plant, industry or 

government facility, there is a need to protect themselves against the potential 

consequences of losing a large employer (Lockheed Martin in Tioga County, NY) or 

experiencing a downturn in a key industry (e.g., coal mining in Knott County, KY; 

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furniture and textiles in Rutherford County, NC). As a result, leaders in these places

seek additional economic activities to both replace lost jobs in declining industries and 

reduce the extent to which the community depends on those firms or industries viewed 

as “declining” or “at-risk” of eventual loss due to global economic forces. 

While economic crises may lead communities to seek greater diversity, those same 

events may also tie leaders’ hands in terms of how best to achieve diversity goals. A 

crisis environment may limit the extent to which places can be intentional about how 

they pursue their economic development objectives. Rather than being strategic about 

their efforts, the crisis itself may force leaders to adopt a “beggars can’t be choosers” 

mindset and, as a result, pursue any available opportunity instead of focusing their 

efforts on quality opportunities. 

Capturing greater opportunities is another motivating factor behind diversity-driven 

strategies. Places with dynamic economies that continuously develop new economic 

activities and are constantly in a state of transformation are more likely to weather 

economic storms and raise overall regional prosperity. However, in order to do so, 

places must be able to maximize their assets to capture current and future 

opportunities. These “opportunistic” strategies might focus on investing in state-of-theart infrastructure (e.g., Corridor H in Upshur County, WV), building market relationships 

with nearby growth centers (e.g., Garrett County, MD’s links to Pittsburgh and DC), or 

effectively connecting firms to local sources of innovation like major research 

universities (e.g., Clemson University, Mississippi State University) and/or national 

research laboratories (e.g., Oak Ridge National Laboratory in TN, National Energy 

Technology Laboratory in WV).

The data show that places that diversify more quickly are those that experience a 

sudden loss of a major employer or industry. By contrast, the case studies demonstrate 

that places that achieve more positive economic diversity tend to do so in smaller 

numbers and over an extended period of time. Therefore, places seeking to implement 

diversity-driven strategies must show patience and commitment to those efforts. This 

requires building consensus around diversity as a goal within the area’s long-term 

economic vision.

DIVERSIFICATION THAT CONTRIBUTES POSITIVELY REQUIRES MANY YEARS

In fact, like making a fine wine, economic diversity requires time to develop local roots 

and support before its fruit can bear prosperity. Diversity that occurs too rapidly often 

reflects significant economic weaknesses from a community overly reliant on a single 

company or industry. These are the stories of crisis and distress that so many regions 

encounter. While greater diversity may result, its rapid occurrence leads to dislocation 

among workers and interdependent businesses alike.

The most successful places can expect their efforts to bear positive economic fruit after 

many years of sustained effort. Several of the case profiles included in this analysis 

developed their initial plans in the 1990s and pursued that plan’s implementation (with 

appropriate modifications along the way) for 15 years before realizing success. In these 

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cases, the community’s citizens (not just its leaders) were committed to a long-term 

investment strategy. For some, that community commitment may have occurred only 

after a significant event—a major company downsizing or relocation, meaning that the 

road back to prosperity was probably much longer and more challenging. 

However, this long-term commitment often resulted from a common realization that 

the community’s future had to be built on leveraging one or more unique local assets (a 

university, a major new highway, or a unique natural geography such as a body of water 

or mountain) as well as a bit of luck. These assets provided the lynchpin, but the 

singular focus on a common plan provided the course for the community’s new 

economic trajectory. Identifying those lynchpins and then building local consensus 

around the plan to leverage those assets are the most challenging tasks—and ultimately 

the key to success. 

DIVERSIFICATION CAN BE ACHIEVED THROUGH A VARIETY OF ECONOMIC 

DEVELOPMENT STRATEGIES

Economic diversity results from a variety of strategy choices. Communities with varying 

economic experiences and situations will pursue those strategies that the community 

supports and that local partners have the capacity to implement. When asked about 

how they are working to diversify their economy, practitioners identified a wide range 

of activities including traditional business recruitment, retention and expansion, 

workforce development, entrepreneurial development, promoting tourism, leveraging 

university capabilities, investing in infrastructure, and many others. As noted earlier, all 

these strategies are typically designed to achieve one of five goals:

• Create collaborative regional planning and implementation systems,

• Build an ecosystem capable of supporting a diverse array of economic 

activities,

• Connect local and regional assets to external markets,

• Develop skills and talent needed in a wide range of industries, and

• Encourage local reinvestment of wealth.

However, at the most basic level practitioners often seek an “anything that works” 

approach to diversification. Clearly, there are multiple ways to achieve diversity, and 

several practitioners pointed out that just as the community should not rely on one 

employer or one industry, nor should the community rely on any single economic 

development strategy. Much like diversity itself, undertaking multiple strategies allows 

practitioners to protect themselves against failed or ineffective initiatives while at the 

same time increasing the likelihood that one of those strategies will succeed. Moreover, 

many strategies create jobs in relatively small numbers; undertaking multiple strategies 

can also increase the overall impact of the broader community efforts.

Another influence over selecting preferred economic diversification strategies is the 

extent to which strategy outcomes can be controlled locally. Practitioners most 

commonly identified workforce development and entrepreneurial support efforts as 

actions they could impact locally. In both instances, these strategies involve building the 

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capacity of people who are already located and/or tied to the local community. In other 

instances, local efforts to foster diversity focused on giving people already living in the 

community a reason to stay, either through local educational or career opportunities. 

Traditional business retention and expansion activities also seek to capture and retain 

investment in the region, but for many businesses, their fate is determined externally as 

part of far-flung corporate headquarters’ decisions or by global market forces. 

Ultimately, the most significant challenge to overcome is that most diversification 

strategies result in creating jobs in the fives or tens over a sustained period of time, 

whereas an economic event can result in the loss of a major employer or industry and 

hundreds of jobs at a single moment in time. As demonstrated in the case studies, 

many Appalachian communities are looking toward tourism development strategies to 

help diversify their economies and replace past economic drivers. These tourism 

strategies, however, are often more about replacing lost jobs than diversifying the local 

economy. While the tourism industry provides jobs for relatively low-skilled workers, 

the new jobs do not pay those same workers as much as industries like coal or 

manufacturing once did. Consequently, tourism alone will not lead to greater diversity, 

but instead must be viewed as but one element of broader economic development 

strategies. 

In this context, the appeal of significant business attraction efforts becomes clear. The 

attraction of a 200-employee manufacturing plant can make a greater impact on the 

community than helping a small business grow from 5 employees to 10 employees. 

While business attraction efforts have a place in many comprehensive economic 

development strategies, these kinds of investments are made sporadically and a focus 

on these types of projects can be risky. Most notably, there are few relatively large 

projects seeking new locations, and it is probably unrealistic for communities lacking 

significant assets to expect to win these types of large projects. Many growth 

companies are also looking to locate in fast-growing, diverse metropolitan areas, closer 

to their customer base. The projects that are looking for more rural locations often tend 

to need a low-cost and relatively low-skilled workforce. These kinds of projects are

intrinsically mobile and their employment levels are not always sustainable over a long 

period of time. As a result, this is an area where local forces are least likely to exert real 

influence over outcomes.

EFFECTIVE DIVERSIFICATION REQUIRES MORE THAN JUST DIVERSIFYING THE

EMPLOYMENT BASE

The case studies demonstrated that approaching diversity issues requires communities 

and practitioners to take a broader perspective and approach. This broad thinking is

required in two ways—in how community leaders and economic developers define 

diversity as well as in how they go about pursuing strategies to promote greater 

economic diversity. Most practitioners conceptualize a diverse economy as one with a 

wide array of industries, but this is a relatively narrow way to define diversity. Since 

wages paid by area employers are not the area’s only form of wealth generation, 

diversity should take into account more than just local employment. Therefore, broader 

definitions of diversity can lead to outcomes beyond just a diverse employment base. 

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For instance, communities might consider thinking about developing a more diverse set 

of wealth generators. As shown in places like Rutherford County, NC, retiree attraction 

efforts can diversify an area’s wealth generation capacity. These retirees bring money 

from outside the region in the form of their retirement accounts as sources of wealth. 

This wealth can then be re-circulated in the region to support activities like construction 

and retail. Similarly, Medicare payments (which also originate external to the 

community) can also generate demand for activities such as those related to health 

care. 

Diversity in the ownership of locally-based companies is another consideration, and 

specifically whether owners are based inside or outside of the community. Locallybased and headquartered companies are more apt to remain and invest in the area than 

are firms where the locus of decision making is external to the region. As the Tioga 

County case study demonstrates, when decisions about a local firm’s future are made 

external to the region, there can be great uncertainty about the region’s economic 

future. In order to mitigate this risk, many places pursue entrepreneurship strategies 

that promote greater local ownership and control. The case study counties provide 

numerous examples of entrepreneurial strategies including promoting entrepreneurship 

to students, recruiting entrepreneurs to the region, connecting entrepreneurs to 

existing support services, and/or investing in key infrastructure like broadband capacity 

and business incubators. By growing the number of locally-based firms, communities 

are more able to exert control over their economic trajectory. 

Skills diversity is another area of consideration. Workforce development represents

another area that local practitioners identified as a real challenge, but also one where 

they felt as though local action could make a real impact. Many former mill towns 

possess a talent pool that has a relatively narrow range of skills that may not translate 

well to emerging new industries. This relative lack of skills slows these communities’ 

ability to take on new and different activities and thereby diminishes their capacity to 

diversify their economies. While there may be a desire to focus training efforts around 

the region’s dominant industry, this can prove risky if that industry experiences 

significant decline. By contrast, a broad and flexible base of skills is required to support 

a diverse economy and respond to rapidly changing demand for skills. The Lycoming 

County case study provides an excellent example of a diversity-driven workforce 

initiative. In this instance, Penn College intentionally sought to invest in training 

programs that met the needs of the natural gas industry, but also provided workers with 

skills that are of use to firms in other industries such as construction, utilities and 

advanced manufacturing. Therefore, diversifying the skill base can allow communities 

to better take advantage of opportunities as they present themselves.

These distinctions are important as they force consideration of issues such as the 

diversity of wealth and ownership, as well as the workforce’s ability to respond to 

changing economic conditions. Consideration of these different forms of diversity also 

influences strategy design and how outcomes are measured. These different definitions 

of diversity are not contradictory or mutually exclusive. Broadly defining diversity can 

lead to the need for a diverse portfolio of development strategies, which in turn can 

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allow communities to not only achieve multiple objectives, but also increase the 

chances of strategies proving successful. 

ECONOMIC DIVERSITY INFLUENCED BY REGIONAL CONTEXT

Communities must also approach diversity with broader geographic considerations. The 

case studies showed that diversity is a scale-dependent issue. Workers often think 

regionally when looking for employment, and so too should communities when seeking 

future opportunities for wealth creation. Understanding the broader regional context 

can help communities better understand the range of available opportunities and 

potential risks. For instance, a community like Tioga County, NY will never be a major 

jobs center but by making themselves an attractive place to live they are able to attract 

people with good paying jobs in nearby job centers like Ithaca or Binghamton. By 

contrast, downturns in the Washington, DC or Pittsburgh economies might have 

negative consequences for a place like Garrett County, MD. Understanding the regional 

context can therefore help to shape the parameters for strategy development as it helps 

communities more fully understand the threats and opportunities they face. 

Specialized counties that contribute to a broader, more diverse region are more likely to 

diversify over time. Building stronger regional connections can enable communities to 

leverage a greater number of assets, and thereby open up greater economic 

opportunities. For instance, proximity to large universities can prove beneficial not only 

for the immediate area, but also surrounding communities. If leveraged properly, large 

universities, such as MSU in Oktibbeha County or Clemson in Pickens County, can be a 

source of innovative research and talented workers for area companies, as well as a

source of demand for local food producers or retailers. While this is important for all 

counties, it is especially important for more rural communities that lack depth of assets. 

However, these regional connections do not always occur naturally and both the 

university and the community must be willing to work together in order to maximize 

these opportunities. 

Regional thinking can also help create assets. For communities seeking to develop 

tourism, the whole is greater than the sum of the parts, and they are best served by 

linking these attractions together in the minds of potential tourists and promoting the 

entire region as a single destination. By thinking regionally, Lauderdale County, for 

example, has not only made itself a regional retail center but also a destination for 

golfers through its place in the Robert Trent Jones Golf Trail. These kinds of economic 

opportunities would not be available had Lauderdale County acted independently to 

promote its golf courses or its tourist attractions. Similarly, the tourism and 

infrastructure initiatives underway in Upshur County and its surrounding region would 

not have succeeded had those counties operated independently of one another. By 

contrast, Knott County’s efforts to establish itself as a center for adventure sports has 

yet to fully materialize in part because the initial efforts have been independent of one 

another and not yet connected to a broader regional effort. 

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BROAD ENGAGEMENT ACROSS SILOS INCREASES POTENTIAL FOR 

SUCCESSFUL DIVERSIFICATION STRATEGIES

Similarly, broad engagement with a wide array of stakeholders can also help move 

diversification efforts forward. Effective strategies engage practitioners from economic 

development, workforce development, higher education, planning, tourism 

development, as well as the private sector. Practitioners in each of these arenas bring 

unique, but sometimes overlapping, networks. These networks offer access to 

knowledge, resources and expertise. For instance, workforce developers may have 

access to training funds or training programs that would benefit companies in industries 

that economic developers seek to promote. Similarly economic developers meet 

regularly with area companies, and can in turn convey information from these 

conversations to educators who can then use that information to develop curriculum. 

As noted above, regional approaches and regional collaboration is often ideal, but local 

collaboration is the minimum required for implementing effective strategies. Top down 

initiatives are not always effective as they often end up being neither regional nor 

collaborative. Instead, some of the more effective collaborative initiatives emerge from 

grass roots efforts where local organizations come together to form a partnership to 

address an issue where there is a consensus need. Economic developers often lead 

these efforts, but the most effective ones are those that see their primary role as one of 

a connector. By connecting different regional actors and networks, they are able to 

gather support and resources to advance regional initiatives. This kind of local 

collaboration often emerges in places where there are open networks, and grassroots 

initiatives are able to surface from a wide array of actors. 

Opportunities are fewer in places where the practitioners remain within the walls of 

their silos. The case studies suggest that the communities with the most coherent 

diversification strategies have overall economic development strategies that are not 

driven by local economic developers alone. As in the case of Garrett County, MD, these 

successful strategies involve multiple stakeholders and enjoy broad-based community 

support. In these instances, area economic developers see that they have a role beyond 

just industrial recruitment and retention and are willing to embrace their role as a 

regional connector or facilitator are more apt to be successful in furthering broad based 

regional initiatives that seek to promote economic diversity. 

RESEARCH CAN GUIDE EFFECTIVE STRATEGY DEVELOPMENT AND 

IMPLEMENTATION

Strategies are more likely to be successful if they are based on a foundation of datadriven research. However, this is an area where many economic development 

practitioners often lack capacity. These research skills are needed for economic 

development practitioners to track regional growth and progress, undertake effective 

market analysis, understand how to harness creativity and entrepreneurship, and keep 

up with trends about how technology is re-shaping economic advantage. Many places 

lack this research capacity, but creative and enterprising practitioners can access these 

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capabilities by looking beyond their silos and partnering with other regional 

organizations that are capable of performing this research. 

At the most basic level, communities should have a basic understanding of the economic 

trends that are shaping their economy. This baseline analysis is an important 

component in undertaking an honest appraisal of the community’s strengths and 

weaknesses and is often captured in regional Comprehensive Economic Development 

Strategies. Understanding these economic trends allows communities and regions to 

better identify and prioritize key issues as well as determine which strategies are 

realistic, which are not, and which are likely to yield the greatest impact. For instance, 

local leaders may seek to turn their community into a tourist destination, but if the 

community has no hotels or restaurants and lacks highway access or appealing tourist 

attractions, then this effort will likely experience difficulty gaining traction. Undertaking 

this kind of honest appraisal allows places to remove less optimal options from 

consideration and instead focus on diversification strategies that are more appropriate 

for their circumstances.

In addition to considering their internal strengths and weaknesses, successful places 

also consider external risks and opportunities. This can be done by better 

understanding how local firms and industries are connected to external markets, and 

how growth or decline in other places may affect the local economy. If communities are 

able to identify comparable places then they might be able to learn from their situation. 

Similarly, community leaders might also seek to learn from other places that have 

experienced significant shocks or been presented with similar opportunities. For 

instance, the Lycoming County case study showed how that community was able to 

learn from another community—Fort Worth, TX—that had been affected by the natural 

gas boom, and was better able to prepare for the multitude of impacts arising from 

these developments. 

Research and analytical capacity can also play an important role in monitoring and 

evaluating strategy progress. By tracking outcomes, places can better identify those 

strategies that are not performing as hoped and either eliminate them or develop new, 

more effective tactics for the future. Moreover, tracking outcomes and performance 

allows stakeholders to demonstrate their progress and impact which is important for 

enlisting additional support or securing funding. 

LEADERSHIP MATTERS 

As noted above, there is no single economic development strategy that alone can lead 

to greater economic diversity. Many different kinds of strategic actions must be 

undertaken to achieve this goal. However, none of this matters if the strategies are not 

effectively executed. Successful implementation often comes down to the people 

involved. For a community to effectively diversify its economy, it must first and 

foremost have people who see economic diversification as a priority. If diversification 

proves to be a community priority, then it must also have community leaders and 

stakeholders who are open to working regionally, collaborating across silos, thinking 

opportunistically about resources, and willing to take risks in order to make the 

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investments necessary to see diversification strategies through to completion. 

Moreover, communities need different people in different roles to implement these 

strategies. At the most basic level, there are two roles that need to be filled—a 

leadership role and a staff role.

Leadership is crucial for any economic development effort. Local leaders are needed to 

articulate, and build consensus for, a community vision. They are also needed to serve 

as champions for strategies and enlist support from the community to help implement 

those strategies. In several of the cases studies, the research team saw that the private 

sector could play an important catalyzing role for many strategies. This was especially 

true for Washington County, VA, where private sector leadership drove the completion 

of the Birthplace of Country Music Museum. In Oktibbeha County, MS, engaged and 

committed leadership from both the community and the Mississippi State University 

administration make it possible for the community to increase the economic impact of 

the campus community on the local economy. However, for many smaller and more 

rural Appalachian communities, this kind of strong private sector leadership is not 

always available, and as a result they often lack the capacity to coalesce local business 

leadership for true public-private efforts in economic development. In these places, 

government can be a critical actor in rural local economic development in Appalachia 

(and elsewhere). 

But no matter where this local leadership may come from, there is also a need for 

multiple leaders. Many of the diversification strategies laid out in the case studies will 

require long-term commitment before they begin to yield significant and sustainable 

benefits for the community. Many of the initiatives underway in Lauderdale County 

began in the mid-1980s and they continue to yield benefits because there has been a 

long-term commitment to see them continued. Sustaining long-term economic 

development and diversification efforts therefore requires a depth of leadership. The 

Knott County, KY case study showed that when there has been significant leadership 

turnover, it is difficult to maintain the momentum behind these efforts. The loss of a 

strong, persuasive and visionary leader can derail efforts unless there is a deep 

leadership bench and broad buy-in to a common vision. As demonstrated by the 

Garrett County, MD case study, the county’s economic development planning process—

which has been ongoing for over 15 years—has been an effective means for onboarding new leaders into the process and ensuring consensus for the economic 

development vision.

Leadership alone cannot guarantee successful implementation of economic 

development and diversification strategies. Strong leaders often succeed when they are 

supported with great staff. For many leaders, particularly private sector leaders, these 

kinds of initiatives are well outside of their core professional responsibilities. Therefore, 

economic development practitioners are needed to do much of the work involved with 

seeing these strategies through to completion. This may involve work such as 

organizing meetings and doing the research. Practitioners also play a hugely important 

role in organizing stakeholders and preparing applications needed to secure state and 

federal funds that help kick start many of these initiatives. This is not to say that 

economic development practitioners themselves are not key leaders in their 

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communities. In most instances, they are quite active in many of the core leadership 

responsibilities (e.g., building a consensus vision, recruiting support) in which private 

sector leaders should also be engaged. However, rarely can a community truly 

transform its economy without significant support from business leaders and elected 

officials and without important support from their economic development practitioners. 

It should also be noted that working both collaboratively and regionally requires a great 

deal of trust among stakeholders. Repeated interaction often leads to greater trust and 

comfort, so continuously seeking regional and collaborative projects is one way to build 

these loose coalitions (even if the efforts are not always successful in attracting external 

funding). The importance of having these coalitions in place cannot be understated. 

For instance, the existence of ongoing partnerships can allow regions to respond quickly 

and effectively to funding opportunities as they arise. Just like diversity itself, these 

coalitions allow places to capture more opportunities and mitigate the risk involved in 

undertaking new initiatives.

MUTLIPLE AVENUES FOR ACHIEVING DIVERSIFICATION

The case studies demonstrate that diversity represents an oft-articulated goal for 

pursuing economic development strategies, but it is seldom the only goal (or even the 

most important goal) of these plans. In fact, places often identify diversity as a goal only 

if they have experienced some kind of crisis (like the loss of a major employer loss), fear 

an imminent crisis, or are frustrated by a long-term lack of growth in their industrial 

base. In essence, leaders are motivated to diversify for two primary reasons:

1) To mitigate the risk associated with being too dependent on a given employer 

or industry or 

2) To capture greater opportunity by being involved in a wider array of economic 

activities. 

When asked about their diversification efforts, local economic development leaders and 

practitioners typically point to a wide array of strategies employed. For instance, several 

practitioners cite their business attraction and retention efforts as a key plank of the 

diversification plan. Perhaps most commonly, practitioners look to activities—like 

agriculture and tourism—that leverage available assets that they currently control – 

their land, their people, or their proximity to natural or man-made amenities. These 

toolbox of development activities are similar to any other economic development goal, 

but the efforts often focus more on replacing what exists rather creating new growth 

opportunities. But, replacement efforts rarely serve to actually transform the regional 

economy. Often, they simply seek to find activities to employ those dislocated in the 

short term, and many communities have learned that this is a futile effort. 

For those communities that are seeking to avoid a crisis (or have accepted the crisis and 

moved beyond it), the focus tends to be different—emphasizing the possibilities tied to 

attracting or creating new economic activities rather than retaining the old. For these 

communities, the economic development toolbox is also the same, but the emphasis 

may differ. Leaders in these communities are more likely to focus their attention and 

energy on initiatives designed to encourage new economic activity that will likely have 

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the potential for longer term pay-off in terms of jobs and wealth creation. This longterm view is likely to be more successful in the long run and is also likely to rely on 

identifying new competitive advantages the community or region should seek.

Some places understand and describe economic diversity in terms that look beyond 

simply having firms and workers in multiple industries. These communities emphasize 

the need for also diversifying their talent base and growing locally-owned firms (that can 

control their own destiny) through entrepreneurial support efforts. In other instances, 

local efforts stress initiatives designed to diversify sources of wealth generation by 

attracting people (e.g., retirees) or developing new assets. Moreover, diversified 

communities also find effective ways to leverage a broader set of assets beyond those 

within their own borders in order to further their economic development efforts. In 

short, no one method for diversifying a local economy and successful diversification 

efforts involve multiple strategies.

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LESSONS FOR DEVELOPMENT PRACTICE

The results of the empirical and case study analyses suggest several lessons about the 

nature, characteristics, and implications of economic diversity for local and regional 

economic development in Appalachia and elsewhere in the U.S. These lessons may be 

viewed as guides for development practice, whether or not the aim is to pursue a 

concerted diversification strategy.

GROWTH OCCURS THROUGH SPECIALIZATION

Other things equal, a local and regional economic base that is diverse in its mix of 

economic activity is likely to be more stable over time; as given industries, markets and 

technologies change, other economic activities have already taken root and are in place 

to absorb labor and capital released through restructuring. However, significant regional 

growth is rarely, if ever, driven by the balanced expansion of a broad mix of industries. 

Rather, growth tends to coalesce around—and be driven by—particular industries, 

which often drives growth in related industries through firm in-migration, business startups, and existing firm expansions. This “unbalanced” growth phenomenon leads to the 

formation of industry clusters, a focus of local economic development practice and 

strategy since the early 1990s. Rapidly growing places, particularly those of small or 

medium size, are likely to appear non-diverse even as their expansion yields abundant 

job and wealth-creation opportunities.

It follows that a competitive regional economy, and one that is also diverse in 

comparison to other regional economies of similar levels of development and scale, is 

likely to be comprised of multiple competitive specializations. The goal should not be 

simply to somehow encourage the emergence and expansion of a diverse mix of 

economic activity, but rather to support the competitiveness and growth of a number of 

specializations or clusters that can serve as the multi-legged foundation for the local 

economy. Put differently, a diversification strategy is a matter of implementing many 

successful specialization strategies simultaneously. Those strategies will typically be 

focused around industries that have already gained a toehold in the place; by doing so, 

they have created a kind of “revealed competitive advantage.” If the location of truly 

new activities in a region is rare, then the rapid, large-scale location of truly new 

activities in a region is even more rare.

The complex relationships between diversity, specialization, and growth, and their 

evolution over time in the context of a specific region’s development path, produces 

“snapshots” of relative diversity that can appear counterintuitive on their face. An 

example is the finding that a common phenomenon in Appalachia is the nominally 

diverse county that is growing more slowly or declining faster than less diverse peer 

counties. What is often happening in such places, particularly those that are small, is 

that a non-competitive specialization—one that long dominated the mix of employment 

opportunities—is in decline. What is left is an apparently diverse set of unrelated 

sectors, some of which constitute stand-alone export-oriented industries and others of 

which provide business support services to the declining specialization or local services 

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ECONOMIC DIVERSITY IN APPALACHIA

to the remaining population base. In this case, then, a diverse economic structure may 

well be the reflection of “what’s left,” or, in essence, an absence of competitive clusters.

DIVERSIFICATION STRATEGY AS RISK ASSESSMENT AND OPPORTUNITY 

CAPTURE

The diversity-specialization-growth dynamic suggests two key roles for the local 

economic developer who wishes to pursue a general goal of building a diversified 

economic base. The first is to fully assess and understand the “risk” associated with the 

existing economic base of his or her locality. A highly specialized economy may face 

comparatively little risk of significant decline over a foreseeable future if robust demand 

for its goods and services is certain. Alternatively, a diverse economic base can be under 

threat if multiple industries face significant disruption. An important role for the local 

economic developer is to fully understand the competitive factors underpinning the 

economic base and use this knowledge to anticipate possible disruptions that might be 

countered through development strategies.

The second key role is to scan for economic opportunities—whether through business 

expansion, entrepreneurship, or attraction strategies, or other economic development 

initiatives—that might be nurtured through appropriate public sector actions. Regional 

economic diversification is not akin to financial portfolio diversification; a region cannot 

choose to actively divest itself of a particular segment of its economy (although it can 

allow a segment to founder or languish). Instead, it can shift its economic mix primarily 

by encouraging new industries and activities. In this sense, diversification strategies 

build on fundamental principles of economic development more broadly.

REGIONALISM SUPPORTS DIVERSIFICATION

The fact that local and regional diversity are rarely independent of one another is one 

among many cases for regionalism in economic development policy and strategy. For 

most communities there is at least some value in regional partnerships for economic 

development. The examination of functional county roles within a larger labor market 

area and region can help to clarify the necessity and potential content of these 

arrangements. In order to compensate for local gaps in factors such as workforce skills 

or infrastructure, individual communities might seek to highlight their ties to other 

communities in their region that play different functional roles. In the same vein, 

business recruitment or cluster strategies may be more successful if they highlight the 

region’s resources, not just those of individual communities—an approach that could 

potentially highlight a variety of workforce and infrastructure resources of interest to a 

wider variety of businesses. Communities might also benefit from partnering on major 

infrastructure projects.

For example, a bedroom community with close economic ties to a regional work center 

may find it in its interest to cooperate with the work center and other surrounding 

counties in funding transit improvements. As part of regional branding efforts, individual 

communities and the region as a whole may benefit by highlighting the functional 

distinctions among the jurisdictions. For example, a campaign that highlights the vibrant 

50
ECONOMIC DIVERSITY IN APPALACHIA

urban centers, peaceful bedroom communities, and outdoor recreation opportunities in 

a region might be attractive to a diverse workforce and, thus, attractive to employers 

seeking to provide employees with a high quality of life.

DIVERSIFICATION IS SUPPORTING THE FUNDAMENTALS

Overall, the best diversification strategy is a sound, well-balanced economic 

development strategy. The case analyses showed that communities that successfully 

implement diversification strategies often share several common traits. First, they 

develop their strategies on a solid foundation of analysis and research. Second, they 

think and operate regionally so as to maximize the resources and assets available to 

them. Third, practitioners work across silos to create broader networks and coalitions 

and to leverage networks and expertise. Fourth, successful places put the right leaders 

and staff in place to ensure effective implementation. As a result, leaders and 

stakeholders commit to a common vision and goals, display patience, and take selected, 

calculated risks. Finally, successful places have a process in place not only for developing 

and implementing their strategies, but also for incorporating new leaders. Economic 

diversity is a legitimate economic aspiration and goal, but like all economic development

goals, it is only accomplished if area leaders and stakeholders thoughtfully and 

effectively implement their economic development strategies.

Conversely, good diversification strategy is not a single-shot game or narrow focus on 

business attraction. True industrial recruitment coups are rare. Most growth in 

employment, whether by existing businesses or new establishments, tends to 

complement the incumbent economic base in a region while drawing upon regional 

assets. The 1992 location of a BMW assembly plant in Greenville-Spartanburg provides 

an illustrative example of growth building on existing assets. While $130 million in state 

and local government incentives served as the popular explanation for BMW’s decision, 

this focus belied the importance of other factors.

Since the late 1950s, Greenville-Spartanburg had consciously sought to compensate for 

its declining, domestic textiles industry by attracting foreign investment—focusing first 

on manufacturers of equipment for the textiles industry and later on a broad 

assortment of industries ranging from chemicals to automotive supplies. By the time of 

BMW’s site selection decision, the region had become one of the nation’s per-capita 

leaders in attracting foreign investment, with German companies including Michelin, 

Bosch, and Bertelesmann AG calling the region home (New York Times, 1992; Saporito 

and Solo, 1992). In addition to creating a friendly business environment for foreign 

investment, state and regional leaders had made significant investments in 

infrastructure, particularly a regional airport, and technical education and workforce 

training programs that proved attractive to BMW (Eichel, 1992; Kanter, 2003). The wellknown and much heralded Research Triangle Park in North Carolina serves as an 

additional example of an economic development success that resulted from at least fifty 

years of sustained public policy effort, much to the consternation of economic 

developers seeking to easily replicate the success enjoyed in that region (Feldman and 

Desrochers, 2003).

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ECONOMIC DIVERSITY IN APPALACHIA

DIVERSIFICATION NEEDS INFORMATION

Good information fuels good economic development strategy and, by extension, 

effective diversification strategy. The local economic developer provides an important 

economic intelligence function—gathering and interpreting data on economic trends, 

diagnosing the vulnerability of the local economy based on the market and 

technological trends buffeting local industries, and working effectively with local 

businesses to identify bottlenecks and constraints to growth and competitiveness that 

could be resolved with appropriate policy actions or public sector investments. Diversity 

metrics like those outlined in this report can be valuable tools if they are used to ask 

useful questions about the nature of the local economic base. Useful questions are most 

often revealed by benchmarking local conditions against other places of similar type or 

character, or which represent “aspirational” targets for local economic development. 

The web tool that accompanies this study is intended to facilitate this kind of 

exploratory analysis and benchmarking.

Diversity metrics have to be interpreted carefully, however. If the aim is to have a high 

diversity score, then diversity metrics are certainly biased in favor of larger, more urban 

communities. The chief technical underpinning of this tendency is the positive 

relationship that exists between the number of industry sectors in a region and that 

region’s diversity level. In smaller places, the maximum diversity is limited by the natural 

tendency for there to be fewer sectors present. Practitioners should consider the 

natural bias of diversity for larger places and benchmark regional diversity to regions 

similar in size and urban population characteristics. This approach will allow for the 

identification of real differences in economic diversity that are not primarily due to size 

differences. Benchmarking against counties of similar size and character will allow 

practitioners to identify realistic goals for economic progress, though it may still be

beneficial for comparisons to be made with larger places in order to track progress on 

more ambitious, transformative economic development goals.

Just as it makes sense to benchmark a county’s diversity to regions with similar urban or 

rural characteristics, benchmarking to the right places in terms of similar functional 

specialization can help to make realistic comparisons that at least partially account for 

differences in diversity due to the structure of counties’ economic specializations. 

Comparing several counties with similar specializations but very different characteristics 

related to factors such as economic outcomes and the tenure of economic 

specializations can also help to assess the trajectory of a region’s development, and 

might provide opportunities for gaining policy insights from the experiences of peer 

counties.

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ECONOMIC DIVERSITY IN APPALACHIA

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Wagner, J. E., and Deller, S. C. (1998). Measuring the effects of economic diversity on 

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ECONOMIC DIVERSITY IN APPALACHIA

APPENDIX: DATA AND METHODS

This appendix provides additional detail on the measurement of economic diversity 

based on industries, functions, occupations, and knowledge clusters; the geographies 

used to calculate and aggregate measures of diversity; and the linkages among counties 

based on commuting ties.

BASE DATA AND METHODS FOR CALCULATING ECONOMIC DIVERSITY

Most of the diversity calculations conducted for this report rely upon county 

employment estimates acquired from Economic Modeling Specialists International 

(EMSI). The particular datasets used were “complete” employment estimates for 2009 

and the third quarter of 2012, along with “covered” employment estimates for 2009 and 

2012.

18 Each dataset provides an individual row of data for each six-digit North 

American Industrial Classification System (NAICS) industry within a county, with these 

rows detailing the estimated employment and earnings in the industry and county in 

question. Based on the Bureau of Labor Statistic’s Quarterly Census of Employment and 

Wages, the covered employment dataset contains estimates for jobs covered by federal 

or state unemployment insurance systems. In addition to earnings and employment 

estimates by industry, the covered employment dataset provides estimates of the 

number of establishments by industry and county, although the 2012 dataset includes 

establishment estimates from 2011. For this report, the covered employment dataset 

was only used to conduct analyses that relied upon establishment estimates. 

The complete employment dataset includes all employment in the covered dataset 

along with wage-and-salary employment exempt from unemployment insurance 

coverage—such as military and railroad employment and employment as a real estate 

or insurance agent—and self-employment that accounts for all or a portion of an 

individual’s income. For both datasets, EMSI relies upon a variety of data sources from 

the Bureau of Economic Analysis, U.S. Census Bureau, and Bureau of Labor Statistics and 

proprietary algorithms to produce estimates of employment that do not suppress 

employment and earnings numbers for any county or industry. With a considerable 

number of employment statistics suppressed to prevent the release of confidential firm 

information, these datasets allow for a more complete analysis of county employment 

than allowed for by standard federal data releases (Isserman and Westervelt, 2006).

ENTROPY MEASURE OF ECONOMIC DIVERSITY

The entropy measure of diversity was used to calculate industry-, function-, occupation-, 

and knowledge--based measures of economic diversity across U.S. counties and a 

variety of other geographies (Malizia & Ke, 1993). These metrics were calculated 

according to Formula 1: 

18 See http://www.economicmodeling.com/data/ for more information on these 

datasets

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ECONOMIC DIVERSITY IN APPALACHIA

(1) ENTR = �(pi) ln �

1

pi

�

k

i=1

where there are i=1 to k industries and pi

is the share of economic activity (i.e., 

employment) in the ith industry. The products of industry shares of economic activity 

and the natural log of the inverse industry shares of economic activity are summed to 

arrive at the final entropy index measurement. The index has a minimum value of 0 

when all economic activity is within one industry, and the value increases as the number 

of industries increases and the distribution of economic activity across these industries 

becomes more equal. 

Where entropy measures were calculated for non-county geographies (e.g., the United 

States as a whole or individual states), the employment data was summed by industry 

and the geography in question before the entropy calculation was performed. Unless 

otherwise noted, economic diversity statistics cited in this report were calculated based 

on the entropy values of individual counties located within a geography or aggregation 

of interest—they do not represent the calculation of entropy based on all economic 

activity within a given geography. For example, the average entropy by ARC subregion

represents the mean value of all county entropy values within each subregion, not the 

calculation of entropy across all economic activity in the subregions. 

MEASURING INDUSTRY-BASED ECONOMIC DIVERSITY

Industry-based economic diversity calculations depend upon EMSI’s complete 

employment dataset. This section details the procedures used to calculate measures 

describing industry-based economic diversity.

BASE DIVERSITY OF EARNINGS AND EMPLOYMENT

The entropy measure serves as the base metric of industry-based economic diversity. 

Measures were calculated for employment, with employment by six-digit NAICS industry 

serving as the share of total economic activity (pi

) specified in Formula 1.

ANALYZING CHANGES IN DIVERSITY

This analysis relies upon 2009 and 2012 complete employment estimates from EMSI and 

1999 suppression-adjusted employment data prepared by Isserman and Westervelt 

(2006) to calculate changes in industrial diversity. Diversity changes were examined for 

the periods 2009-2012 and 1999-2009. For the 1999-2009 analysis, the industries 

included with the 1999 dataset were used as the baseline for comparison when 

accounting for differences between the Isserman and Westervelt (2006) and EMSI 

datasets. 

MEASURING FUNCTION-BASED ECONOMIC DIVERSITY

A region’s economic function or functions represent the collection of broad economic 

activities that the region’s workforce and firms engage in. Practically, functions can be 

56
ECONOMIC DIVERSITY IN APPALACHIA

identified by grouping industries together into categories that are broadly similar on 

factors such as inputs, outputs, and/or the technological or skill requirements necessary 

to perform the work customary to these industries. Grouping industries according to 

function, rather than simply accepting the NAICS industry categories, can help to 

broadly characterize the economic roles a county plays in its region; provide insight into 

the economic relationships and similarities counties have with other regions; identify 

factors that make regions comparatively better fits for certain economic activities; and 

speak to the broader economic and demographic forces that are likely to impact a 

county’s economic prospects.

This section describes the methods used to classify industries into functional groupings, 

categorize counties according to their functional specialization, and calculate countylevel, function-based economic diversity. 

CREATING FUNCTIONAL INDUSTRY CLASSIFICATIONS

One purpose of a functional industry classification is to broadly define the types of work

that are prevalent in a region. For example, Thompson and Thompson (1987) suggest 

grouping industries and occupations into functional classes to identify regional 

specializations in “routine work, precision operations, central management, research 

and development, and entrepreneurship” (p. 558). In an examination of the rise of 

services as a proportion of employment, Noyelle (1983) advanced a functional 

classification system for services “based on the type of outputs (intermediate or final 

outputs) and the institutional setting under which services are provided (private, public, 

or nonprofit sectors)” (p. 282). Lawrence (1984) classified manufacturing industries on 

the basis of the primary end use of the product (e.g., intermediate goods; consumer 

durables; producer durables; consumer nondurables) and the necessary inputs to the 

industry (e.g., research and development expenditures; scientists and engineers; capital-

, labor-, and resource-intensive). 

This analysis draws primarily from the work of Lawrence (1984) and Noyelle (1983) to 

categorize industries according to functional types. In an effort to focus on the 

economic base of counties, industries that often serve local populations, such as retail 

trade, personal services, doctor’s offices, local government, and construction, were 

excluded from the analysis of functions. Eleven functional categories were delineated 

and Table A1 lists the category titles and selected examples of industries within each 

class.

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ECONOMIC DIVERSITY IN APPALACHIA

Table A1: Functional Categories with Selected Industry Examples

CATEGORIZING COUNTIES BASED ON FUNCTIONAL INDUSTRY 

SPECIALIZATION

The concept of extra jobs quantifies specializations in terms of the absolute number of 

jobs employed in a particular category above or below the national average. To assign a 

single functional industry specialization to all counties, an extra jobs value was 

calculated for the groups of industries comprising each functional category in each 

county. The functional category with the largest number of extra jobs was assigned as 

the functional industry specialization for the county in question.

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ECONOMIC DIVERSITY IN APPALACHIA

For each county, extra jobs were calculated for each functional category as shown in 

Formula 2: 

(2) Extrai = �

Eic

Ec

− Ein

En

� Ec

where Eic is employment in the functional category of interest (i) for a county (c), Ec is 

total employment in the county of interest, Ein is the nation’s employment in the 

functional category of interest, and En is total national employment. 

CALCULATING FUNCTION-BASED ECONOMIC DIVERSITY

The entropy measure was used to calculate function-based economic diversity. The 

metric was calculated for each county with employment by functional category serving 

as the share of total economic activity (pi

) specified in Formula 1.

MEASURING OCCUPATION-BASED ECONOMIC DIVERSITY

Understanding what a region “does”—in addition to what a region “makes”—can help 

an analyst to better gauge the adaptability and suitability of a region to shocks and 

opportunities (Feser, 2003; Thompson and Thompson, 1987). In part, knowing what a 

region “does” requires data on the occupations of workers employed in the region’s 

industries and the skills required to perform those occupations. This section reviews the 

methods used to estimate county-level employment by occupations. These occupational 

employment estimates are then used as the basis for the calculation of an occupationbased measure of economic diversity.

DEFINING OCCUPATION GROUPS AND CALCULATING OCCUPATION-BASED 

ECONOMIC DIVERSITY

Occupation-based economic diversity uses the 96 minor occupational groups defined in 

the Bureau of Labor Statistics’ 2000 Standard Occupational Classification19 as the units 

of analysis in the calculation of entropy, with employment by minor occupational group 

serving as the share of total economic activity (pi

) specified in Formula 1. According to 

the Bureau of Labor Statistics, occupations are grouped based on similarity of “work 

performed, skills, education, training, and credentials.” Example occupational groups 

including agricultural workers, life scientists, secretaries and administrative assistants, 

and top executives. To estimate employment by minor occupational grouping, data 

from the Bureau of Labor Statistics’ national Staffing Pattern Matrix were used to 

translate county employment by industry data to county-level employment by 

occupation. 

MEASURING KNOWLEDGE-BASED ECONOMIC DIVERSITY

To estimate the diversity of workforce knowledge at the county level, occupation-based 

knowledge clusters were derived and employment across these clusters provided the 

19 See http://www.bls.gov/soc/2000/socguide.htm

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ECONOMIC DIVERSITY IN APPALACHIA

basis for an additional entropy calculation. Knowledge clusters are 12 groups of 

occupations categorized based on similarities in the type and level of knowledge 

required to work in these professions. Feser (2003) details the procedures used to

identify these clusters. Again, the Bureau of Labor Statistics’ national Staffing Pattern 

Matrix were used to translate county employment by industry data to county-level 

employment by knowledge cluster. Employment in government industries, including 

military employment, is not accounted for by these knowledge clusters. Employment by 

knowledge cluster serves as the share of total economic activity (pi

) specified in Formula

1. Table A2 lists the 12 knowledge clusters and provides examples of common 

occupations and average education or training levels associated with them. 

Table A2: Illustrative Descriptions of Knowledge Clusters

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ECONOMIC DIVERSITY IN APPALACHIA

CATEGORIZING COUNTIES BASED ON KNOWLEDGE CLUSTER SPECIALIZATION

Modeling the methods used to classify counties by functional specialization, counties 

were also categorized by knowledge cluster specialization. For this application, the 12 

knowledge clusters substitute for the category of interest (i) in formula 2, above. A 

knowledge cluster specialization is assigned to a county based on the one cluster that 

accounts for the most extra jobs relative to the other eleven clusters.

GEOGRAPHIC AGGREGATIONS

To examine the differences in economic diversity across urban and rural counties, we 

adopted the urban-rural typology method suggested in Isserman (2005). This method 

required the classification of counties as one of four county characters—Urban, Mixed 

Urban, Mixed Rural, or Rural—based on the population density of the counties and the 

relative size of urban and rural areas within the counties. To complete the classification, 

we used U.S. Census 2010 data on total population, rural population, urbanized area 

and urban cluster population, and total urban population by county. Census information 

on land area by county was used to calculate population density. Finally, the Census 

2010 Urban Area to County Relationship File Layout was used to determine the 

population of portions of urban areas located completely within individual counties.20

This file splits all urban areas based on county boundaries and reports the population 

associated with each portion.

We classified counties according to the following criteria:

Rural county: (1) The county’s population density is less than 500 people per 

square mile, and (2) 90 percent of the county population is in rural areas or the 

county has no urban area with a population of 10,000 or more. 

Urban county: (1) The county’s population density is at least 500 people per 

square mile, (2) 90 percent of the county population lives in urban areas, and 

(3) the county’s population in urbanized areas is at least 50,000 or 90 percent 

of the county population.

Mixed rural county: (1) The county meets neither the urban nor the rural 

county criteria, and (2) its population density is less than 320 people per square 

mile. 

Mixed urban county: (1) The county meets neither the urban nor the rural 

county criteria, and (2) its population density is at least 320 people per square 

mile. (Isserman, 2005, p. 475)

DETERMINING COMMUTING LINKAGES

20 See http://www.census.gov/geo/maps-data/data/ua_rel_layout.html for a 

description of the Census 2010 Urban Area to County Relationship File Layout contents.

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ECONOMIC DIVERSITY IN APPALACHIA

In most U.S. counties, workers, firms, and consumers depend upon employment, 

shopping, and service opportunities that lie both within and outside their home county. 

While there are many potential regions that could be defined to approximate the 

multiple economic relationships among places (e.g., firm-to-firm; worker-to-employer; 

consumer-to-store), the analysis of commuting patterns provides one method for 

defining inter-county economic relationships.

Using 2006-2010 county-to-county commuting flow data prepared by the U.S. Census 

Bureau, we defined commuting sheds for each U.S. county.

21 A county’s commuting 

shed includes all those counties that account for a significant share of the journey-towork commuting flow headed toward or away from that county. Journey-to-work data 

from the U.S. Census Bureau’s 2006-2010 American Community Survey were used to 

determine membership in a commuting shed. Specifically, counties are included in a 

commuting shed if they account for at least five percent of the worker flow toward or 

away from the county in question. Commuting sheds range in size from one to nine 

counties in size, including the county of interest. In order to calculate measures of 

commuting shed diversity, employment by industry data for all counties in a commuting 

shed were combined.

PRESENTATION OF METRICS

The diversity metrics calculated for this analysis cannot be easily interpreted unless 

counties are compared relative to one another or the overall distribution of county 

diversity values. To accomplish these comparisons and allow for interpretation, diversity 

measures were standardized and classified according to the procedures described in this 

section.

DATA STANDARDIZATION 

For each diversity measure calculated on a county-by-county basis, data standardization 

required three steps. First, the diversity measure was calculated, resulting in a raw 

diversity score for each county. Second, the mean, raw diversity value was calculated for 

each measure by summing the raw diversity values across all counties and dividing by 

the number of counties. Third, each county’s raw diversity value was divided by the 

mean, raw diversity value, resulting in a standardized value for each county. 

Standardized values can be interpreted as follows:

• A standard value (X) less than 1.0 is (1 – X) * 100 percent less diverse than the 

mean county diversity value (e.g., a standard value of 0.67 is 33 percent less 

diverse than the mean diversity value);

• A standard value of 1.0 is equivalent to the mean county diversity value;

21 See http://www.fhwa.dot.gov/planning/census_issues/ctpp/ for information on 

Census Transportation Planning Products

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ECONOMIC DIVERSITY IN APPALACHIA

• A standard value (X) greater than 1.0 is (X - 1) * 100 percent more diverse than 

the mean county diversity value (e.g., a standard value of 1.25 is 25 percent 

more diverse than the mean diversity value).

Z-SCORES CLASSIFICATION

While standardized diversity scores serve as a simple indicator of the relationship of a 

county’s diversity to the average diversity, z-scores provide information on the 

relationship of a value to the mean and the value’s placement relative to the 

distribution of diversity (or another measure, such as average establishment size) across 

all counties. Z-scores for each value and diversity measure were calculated as follows: 

• Calculate the mean (x̅) and standard deviation (s) for a particular diversity 

measure; 

• For each diversity value (xi), calculate the difference (di

) between the value and 

the mean value (di = xi- x̅); 

• Calculate the z-score for each county’s diversity value (zi

) to equal the quotient 

of the difference between the county’s value and the mean value and the 

standard deviation (zi = di

/s) 

While the distributions of the diversity values vary by measure, and none of the 

measures have a perfect normal distribution, z-scores can be used to provide a 

shorthand classification of individual values into groups with high, low, or about average 

values. Z-scores were classified into groups as follows:

• Very high (zi >= 2) 

• High (1 <= zi < 2)

• Above average (0 < zi < 1) 

• Below average (0 > zi > -1) 

• Low (-1 >= zi > -2) 

• Very low (zi <= -2) 

To control for county character, z-scores were also calculated according to the 

distribution of diversity scores across each of the four county character categories. Each 

county was then classified on the very low-very high scale according to its z-score based 

on the distribution of values in the same county character grouping. Where county 

character-based z-scores were used to classify counties, tables or maps are identified by 

language indicating the displayed values control for county character.

63
Case Studies in Economic Diversification

February 2014 

Prepared for the Appalachian Regional Commission 
CONTENTS

About this Report................................................................................................................ii

Introduction: Why Studying Economic Diversity is Important to Appalachia.................... 1

Keys to establishing a more diverse economy ............................................................... 2

Identifying case study candidates .................................................................................. 3

Criteria for selecting case studies................................................................................... 5

Case study findings............................................................................................................. 6

Tioga County, NY ............................................................................................................ 7

Lycoming County, PA....................................................................................................10

Garrett County, MD......................................................................................................13

Upshur County, WV......................................................................................................17

Knott County, KY...........................................................................................................20

Washington County and Bristol City, VA ......................................................................23

Rutherford County, NC................................................................................................. 27

Pickens County, SC .......................................................................................................31

Lauderdale County, AL ................................................................................................. 33

Oktibbeha County, MS ................................................................................................. 36

Common themes and trends............................................................................................39

Conclusion ........................................................................................................................48

i
ABOUT THIS REPORT

This report is one of four documents prepared as part of an extensive analysis of 

economic diversity for the Appalachian Regional Commission by the University of Illinois 

at Urbana-Champaign’s Regional Economics Applications Laboratory and the Center for 

Regional Economic Competitiveness, with assistance from the RUPRI Center for Rural 

Entrepreneurship and EntreWorks Consulting. Project leaders were Edward Feser, Ken 

Poole, Mark White, and Geoffrey Hewings, with Troy Mix serving as Project Manager.

Authors of this report were Mark White, Deb Markley, Erik Pages and Ken Poole. 

In addition to this report, the documents include a summary report (Economic Diversity 

in Appalachia: Statistics, Strategies, and Guides for Action), a guide to the web tool (A 

Practitioner's Guide for Planning and Analysis with the Appalachian Economic Diversity 

Web Tool), and a technical report detailing methods, data, and additional findings (A 

Statistical Portrait of Economic Diversity in Appalachia). All reports associated with the 

project can be found here: 

http://economicdiversityinappalachia.creconline.org/Report/.

ii
CASE STUDIES IN ECONOMIC DIVERSIFICATION

INTRODUCTION: WHY STUDYING ECONOMIC DIVERSITY IS 

IMPORTANT TO APPALACHIA

Mines and mills have dominated the economies of many Appalachian communities for 

many years. The dependence on the employers in these industries mean that some 

areas are highly vulnerable to economic shocks resulting from boom and bust cycles. 

When those industries decline, communities relying on one or two companies are 

typically poorly positioned to chart a new economic future. Recognizing this challenge, 

Appalachian leaders frequently cite greater economic diversity as a fundamental 

development goal. These leaders hope to better position their communities to mitigate 

economic risks or seize emerging new growth opportunities. Much like individual 

investors seeking a diverse financial portfolio, a community must also seek to develop a 

broad base of employment and wealth generators to comprise its economic portfolio. 

Diversity-driven strategies are therefore one way that local leaders tend to build a more 

stable economy in anticipation of economic cycles as well as a strategy for responding to 

crises when they occur.

Demonstrating a clear connection between anticipated outcomes from economic 

diversification and specific strategies to promote economic diversity can prove 

particularly difficult. Some communities begin with a greater mix of industries than 

others. At the same time, some localities serve as regional trading centers while others 

do not. Some areas attract more in-migrants because they are near growing urbanizing 

areas. 

Furthermore, economic luck (both good and bad) can also influence the economic mix 

and diversity of a local economy. For instance, the loss of a major employer often leaves 

a region’s economy more diverse because the loss often eliminates an important 

company that represented an “economic specialty.” This is especially true in a rural or 

small metro area where only a few companies drive the overall economy. In such cases, 

diversification can bring on wrenching changes and may not necessarily lead to more 

jobs. At the same time, diversification may result from intentional efforts designed to 

attract or develop alternative employment options for local residents or to help 

emerging industries grow. But for larger economies, these efforts alone may be 

inadequate to the task if they cannot leverage natural economic growth patterns that 

complement the diversification efforts. 

In order to understand this issue in a more applied manner, we sought to identify the 

key features of strategies in diverse communities, or communities that have 

experienced increases in diversity and economic growth. In order to better understand 

these issues, the University of Illinois/CREC team analyzed ten case studies in ten 

Appalachian counties located in different parts of the region. This report summarizes 

the findings from this research by laying out the motivations for economic 

diversification and several ways in which communities might pursue diversification. 

That analysis includes the ensuing discussion, which provides a brief review of the 

process for selecting the case studies followed by a synopsis of the findings in each of 

the ten locations. Each case study offers a brief background about the county and its 

1
CASE STUDIES IN ECONOMIC DIVERSIFICATION

economy, but the cases primarily lay out the elements of each county’s economic 

diversification efforts. The report concludes with a review of common themes and 

trends discovered or validated from the case study research. 

KEYS TO ESTABLISHING A MORE DIVERSE ECONOMY

The case study research revealed that no single strategy represents a “silver bullet” 

leading to a more diverse economy. Each of the case study communities face very 

different situations that offer unique opportunities. Not surprisingly, communities use a 

variety of approaches, often simultaneously, to transform their economies and create 

more opportunities. The five broad diversification strategies identified by the research 

team are summarized below.

Create collaborative regional planning and implementation systems focused on 

economic diversification

Structured regional planning processes can motivate and guide the development and 

implementation of economic diversification strategies. Relationships are important for 

advancing any kind of economic development goal, and building these relationships 

throughout the region is a process that requires the time and energy of local leaders. 

An environment that promotes regional collaboration and planning not only allows for 

intentional action and accountability, but can better position regions to take advantage 

of opportunities as they arise. Having these regional relationships established and in 

place allow places to react to federal or state funding opportunities as they arise. 

Build local/regional economic ecosystems that support diversification

These strategies seek to create an ecosystem that is supportive of a wide array of 

different activities, as well as increasing the region’s capacity to support business 

formation and growth. Common strategies include creating an ecosystem more 

supportive for entrepreneurs. These strategies involve promoting entrepreneurial 

opportunities, providing technical assistance for entrepreneurs, and creating 

infrastructure or space, such as business incubators (or specialized incubators like 

kitchen incubators for food companies). Investing in entrepreneurship increases the 

likelihood that new companies will emerge in the region to replace those that inevitably 

die as a natural part of the economic cycle. Similarly, many regions may seek to invest

in infrastructure, whether it involves building interstate-quality roads, constructing

modern facilities in which businesses can expand their output, or improving regional

broadband capacity to meet the needs of small and medium-sized businesses. 

Connect regional assets to external demand/markets

Successful economic development strategies seek to maximize available assets. Many 

places may not have significant assets from which to build, but they can leverage assets 

located in neighboring counties (e.g., colleges and universities, large employment 

centers, significant tourist attractions) to support local growth. A more regional 

approach to inventorying assets can allow communities to better access additional 

2
CASE STUDIES IN ECONOMIC DIVERSIFICATION

resources, expertise, and market demand. Similarly, many places pursue strategies that 

seek to grow existing specializations and to attract new ones through cluster 

development strategies. This may occur by connecting local firms to larger supply 

chains or helping local firms seek out export markets. These efforts might also involve 

promoting tourist attractions in nearby metro areas as part of their local efforts to 

attract visitor, or connecting local food growers to large institutional buyers in the form 

of nearby hospitals or universities. The key to this approach is to recognize that assets 

located in other places may create economic value locally.

Enhance the skills and capacity of the area workforce

A wide array of workforce skills is necessary to support a diverse economy. As a result, 

regions need to broaden and upgrade the regional skill base or make efforts to attract 

workers with necessary skills to the area. Strategies for diversifying the regional skill 

base often involve making investments in education and training, creating greater 

communication between educational institutions and employers, and promoting area 

employment opportunities to talent that may not already live in the area.

Encourage reinvestment of wealth within the region

Capturing greater local wealth is an important element to a stable and sustainable 

economy. There are multiple strategies for encouraging reinvestment. At the most 

basic level, local business retention and expansion (BRE) activities undertaken by most

economic development organizations are intended to help local firms grow and thrive 

where they are already located. Sound BRE principles often overlap with many of the 

strategies mentioned above (e.g., workforce training, infrastructure improvements), but 

the key element is the open communication between business and area stakeholders 

and service providers. Additionally, communities might work with local philanthropists 

and foundations to enlist funding support for local projects. Reinvestment strategies 

may also focus on connecting local demand for goods and services with local suppliers 

to increase the amount of money recycled within the community.

These approaches are not mutually exclusive and as the case studies demonstrate,

many communities seek to increase their economic diversity by taking multiple 

approaches simultaneously. Many of these strategic approaches will feature 

prominently in the case studies profiled later. 

IDENTIFYING CASE STUDY CANDIDATES

Determining which counties would represent useful cases for study involved a process 

that included several steps. The research team first assembled data about the 

economies of all U.S. counties to assess their relative economic diversity and growth 

patterns. The team then identified a preliminary group of candidate counties for study 

by analyzing three key data inputs: 

• Measurements of county-level economic diversity allowed the research team

to identify candidate counties that either had above-average diversity 

characteristics, or counties that exhibited an increase in economic diversity. 

3
CASE STUDIES IN ECONOMIC DIVERSIFICATION

These counties reflected an initial list of locations where economic diversity 

may be leading to some kind of economic change. To measure county-level 

economic diversity, the research team used employment by four-digit NAICS 

industry group provided by Economic Modeling Specialists International 

(EMSI).1

 These data enabled the calculation of “entropy of employment” for 

the time period following the recession (2009 and 2012). One such indicator 

takes the shares of each type of activity in the local economy, multiplies them 

by the logarithms of their inverses, and sums up the values. 2 The mathematical 

details are less important than the result: the measure yields a higher value for 

places with a broader and more even mix of economic activities, and it registers 

as zero in the hypothetical case of a location with only a single type of 

economic activity. The value will be quite low for a community with just a few 

industries that account for most economic activity. Conversely, the value will 

be high for a community with more balanced employment across many 

industries. Measuring this diversity indicator for every county, the team 

calculated an index and then categorized the counties into percentile groups.

• Data from the Bureau of Economic Analysis’ Regional Economic Accounts were 

used to calculate and rank counties according to their change in employment 

and per capita income over the periods 2002-2009 and 2010-2011.3

 Using 

these data, the research team conducted a shift-share analysis that estimated

employment changes that could be attributed to broad national economic 

trends, unique industry trends, and regional economic competitiveness

factors.

4

 The research team considered locations that experienced significant 

employment changes that could be attributed to regional conditions as 

potentially good candidates for understanding what local leaders could do to 

foster economic growth. These represented potentially good examples of how 

successful local economic development policy interventions might influence 

actual outcomes that resulted in greater economic diversity. These data were 

aligned with ARC’s index-based county economic classification system that uses 

three economic indicators—three-year average unemployment rate, per capita 

market income, and poverty rate. ARC uses these indicators to categorize all 

ARC counties as either “distressed,” “at-risk,” “transitional,” “competitive,” or

“attainment.” That classification is updated annually. For the purposes of this 

study, ARC’s Fiscal Year 2013 designations are used to describe the studied 

counties.5

 

1 “EMSI Data,” Economic Modeling Specialists International, 

http://www.economicmodeling.com/data/.

2 This is called an entropy measure of diversity. A more detailed description of the 

methods and findings summarized in this section is provided in the companion technical 

report Statistical Portrait of Economic Diversity in Appalachia.

3 “Regional Economic Accounts,” Bureau of Economic Analysis, U.S. Department of 

Commerce, http://www.bea.gov/regional/

4 Blakely, E. J., & Leigh, N. G. (2009). Planning local economic development: Theory and 

practice. Sage Publications, Incorporated.

5 Appalachian Regional Commission, Distressed Designation and County Economic 

Status Classification System, FY 2007 – FY 2014, website, accessed August 2013. 

http://www.arc.gov/research/SourceandMethodologyCountyEconomicStatusFY2007FY2

014.asp

4

 
CASE STUDIES IN ECONOMIC DIVERSIFICATION

• The research team also considered data on industry dependence, location, and 

urban and rural characteristics in order to identify locations relevant to 

conditions in Appalachia’s rural, distressed counties. The researchers used a 

methodology developed by Andrew Isserman that classified counties as either: 

“Rural,” “Mixed Rural,” “Mixed Urban,” or “Urban,” based primarily on their 

population density characteristics according to U.S. Census 2010. 6

 In addition, 

reporting the USDA Economic Research Service’s county typology codes for 

2004 provided insight into counties functional specializations and dependence 

on a particular type of economic activity (e.g., manufacturing, government).

7

 

To reflect the unique strategies that may arise from the presence of higher 

education, the team also considered the distance from each county to the 

nearest research university.8

 Finally, counties were also classified according to 

their location in the ARC region and particular in ARC sub-regions. 

These data on diversity and economic change were assembled for all U.S. counties, but 

smaller lists of growing and diverse or diversifying rural counties were also generated to 

assist case selection. Combined, these data elements served as inputs in the research 

team’s process of compiling a list of preliminary candidate case study counties. Using 

these data and metrics, the project team identified 32 candidate counties that displayed 

evidence of measured diversification, and/or growth. These candidate counties were 

initially drawn from both the ARC region and beyond. 

CRITERIA FOR SELECTING CASE STUDIES

As part of the effort to narrow the candidates, the team undertook initial research on 

the economic make up and the economic development policies being implemented in 

these counties. This revealed whether the county or its broader region had identified 

economic diversity as an issue or whether the county was undertaking or participating in 

initiatives to strengthen and diversify the county and/or region’s economic base. The

subsequent selection criteria then took into consideration the following factors: 

• Whether the county or its surrounding region had a current economic 

development strategy

• Whether the county leaders were engaged in some kind of meaningful 

economic development efforts, or

• Whether the county was participating in current regional initiatives, such as 

federal funding opportunities provided through the Rural Jobs and Innovation 

6 Isserman, Andrew M. (2005). In the National Interest: Defining Rural and Urban 

Correctly in Research and Public Policy. International Regional Science Review, 28(4), 

465-499. 

7 “County Typology Codes,” Economic Research Service, U.S. Department of Agriculture, 

2004, http://www.ers.usda.gov/data-products/county-typology-codes.aspx. 

8 “The Carnegie Classification of Institutions of Higher Education,” Carnegie Foundation 

for the Advancement of Teaching, 2012, http://classifications.carnegiefoundation.org/.

5

 
CASE STUDIES IN ECONOMIC DIVERSIFICATION

Accelerator grants9 or HUD Sustainability grants (reflecting an assumption that 

the county is proactively engaged in collaborative activities in order to 

successfully win one of these Federal grants).10

Based on this preliminary research and consultation with ARC staff, the research team

selected ten counties for case studies. It is important to note that these case studies are

not necessarily examples of best practices; some case studies are of counties with very 

high levels of economic diversity and others have very low diversity. The ultimate goal 

was to select a set of case studies that spoke to a wide range of diversity-related issues. 

The project team conducted the case study research through a series of site visits and 

phone interviews in April and May of 2013. During the course of these case studies, the 

project team spoke with a wide array of stakeholders including local economic 

development and planning organizations, educational institutions, community groups, 

county and local government, and representatives from the business community. The 

stakeholders interviewed were asked about their economic development activities, and 

the role that economic diversity plays in motivating those activities. The case study 

findings are described in the next section.

CASE STUDY FINDINGS

The ten cases studies selected for this analysis represent a number of aspects related to 

the issue of economic diversity. As noted above, these counties include some that have

diverse economies, some that have much more specialized economies, and some that 

have experienced relatively significant changes in diversity over time. The cases also 

include examples of both urban and rural counties throughout the ARC region. 

Additionally, the cases include counties with a wide mix of functional specializations, 

driven by manufacturing, education, or extractive industries as well as a few counties 

with no discernible dominant industry. The counties profiled include:

• Tioga County, NY

• Lycoming County, PA

• Garrett County, MD

• Upshur County, WV

• Knott County, KY

• Washington County, VA

• Rutherford County, NC

• Pickens County, SC

• Lauderdale County, AL

• Oktibbeha County, MS

9 http://www.rurdev.usda.gov/RuralJobsAcceleratorAbout.html

10http://portal.hud.gov/hudportal/HUD?src=/program_offices/sustainable_housing_co

mmunities/sustainable_communities_regional_planning_grants

6

 
CASE STUDIES IN ECONOMIC DIVERSIFICATION

For each of these case studies, the analysis begins with a background discussion about 

the county and its economy. The case studies, however, focus primarily upon how each 

community thinks about the issue of diversity and the actions taken to achieve greater 

economic diversification. The cases also highlight any key issues or lessons learned from 

each county’s individual experience.

TIOGA COUNTY, NY

Tioga County is a “rural” county (as defined by the Isserman 2005 typology) located in 

New York State’s southern tier region. Its largest community is Owego, NY, but given 

that it is situated between Binghamton, Ithaca, and Elmira, it primarily serves as a 

bedroom community to those relatively larger metro areas. In fact, according to the

U.S. Census Bureau, almost 75 percent of workers living in Tioga County work outside of 

the county, with most commuting to Broome County (Binghamton) to the east. The 

county has a long history of relying heavily on single, large employers. In the past it was 

Endicott Johnson, then IBM, and now Lockheed Martin. 

Tioga County experienced a large increase in employment diversity between 2009 and 

2012. Tioga County moved from having below average economic diversity (30th

percentile of counties nationally) to near average diversity (46th percentile). In most 

instances, large increases in diversity during this particular economic period resulted 

from major recession-inspired economic shocks—such as the loss of one major 

employer or significant downsizing in one dominant industry.11 This has been the case in 

Tioga County, where Lockheed Martin recently employed as many as 4,000 employees, 

but lost nearly 1,600 jobs due to the company’s loss of the Marine One Presidential 

Helicopter contract. These job losses highlighted the extent to which the county relies 

upon this one employer, and in a sense, shows how the county faces a crisis of diversity.

Tioga County’s 2010 Economic Development Plan12 (approved in 2005) listed the 

erosion of the county’s economic base as a critical challenge. The plan also identified 

greater diversity as an economic goal and sought to target other activities such as 

tourism, wood products, agriculture, electronics, and metal fabrication for 

development. Recently, Tioga County received some modest state funding through the 

2012 New York State Regional Economic Development Councils awards to renovating 

buildings in the Town of Nichols and to support two multi-county initiatives aimed at 

developing tourism and food processing.

11 To illustrate this point, consider that the U.S. county with the greatest percentile 

change in diversity between 2009 and 2012 was Clinton County, OH where DHL closed a 

major distribution hub that had employed 7,000 people (representing about 30 percent 

of that county’s total employment). Within the Appalachian Region, other large 

increases in diversity were found in places like Whitfield County, GA—home to Dalton, 

GA and a declining carpet manufacturing industry.

12

http://www.tiogacountyny.com/images/stories/PDFs/departments/edp/tioga2010planF

.pdf

7

 
CASE STUDIES IN ECONOMIC DIVERSIFICATION

ECONOMIC DEVELOPMENT AND DIVERSIFICATION STRATEGIES

Tioga County’s diversification and economic development efforts rely on practitioners 

operating in a collaborative manner. Given its small size, the county lacks the resources 

to pursue extensive business recruitment and attraction. Moreover, these activities 

would not necessarily be appropriate given its rural character. Instead, county 

economic development stakeholders focus on strengthening relationships both locally 

and regionally. Locally, area stakeholders make continuous efforts to build cohesion 

amongst local service providers. The goal is to provide a ‘no wrong door’ approach to 

providing business assistance to Tioga County companies. Members of this stakeholder

network know their respective roles and responsibilities and can refer companies to the 

office that can best meet their needs. Therefore, if a business has a land-use question,

they know to contact the County Department of Economic Development and Planning, 

and if they have a question about certain types of industrial loan programs they can be 

directed to the Tioga County Industrial Development Agency. Building these 

connections has also helped to support entrepreneurial efforts. For instance, potential 

entrepreneurs can receive mentoring from SCORE (Service Corps of Retired Executives) 

representatives. Similarly, further technical assistance is available by directing 

entrepreneurs to the Small Business Development Center at nearby Binghamton 

University in Broome County. The ultimate goal is to avoid having a fractured system. 

Further efforts are being made to build regional relationships as well, especially with

Broome County (Binghamton) to the east, and Chemung County (Elmira) to the west. 

These efforts have largely centered on information sharing. This often plays out when 

dealing with businesses that are looking to relocate across county lines. By having these 

open lines of communication, these counties can avoid wasteful beggar-thy-neighbor 

activities by not providing incentives to companies that are staying in the region, but 

just changing their address. These relationships have been strengthened in part due to 

stakeholder participation in the Southern Tier Regional Economic Development Council 

(one of ten councils tasked with developing long-term regional economic growth 

strategies). 

Tioga County has further applied a regional lens, by looking at opportunities available in 

neighboring Pennsylvania. New York State currently bans the drilling for natural gas, but 

Tioga County is home to about a dozen support companies that are involved in 

Marcellus Shale drilling in Pennsylvania. For instance, one company takes advantage of 

a rail line that brings sand and gravel needed to make the drilling platforms. The sand 

and gravel are offloaded in Tioga County and then shipped just across the border to 

Pennsylvania. Drilling the Marcellus Shale would offer a new source of economic 

activity for the county, but decisions about drilling will be made in Albany and are 

therefore out of the county’s direct control.

While many of the activities described above are designed to increase the number of 

firms and the types of industrial activity in the county, Tioga County is also engaged in 

other efforts to diversify its sources of wealth. For instance, it has embraced its role as a 

bedroom community and has invested in making itself an appealing place for 

commuters to live. For instance, investments have been made in developing Owego’s 

8
CASE STUDIES IN ECONOMIC DIVERSIFICATION

downtown. As a result, the downtown has numerous shops and businesses, many of 

which have recovered from the flooding damage brought by Hurricane Irene in 2011. 

Focusing on making Tioga County attractive to commuters has several benefits. For 

instance, these workers pay taxes and spend a good portion of their money in Tioga 

County even though they do not work in the county. It also helps to counter balance 

the ‘Brain Drain’ affecting Tioga County and many other Upstate New York communities. 

In some instances, these residents have brought companies with them. For example, 

one life sciences company in Owego came out of a Cornell University spinoff. The 

founder of the company previously worked at Cornell University in Ithaca, but lived in 

Tioga County. 

In spite of these efforts to establish new sources of wealth creation, Tioga County still 

faces many challenges in diversifying its economy. Most notably, those activities most 

likely to change the trajectory of local growth or to add significant new employment and 

wealth will result from decisions that are well beyond local control. For instance, the 

fate of Lockheed Martin’s facility lies in Congress and with Lockheed Martin’s corporate 

leadership. Similarly, opportunities to leverage Marcellus Shale opportunities or add a 

casino to the Tioga Downs racetrack will be decisions that will be made at the state level 

in Albany. 

At the same time, more locally-based diversification efforts tend to be far more 

incremental in nature. Ten or twenty new entrepreneurs per year, for instance, cannot 

outweigh the loss of 1,600 good paying manufacturing jobs at Lockheed Martin. This 

conundrum is a real issue facing many places like Tioga County that rely heavily on one 

large employer or industry. Those economic pillars cannot be easily replaced.

KEY FINDINGS AND OTHER OBSERVATIONS

• When control and decision-making about key assets lies external to the region, 

local communities can experience difficulty in shaping their own economic 

future. 

o E.g., Future of Lockheed Martin in an age of declining Federal budgets, 

decisions about natural gas drilling in New York State, etc.

• When budgets are limited, communities must pursue activities appropriate to 

their situation (size, scale, region, etc.). 

o E.g., small communities with few assets and limited budgets should 

limit risky and expensive industrial recruitment efforts

o Emphasis should instead be placed on the efficient delivery of existing 

services

• Regional perspectives create broader opportunities.

o Greater access to markets and employment opportunities

o Greater access to specialized services and expertise (available through 

Cornell or Binghamton University)

9
CASE STUDIES IN ECONOMIC DIVERSIFICATION

• Diversifying the area’s industrial base helps, but so too does diversifying its 

sources of income.

o Benefits can be derived from being an attractive bedroom community

LYCOMING COUNTY, PA

Lycoming County is home to 117,000 residents, with Williamsport being its largest 

community. Its economic base has traditionally focused around activities such as

lumber production and manufacturing. More recently, Lycoming County and 

Williamsport, PA have become one of the epicenters of the natural gas industry and 

Marcellus Shale development, contributing to the county’s recent economic growth. 

Between 2009 and 2012, county employment grew 2.0 percent annually, well above the 

national rate of 1.0 percent during the same period. Lycoming County is also one of the 

most diverse economies in the ARC region; in 2012, it was more diverse than 97 percent 

of U.S. counties. 

The direct and indirect jobs and investment that have emerged from the recent 

Marcellus Shale development have helped to grow Lycoming County’s economy, but 

area stakeholders do not want these activities to overwhelm their economy. As a result, 

the need to maintain a diversified economic base is a clearly articulated goal within the 

community. As stated within the Williamsport/Lycoming Chamber of Commerce 2013 

Action Plan: 

“It is no secret that the Natural Gas Industry has played a critical role 

in the area’s economy and, despite a slowdown caused mostly by low 

prices, it will be a dominant force in our region for decades to come. 

However, the Chamber understands the critical balance that must be 

maintained to have a diversified and healthy economy and during 

2012 we worked to maintain a strong local business community across 

all sectors of our economy including our traditional ones as well as the 

natural gas industry. We will continue to work hard to maintain our 

area’s economic diversity and improve upon it. A sluggish national and 

world economy continues to be a danger to our prosperity and the 

more diverse we become, the better the chance we have of surviving a 

recession or a continuation of a very slow recovery.”

13

This diversity-driven thinking has helped to inform Lycoming County’s economic 

development activities. As a result, its economic development strategies have generally 

focused around ensuring that Lycoming County’s workforce and infrastructure can 

effectively support a wide array of economic activities.

13 http://www.williamsport.org/pdf/PlanofAction.pdf

10

 
CASE STUDIES IN ECONOMIC DIVERSIFICATION

ECONOMIC DEVELOPMENT AND DIVERSIFICATION STRATEGIES

The Marcellus Shale development in Lycoming resulted in significant employment gains. 

Direct jobs in natural gas have grown from 140 in 2009 to over 1,800 in 2012.14 The 

benefits of adding a new component to the economic base are evident. Not only has 

the county added jobs in industries that directly support natural gas such as trucking, 

but this economic activity has also led to growth in other industries such as retail and 

accommodations. Moreover, it has led to investments in infrastructure, such as paving 

gravel roads, which can help drive additional economic development activity. Greater

natural gas activity has, to a certain extent, contributed to a more diverse economy. 

Lycoming County’s diversification has centered on efforts to ensure that wealth creation 

is reinvested in the community. Before the natural gas boom occurred in 2008-2009,

leaders made investments to position the county for new economic activity. As part of 

an economic development strategy process in the early 2000s, the county identified a 

lack of competitive industrial sites and buildings as a key challenge for the area’s 

economic development. Given the county’s terrain, there are limited numbers of sites 

that are both flat and outside of flood zones. As a result, investments were made to 

develop six sites throughout the county. Originally, it was hoped that the county would 

attract food manufacturers. These operations failed to materialize at the scale hoped, 

but as efforts to develop the Marcellus Shale natural gas reserves began in earnest, 

Lycoming County had ready sites available to the natural gas companies and their 

related support companies. Consequently, these early investments allowed the county 

to be well positioned to quickly take advantage of emerging opportunities.

As the developments around the Bakken Shale gas fields in North Dakota have shown, 

natural gas development can transform economies and regions in very powerful (but 

not always positive) ways. While the changes resulting from natural gas activities in 

Pennsylvania are not nearly as dramatic as those in North Dakota, area stakeholders 

wanted to ensure that they were adequately prepared for the natural gas boom. Once 

it became clear that these activities were going to significantly accelerate the regional 

economy, this planning began in earnest. As part of this preparation, approximately 10 

area stakeholders went to Fort Worth, TX to see how that community had been affected 

by the rapid development of natural gas. They sought to understand not only how the 

gas boom would affect economic development, but also the area’s schools, public 

services, health care providers, and infrastructure. In short, they wanted to learn from 

the Fort Worth experience to minimize the number of unintended consequences that 

can arise from rapid development. 

The natural gas boom helped Lycoming County grow, even during the recession. Yet as 

noted above, county leaders continue to emphasize the importance of keeping a 

diversified economy.15 Local leaders have clearly articulated that they want

Williamsport to be much more than just a “gas town.” For the Williamsport/Lycoming 

14 Data provided by Economic Modeling Specialist International 

(www.economicmodeling.com) 

15 http://www.williamsport.org/pdf/PlanofAction.pdf (Page 3)

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Chamber of Commerce, the main strategy for maintaining and improving diversity is 

through its Business Retention and Expansion (BRE) efforts. As a result, the Chamber 

invests time into meeting with area companies to ensure that their needs are being met. 

Even though natural gas has been important, it has the potential to crowd out 

investment in new industries or distract from the needs of existing industries. These 

activities help ensure that there are efforts in place to maintain the diversity within the 

area’s economic base. Moreover, county leaders would prefer that gas companies do 

not take up all of Lycoming County’s available industrial space. 

Broadening the industrial base is not the only form of diversification motivated by the 

natural gas boom. The county has also made efforts to diversify the skills within the 

area’s workforce. Pennsylvania College of Technology (Penn College) has gone a long 

way in preparing the area’s workforce for the gas boom, but they have done this with 

diversity in mind. Since 2009, Penn College has trained approximately 10,000 people for 

natural gas related activities. This training, delivered primarily through Penn College’s 

Shale Technology and Education Center (ShaleTEC), ranges anywhere from one-day,

non-degree programs to 2-year degree programs. When the natural gas boom first hit 

in 2008-2009, approximately 25 percent of people in these training programs were from 

outside of Pennsylvania but now most are Pennsylvanians.

When Penn College sought to invest in new degree programs, they intentionally avoided 

having a degree specifically and narrowly dedicated to natural gas. Instead they sought 

to develop programs that were relevant to the natural gas industry, but also had 

broader applicability to the workforce needs of other industries. As a result, they put 

resources into developing new programs in mechatronics (which also supports area 

manufacturers) and emergency management (which also supports public services and 

utilities). Several factors figured into these investments, and diversity was one aspect of 

these decisions. 

Prior to making these investments, Penn College had conducted a workforce analysis to 

identify the jobs, skills and competencies that were demanded by the natural gas 

industry, but also tried to identify those skills and competencies that were transferable 

to other industries. This was designed not only to meet the most demand, but also to 

guard against any changes in the natural gas industry. The industry itself tends to be 

relatively mobile, and Penn College did not want to invest in programs that only served 

that one industry. In addition to not having degree programs for one industry, Penn 

College also avoided competing directly with Penn State University’s existing Petroleum 

and Natural Gas Engineering program. Instead, the college would seek to leverage 

these programs which are only a little over an hour away. 

The Lycoming County case highlights several important issues. First, it illustrates the 

importance of planning and research. Through a planning process, the county identified 

a key challenge (a lack of adequate industrial sites). By addressing that challenge 

directly, the county eventually was prepared to meet the needs of a quickly emerging 

industry. Research also played an important role in preparing the county for the rapid 

growth that accompanied the natural gas industry. By understanding the consequences 

of this growth, the county was able to manage it in an intentional and planned manner. 

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Similarly, research allowed Penn College to strategically invest in new programs that not 

only met the needs of the emerging gas industry, but the needs of other industries as 

well. Second, the Lycoming County case highlights the importance of focusing economic 

and workforce development efforts on the entirety of the economic base, rather than 

just emerging growth opportunities. 

KEY FINDINGS AND OTHER OBSERVATIONS

• The planning process that occurred in the early 2000s identified critical 

challenges to be addressed (e.g., the lack of adequate industrial sites).

o Investments in infrastructure allowed the county to take advantage of 

emerging opportunities

• Research can effectively guide strategy efforts.

o To prepare for natural gas developments, a delegation of 10 Lycoming 

County stakeholders went to Fort Worth to see the impact of natural 

gas development

 Examine the impact on public services, infrastructure, 

economic development, etc.

o Penn College took data-driven approach so they knew what 

programming to offer

 Workforce assessment of growth jobs and needed skill 

competencies

• In meeting industry workforce needs, it is important to consider skills utilized 

by multiple industries so as to avoid instilling the area workforce with too 

narrow a set of skills.

• Dominant industries have the potential to crowd out other industries or 

distract from the needs of existing industries.

o Community consensus helps to maintain the focus of economic 

development efforts on the whole economic base, not just growth 

opportunities presented by mobile industry.

GARRETT COUNTY, MD

Garrett County is Maryland’s westernmost county and known for being home to Deep 

Creek Lake, a prominent resort area in the Mid-Atlantic. It is located between 

Morgantown, WV (home of West Virginia University) to the west and Cumberland, MD

to the east. Garrett County represents a strong example of a community successfully 

responding to a diversity crisis. In 1996, Bausch and Lomb closed its glasses factory in 

Oakland (the county’s largest community) and relocated 600 jobs to San Antonio, TX. 

This economic shock served as the impetus for an ongoing strategic planning process. 

As a result of the sustained response to that event and a strong local belief in local 

opportunities, the county created a stronger, more diverse economy. Ongoing planning 

(and implementation of that plan) has played a key role in laying the foundation for the 

current economy.

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CASE STUDIES IN ECONOMIC DIVERSIFICATION

Garrett County is among the top 25 percent most diverse counties in the ARC region, 

and experienced above average employment and income growth between 2002 and 

2009. Overall, county employment has remained relatively stable over the past three 

years, and it has had modest employment gains in areas such as manufacturing and 

recreation. Although, many county residents commute to Cumberland, MD and 

Alleghany County to the east, Garrett County’s economy remains relatively selfcontained. According to the U.S. Census Bureau, nearly two-thirds of people that work 

in the county also live in the county.

Garrett County’s current Economic Development Plan (which was completed in 2011

and was being updated in 2013) is a broad-based and detailed plan that has wide 

community support. The plan was completed with significant input from five key county 

organizations including the Garrett County Economic Development Department, the 

Garrett County Development Corporation, the Garrett County Chamber of Commerce, 

Garrett County Community Action Committee, and Garrett College. One element of the 

strategy’s vision is to make Garrett County a place with “vibrant, growing, profitable and 

diverse businesses.” In order to realize this vision, the strategy calls for several actions 

including leadership development, entrepreneurial and small business support, local 

purchasing initiatives, and promoting more sustainable business practices. As a result, 

Garrett County’s approach to economic development is not reactive or done in an ad 

hoc fashion, but rather involves a reasonable amount of intentionality.

ECONOMIC DEVELOPMENT AND DIVERSIFICATION STRATEGIES

In 1996, Bausch and Lomb’s 600 jobs accounted for about half of the county’s 

manufacturing base. The loss of this important facility devastated the community as 

those economic base jobs supported many other indirect jobs. Not only had the 

community lost a significant portion of its economic base, but many of the displaced 

workers lacked transferable skills. There was also a sense at the time that the local 

higher educational institution, Garrett College, did not possess the capacity to respond 

effectively to the situation. The crisis atmosphere created by significant job losses 

nevertheless spurred community action. 

In the wake of this crisis, 25 area leaders met for several days in the Wisp Resort hotel 

to craft a response. This response was an honest assessment of the county’s existing 

strengths, weaknesses, opportunities and threats (SWOT), and that discussion led to a

short and concise strategic plan. The general thrust of this plan was to place greater 

focus on economic development by leveraging existing assets and investing in economic 

development infrastructure with the intended goals of growing and diversifying the 

Garrett County economy.

Since then the county has been effective in growing and diversifying its economy. 

Tourism and second-home demand have been an important component of that growth. 

These activities began to emerge following the completion of Interstate 68 (I-68) in 

1991, but economic growth really took hold during the 2000s. This was in part fuelled 

by the growth of the Washington, DC metropolitan area, which generated greater

demand for second homes and tourism. The area experienced a bit of a property 

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bubble leading up to the recession, but Garrett County was able to weather the 

recession due to a broad set of activities ranging from agriculture, wind energy and a 

more diversified (albeit smaller) manufacturing base. 

This ongoing planning process played an important role in Garrett County’s continued 

economic development. In constructing the county’s economic development plan, 

listening sessions were held to gather community input from several different industry 

sectors including: manufacturing, retail, real estate, tourism, and agriculture and 

natural resources among others. These listening sessions allowed county stakeholders 

to discuss these industries strengths, weaknesses, and current and future trends so that 

they could inform the resulting plan’s strategies and vision. By considering the 

influences on these different industry sectors, the plan therefore seeks to incorporate 

strategies that are capable of supporting a diverse range of economic activities. In 

addition to strengthening its capacity to support its existing and emerging economic 

base, Garrett County also emphasizes business retention and expansion efforts for 

existing businesses. These efforts involve continuous communication with area 

companies about their needs.

The diversification efforts that have emerged from this ongoing planning process have 

addressed many different issues, beyond just supporting multiple economic sectors. For 

instance, the community has devoted significant efforts toward improving the 

environment for entrepreneurs, and has done so in part by expanding and improving 

the broadband infrastructure. Working in partnership with the One Maryland 

Broadband Network (and with Appalachian Regional Commission financial support), the 

county is expanding its fiber optic network. These investments in its broadband 

infrastructure are vital. Many of the county’s entrepreneurs begin as second-home 

buyers who split their time between Garrett County and the DC or Pittsburgh areas. 

Natural amenities like Deep Creek Lake entice potential entrepreneurs to vacation or 

buy second homes in the area, but a strong broadband infrastructure allows those kinds 

of entrepreneurs to work from or even move to Garrett County permanently. 

In addition to expanding and strengthening the broadband infrastructure, there are 

other county assets available to support entrepreneurs. For instance, the Garrett 

County Information Enterprise Center is a technology-based business incubator located 

at Garrett College. More regional efforts and assets expand the range of support 

available to Garrett County entrepreneurs such as improving the availability of 

investment capital in the area. For instance, Garrett County participates in the 

Mountain Maryland Angel Investors Group, which is a regional effort that pools angel 

investment funds in Garrett, Alleghany and Washington counties. Additional 

entrepreneurial support services are available through the Small Business Development 

Center based at Frostburg State University in Cumberland, MD in neighboring Alleghany 

County.

With committed leadership from a number of stakeholders, including the President of 

Garrett College, the county has embarked on several workforce development initiatives. 

One of these initiatives has been the Garrett Promise whereby Garrett County high 

school graduates and GED recipients are eligible for a scholarship that ensures full 

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CASE STUDIES IN ECONOMIC DIVERSIFICATION

tuition payment in the year of their graduation. These scholarships are intended to not 

only provide additional education and training, but also to entice students to stay in 

Garrett County. Other workforce development efforts include the establishment of 

career pathways programs that will train area students and workers for jobs that local 

businesses are looking to fill. In doing so, the county hopes to establish a pipeline of 

talent that can support the area’s long-term workforce needs. The county also 

continues to invest in its training infrastructure as well. For instance, Garrett College’s 

new Career Technology Training Center provides the equipment and facilities necessary 

to support many of the County’s workforce preparation and training needs. As a result, 

many of the courses that will be taught there will lead to industry certifications in areas 

such as manufacturing, the construction trades, and healthcare. The space will also be 

utilized by individual companies for customized training. 

It is important to note that designing strategies to achieve growth and diversification 

means little if they are not effectively implemented. Garrett County exemplifies the 

benefits of an ongoing planning process tied to ongoing implementation. Furthermore, 

identifying and agreeing on metrics allows local leaders to measure the outcomes from 

these efforts, providing accountability for implementation partners. Community efforts 

to monitor outcomes closely ensures that progress is actually being made and to 

demonstrate that progress. By revisiting the county’s strategy every few years, local 

leaders also ensure that the strategies continue to meet community needs and are 

responsive to changing economic conditions. 

The process itself also creates cohesion among stakeholders, builds consensus and 

brings in new leaders which help to continually reenergize the process. This cohesion 

further allows the county to respond quickly to funding opportunities either at the state 

or federal level. Rather than spending time building coalitions, local partners can 

instead be prepared with pre-established coalitions and partnerships ready to respond 

quickly to funding opportunities. This kind of organization and process has allowed 

Garrett County organizations to effectively implement the county’s economic 

development strategies and will serve the county well as leaders encounter future 

challenges, including developing the county’s wind industry, making future land use and 

zoning decisions, and determining how best to leverage the potential from future 

natural gas development. 

KEY FINDINGS AND OTHER OBSERVATIONS

• A crisis is a terrible thing to waste.

o In 1996, the loss of 600 manufacturing jobs from Bausch and Lomb—

half the county’s manufacturing base—mobilized community action

• Garrett County’s ongoing planning process yields many benefits for the 

community.

o Builds consensus about regional vision and a framework for action

o Sets clear goals and provides accountability

o Incorporates new leaders

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• Attracting and retaining talent an important element in building wealth.

o Effective marketing of amenities

o Connectivity (Broadband/I-68) allow people in growth centers to stay 

for more than a vacation

o Creating opportunity for youth to stay in the community

• Thinking regionally opens up greater possibility for local opportunity

o Connecting into growth centers (e.g., Washington, DC, Pittsburgh)

o Creating scale through regional angel investor networks

UPSHUR COUNTY, WV

Upshur County is a small, rural county located in the foothills of the Alleghany 

Mountains. Its population is just over 24,000 people, with Buckhannon, WV 

representing its largest community. There are roughly 11,300 jobs in Upshur County, 

and its employment grew at an annual rate of 1.1 percent between 2009 and 2012—an 

annual rate faster than either the U.S. (1.0 percent) or West Virginia (0.7 percent). Its 

economy is also relatively diverse, as its employment diversity ranked within the 63rd

percentile in 2012.

Natural resources have been an important economic driver for Upshur County as it 

possesses a wide array of resources ranging from lumber to coal and natural gas 

deposits. Like much of West Virginia, coal has long been important to Upshur County, 

but that has been in decline. The county has also been a site for natural gas 

development associated with Marcellus Shale, but as the focus of the natural gas 

industry has shifted to Pennsylvania, that too has slowed. Manufacturing is another

economic driver and the county does support a number of specialty manufacturing 

businesses that sell outside the region and the state. As the county seeks to diversify its 

economic drivers, its economic development efforts now focus on three other sectors—

agriculture, hardwood products, and tourism. These sectors are regional in nature and 

have the potential to support a more diverse county and regional economy.

Given their history of resource extraction – coal, timber and more recently natural gasdiversification has always been a regional goal. Upshur County’s economic 

development activities are often regional in nature, and this regional mindset forms the 

foundation for many of its diversification and development efforts. Upshur County is 

part of the Region VII Planning and Development Council16 and has a strong history and 

success with regional collaboration. The county is located along Corridor H, an EastWest route in which WV 55 and U.S. 33 are being upgraded into an interstate quality 

road. Once this major transportation corridor is completed, the region will only be a 

three-hour drive from the Washington, D.C. metro area. This, in turn, will open up a 

wide range of development opportunities. The county’s economic development focus 

has been primarily on business retention and growth, rather than recruitment. 

16 The Region VII Planning and Development Council also includes Barbour, Braxton, 

Gilmer, Lewis, Randolph and Tucker counties.

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CASE STUDIES IN ECONOMIC DIVERSIFICATION

However, the county is also looking to establish itself as a place for other activities 

related to agriculture, tourism and value-added timber production, and in the process 

provides a number of important lessons learned for other small, natural resource-based 

communities that are seeking alternatives to the resource-extraction activities of the 

past.

ECONOMIC DEVELOPMENT AND DIVERSIFICATION STRATEGIES

Agriculture has always been an important sector and there is a strong focus now on 

local foods. As part of the efforts to support agriculture, the county is currently 

participating in a regional Rural Jobs Accelerator grant that focuses on promoting and 

growing local foods. This grant provides support for creating food value chains in the 

state, and can strengthen existing efforts. For instance, one current local effort—led by 

a local agripreneur—helps local organic growers produce for homes and restaurants and 

creates a community kitchen for food processing. It also further supports the farmers’ 

market in Buckhannon (population: 5,639)—the only incorporated community in the 

county. 

The forest products sector is another area of historic significance in the county. For 

much of its history, Upshur County saw its timber resources harvested and then shipped 

out of the county in raw form. A more recent effort seeks to capture more of the valueadded activities associated with timber production and the county has participated in a 

recent regional effort—the Hardwood Alliance Zone. This regional collaboration focuses

on marketing and investing in the infrastructure needed to attract value-added 

hardwood companies to the region. The region has abundant natural hardwood 

resources, as well as the local sawmills and other services needed to grow value-added 

production in the region. This regional effort grew out of recognition that together the 

counties would have greater opportunities to build and support this sector than they 

would if they were working independently of one another. 

The county is also taking a regional approach to promoting and growing its tourism 

sector. The state of West Virginia has placed significant emphasis on tourism 

development, and the region is trying to tap into the tourism resources made available 

by the state. Three counties—Randolph, Lewis, and Upshur—recognized that alone 

they do not provide enough of a destination to attract tourists to the region. As a result, 

they are building a regional strategy that links individual attractions along U.S. 33. In 

doing so, they plan to turn the whole region into a tourist destination by promoting “33 

Things To Do Along Route 33.”

The economic development focus on agriculture, tourism and forest products, and the 

regional approach being utilized, is unlikely to change in the years ahead. However, the 

success of these efforts will depend greatly on the completion of the major interstate 

(Corridor H) between the Washington, D.C. metro area and this region. Corridor H 

provides a prime example of the power of regional collaboration. No single county in 

this region has the political clout to advocate for this transportation lifeline, but by 

working together they demonstrate the benefits of completing the corridor as a way to 

boost the entire state’s economy. While the new transportation corridor has economic 

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development benefits to the individual counties and their businesses, it provides clear 

evidence that regional collaboration can achieve meaningful outcomes. This 

collaboration established a precedent that is now being used to promote the three 

counties’ regional tourism efforts.

This spirit of collaboration is also occurring within Upshur County itself. Significant 

private sector leadership, particularly in Buckhannon, has helped to advance many of 

these development efforts. Local leaders participated in CreateWV, a conference 

designed to encourage local leaders to create a new economic future for their 

communities. While city leaders did not formally adopt the plan that grew out of this

process, the conference nevertheless empowered local business and community leaders

to start CreateBuckhannon—a locally driven effort whose goals are to improve the 

quality of life and upgrade the town’s amenities. CreateBuckhannon has no formal 

organizational structure, but leaders meet at a regular weekly luncheon open to all. 

Through this venue, the community secured a USDA grant to build the farmers’ market, 

won a grant to create a downtown park, created new raised beds at the senior center, 

restored a civil war era home, and built bike and walking trails throughout town. 

Through these successes, CreateBuckhannon instilled a strong spirit of private sector 

leadership in the community and the county. The work of this informal group has 

brought energy to community residents and created a positive image for those outside 

the community. 

Upshur County is also home to West Virginia Wesleyan University, which is a small 

liberal arts institution located in Buckhannon. Although playing only an informal role in 

CreateBuckhannon activities, the university has been important in providing community 

leadership in other ways. The university president has embraced her leadership 

position in the community, serving formally on economic development committees and 

boards. As a result, the community has been able to more effectively leverage the 

university in support of its workforce and community development efforts. 

KEY FINDINGS AND OTHER OBSERVATIONS 

• Regional partnerships are the key to increasing the county’s economic 

development capacity, as none of Upshur County’s major economic 

initiatives—the Hardwood Alliance, completing Corridor H, and the multicounty tourism marketing effort—would have been possible working on its 

own. 

• Local volunteers and civic leaders can be powerful drivers of change

o Committed community leaders working collaboratively with elected 

officials can increase the capacity of the community to address both 

the challenges and opportunities associated with economic 

development. 

• Adding value to natural assets in a sustainable way is an important goal for 

resource-driven economies seeking greater diversity. 

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o Development focused on value-added opportunities, whether in

agriculture or forest products, ensures that the impact of

development is greater in the county and more sustainable.

o Combining these value-added efforts with regional tourism strategies

that leverage these natural assets can further contribute to greater

diversification.

KNOTT COUNTY, KY

Located in the eastern Kentucky coalfields, the Appalachian Regional Commission 

designated Knott County as distressed in fiscal year 2013. A rural county with a longtrend of out-migration and population decline, it is currently home to 16,124 residents. 

Knott County has lost almost 9 percent of its population since 2000 when over 17,600 

people lived in the county. The county has roughly 4,600 jobs, but its economy is losing 

jobs. Mining is the county’s single largest employing sector and employment in this 

sector has declined from almost 1,500 jobs to just under 900 jobs between 2009 and 

2012. The mining job losses contribute greatly to an unemployment rate that, at 15.9 

percent,

17 remains almost twice the state and U.S. rate. This dependence on mining 

contributes greatly to a lack of diversity within the Knott County economy; in 2012, 

Knott County only ranked in the 17th percentile for employment diversity.

Lack of economic diversity is recognized as a concern among regional leaders. Knott 

County is situated within the broader Kentucky River Area Development District 

(KRADD), an eight county region in eastern Kentucky.18 The KRADD comprehensive 

economic development strategy makes explicit mention of the importance of economic 

diversification:

“Diversification of the economy must be a priority of the area. Efforts 

must be made to provide employment opportunities for the dislocated 

worker, with proper training made available to give these individuals 

the new skills they will need in a diversified economy. Education of the 

workforce, with adult literacy programs and other education or 

training programs must be a priority. 

Efforts to diversify in the region include focusing on the areas of 

telecommunications, the wood industry, development of small 

businesses from within the Kentucky River ADD, tourism, and the 

recruitment of industry into the area.”19 

17 U.S. Bureau of Labor Statistics Local Area Unemployment Statistics, June 2013.

18 In addition to Knott County, KRADD also serves Breathitt, Lee, Leslie, Letcher, Owsley, 

Perry, and Wolfe counties.

19 KRADD Comprehensive Economic Development Strategy Update FY 2012-2013, p. 7. 

Available at: 

http://kradd.org/CommunityAndED/2012%202013%20KRADD%20CEDS%

20REWRITE.pdf (PDF: 23 MB)

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Within Knott County, diversification efforts are based around using the county’s assets 

to promote activities like tourism. Although Knott County’s people and economy have 

long been defined by coal mining, diversification efforts are based around using the 

county’s assets to promote activities like tourism. The county seat, Hindman, is home to 

the Hindman Settlement School which was established in 1902 as the first rural 

settlement school designed to provide education and social services to “people of the 

mountains” while also preserving Appalachian cultural heritage. This mix of the coal 

industry and Appalachian cultural heritage forms the foundation of more recent efforts 

to diversify the county’s economy.

ECONOMIC DEVELOPMENT AND DIVERSIFICATION STRATEGIES

Interest in diversification is driven by the significant loss of coal industry jobs and 

income over the past decade. In Knott County, this diversification strategy has roots in 

an earlier effort as part of Kentucky’s Community Development Initiative (CDI) in the 

late 1990s. In 1997, Knott County residents and leaders developed a sustainable 

economic development plan that was funded by the state as part of the CDI. The state 

allocated $18 million to help Knott County achieve its vision of a sustainable local 

economy driven by the county’s heritage of arts and Appalachian crafts.

A key piece of this strategy was the creation of the Kentucky School of Craft, located in 

the renovated Hindman High School building and operated as part of Hazard Community 

and Technical College (HCTC). The idea behind the school was to offer residential 

opportunities for people to train to become master craftsmen in Appalachian crafts 

(e.g., dulcimer making, pottery). The strategy also included the establishment of the 

Kentucky Appalachian Artisan Center and incubator on Hindman’s Main Street as a 

potential retail outlet for the work of these artisans. With CDI support, the school was 

launched but for a number of reasons—including staff turnover, lack of housing 

opportunities for students, and limited demand for long-term residential programs—it 

was not successful in creating new sources of economic opportunity. Although it did not 

achieve everything it set out to accomplish, this CDI process did help to attract

significant investment in Knott County infrastructure, including a new Opportunity 

Center in Hindman that houses the library, a Head Start center and facilities for HCTC. 

The CDI process helped local residents witness the power of collaboration and 

engagement among local citizens and brought hope to people for whom this was a 

scarce commodity. 

One of the outgrowths of the CDI experience was the focus by local leadership on 

adventure tourism. Although Knott County’s relative isolation does not bode well for 

the recruitment of outside industry, the county does have entrepreneurial spirit in the 

“hollows” across the region, and the emphasis on tourism is designed to capitalize on 

that spirit. While the School of Craft and focus on heritage tourism was aided by the 

state’s CDI, energy for adventure tourism came from county leadership, both public and 

private. In 2006, county leaders made a priority of creating an adventure tourism park. 

As a legacy of the county’s dependence on natural resource extraction, there are 

thousands of acres of reclaimed strip mine and forest lands uniquely suited to a range of 

outdoor recreational activities. In 2007, Mine Made Paradise Park opened – a 

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partnership between a prominent regional coal company, local residents and the 

county. The park covers over 43,000 acres and has 100 miles of trails for off-road biking 

and additional horseback riding trails and stables. Semi-annual horseback trail rides

(spring and fall) began in 2007 with 500 visitors and grew to 10,000 visitors in 2010. A 

motorcycle/ATV training center and campgrounds complete the park. 

The tourism-focused efforts in Knott County, Kentucky offer the potential for 

diversifying the economy and replacing some of the jobs lost in the coal industry. The 

School of Craft effort has experienced some difficulty gaining momentum in part due to 

its lack of connection to other tourism activities. Moving forward, new leadership at 

HCTC has expressed interest in finding a more sustainable model for the school as one 

piece of a larger tourism strategy that will increase its potential for success. 

By contrast, the grassroots adventure tourism effort has gained momentum and 

achieved some positive results. The steady increase in the number of visitors (a key 

metric for tourism activities) and the expansion of facilities and trails at the park 

provides evidence of this impact. But much like the School of Craft effort, the adventure 

sports park has room to grow. As it currently operates, the park is relatively selfcontained. Park visitors come and camp on the grounds and that tends to limit their 

potential impact on the broader county and regional economies. This is due in part to 

the lack of motels near the park, limited restaurants, and few additional entrepreneurial 

ventures in support of tourism.

Knott County’s tourism development efforts are in some ways a replacement strategy, 

as tourism can create jobs for lower-skill workers that have lost their jobs. As the 

county’s tourism efforts gain momentum and grow, they are more likely to create 

greater diversification, as a growing number of visitors will likely demand greater 

opportunities in other activities such as lodging, restaurants and retail. A broader 

regional effort might help the county create an adventure and heritage tourism brand 

and destination and would further increase the economic impacts on the county and the 

region.

To advance these projects, Knott County will need strong, patient leadership. Knott 

County showed strong leadership in competing for the CDI grant from the state. Private 

and public sector leaders rallied to make the Mine Made Paradise Park a reality. 

However, economic development and specifically, tourism development, requires longterm investment in both time and resources. There has been significant leadership 

turnover in the county, making it difficult to maintain the momentum behind these 

efforts. The loss of a strong, persuasive and visionary leader can derail efforts unless 

there is a deep leadership bench and broad buy-in to a compelling community vision.

KEY FINDINGS AND OTHER OBSERVATIONS 

• A regional approach to tourism that positions the region as a destination offers 

more promise than a single county or single attraction approach. 

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• There is strength in focusing on a sector strategy, e.g., tourism, and not on a set 

of specific projects. 

o Coordination of efforts can lead to greater economic impacts.

• Depth of leadership and commitment is critical to sustaining long-term 

economic development efforts. 

o The loss of a strong, persuasive and visionary leader can derail efforts 

unless there is a deep leadership bench and broad buy-in to a 

common vision.

• Tourism development requires long-term investment in both time and 

resources.

• Leveraging natural amenities in support of tourism development provides 

economic opportunities and can lead to greater diversification, but tourism 

alone is not going to generate the economic impacts necessary to replace past 

economic drivers (e.g., coal, manufacturing).

WASHINGTON COUNTY AND BRISTOL CITY, VA

Washington County (2012 population—55,190) and the independent city of Bristol 

(2012 population—17,662) are located along the Interstate 81 (I-81) corridor in 

southwest Virginia. Bristol is on the Virginia-Tennessee border; the border runs down 

the city’s Main Street. Washington County is a mixed rural county as defined by 

Isserman (2005) and a transitional county by ARC standards. The county has shown

signs of economic improvement over time. Between 2009 and 2012, Washington 

County grew at a rate of 1.0 percent annually, a rate comparable to both the U.S. and 

Virginia. However, these gains were partially offset by economic conditions in the 

independent city of Bristol which lost employment at a rate of 1.2 percent during the 

same period. 

Washington County has not been overly dependent on a single industry, and both 

Washington County and Bristol City display above average levels of employment 

diversity, although Washington County (76th percentile) has a somewhat more diverse 

economy than Bristol City (57th percentile). Washington County very much benefits 

from its position along the I-81 transportation corridor. Historically a manufacturing 

dependent area, Washington County and Bristol City have seen commercial expansion 

along the interstate, and they have also benefitted from several broader regional 

initiatives to develop tourism in southwest Virginia. 

In many ways, the county and city provide an example of opportunity-driven economic 

development. Diversifying the city and county economies is an explicit goal shared by 

leaders across these jurisdictions. The Mount Rogers Planning and Development 

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Commission20 (the region’s local development district) identified diversity as a key plank 

of its Comprehensive Development Strategy.

21 The vision for the region, as articulated 

in that strategy is for a region with:

“...a diverse, stable economy, grounded in traditional economic 

development, tourism, and entrepreneurship, that provides high 

quality jobs while maximizing a competitive cost of doing business;”

Washington County’s location feeds into a number of its various diversification efforts. 

The county’s commercial and industrial development efforts are tied directly to its two 

interstate interchanges and the opportunities those interchanges pose for commercial 

and retail development. At the local and regional level, tourism drives diversification 

efforts. Local tourism efforts in Bristol City and Abingdon (the Washington County Seat) 

emphasize main street developments and tapping into a history of tourism development 

focused on the Bristol Motor Speedway, host to NASCAR and other races. More 

regionally-oriented tourism initiatives leverage the region’s musical heritage and 

outdoor recreational assets. All of these activities are underway in the region, but the 

emphasis placed on each varies by scale and jurisdiction. 

ECONOMIC DEVELOPMENT AND DIVERSIFICATION STRATEGIES

The area’s economic development strategies have three relatively distinct focus levels—

county, city, and region. At the county level, the primary focus has been on commercial 

and industrial development along the I-81 corridor. This development activity has been 

driven primarily by the county commission and the county economic developer. They 

are seeking to attract not only projects related to manufacturing and distribution, but 

they are also looking for ‘destination’ retail as exemplified by the location of a Bass Pro 

Shop or Cabela’s facility. To date, these efforts have been largely successful. 

Development at one of two major interchanges along I-81 is almost complete and there 

is significant new development planned for the second. 

The cities of Bristol and Abingdon employ quite distinctive economic development 

strategies from Washington County. Instead, the cities focus their attention on 

downtown revitalization, supporting small business owners and tourism. There is a 

growing recognition, particularly in Bristol, that entrepreneurial and small business 

support services are needed to help local businesses actually succeed in their main 

street locations. Partnerships with other support providers may be the most effective 

way to increase the capacity in these communities to effectively support entrepreneurs. 

The community is also looking for effective models elsewhere that can be adapted and 

implemented locally.

20 In addition to Washington County and Bristol City, the Mount Rogers Planning and 

Development Commission also serves the counties of Bland, Carroll, Grayson, Smyth, 

and Wythe and the independent city of Galax.

21 Available at: http://www.mrpdc.org/docs/CEDS.pdf

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Bristol has an active Main Street program—Believe in Bristol—and this program is 

driven primarily by private sector leadership. Private sector leaders play a catalyzing 

role in advancing Bristol’s tourism development and main street projects. This has been 

most evident through the construction of the Birthplace of Country Music museum. 

Support for the construction of this museum, which is being built in a central downtown 

location, has come from the private sector and not local government. As part of the 

museum’s development, local organizers have established an affiliate relationship with 

the Smithsonian Institution. This relationship not only adds to the museum’s credibility, 

but also opens up opportunities to leverage the resources of the Smithsonian in creative 

ways (e.g., hosting traveling exhibitions and educational programs). The museum is set 

to open in August 2014, and its future was ensured in part through strong leadership 

and a recent merger with Bristol Rhythm and Roots Reunion (a multi-day music festival 

in downtown Bristol). 

Local efforts like the development of the Birthplace of Country Music museum have 

benefited from strong multi-county regional collaboration in support of tourism 

development and promotion. For instance, Washington County is crossed by the 

Crooked Road, which is Virginia’s music heritage trail that runs through Southwest 

Virginia. Washington County is also involved with ‘Round the Mountain’—Southwest 

Virginia’s artisan network. Communities need to build on their assets and connect those 

assets to external demand. To further support these efforts, Heartwood was opened in 

2010. Heartwood is an artisan center located along the interstate and is designed to

serve as the region’s gateway to heritage and cultural tourism. Heartwood was funded 

with both ARC and state tobacco trust fund resources. 

While Heartwood was designed to connect the musical and artisan heritage in the 

region, it has very limited connection to parallel efforts to promote nature-oriented and 

outdoor recreational tourism. In order to further connect these tourism assets,

Washington County collaborated with 18 other Southwest Virginia counties as a partner 

in a Rural Jobs Accelerator grant (with funding in part from the U.S. Department of 

Agriculture or USDA, the U.S. Economic Development Administration or EDA, and ARC). 

This larger project—‘Appalachian Spring’—seeks to connect many of the downtown 

development efforts with the regional tourism efforts in order to establish a consistent 

theme and more closely connected network of assets that would help the region market 

itself as a single destination. This project will also connect not only the cultural and 

heritage assets, but also the region’s many outdoor recreational assets (e.g., the 

Appalachian Trail for hikers, the Virginia Creeper Trail for bikers). 

While tourism-related activities provide the focus for many of the broader regional

economic development and diversification efforts, they are not the only activities 

underway locally. Other emerging efforts seek to strengthen regional food systems such 

as the establishment of a significant farmers’ market in Abingdon and the development 

of private and non-profit resources through Appalachian Sustainable Development and 

Harvest Table—a local and influential farm-to-table restaurant.

The three elements of the area’s economic development— commercial and industrial 

development, main street development (in both Bristol and Abingdon) and tourism—

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have built a solid foundation for a more diverse regional economy. While local 

economic development actors are cooperating on an ad hoc basis, future success may 

require greater collaboration in a more systematic way as well as a more clearly 

articulated, shared vision for the region’s most critical priorities. Creating greater 

alignment of these different strategic efforts will be a key factor going forward, as it will 

be necessary to ensure that these assets are developed in ways that do not create 

conflict or work at cross purposes. For example, the commercial and industry 

development along the interstate could come into conflict with efforts to promote the 

region as a destination for outdoor recreation and tourism. There is a growing sense 

among local leaders that they need to work together to tackle these issues, but the lack 

of a single driving force to guide this regional approach makes collaboration difficult. 

KEY FINDINGS AND OTHER OBSERVATIONS 

• The Bristol/Washington County region benefits from economic development 

strategies that are multi-dimensional. 

o Different units of government (county vs. city) and different sectors 

(public vs. private) play different roles that collectively contribute to a 

more diverse and resilient county economy. 

• Communities need to build on their assets and connect those assets to demand 

outside the region.

o Washington County also benefits from a location that enables easy 

access – for tourists, for shoppers, for commerce and industry – and 

from regional initiatives that attracted outside support.

o Most of the local diversification efforts have focused on leveraging 

existing assets and linking them together in ways that create a tourist 

destination that could be marketed together.

• Private sector leadership has an important catalyzing role to play in economic 

development. 

o Economic development is not the sole domain of paid economic 

developers. 

o The private sector has played a strong and active role, particularly in 

Bristol’s tourism development efforts and the emerging regional food 

system efforts. 

• Effective public-private partnerships are vital for moving projects forward and 

connecting to resources.

o The private sector has played a catalyst role, allowing the region to 

access a wide range of state and federal resources, including ARC, to 

support their efforts.

• Downtown revitalization efforts need to be tied to entrepreneurship 

development.

o Entrepreneurial and small business support services are needed to 

help local businesses actually succeed in their main street locations. 

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o Partnerships with other support providers may be the most effective 

way to increase the capacity in these communities to effectively 

support entrepreneurs.

RUTHERFORD COUNTY, NC

Rutherford County is located between Asheville and Charlotte in North Carolina, and 

Greenville-Spartanburg in South Carolina. ARC categorizes the county as distressed. 

The unemployment rate of 12.7 percent is well above average compared to both the 

U.S. and North Carolina.

22 Rutherford has lost employment over the previous decade.

Between 2009 and 2012, Rutherford County lost employment at a rate of 1.3 percent

annually. By contrast, North Carolina and the U.S. grew at 0.8 percent and 1.0 percent 

respectively during the same period. 

In spite of these difficult economic headwinds, Rutherford County remains a relatively 

diverse county and is within the top quartile of counties in terms of employment 

diversity (79th percentile in employment diversity). Rutherford County has traditionally 

relied upon manufacturing activities, but many of the manufacturing jobs that formed 

the economic foundation of mill towns like Rutherfordton, Forest City and Spindale are 

now gone. While manufacturing remains an important part of the county’s economy, 

local development efforts today emphasize other economic activities. For instance, the 

county has attracted several data centers—most notably Facebook. There is also a 

growing retiree population, particularly around Lake Lure and Chimney Rock in the 

western part of the county. The county is also seeking to boost the agricultural sector 

by increasing linkages with local food producers.

Given the recent history of Rutherford County and the surrounding region as one 

dominated by mill towns, it is no surprise that diversity-driven thinking informs the 

region’s strategy development. It is listed as one of the goals in the Isothermal Planning 

and Development Commission’s23 regional Comprehensive Economic Development 

Strategy:

“The Isothermal region was once too dependent on a very limited 

number of manufacturing industries. In the future, the region will have 

a more diverse economy that will help insulate the community against 

future economic downturns. It will develop a strong entrepreneurial 

culture so that new companies and ideas will continuously emerge and 

better insulate the region from external economic forces. The region 

will also grow its economic strength in globally competitive industries –

including advanced manufacturing, manufacturing-related producer 

and distribution services, entrepreneurial enterprises, as well as 

tourism and retirement-related industries. The region will also make 

22 U.S. Bureau of Labor Statistics Local Area Unemployment Statistics, June 2013.

23 The service area of the Isothermal Planning and Development Commission includes 

Rutherford County, as well as Cleveland, McDowell and Polk counties.

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use of its land to support activities such as high value added 

agriculture and viticulture, as well as the continued growth of its 

already strong equine industry. With more growing companies, 

relocated firms, and entrepreneurial start-ups in these industries, the 

Isothermal region can develop a diverse economic base that will 

provide jobs, wealth, and a sound tax base to ensure prosperity for 

current and future residents.”24

As noted in the above vision, the region (and Rutherford County) is pursuing a diverse 

array of strategies to become a more diverse economy. There is a very clear recognition 

that no single strategy will rejuvenate the county economy, and the path forward will 

require the efforts of multiple organizations and stakeholders. Therefore the strategies 

pursued in Rutherford County fall into several groups of diversification strategies.

DIVERSIFICATION EFFORTS

Given its location between three metro areas (Asheville and Charlotte in NC, and 

Greenville-Spartanburg in SC), it is logical that Rutherford County has been generally 

active in seeking out regional opportunities. In the broadest sense, thinking regionally 

allows area businesses to access broader markets and area workers to find a more 

extensive array of job opportunities. Regional collaboration also benefits economic 

development practitioners. For instance, the county participated with 22 other counties 

in the Advantage West Region to successfully win a Rural Jobs and Innovation 

Accelerator designed to promote local foods (WNC Agri-ventures). Stakeholders have 

also participated in other regional endeavors including the Future Forward Economic 

Alliance (a 12-county group covering the NC Foothills) and the area’s workforce 

development board frequently collaborates with neighboring boards. These 

partnerships often prove beneficial as they allow the county to leverage other 

resources, programs and expertise. Moreover, these regional configurations shift as 

needed. Depending on the issue, stakeholders may pursue regional collaboration with 

groups from Upstate South Carolina, Asheville, Charlotte or all three. However, to fully 

derive the benefits from these regional collaborations, there must be very active 

participation. As budgets and staff resources diminish, it is increasingly difficult for 

organizations to commit time and staff resources to these efforts which typically fall 

outside of their core set of responsibilities.

Promoting entrepreneurship is one of the key planks of Rutherford County’s 

diversification efforts. For a number of years, the Foothills Connect Business and 

Technology Center had been a leader in these efforts by supporting entrepreneurial 

development and broadband expansion. However, the funding for Foothills Connect 

expired. While many of the programs put in place through Foothills Connect have found 

homes, the county no longer has the primary point of contact that it once did. There is 

a sense in the community that efforts to support entrepreneurship could be stronger. 

For instance, it was noted that many of these efforts would be more effective if they 

24 http://www.regionc.org/IPDC/Docs/CEDS%202012%20FinalDraft1_2013.pdf Page 41.

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focused less on encouraging people to pursue entrepreneurial ventures, and more on 

helping established entrepreneurs grow their business.

Rutherford County is working to develop several different sources of both employment 

and wealth. While advanced manufacturing remains an important economic driver for 

the area, efforts are underway to broaden the economic base. For instance, due to 

relatively cheap electricity and water the county has been able to attract several data 

centers—most notably Facebook. These investments, as well as Google and Apple data 

centers in nearby counties, has led Isothermal Community College to establish the Data 

Center Institute to provide short-term, specialized training for workers at these centers. 

Efforts have also been made to promote the local agricultural sector as well. With 

resources from the Rural Jobs and Innovation Accelerator grant, local leaders are 

connecting local farmers to buyers in Asheville and Charlotte and connecting local 

farmers to the expertise available through the North Carolina Agricultural Extension 

Service. 

While diversifying the employment base can prove beneficial, it is also important to 

diversify sources of wealth. In Rutherford County, this has perhaps been best 

exemplified by its retirement attraction efforts. The county leverages several natural 

assets (e.g., the nearby Blue Ridge Mountains, Lake Lure) to attract retirees to the area. 

These efforts bring new money into the community through the spending of their 

retirement savings or Social Security income. This spending helps to support activities 

such as construction, local retail, and recreational facilities like golf courses or 

equestrian facilities. Further efforts are being made to grow Rutherford County’s 

attractiveness as a retirement destination. For instance, the county is working to 

partner with an Asheville-based health care provider to expand access to health care in 

the Lake Lure area. Similarly, efforts are being made to expand the broadband network, 

which is often limited due to the mountainous terrain that can block satellite signals. In 

addition, the county is also exploring ways to increase the diversity of that area’s 

available housing options. Currently the area has a number of retirement homes, but 

fewer options for people looking for condominiums or assisted living facilities. 

Workforce development is an area where local stakeholders feel as though they can 

make progress through local efforts. The Region C Workforce Development Board 

(WDB) which represents Rutherford, McDowell, Cleveland, and Polk counties is 

partnering with the neighboring High Country and Western Piedmont WDBs on a 

regional literacy initiative. Other workforce initiatives currently underway include 

promoting greater usage of worker certifications (e.g., ACT WorkKeys) for both 

employers and workers, job shadowing programs, and other efforts to communicate 

employer needs to students and other potential workers. 

Rutherford County also promotes local reinvestment amongst existing businesses 

through dedicated business retention and expansion efforts. These efforts involve 

ongoing communication with area businesses so as to understand their needs. Based on 

these needs, the Rutherford County Economic Development Commission ( EDC )then 

seeks to connect them to services that might best meet their needs be it assistance from 

the Small Business Technology Development Center, Industrial Extension Service, 

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Isothermal Community College, or a local utility company representative. In one 

instance, one Rutherford County manufacturer sought to get into defense contracting, 

and the Rutherford EDC helped connect them with the North Carolina Military Business 

Center which has expertise in these areas. 

In spite of these efforts, transforming elements of the old economy to prepare for new 

industries represent a constant challenge. For instance, Rutherford County has a 

number of older, obsolete industrial buildings with low ceilings, inadequate lighting, or 

environmental hazards that require remediation. In other cases, existing facilities may 

not have adequate infrastructure or road access. These buildings are not assets, but 

instead are liabilities without the resources to invest in adapting or replacing them. 

Similar legacy issues exist within the area’s workforce. Many of the county’s workers 

are equipped with the skills that were suitable for declining industries, but not adequate 

for the demands of today’s growing employers. While some workers can be retrained 

to meet emerging needs, many dislocated workers do not see retraining as a realistic 

option. Local leaders have found that many older workers either will not seek out 

additional training or education. Other dislocated workers simply cannot afford to 

invest the time in undertaking retraining programs. At the same time, skilled younger

workers are apt to leave the area for places with more opportunities and a wider array 

of appealing amenities. However, one of the ancillary benefits of Facebook’s recent 

move to the region was that a number of the employees at the Rutherford County 

facility are from outside the region and therefore provide a small infusion of relatively 

young talent. New companies locating to the area are seen as a source for jobs, not 

only for existing workers, but also for a new generation of talent that could help to 

create a new sense of energy in the county.

Rutherford County stakeholders fully understand that no single strategy or initiative can

revive and diversify the local economy. Rather, multiple efforts—over an extended 

period of time—are needed to ultimately transform the county’s economy. Patience, 

however, is not a virtue that people in the midst of an economic crisis can afford. As a 

result, many economic development practitioners face increased pressure from local 

elected officials who want to see immediate progress. A crisis atmosphere can make 

many local elected officials reluctant to invest in the staffing, services or infrastructure 

that might yield long-term benefits. Instead, the investments are often short-term in 

nature, even if the investment is not seen as optimal in the long-term. For instance, 

communities in crisis may not be as selective in the businesses they seek to attract. As a 

general rule, communities often prioritize their investments to encourage businesses 

that require unique skills or pay higher than average wages. But, in a crisis atmosphere,

those same places may not be able to adhere to those priorities, settling for more 

mobile companies that may offer less than desirable wages or that require greater than 

expected incentives.

KEY FINDINGS AND OTHER OBSERVATIONS

• Multiple approaches are needed to achieve economic diversification (the 

shotgun approach). 

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• Many places would like to attract “good” industries that offer the best wages 

and are committed to the community, but being truly selective can be a 

challenge in times of need.

o Mobile firms may address short-term job needs, but they will 

continuously seek more advantageous locations.

• Investing in new industrial sites and buildings can potentially support 

diversification efforts. 

o Buildings that supported the old economy may no longer work for the 

new economy.

o Connecting sites/buildings to high quality roads and telecom 

infrastructure is another important consideration.

• Economic crisis (and often the related fiscal crisis) can impart a risk-averse 

mindset into local elected officials, and discourage them from making the 

investments necessary to support diversification strategies. 

• Different issues require different stakeholders and regional alliances.

o Stakeholders must actively participate in order to reap the rewards of 

regional initiatives.

PICKENS COUNTY, SC

Pickens County, located in Upstate South Carolina, is part of the Greenville-Spartanburg 

MSA and home to 117,000 people. Local economic developers often refer to three 

different parts of Pickens County: 1) a western section focused on the city of Clemson 

and Clemson University (South Carolina’s land grant university), 2) a central section that 

remains very rural and focused on agriculture, and 3) an eastern section that serves as a 

bedroom community to Greenville, SC. The county has had little net employment 

growth since the end of the recession. Employment between 2009 and 2012 has 

remained just under 49,000.

The Pickens County economy has traditionally relied on a very narrow base of economic 

drivers. For much of its history, the county was dominated by cotton, which was both 

grown and processed in the community. Until quite recently, textiles represented the 

county’s largest manufacturing sector. Pickens County today is more diverse than twothirds of U.S. counties. This diversity is driven in part by the presence of Clemson 

University which serves as an important economic driver for the county and region, 

particularly neighboring Anderson County. More recently, the county has benefited 

from rapid growth in the nearby Greenville-Spartanburg metropolitan area.

DIVERSIFICATION EFFORTS

In terms of economic development policy, competitiveness issues, more so than 

diversification, motivates Pickens County’s economic development efforts. This is 

reflected in the efforts to focus business attraction and retention efforts around key 

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clusters such as automotive, plastics and metal working, and medical devices and 

pharmaceuticals. To support this cluster development, Pickens County—under the 

auspices of the county government and Alliance Pickens (the county’s public/private 

economic development arm)—pursues a relatively traditional economic development 

strategy by focusing its efforts on attracting new firms, primarily manufacturers. 

Pickens County’s primary selling points have been its lower labor and business costs, but 

it has also sought to leverage its proximity to the markets and automotive 

manufacturing cluster in and around Greenville/Spartanburg. These business attraction 

efforts have proven relatively successful, as Pickens County has succeeded in attracting 

several major employers to the county. For instance, recent site selection and 

expansion announcements include a new TaylorMade golf facility and an expanded 

circuit manufacturing facility for Sealevel, a longtime local manufacturer. 

The county has also made investments designed to strengthen its business attraction 

and retention efforts. For instance, many of these firms have opted to set up operations

in the Pickens County Commerce Park, which first opened for business in 2005. The 

park is equipped with modern utilities, a dedicated fiber switch, and access to a 4-lane 

highway. The park has been extremely successful, and county officials are now

considering creation of a second industrial park located on 500 acres near Clemson. 

While the primary focus of Pickens County’s economic development efforts is on 

industrial recruitment, it is not the only focus area. Like many other communities in the 

Southern Appalachians, Pickens County also tries to capitalize on its natural beauty by 

promoting tourism and retiree communities. Within Pickens County, there are several 

high-end retirement communities and golf courses located near Lake Keowee and other 

local lakes. In keeping with Pickens County’s focus on business recruitment, these high 

end communities are often touted as a local asset to visiting corporate executives. 

While these communities attract many weekend visitors from around the region, they

have not had a huge impact in generating other tourism-focused activities. 

Clemson University is another local asset that has not been fully maximized as 

collaboration between Pickens County and Clemson University appears to be relatively 

limited. Pickens County clearly benefits from the university’s economic ripple effects. 

For instance, not only is Clemson University a major employer in the county, but 

spending by faculty, staff and students are significant sources of retail and real estate 

demand. Moreover, nationally-recognized research centers like the Clemson University 

International Center for Automotive Research (CU-ICAR, which is based in Greenville) 

help to attract potential investors. However, conscious strategies and programs to link 

the county to the university appear limited. Given its role as the South Carolina’s land 

grant university, Clemson University prioritizes serving the entirety of the state (through 

‘Centers of Economic Excellence’ like CU-ICAR) more than building closer connections 

within Pickens County. 

Moreover, for as much as Pickens County benefits from its place within the Upstate 

South Carolina region (e.g., the Greenville job center, the automotive manufacturing 

cluster around Greenville-Spartanburg, etc.), it has not embraced active partnerships 

with neighboring communities. For instance, Pickens County recently opted to end its 

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formal partnership with the Upstate SC Alliance, a 10-county regional partnership that 

markets the region worldwide. Pickens County is still considered part of the Alliance, 

but the county no longer provides direct investment in the group.

Pickens County’s economy has performed fairly well in recent years, despite the fact 

that the county economy lags on many key indicators such as workforce education 

levels. In spite of these shortcomings, the county remains an attractive location to 

many potential investors due to its low cost structure, location between Charlotte and 

Atlanta, proximity to the automotive cluster around Greenville-Spartanburg, and the 

presence of Clemson University. For the foreseeable future, Pickens County officials 

intend to use the assets in support of their continued focus on business attraction and 

recruitment. However, pursuing more extensive regional partnerships—particularly 

with Clemson University—would not only strengthen these core strategies, but would 

also provide a great boost to other strategies that could lead to a more diversified 

employment base such as tourism development and retiree attraction.

KEY FINDINGS AND OTHER OBSERVATIONS

• A smart and focused business attraction strategy can improve economic 

performance when a region has true in-demand competitive assets such as a 

major research institution, natural amenities and low cost of doing business. 

• There are benefits from being located near large metro regions like Greenville, 

SC or major research universities like Clemson, but active engagement is 

required to fully maximize these opportunities. 

• Mutually reinforcing strategies can increase strategy impact.

o E.g., promoting the auto industry by linking business attraction efforts 

to university research centers that undertake automotive research as 

well as by building executive housing to appeal to automotive 

company executives and managers.

LAUDERDALE COUNTY, AL

Located in northwest Alabama, Lauderdale County is home to over 92,000 residents. 

Over 40 percent of these residents live in the city of Florence, which along with Muscle 

Shoals in Colbert County form the core of the two-county Florence-Muscle Shoals MSA. 

Lauderdale County is home to the University of North Alabama (UNA), which first 

opened in 1830, and now has an enrollment of 7,000 students. The region also has a 

long history as a center for music. Blues originator W.C. Handy and Sam Phillips, the 

record producer who discovered Elvis, were both born in Florence, and the Shoals 

region is globally known as a recording center for rock, gospel, and country music. This 

musical heritage is a core part of the region’s tourism promotions efforts today. 

The broader Shoals region has also capitalized on its long historical connection with the 

Tennessee Valley Authority (TVA), as the nearby Wilson Dam was the first dam on the 

Tennessee River and later became the first dam under TVA’s authority. The TVA 

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connection remains important to the region. Due to the Tennessee River access and 

TVA-provided power, the region has long been home to major manufacturing facilities

and Lauderdale County and the Shoals region has long served as one of Alabama’s 

leading manufacturing centers. Large manufacturers like Ford, Occidental Chemical, 

and Reynolds Aluminum were long-time anchors for the local economy. 

Overall, county employment grew at a modest rate of 0.3 percent annually between 

2009 and 2012, well below the national rate of 1.0 percent during the same period. 

However, the region’s manufacturing sector has experienced net positive job growth 

between 2009 and 2012, and this has partially helped to offset job losses in other areas 

such as government and construction. As a result, the county’s economy remains 

relatively diversified and is in the top 25 percent of the nation’s most diversified 

counties (79th percentile in 2012). Although economic development strategies within 

the region are not explicitly driven by concerns about diversity, the Muscle Shoals 

region has a relatively diverse economy, and Lauderdale County is working on several

efforts to enhance diversification. Many of these efforts are framed and implemented 

within a broader regional context, and this regional approach has contributed to their 

success.

DIVERSIFICATION EFFORTS

Like many parts of the traditional “Rust Belt,” Northwest Alabama manufacturers were 

hit hard in the 1980s and the local economy suffered major setbacks. In response to 

these economic shocks, the community undertook a variety of initiatives to strengthen 

its economic foundation. Given the interconnected nature of the Florence-Muscle 

Shoals region, it is not surprising that these efforts were primarily regional in nature. 

For instance, the Shoals Industrial Development Authority (now known as the Shoals 

Economic Development Authority—SEDA) was established in 1986 to serve the entire 

region. SEDA serves as the region’s primary business recruitment arm and was 

Alabama’s first multi-county industrial recruitment entity. Creating this entity 

formalized a more regional approach to business recruitment. SEDA continues to invest 

in extensive industrial recruitment efforts although much of this recent activity has been 

located in Colbert County. While many of these investments are made outside of 

Lauderdale County, they still create employment opportunities for county residents. 

Many of Lauderdale County’s economic development organizations operate on a 

regional basis and serve all of the communities located in Lauderdale County and 

neighboring Colbert County. SEDA was the first regional economic development entity 

for these two counties, but the Shoals Chamber of Commerce also serves the twocounty region and its four largest communities (Florence, Sheffield, Muscle Shoals, and 

Tuscumbia). Since 2007 the region has also operated an independent Shoals Industrial 

Development Committee. The Committee is composed of public and private sector 

leaders and oversees a large “deal-closing” fund for prospective economic development 

projects. The Northwest Alabama Council of Governments (NACOLG)—the area’s Local 

Development District—is another regional development organization. The NACOLG 

serves a wider region that includes Lauderdale and Colbert counties, along with 

Franklin, Marion and Winston counties. NACOLG manages a number of federal and 

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state-backed business loan programs. The collaboration between these regional 

organizations enables more effective economic development practice throughout the 

region, as it facilitates scale and coordination of economic development activities.

While manufacturing remains an important part of the regional economy, efforts are 

underway to leverage many of Lauderdale County’s broader regional assets in order to 

develop other industry sectors. A strong base of manufacturing firms still operates in 

the region, and the area benefits from growth occurring across the wider region in 

nearby communities such as Huntsville and Decatur. Growth in these neighboring 

regions also benefits other sectors of the Florence and Lauderdale County economy. 

For instance, Florence has emerged as a major retail center for the surrounding region, 

attracting numerous shoppers from Northern Alabama, Mississippi and Tennessee. 

Lauderdale County continues to attract major retail investments, with a second WalMart superstore opening for business in the near future. Florence is also a regional 

medical hub and is home to ECM Hospital system which is one of the region’s largest 

employers with more than 1,200 employees. 

Tourism is another area of emphasis. Spurred by organizations like the Lauderdale 

County Tourism and Downtown Florence United, Lauderdale County leverages many of 

its broader regional assets to support its tourism industry. For instance, the region’s 

place on the Robert Trent Jones Golf Trail makes the region a golfing destination more 

so than any single local golf course would. Similarly, there are efforts underway to 

capitalize on the Muscle Shoals region’s musical heritage. While the area hosts many 

local events and festivals (e.g., the W.C. Handy Music Festival), there are also efforts to 

develop these assets in a broader regional strategy. The Americana Music Triangle is a 

proposal to link five states (AL, AR, LA, MS, and TN) along a tourist trail that introduces 

visitors to key spots in the development of American roots music. Along with Nashville 

and New Orleans, the Shoals would be a key stop within the Triangle. This concept is 

modeled on Virginia’s Crooked Road, a highly successful project developed with past 

ARC investments. Once again, promoting these tourism assets in a unified and strategic 

manner is more likely to draw tourists than promoting multiple attractions individually. 

Beyond golf and music, Lauderdale County can also draw on other regional tourism 

assets such as the Tennessee River and a host of heritage tourism assets.

The University of North Alabama is another asset that can be leveraged to further the 

region’s economic diversity. Lauderdale County has benefited from the recent growth 

of enrollment at UNA. Not only does UNA serve as an important source for talent and 

skills, but its student and faculty are also important contributors to retail demand. 

Moreover, the faculty and staff from both UNA and Colbert County-based Northwest 

Shoals Community College (NSCC) are important contributors to the region’s economic 

development activities. While UNA does not have a dedicated economic development 

office, key faculty members sit on local boards and contribute to economic development 

efforts in other ways. For example, UNA staff members were key players in the initial 

planning for SEDA. 

In addition, UNA also plays an important role in promoting and supporting local 

entrepreneurship. UNA manages the local Small Business Development Center network 

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whose staff members work closely with key agencies and with the Shoals Entrepreneur 

Center. The latter helped build an economic ecosystem capable of supporting a diverse 

array of economic activities. Since its launch in 1992, the Shoals Entrepreneurial Center 

has incubated over 150 businesses and created more than 1,500 jobs. As a result, it has 

proven to be one of the country’s most effective business incubation programs. The 

Shoals Entrepreneurial Center draws support from a wide array of regional stakeholders 

including many of the regional organizations mentioned above (e.g., UNA, NSCC, 

NACOLG, SEDA, etc.) and several extra-regional organizations such as TVA, ARC and the

U.S. Department of Agriculture.

The Lauderdale County case study demonstrates the importance of regional approaches 

to economic development and how these activities contribute to a more diversified 

economy. Regionalism underlies efforts to develop a wide array of economic activities 

ranging from industrial recruitment, retail development and tourism related to golf and 

music. In each of these instances, practitioners have sought to position Lauderdale 

County as part of a broader region in order to market and promote local opportunities. 

At a more local level, Lauderdale County has also effectively leveraged its available 

assets like UNA in support of its workforce and entrepreneurial development efforts.

KEY FINDINGS AND OTHER OBSERVATIONS

• A multi-pronged diversification strategy makes sense, as communities 

proactively pursue multiple approaches and seek to build assets around 

multiple sectors (e.g., manufacturing, retail, and tourism). 

• Large scale impacts take time as shown by the fact that many of Lauderdale

County’s efforts began in the 1980s and took at least a decade to bear fruit.

• Entrepreneurship can be an effective core strategy if properly supported.

o The local presence of the Shoals Entrepreneur Center has been an 

important spur to new businesses in the region.

• Promoting individual tourism assets in a unified, regional manner is more likely 

to draw tourists than promoting multiple attractions individually.

• While tourism jobs do not always yield high paying jobs, developing tourism 

can lead to many ancillary benefits such as more desirable downtowns, new 

hotels, and new amenities.

o These benefits may improve the quality of life for local residents and 

serve as a magnet for potential new residents.

OKTIBBEHA COUNTY, MS

Located in east central Mississippi, Oktibbeha County is home to the city of Starkville, 

Mississippi State University (MSU), and about 48,000 residents. Oktibbeha County is 

part of a larger region known as Mississippi’s Golden Triangle, which also encompasses 

Clay and Lowndes Counties. Several economic development organizations serve these 

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three counties, with the newest being the Golden Triangle Development LINK (GTDL). 

The GTDL is the region’s primary business attraction and marketing agency. The Golden 

Triangle Planning and Development District (GTPDD) is the region’s local development 

district, serving seven counties including Oktibbeha, Lowndes, and Clay as well as

Choctaw, Noxubee, Webster and Winston counties.

MSU—Mississippi’s land grant university—and its 20,000 students are the county’s 

largest economic engine. However in spite of the university presence, ARC designated 

Oktibbeha County as a distressed economy in 2013. The county has experienced little to 

no employment growth over the past few years, and at 9.4 percent the unemployment 

rate remains above the national average.25 Average earnings in the county also reflect 

this distress as the county’s average earnings of $32,705 are lower than the average 

earnings for the U.S. ($49,468), the ARC region ($40,821), and the state of Mississippi 

($33,073).

The dominance of Mississippi State has also led to Oktibbeha County having an 

economy that lacks diversity. In 2012, it was only in the 26th percentile of U.S. counties 

in terms of the diversity of its employment. Efforts to grow and diversify the county’s 

economy have focused around two key elements. One strategy follows a more 

traditional economic development focus on industrial recruitment and retention, while 

the other emphasizes leveraging MSU as a resource in generating a wider array of 

economic activity.

DIVERSIFICATION EFFORTS 

Local economic development leaders acknowledge that MSU’s rapid growth has been a 

primary driver of recent positive economic news in Oktibbeha County. Between 2002 

and 2010, enrollment grew by roughly 17 percent. By 2012, there were more than 

20,000 students enrolled at MSU. This growth has created strains related to 

infrastructure on campus and housing options in Starkville, but it has also created new 

opportunities for area businesses. Local initiatives, such as those sponsored by Main 

Street Starkville, have smartly sought to capitalize on these new opportunities in order 

to create a stronger and more diversified economy.

The town and gown linkages have not always been close between Starkville and MSU, 

and the nature of this relationship often depends on the commitment and focus of 

MSU’s President and other university leadership. Under the current MSU 

administration, MSU and Starkville’s community leadership are enjoying a close and 

fruitful partnership. Community leaders are trying to link the community to many of the 

MSU-related activities like football games. In branding Starkville as “Mississippi’s 

College Town” they hope to turn Starkville into a destination for football fans or parents 

of MSU students. Success in these efforts should create more opportunities to grow

other activities such as tourism and retail. Continued efforts to attract visitors to 

Starkville are also a top priority. Starkville and MSU are currently collaborating to 

develop a hotel and convention center that should help attract more visitors to the 

25 U.S. Bureau of Labor Statistics Local Area Unemployment Statistics, June 2013.

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community. Besides making Starkville a destination, the community’s leadership also 

hopes to better connect the community with the campus. Consequently, the Starkville 

Main Street program promotes local shops and community districts—like the popular 

Cotton District—to the campus community (faculty, staff and students) so that they are 

more likely to see these areas as attractive destinations for shopping and nightlife. 

In addition to trying to capture the demand generated by MSU, Oktibbeha County’s 

economic development community also seeks to leverage its innovative capacity. As a 

result, efforts to access skilled talent and technology from MSU are centered on 

investments made at the local Thad Cochran Research and Technology Park. The park 

first opened in 1985, and is growing rapidly, with newly constructed buildings quickly 

reaching full capacity and further expansion plans underway. At present, the park is 

home to seven buildings with more than 1,500 employees working at technology-based 

businesses. 

The other major plank of Oktibbeha County’s economic development plan focuses on

more traditional business attraction and retention efforts. However, these economic 

development activities are being undertaken in an increasingly regional context. Key 

economic development partners are now working to attract new industries—

particularly manufacturers—to the broader Golden Triangle region as opposed to 

focusing on their individual counties. These initiatives further link Oktibbeha County to 

the wider regional economy by connecting the intellectual assets and talent based at 

MSU to emerging manufacturing centers in both Clay and Lowndes County. These 

partnerships, formalized through the GTDL, represent a significant change for the 

region. Like many areas of Appalachia, the region had previously been characterized by 

business recruitment competition between county economic development agencies. 

The GTDL will lessen this competition, and it is hoped that this model will be used across 

the state. 

Other regional organizations have further supported these efforts. For instance, the 

GTPDD’s Comprehensive Economic Development Strategy describes several specific 

action items such as identifying and preparing new industrial sites, assisting localities in 

attracting new firms, providing capital through revolving loan funds and other tools, and 

making workforce development services available to firms needing training support. 

This regional economic development approach has yielded some early successes, such 

as the recent location of a new Yokohama Tire facility in West Point. Expected to open 

in 2015, this new facility is slated to employ more than 2,000 people.

Oktibbeha County has also engaged in a number of other regional initiatives, including 

several high profile federal grant opportunities in Eastern Mississippi. In 2005, regional 

leaders in both East Mississippi and West Alabama won a Federal WIRED (Workforce 

Innovation in Regional Economic Development) grant that was used to develop a 

regional entrepreneurship support strategy, the MyBiz Mississippi Entrepreneur 

Alliance. The project also supported enhanced technical training programs at local 

community colleges, which has continued to expand and was included as part of the 

Wal-Mart Foundation’s national America Works initiative. Oktibbeha County and MSU 

are also lead partners in an ongoing Rural Jobs and Innovation Accelerator grant project 

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jointly funded by ARC, USDA, and EDA. This effort will support a host of programs 

targeting key regional industry clusters such as automotive, furniture and agribusiness.

The Oktibbeha County case study demonstrates the importance of connecting 

communities to anchor institutions like universities. Focusing economic development 

efforts around the community’s largest employer might seem like a strategy that would 

reduce economic diversity, but in the case of a major research university like MSU, the 

school’s many activities actually help spur greater diversity. New opportunities are 

created not only in high tech and advanced manufacturing activities, but also in a 

diverse set of industries such as retail, recreation and tourism, and real estate. Making 

these connections and linkages can be important for communities that do not possess 

large institutions, but are nevertheless close to a large regional institution. The 

importance of regional approaches can also be seen in Oktibbeha County’s industrial 

recruitment efforts which in the past focused on the county level, but have become 

more effective since the county became part of a wider regional consortium. 

KEY FINDINGS AND OTHER OBSERVATIONS

• Concerted and intentional efforts are required to effectively maximize the 

economic development potential of major research institutions.

o Committed leadership from both the community and university are 

also required.

• Business attraction efforts tend to be most effective when undertaken in a 

regional context rather than individually.

o Making these efforts work requires trust and an understanding of 

roles and relationships between the partners.

COMMON THEMES AND TRENDS

The 10 case studies profiled in this report offer both lessons and cautionary tales. This 

section seeks to identify the common themes and findings that emerged from these 

case studies. The goal is not only to improve practitioners’ understanding of diversity as 

a motivating factor in economic development, but also to give those same practitioners 

insights that might help as they develop their own economic diversification strategies. 

DIVERSITY IS MORE OFTEN A GOAL THAN AN ARTICULATED STRATEGY

Many communities identify a more diverse economy as a goal or a value, but diversity is 

less likely to motivate individual economic development strategies. As a consequence, 

diversity does not tend to drive strategy development in the same way as a concept like 

competitiveness. Competitiveness-driven strategies are not necessarily mutually 

exclusive with regional efforts to diversify, but they do lead to different sets of priorities 

and types of strategies. A focus on competitiveness can often lead places to select 

specific clusters around which to focus energy and resources. This, in turn, may limit 

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their activities to only those that support those clusters, foregoing potential 

opportunities in other non-core activities. 

Diversification does not necessarily need to be seen as a goal in itself to be an important 

concept in framing decisions about economic development strategy. Instead, explicit 

consideration of diversification within a strategic planning process may help reveal

different kinds of strategies and also lead to the implementation of programs or policies 

that ultimately help diversify the community’s economic base. As a result, 

diversification as a priority might encourage leaders to consider developing a broader 

array of skills or focusing on finding new sources of wealth rather than just emphasizing 

job creation. Thinking carefully about diversity can be helpful for many places, even as 

leaders consider other goals.

Where diversity does drive thinking, it is usually the result of crisis (e.g., the loss of a 

major employer or major industry) or the recognition that a crisis might be imminent 

because the community is overly reliant on a single industry or employer. The 

justification for seeking economic diversity often centers on mitigating risk and/or 

capturing opportunity. For those places that rely on a single plant, industry or 

government facility, there is a need to protect themselves against the potential 

consequences of losing a large employer (Lockheed Martin in Tioga County, NY) or 

experiencing a downturn in a key industry (e.g., coal mining in Knott County, KY; 

furniture and textiles in Rutherford County, NC). As a result, leaders in these places

seek additional economic activities to both replace lost jobs in declining industries and 

reduce the extent to which the community depends on those firms or industries viewed 

as “declining” or “at-risk” of eventual loss due to global economic forces. 

While economic crises may lead communities to seek greater diversity, those same 

events may also tie leaders’ hands in terms of how best to achieve diversity goals. A 

crisis environment may limit the extent to which places can be intentional about how 

they pursue their economic development objectives. Rather than being strategic about 

their efforts, the crisis itself may force leaders to adopt a “beggars can’t be choosers” 

mindset and, as a result, pursue any available opportunity instead of focusing their 

efforts on quality opportunities. 

Capturing greater opportunities is another motivating factor behind diversity-driven 

strategies. Places with dynamic economies that continuously develop new economic 

activities and are constantly in a state of transformation are more likely to weather 

economic storms and raise overall regional prosperity. However, in order to do so,

places must be able to maximize their assets to capture current and future 

opportunities. These “opportunistic” strategies might focus on investing in state-of-theart infrastructure (e.g., Corridor H in Upshur County, WV), building market relationships 

with nearby growth centers (e.g., Garrett County, MD’s links to Pittsburgh and DC), or 

effectively connecting firms to local sources of innovation like major research 

universities (e.g., Clemson University, Mississippi State University) and/or national 

research laboratories (e.g., Oak Ridge National Laboratory in TN, National Energy 

Technology Laboratory in WV).

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The data show that places that diversify more quickly are those that experience a 

sudden loss of a major employer or industry. By contrast, the case studies demonstrate 

that places that achieve more positive economic diversity tend to do so in smaller 

numbers and over an extended period of time. Therefore, places seeking to implement 

diversity-driven strategies must show patience and commitment to those efforts. This 

requires building consensus around diversity as a goal within the area’s long-term 

economic vision.

DIVERSIFICATION THAT CONTRIBUTES POSITIVELY REQUIRES MANY YEARS

In fact, like making a fine wine, economic diversity requires time to develop local roots

and support before its fruit can bear prosperity. Diversity that occurs too rapidly often 

reflects significant economic weaknesses from a community overly reliant on a single

company or industry. These are the stories of crisis and distress that so many regions 

encounter. While greater diversity may result, its rapid occurrence leads to dislocation 

among workers and interdependent businesses alike.

The most successful places can expect their efforts to bear positive economic fruit after 

many years of sustained effort. Several of the case profiles included in this analysis 

developed their initial plans in the 1990s and pursued that plan’s implementation (with 

appropriate modifications along the way) for 15 years before realizing success. In these 

cases, the community’s citizens (not just its leaders) were committed to a long-term 

investment strategy. For some, that community commitment may have occurred only 

after a significant event—a major company downsizing or relocation, meaning that the 

road back to prosperity was probably much longer and more challenging. 

However, this long-term commitment often resulted from a common realization that 

the community’s future had to be built on leveraging one or more unique local assets (a 

university, a major new highway, or a unique natural geography such as a body of water 

or mountain) as well as a bit of luck. These assets provided the lynchpin, but the 

singular focus on a common plan provided the course for the community’s new 

economic trajectory. Identifying those lynchpins and then building local consensus 

around the plan to leverage those assets are the most challenging tasks—and ultimately 

the key to success. 

DIVERSIFICATION CAN BE ACHIEVED THROUGH A VARIETY OF ECONOMIC 

DEVELOPMENT STRATEGIES

Economic diversity results from a variety of strategy choices. Communities with varying 

economic experiences and situations will pursue those strategies that the community 

supports and that local partners have the capacity to implement. When asked about 

how they are working to diversify their economy, practitioners identified a wide range 

of activities including traditional business recruitment, retention and expansion, 

workforce development, entrepreneurial development, promoting tourism, leveraging 

university capabilities, investing in infrastructure, and many others. As noted earlier, all 

these strategies are typically designed to achieve one of five goals:

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• Create collaborative regional planning and implementation systems,

• Build an ecosystem capable of supporting a diverse array of economic 

activities,

• Connect local and regional assets to external markets,

• Develop skills and talent needed in a wide range of industries, and

• Encourage local reinvestment of wealth.

However, at the most basic level practitioners often seek an “anything that works” 

approach to diversification. Clearly, there are multiple ways to achieve diversity, and 

several practitioners pointed out that just as the community should not rely on one 

employer or one industry, nor should the community rely on any single economic 

development strategy. Much like diversity itself, undertaking multiple strategies allows 

practitioners to protect themselves against failed or ineffective initiatives while at the 

same time increasing the likelihood that one of those strategies will succeed. Moreover,

many strategies create jobs in relatively small numbers; undertaking multiple strategies 

can also increase the overall impact of the broader community efforts.

Another influence over selecting preferred economic diversification strategies is the 

extent to which strategy outcomes can be controlled locally. Practitioners most 

commonly identified workforce development and entrepreneurial support efforts as 

actions they could impact locally. In both instances, these strategies involve building the 

capacity of people who are already located and/or tied to the local community. In other 

instances, local efforts to foster diversity focused on giving people already living in the 

community a reason to stay, either through local educational or career opportunities. 

Traditional business retention and expansion activities also seek to capture and retain 

investment in the region, but for many businesses, their fate is determined externally as 

part of far-flung corporate headquarters’ decisions or by global market forces.

Ultimately, the most significant challenge to overcome is that most diversification 

strategies result in creating jobs in the fives or tens over a sustained period of time, 

whereas an economic event can result in the loss of a major employer or industry and

hundreds of jobs at a single moment in time. As demonstrated in the case studies, 

many Appalachian communities are looking toward tourism development strategies to 

help diversify their economies and replace past economic drivers. These tourism 

strategies, however, are often more about replacing lost jobs than diversifying the local 

economy. While the tourism industry provides jobs for relatively low-skilled workers, 

the new jobs do not pay those same workers as much as industries like coal or 

manufacturing once did. Consequently, tourism alone will not lead to greater diversity, 

but instead must be viewed as but one element of broader economic development 

strategies. 

In this context, the appeal of significant business attraction efforts becomes clear. The 

attraction of a 200-employee manufacturing plant can make a greater impact on the 

community than helping a small business grow from 5 employees to 10 employees. 

While business attraction efforts have a place in many comprehensive economic 

development strategies, these kinds of investments are made sporadically and a focus 

on these types of projects can be risky. Most notably, there are few relatively large 

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projects seeking new locations, and it is probably unrealistic for communities lacking 

significant assets to expect to win these types of large projects. Many growth

companies are also looking to locate in fast-growing, diverse metropolitan areas, closer 

to their customer base. The projects that are looking for more rural locations often tend 

to need a low-cost and relatively low-skilled workforce. These kinds of projects are

intrinsically mobile and their employment levels are not always sustainable over a long 

period of time. As a result, this is an area where local forces are least likely to exert real 

influence over outcomes.

EFFECTIVE DIVERSIFICATION REQUIRES MORE THAN JUST DIVERSIFYING THE 

EMPLOYMENT BASE

The case studies demonstrated that approaching diversity issues requires communities 

and practitioners to take a broader perspective and approach. This broad thinking is

required in two ways—in how community leaders and economic developers define 

diversity as well as in how they go about pursuing strategies to promote greater 

economic diversity. Most practitioners conceptualize a diverse economy as one with a 

wide array of industries, but this is a relatively narrow way to define diversity. Since 

wages paid by area employers are not the area’s only form of wealth generation, 

diversity should take into account more than just local employment. Therefore, broader 

definitions of diversity can lead to outcomes beyond just a diverse employment base. 

For instance, communities might consider thinking about developing a more diverse set 

of wealth generators. As shown in places like Rutherford County, NC, retiree attraction 

efforts can diversify an area’s wealth generation capacity. These retirees bring money 

from outside the region in the form of their retirement accounts as sources of wealth. 

This wealth can then be re-circulated in the region to support activities like construction 

and retail. Similarly, Medicare payments (which also originate external to the 

community) can also generate demand for activities such as those related to health 

care. 

Diversity in the ownership of locally-based companies is another consideration, and 

specifically whether owners are based inside or outside of the community. Locallybased and headquartered companies are more apt to remain and invest in the area than 

are firms where the locus of decision making is external to the region. As the Tioga 

County case study demonstrates, when decisions about a local firm’s future are made 

external to the region, there can be great uncertainty about the region’s economic 

future. In order to mitigate this risk, many places pursue entrepreneurship strategies 

that promote greater local ownership and control. The case study counties provide 

numerous examples of entrepreneurial strategies including promoting entrepreneurship 

to students, recruiting entrepreneurs to the region, connecting entrepreneurs to 

existing support services, and/or investing in key infrastructure like broadband capacity 

and business incubators. By growing the number of locally-based firms, communities 

are more able to exert control over their economic trajectory. 

Skills diversity is another area of consideration. Workforce development represents

another area that local practitioners identified as a real challenge, but also one where 

they felt as though local action could make a real impact. Many former mill towns 

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possess a talent pool that has a relatively narrow range of skills that may not translate 

well to emerging new industries. This relative lack of skills slows these communities’ 

ability to take on new and different activities and thereby diminishes their capacity to 

diversify their economies. While there may be a desire to focus training efforts around 

the region’s dominant industry, this can prove risky if that industry experiences 

significant decline. By contrast, a broad and flexible base of skills is required to support 

a diverse economy and respond to rapidly changing demand for skills. The Lycoming 

County case study provides an excellent example of a diversity-driven workforce 

initiative. In this instance, Penn College intentionally sought to invest in training 

programs that met the needs of the natural gas industry, but also provided workers with 

skills that are of use to firms in other industries such as construction, utilities and 

advanced manufacturing. Therefore, diversifying the skill base can allow communities 

to better take advantage of opportunities as they present themselves.

These distinctions are important as they force consideration of issues such as the 

diversity of wealth and ownership, as well as the workforce’s ability to respond to 

changing economic conditions. Consideration of these different forms of diversity also 

influences strategy design and how outcomes are measured. These different definitions 

of diversity are not contradictory or mutually exclusive. Broadly defining diversity can 

lead to the need for a diverse portfolio of development strategies, which in turn can 

allow communities to not only achieve multiple objectives, but also increase the 

chances of strategies proving successful. 

ECONOMIC DIVERSITY INFLUENCED BY REGIONAL CONTEXT

Communities must also approach diversity with broader geographic considerations. The 

case studies showed that diversity is a scale-dependent issue. Workers often think 

regionally when looking for employment, and so too should communities when seeking 

future opportunities for wealth creation. Understanding the broader regional context 

can help communities better understand the range of available opportunities and 

potential risks. For instance, a community like Tioga County, NY will never be a major 

jobs center but by making themselves an attractive place to live they are able to attract 

people with good paying jobs in nearby job centers like Ithaca or Binghamton. By 

contrast, downturns in the Washington, DC or Pittsburgh economies might have 

negative consequences for a place like Garrett County, MD. Understanding the regional 

context can therefore help to shape the parameters for strategy development as it helps 

communities more fully understand the threats and opportunities they face. 

Specialized counties that contribute to a broader, more diverse region are more likely to 

diversify over time. Building stronger regional connections can enable communities to 

leverage a greater number of assets, and thereby open up greater economic 

opportunities. For instance, proximity to large universities can prove beneficial not only 

for the immediate area, but also surrounding communities. If leveraged properly, large 

universities, such as MSU in Oktibbeha County or Clemson in Pickens County, can be a 

source of innovative research and talented workers for area companies, as well as a

source of demand for local food producers or retailers. While this is important for all 

counties, it is especially important for more rural communities that lack depth of assets. 

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However, these regional connections do not always occur naturally and both the 

university and the community must be willing to work together in order to maximize 

these opportunities. 

Regional thinking can also help create assets. For communities seeking to develop 

tourism, the whole is greater than the sum of the parts, and they are best served by 

linking these attractions together in the minds of potential tourists and promoting the 

entire region as a single destination. By thinking regionally, Lauderdale County, for 

example, has not only made itself a regional retail center but also a destination for 

golfers through its place in the Robert Trent Jones Golf Trail. These kinds of economic 

opportunities would not be available had Lauderdale County acted independently to 

promote its golf courses or its tourist attractions. Similarly, the tourism and 

infrastructure initiatives underway in Upshur County and its surrounding region would 

not have succeeded had those counties operated independently of one another. By 

contrast, Knott County’s efforts to establish itself as a center for adventure sports has 

yet to fully materialize in part because the initial efforts have been independent of one 

another and not yet connected to a broader regional effort. 

BROAD ENGAGEMENT ACROSS SILOS INCREASES POTENTIAL FOR 

SUCCESSFUL DIVERSIFICATION STRATEGIES

Similarly, broad engagement with a wide array of stakeholders can also help move 

diversification efforts forward. Effective strategies engage practitioners from economic 

development, workforce development, higher education, planning, tourism 

development, as well as the private sector. Practitioners in each of these arenas bring 

unique, but sometimes overlapping, networks. These networks offer access to 

knowledge, resources and expertise. For instance, workforce developers may have 

access to training funds or training programs that would benefit companies in industries 

that economic developers seek to promote. Similarly economic developers meet 

regularly with area companies, and can in turn convey information from these 

conversations to educators who can then use that information to develop curriculum. 

As noted above, regional approaches and regional collaboration is often ideal, but local 

collaboration is the minimum required for implementing effective strategies. Top down 

initiatives are not always effective as they often end up being neither regional nor 

collaborative. Instead, some of the more effective collaborative initiatives emerge from 

grass roots efforts where local organizations come together to form a partnership to 

address an issue where there is a consensus need. Economic developers often lead 

these efforts, but the most effective ones are those that see their primary role as one of 

a connector. By connecting different regional actors and networks, they are able to 

gather support and resources to advance regional initiatives. This kind of local 

collaboration often emerges in places where there are open networks, and grassroots

initiatives are able to surface from a wide array of actors. 

Opportunities are fewer in places where the practitioners remain within the walls of 

their silos. The case studies suggest that the communities with the most coherent 

diversification strategies have overall economic development strategies that are not 

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driven by local economic developers alone. As in the case of Garrett County, MD, these 

successful strategies involve multiple stakeholders and enjoy broad-based community 

support. In these instances, area economic developers see that they have a role beyond 

just industrial recruitment and retention and are willing to embrace their role as a 

regional connector or facilitator are more apt to be successful in furthering broad based 

regional initiatives that seek to promote economic diversity. 

RESEARCH CAN GUIDE EFFECTIVE STRATEGY DEVELOPMENT AND 

IMPLEMENTATION

Strategies are more likely to be successful if they are based on a foundation of datadriven research. However, this is an area where many economic development 

practitioners often lack capacity. These research skills are needed for economic 

development practitioners to track regional growth and progress, undertake effective

market analysis, understand how to harness creativity and entrepreneurship, and keep 

up with trends about how technology is re-shaping economic advantage. Many places 

lack this research capacity, but creative and enterprising practitioners can access these 

capabilities by looking beyond their silos and partnering with other regional 

organizations that are capable of performing this research. 

At the most basic level, communities should have a basic understanding of the economic 

trends that are shaping their economy. This baseline analysis is an important 

component in undertaking an honest appraisal of the community’s strengths and 

weaknesses and is often captured in regional Comprehensive Economic Development 

Strategies. Understanding these economic trends allows communities and regions to 

better identify and prioritize key issues as well as determine which strategies are 

realistic, which are not, and which are likely to yield the greatest impact. For instance, 

local leaders may seek to turn their community into a tourist destination, but if the 

community has no hotels or restaurants and lacks highway access or appealing tourist 

attractions, then this effort will likely experience difficulty gaining traction. Undertaking 

this kind of honest appraisal allows places to remove less optimal options from

consideration and instead focus on diversification strategies that are more appropriate 

for their circumstances.

In addition to considering their internal strengths and weaknesses, successful places 

also consider external risks and opportunities. This can be done by better 

understanding how local firms and industries are connected to external markets, and 

how growth or decline in other places may affect the local economy. If communities are 

able to identify comparable places then they might be able to learn from their situation. 

Similarly, community leaders might also seek to learn from other places that have 

experienced significant shocks or been presented with similar opportunities. For 

instance, the Lycoming County case study showed how that community was able to 

learn from another community—Fort Worth, TX—that had been affected by the natural 

gas boom, and was better able to prepare for the multitude of impacts arising from 

these developments. 

46
CASE STUDIES IN ECONOMIC DIVERSIFICATION

Research and analytical capacity can also play an important role in monitoring and 

evaluating strategy progress. By tracking outcomes, places can better identify those 

strategies that are not performing as hoped and either eliminate them or develop new, 

more effective tactics for the future. Moreover, tracking outcomes and performance 

allows stakeholders to demonstrate their progress and impact which is important for 

enlisting additional support or securing funding. 

LEADERSHIP MATTERS

As noted above, there is no single economic development strategy that alone can lead 

to greater economic diversity. Many different kinds of strategic actions must be 

undertaken to achieve this goal. However, none of this matters if the strategies are not

effectively executed. Successful implementation often comes down to the people 

involved. For a community to effectively diversify its economy, it must first and 

foremost have people who see economic diversification as a priority. If diversification 

proves to be a community priority, then it must also have community leaders and 

stakeholders who are open to working regionally, collaborating across silos, thinking 

opportunistically about resources, and willing to take risks in order to make the 

investments necessary to see diversification strategies through to completion. 

Moreover, communities need different people in different roles to implement these 

strategies. At the most basic level, there are two roles that need to be filled—a 

leadership role and a staff role.

Leadership is crucial for any economic development effort. Local leaders are needed to 

articulate, and build consensus for, a community vision. They are also needed to serve 

as champions for strategies and enlist support from the community to help implement 

those strategies. In several of the cases studies, the research team saw that the private 

sector could play an important catalyzing role for many strategies. This was especially 

true for Washington County, VA, where private sector leadership drove the completion 

of the Birthplace of Country Music Museum. In Oktibbeha County, MS, engaged and 

committed leadership from both the community and the Mississippi State University 

administration make it possible for the community to increase the economic impact of 

the campus community on the local economy. However, for many smaller and more 

rural Appalachian communities, this kind of strong private sector leadership is not 

always available, and as a result they often lack the capacity to coalesce local business 

leadership for true public-private efforts in economic development. In these places, 

government can become a critical actor in rural local economic development in 

Appalachia (and elsewhere). 

But no matter where this local leadership may come from, there is also a need for 

multiple leaders. Many of the diversification strategies laid out in the case studies will 

require long-term commitment before they begin to yield significant and sustainable 

benefits for the community. Many of the initiatives underway in Lauderdale County 

began in the mid-1980s and they continue to yield benefits because there has been a 

long-term commitment to see them continued. Sustaining long-term economic 

development and diversification efforts therefore requires a depth of leadership. The 

Knott County, KY case study showed that when there has been significant leadership 

47
CASE STUDIES IN ECONOMIC DIVERSIFICATION

turnover, it is difficult to maintain the momentum behind these efforts. The loss of a 

strong, persuasive and visionary leader can derail efforts unless there is a deep 

leadership bench and broad buy-in to a common vision. As demonstrated by the 

Garrett County, MD case study, the county’s economic development planning process—

which has been ongoing for over 15 years—has been an effective means for onboarding new leaders into the process and ensuring consensus for the economic 

development vision.

Leadership alone cannot guarantee successful implementation of economic 

development and diversification strategies. Strong leaders often succeed when they are 

supported with great staff. For many leaders, particularly private sector leaders, these 

kinds of initiatives are well outside of their core professional responsibilities. Therefore,

economic development practitioners are needed to do much of the work involved with 

seeing these strategies through to completion. This may involve work such as 

organizing meetings and doing the research. Practitioners also play a hugely important 

role in organizing stakeholders and preparing applications needed to secure state and 

federal funds that help kick start many of these initiatives. This is not to say that 

economic development practitioners themselves are not key leaders in their 

communities. In most instances, they are quite active in many of the core leadership 

responsibilities (e.g., building a consensus vision, recruiting support) in which private 

sector leaders should also be engaged. However, rarely can a community truly 

transform its economy without significant support from business leaders and elected 

officials and without important support from their economic development practitioners. 

It should also be noted that working both collaboratively and regionally requires a great 

deal of trust among stakeholders. Repeated interaction often leads to greater trust and 

comfort, so continuously seeking regional and collaborative projects is one way to build 

these loose coalitions (even if the efforts are not always successful in attracting external 

funding). The importance of having these coalitions in place cannot be understated. 

For instance, the existence of ongoing partnerships can allow regions to respond quickly 

and effectively to funding opportunities as they arise. Just like diversity itself, these 

coalitions allow places to capture more opportunities and mitigate the risk involved in 

undertaking new initiatives.

CONCLUSION

These case studies demonstrate that many Appalachian communities view economic 

diversity as a stated goal, but there are few articulated strategies that are specifically 

intended to achieve greater economic diversification. Instead, diversity is often used as 

one of several justifications for pursuing broader economic development goals. In many 

instances, places identify economic diversity as a goal after they have experienced some 

kind of crisis like the loss of a major employer or job losses in a regionally significant 

industry. The motivation for a more diverse economy therefore manifests itself in two 

ways. First, places seek to mitigate the risk inherent with being too dependent on a 

given employer or industry. Second, places seek to capture greater opportunity by 

being involved in a wider array of economic activity. However, it is most often the 

former that allows diversification to become a core community value. If diversification 

48
CASE STUDIES IN ECONOMIC DIVERSIFICATION

is not a core value or key goal, it still may receive some rhetorical support albeit with 

very limited resources supporting action.

When asked about their diversification efforts, economic development practitioners

note a wide array of strategy types. For instance, several practitioners cite their 

business attraction and retention efforts as a key plank of the diversification efforts. 

Perhaps most commonly, practitioners look to targeted efforts—like those focused on 

the agriculture or tourism sector. Frequently, these may not even be central to their 

traditional economic development efforts. While these activities often take advantage 

of assets—namely existing developable land or even available wilderness—developing 

these industries are in some instances more of an economic replacement strategy than 

a diversification strategy. The initiatives are designed to provide jobs for relatively 

lower-skilled workers who are no longer able to work in mines or mills. As a result, 

tourism and agriculture may represent a return for communities to their rural roots due 

to a lack of alternatives rather than an effort to find a new economic rationale for their 

community. This approach can be valuable, especially in cases where no alternative 

options exist, but this approach seldom contributes sufficiently to local wealth as to help 

transform the local economy in response to global market forces.

Some places think more broadly about diversification than just seeking to encourage a 

broad base of industry employment. In those cases, community leaders emphasize 

efforts aimed at diversifying the local talent base and the skills available from the local 

workforce while others may focus on growing the number of locally-owned firms 

through entrepreneurial support efforts. In other instances, efforts are placed on 

diversifying sources of wealth generation by pursuing activities like retiree attraction. 

Overall though, successful diversification efforts involve identifying and successfully 

implementing multiple strategies simultaneously.

The case studies also show that diversity is a scale dependent concept. If places are able 

to look beyond the borders of their own town or county, they may be able to better 

capitalize on more regional assets. This is demonstrated in a number of ways. For 

instance, places might seek to better integrate their community with larger job centers 

in neighboring counties. They might also attempt to leverage the expertise or demand 

generated by nearby institutions to support their own locally based efforts. Another 

common strategy is to work with individual local industries in a context in which those 

companies are connected to similar industries across a broader area—recognizing the 

importance that industry clusters can have in creating a regional identity. This is a 

common technique in developing the tourism industry, organizing individual tourist 

attractions from across a region in such a way as to create a more appealing tourist 

destination. The same principles can be used in organizing the agricultural sector, 

selected manufacturing industries, or other targeted sectors. The better able the

community is in connecting these industries to key economic assets, the more equipped 

the community will be to mitigate risk and capture opportunity for those industries (and 

the community’s citizens).

Communities that successfully implement diversification strategies often share several 

common traits. First, they develop their strategies on a solid foundation of analysis and 

49
CASE STUDIES IN ECONOMIC DIVERSIFICATION

research. Second, they think long-term, recognizing that sound development 

approaches can take time to mature. Third, they think and operate regionally so as to 

maximize the resources and assets available to them. Fourth, practitioners work across 

silos to create broader networks and coalitions and to leverage networks and expertise. 

Fifth, successful places put the right leaders and staff in place to gain common 

understanding about the challenges ahead and the most appropriate risks to take in 

addressing those challenges. Finally, successful places have a process in place not only 

for developing and implementing their strategies, but also for incorporating new 

leaders. Economic diversity is a legitimate economic aspiration and goal, but like all 

economic development goals, it will only be accomplished if area leaders and 

stakeholders thoughtfully and effectively implement their economic development 

strategies.

50
A Practitioner's Guide for Planning and Analysis with the 

Appalachian Economic Diversity Web Tool

February 2014

Prepared for the Appalachian Regional Commission
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

CONTENTS

About this Guide.................................................................................................................. i

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

About The Appalachian Economic Diversity Web Tool....................................................... 2

Question 1: How is my economy doing? ............................................................................ 4

Question 2: How diverse is my economy and what risks and opportunities does that 

diversity present? ............................................................................................................... 8

Question 3: What kind of asset is my local workforce?....................................................14

Question 4: How is my county doing relative to similar counties elsewhere?.................18

Diversity and Economic Development Planning ...............................................................22

Value to the Planning Process ..........................................................................................23

Bibliography......................................................................................................................26

ABOUT THIS GUIDE

This guide is one of four documents prepared as part of an extensive analysis of 

economic diversity for the Appalachian Regional Commission by the University of Illinois 

at Urbana-Champaign’s Regional Economics Applications Laboratory and the Center for 

Regional Economic Competitiveness, with assistance from the RUPRI Center for Rural 

Entrepreneurship and EntreWorks Consulting. Project leaders were Edward Feser, Ken 

Poole, Mark White, and Geoffrey Hewings, with Troy Mix serving as Project Manager.

Authors of this guide were Mark White and Ken Poole. Erol Yildirim and William Cook 

were primarily responsible for building the web tool.

In addition to this guide, the documents include a summary report (Economic Diversity 

in Appalachia: Statistics, Strategies, and Guides for Action), a detailed synopsis of case 

studies (Case Studies in Economic Diversification in Appalachia), and a technical report 

detailing methods, data, and additional findings (A Statistical Portrait of Economic 

Diversity in Appalachia). All reports associated with the project can be found here: 

http://economicdiversityinappalachia.creconline.org/Report/. 

i 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

INTRODUCTION

Appalachian communities have long relied on one or two major industries to support 

their economies. To paraphrase an old saying: when those industries sneezed, 

Appalachian communities caught pneumonia. Not surprisingly, economic development 

practitioners working in Appalachia recognize the importance of promoting greater 

economic diversity. However, they may not be fully aware of just how dependent their 

local communities are on a single industry or handful of industries. 

As part of a comprehensive analysis of economic diversity conducted for the 

Appalachian Regional Commission (ARC), the University of Illinois at Urbana-Champaign 

and Center for Regional Economic Competitiveness research team created a unique 

website designed to help practitioners better understand the structure of their local 

economies, both singly and in comparison to other counties and regions of similar scale 

and type within Appalachia and across the United States. The website taps a 

compilation of data elements to facilitate user-customized quantitative portraits of 

Appalachia’s counties and regions. The data, indicators, and visualization facilities 

together constitute a web tool rather than a simple website.1

The web tool includes data points designed to help practitioners and policy makers 

understand and explore several key concepts related to economic diversity. Through 

tables, charts, graphics, and maps, users are able to undertake selected analyses of their 

economies’ diversity quickly and easily.

While the web tool is not intended to be a single source of information and analytics to 

support all economic development planning, the tool is best suited to help address four 

commonly asked applied research questions:

1. How is my economy doing?

2. How diverse is my economy and what risks and opportunities does that 

diversity present? 

3. What kind of asset is my local workforce? 

4. How is my county doing relative to similar counties elsewhere? 

This guide is organized around plausible scenarios that economic development 

practitioners frequently encounter, often as part of a local or regional planning process. 

After providing a brief orientation to the website, the guide directs users through the 

mechanics of investigating each question using the data and tools the site offers. The 

guide also points users to additional resources that supplement the information on the 

website.

1 http://economicdiversityinappalachia.creconline.org/ 

1 

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

ABOUT THE APPALACHIAN ECONOMIC DIVERSITY WEB TOOL

There are several core elements to the Appalachian Economic Diversity Web Tool; many 

of the features can be accessed through the tabs at the top of the Home page (see 

Figure 1). Practitioners who are new to the tool can familiarize themselves with the site

via a basic tour accessible by a link in the green box. Users can also find definitions for 

key terms by rolling the mouse over the information icon ( ) found next to key terms.

After clicking on the icon, users will see a pop-up window that includes a definition of 

the term. More detailed definitions are also available on the ‘About’ link. 

We will provide more detail about the information and data contained on each of the 

pages as we address each of the key questions throughout this guide. The following is 

an inventory of items located on each page.

• Home page: The home page provides data tied to three different measures of

economic diversity—industrial, functional, and occupational. Those concepts 

are explained in depth in a companion report.2 In general, the measures 

describe how well-endowed each of the region’s 420 counties are in terms of 

the number of industries and their employment levels, the prevalence of broad 

2 See Economic Diversity in Appalachia, a report prepared for the Appalachian Regional 

Commission by the UIUC Regional Economics Applications Laboratory, the Center for 

Regional Economic Competitiveness, and the RUPRI Center on Entrepreneurship 

(Washington, DC, 2014).

Figure 1: Key Pages of the Appalachian Diversity Web Tool

Information 

for new 

users

2 

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

types of economic functions or roles regions often play (e.g., a government 

center, a retail trade center, a manufacturing hub, etc.), and the number and 

size of occupations represented in the workforce. The level of diversity in each 

county is captured via a statistical index and mapped. Using the maps, 

practitioners can explore county-by-county patterns of economic diversity

within each of the states or across the entire ARC region.

The economic diversity maps can be filtered in multiple ways so that users can 

focus on a few important variables if they wish. For instance, users can opt to 

look at just those counties with high or low levels of industrial diversity, to 

focus on a county’s economic status or functional specialization, or to access 

basic employment and population data.

• County Profile page: The County Profile page provides much of the web tool’s 

key data and information. In the left- hand column, users will find data about 

individual counties. Those data pertain to the selected county’s economic 

performance, the names of five comparison counties, and employment data by 

economic function and occupational knowledge cluster. In the right-hand

column users will find a number of diversity-related metrics, including metrics 

for other geographies, such as the county’s commuter shed and development

district.

• Compare Counties page: The Compare Counties page allows users to make 

side-by-side comparisons for up to three counties quickly and easily. Using the 

available filters, other Appalachian counties may be selected for comparison 

based on ARC sub-region or economic status designations. 

• Report page: The report page contains links to PDF versions of the Economic 

Diversity in Appalachia reports that contain more detailed information about 

the diversity metrics and data. 

• About: This page contains information about the Economic Diversity in

Appalachia website and a glossary of key terms and concepts. It also includes a 

description of the methodology used to develop the metrics presented in the 

web tool.

3 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

QUESTION 1: HOW IS MY ECONOMY DOING?

The first and simplest use of the Appalachian Economic Diversity Web Tool is to explore 

basic trends for a given county and its neighboring counties. A number of key indicators 

may be explored, such as population, employment, and income growth. Such 

explorations are valuable for monitoring of the overall health of a county’s economy.

Data from the site are pulled from a variety of public and private sources. Included are 

detailed population estimates from the U.S. Bureau of the Census and employment 

estimates from Economic Modeling Specialists International (EMSI). EMSI uses Bureau 

of Labor Statistics (BLS) employment data and applies an algorithm designed to estimate 

data that BLS suppresses for small area geographies. This estimation technique is 

important because, at the 5-digit NAICS level of industry detail, more than half of the 

employment data in a given county may be suppressed to meet federal confidentiality 

requirements. To calculate a complete set of diversity measures, the research team 

required estimates of employment for all suppressed data cells.

When tracking economic indicators, practitioners must also understand the context of 

individual data points. County employment or population may be growing at a given 

rate, but how does that rate compare to the state or nation? The tool provides such 

information readily. Even if growth is occurring at a relatively fast pace, it is critical to 

understand the sources of growth to assess whether it is manageable and sustainable.

Figure 2 shows the location on the Home page where users can find diversity 

characteristics and growth trends within the ARC region or in any state. By clicking on 

counties in the map, users can explore basic population and employment growth data 

for different places. The growth trends are presented in a way that allows the 

comparison of one county to another county, the state, or the nation. 

Population and employment growth rates for each county are provided as compound 

annual growth rates (CAGR) for the period 2009 to 2012, which covers the last year of 

the recession through the most recent full year of data available. Percentile rankings aid 

interpretation of a county’s relative performance.

USING THE WEB TOOL TO CREATE A PROFILE: A SCENARIO

As an example, consider the data for Lycoming County, Pennsylvania. Users can access 

individual county data in two ways. By selecting the county of their choice—in this 

example, Lycoming County, PA—on the Home page they will be able to see basic 

population, employment, and earnings data. Clicking on the ‘View Detailed County 

Profile’ link on the bottom of the right column opens more individual county data. 

Alternatively, users can access this same detailed county profile by selecting the ‘County 

Profiles’ link at the top of the Home page. Once on the ‘County Profiles’ page, use the 

State and County drop down menus to select the county of interest—in this case, 

Pennsylvania and Lycoming County. The Lycoming County profile page is illustrated in 

Figure 3.

4 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

Lycoming County is at the center of the Marcellus Shale natural gas boom in 

Pennsylvania, and trends reveal how its growing employment has far exceeded 

population growth. Between 2009 and 2012, Lycoming County’s population grew at a 

rate of only 0.1 percent annually. When compared with the Pennsylvania and national 

growth rates, Lycoming County’s population has grown relatively slowly within a state 

that has also seen relatively slow population growth. The percentile rankings for 

population growth offer further context and show that nearly 67 percent of U.S. 

counties and 60 percent of ARC counties added population at a faster rate than 

Lycoming County.

Figure 2: Accessing basic trends through the home page

Map of 

diversity 

trends

Map 

filters

Select 

counties 

in any 

state

Basic 

county 

indicators

Link to 

detailed 

county profile 

page

5 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

Lycoming is a portrait of contradictions. Even as its population remained stable, the 

county’s employment grew at an annual rate that was twice the national rate and 2.5 

times faster than the Pennsylvania rate between 2009 and 2012. Lycoming is a fast 

growing county in terms of employment in a relatively slow growing state. In spite of 

this rapid job growth, the percentile rankings show that there were still 16 percent of 

U.S. counties, and 13 percent of ARC counties, that added jobs at a faster pace. 

The web tool will align counties side-by-side to facilitate comparisons. For our example, 

we have chosen to compare Lycoming County with another county—Williams County, 

North Dakota—that has also been greatly affected by natural gas activity. To produce 

these side-by-side comparisons, select the ‘Compare Counties’ link at the top of the 

webpage. Then select the comparison counties by first using the ‘Filter by State’ drop 

down menu, and then the ‘Select a County’ drop down menu. Comparison counties 

must be selected individually, up to three counties in total. For users only interested in 

ARC counties, additional filters have been included to facilitate comparisons by 

Appalachian Sub-region or by ARC Economic Status. 

Returning to the Lycoming County, PA and Williams County, ND example, Figure 4 

displays the side-by-side comparison. Williams County lies at the heart of the Bakken 

Shale natural gas development in the Upper Great Plains and has encountered many of 

the same development pressures as Lycoming County. Economic trends have been very 

Figure 3: Basic Indicators on the detailed County Profile page

Basic 

Economic 

indicators 

and their 

percentile 

rank

6 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

different, however. Williams County’s population and employment annualized growth 

rates between 2009 and 2012 were in the 100th percentile of U.S. counties, meaning 

that it had grown faster than almost every other county in the nation. Moreover, 

average earnings in Williams County were also in the 100th percentile and well above 

the state and national average earnings. By contrast, Lycoming County’s average 

earnings were in the 78th percentile of counties and remain below the average earnings 

of both the U.S. and Pennsylvania.

These simple data points help us interpret the relative magnitudes of basic economic 

and demographic trends and to ask whether our given study county is unique or 

following patterns evident in other similar places. More data and research are required 

to fully understand the similarities and differences underlying the economic and 

demographic changes occurring in these two locations. While the web tool is designed 

to highlight differences and similarities, it is not designed to answer every comparative 

question.

To help users continue their exploration and to conduct more in-depth research, the 

tool’s County Profile page also includes links to relevant data in the Census Bureau's 

Quick Facts page and the BEARFACTS page of the Bureau of Economic Analysis. Those 

resources, along with data from the U.S. Bureau of Labor Statistics, can provide 

Figure 4: Side-by-side county comparisons

Side by side 

comparisons of 

counties 

available in the 

compare 

counties page

7 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

additional information for users who wish to analyze the drivers behind the trends they 

are observing.

QUESTION 2: HOW DIVERSE IS MY ECONOMY AND WHAT RISKS AND 

OPPORTUNITIES DOES THAT DIVERSITY PRESENT? 

Promoting economic diversity is largely focused on identifying economic risks and 

capturing opportunities. Practitioners can look at economic diversity to understand 

those risks better and explore prospects for emerging growth. A diverse economy is not 

synonymous with a growing economy, but conventional wisdom suggests that diverse 

economies are much less likely to suffer debilitating losses when an industry falters or a 

company shuts down. Because alternative employment opportunities exist for workers, 

the painful impacts of loss are usually more quickly mitigated in places with greater 

economic diversity.

The Appalachian Economic Diversity Web Tool includes three basic ways of 

understanding economic diversity.3

• Industrial diversity measures the level and evenness of employment across a 

maximum of 1,110 industry types that might employ a region’s residents.4

Industry diversity is important to practitioners because the more industries

represented in a local economy, the greater the alternatives for area 

residents in seeking jobs. Furthermore, places that have higher industrial 

diversity are less likely to be severely impacted by negative trends associated 

with a particular industry or dominant company.

• Functional diversity measures the evenness of employment across eleven 

broad industry groups that capture major economic roles that regions often 

play, such as education centers (e.g., if a university is present), government 

centers (capital city or prevalence of government agencies or installations), a 

manufacturing hub, a retail trade center, and so on. Functional diversity is 

important to practitioners because regions diverse enough to serve multiple 

“economic functions” might be expected to experience greater moderation in 

growth or decline that result from cyclical changes.

3 More detailed definitions of the terms and measures used in the tool can be found by 

selecting the ‘About’ tab on the webtool 

(http://economicdiversityinappalachia.creconline.org/About/#definitions). For an indepth description of how these measures were calculated, consult A Statistical Portrait 

of Economic Diversity in Appalachia 

4 These industries types are based on 6-digit NAICS (North American Industry 

Classification System) definitions. More information about NAICs can be found at: 

http://www.census.gov/eos/www/naics/ 

8 

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

• Occupational diversity measures the evenness of employment across 96 

occupational groups (as defined by the Bureau of Labor Statistics).5

Occupations are grouped based on “work performed, skills, education, training, 

and credentials” and examples of groupings include agricultural workers, life 

scientists, secretaries and administrative assistants, and top executives. Unlike 

industries, which are defined by what companies make, occupations reflect 

what workers actually do. Counties may realize benefits when they have 

specializations in multiple occupations that collectively require a wide variety 

of local workforce skills. Occupational diversity can translate to greater 

adaptability for the workforce as economic activities change and job 

opportunities shift.

Practitioners using the tool can view maps showing different levels of industrial and 

functional diversity for every county in the ARC region and for all counties in each of the 

50 states. Users can apply the available filters to focus on those counties with different 

diversity levels. 

The Home page also allows users to explore other county features such as “county 

character,” “economic status,” “industry function specialization,” and “occupational 

knowledge cluster.”

• County character categorizes how urban or rural a county may be, and is based 

upon its population density and the proportion of its population that lives 

either in Census-defined urban or rural locations.

• Economic status represents the state of the county's economy in relation to 

the national economy, as defined by ARC. ARC combines three variables— the 

three-year average unemployment rate, per capita market income, and the 

poverty rate— to specify economic status. Since this is a formal ARC definition, 

the tool does not assign economic status characteristics to counties outside of 

the Appalachian region.6

• Industry Function Specialization identifies a county’s largest employment 

concentration relative to the U.S. in one of eleven broad categories. This 

information serves as a quick way to identify a county’s predominant type of 

economic activity and the role (or function) it plays in the national economy.

• Occupational Knowledge Cluster Specialization represents its largest 

specialization relative to the share of U.S. employment accounted for by each 

of 12 occupation-based clusters. These clusters were determined by identifying 

groups of occupations that had similarities in the knowledge required to work 

5 For more information, see: http://bls.gov/oes/current/oes_stru.htm 

6

www.arc.gov/appalachian_region/CountyEconomicStatusandDistressedAreasinAppalac

hia.asp 

9 

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

in these professions. This information allows users to quickly identify the 

predominant skills of the county’s workforce.7

The measures allow users to quickly compare their various characteristics with other 

counties in the arc region or in their state. Practitioners can also use the measures to 

identify other counties that may be experiencing similar economic conditions.

INDUSTRIAL AND FUNCTIONAL DIVERSITY: A SCENARIO

Consider the case of McDowell County, West Virginia. McDowell is a rural place with a 

distressed economy according to ARC’s definition. The county’s main economic drivers 

are mining and prisons. Much like the examples discussed in the previous section, users 

can access more detailed data about McDowell County, WV by either selecting the 

county on the Home page map, or by using the state and county drop-down menus 

found on the ‘County Profiles’ page. Once users have accessed the McDowell County 

Profile page they will see that the county’s employment base has grown at a relatively 

quick rate since the recession and it also has relatively high average wages. Yet in spite 

of that employment growth, McDowell lost residents between 2009 and 2012. 

The county’s economy relies on a few economic drivers and very little diversity. As 

illustrated in Figure 5, diversity metrics for the county are found in the right-hand 

column of the County Profile page. The economic diversity metrics shown on McDowell 

7 It should also be noted that nearly 75 percent of counties have a specialization of 

"skilled, semi-skilled labor & machine operation" or "semi-skilled service", reflecting the 

prevalence of basic skill jobs across the U.S. economy, regardless of location.

Figure 5: County diversity scores by percentile ranking

County 

diversity scores 

by percentile 

ranking

10

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

industrial diversity and the 13th percentile for functional diversity. Those statistics 

suggest how dependent McDowell is upon a limited number of local economic engines. 

To help better understand the scale of the county’s key industry functions, users can 

consult the ‘Industry Function Employment’ table toward the bottom of the left hand 

column on the ‘County Profile’ page. In the McDowell County, WV example, there are 

almost 1,900 jobs in agriculture and natural resources extraction, which is far more than 

any other functions. To put this into better context, users can click on the ‘View Radar 

Chart’ link at the bottom of the Industry Function Employment Table. Figure 7 shows the 

functional employment radar chart for McDowell County. It illustrates that natural 

resources extraction (predominantly coal mining) accounts for 26 percent of the 

county's total employment. Such an over-reliance on mining poses potential risk to the 

county's economy in the event this activity declines.

Of course, it is important to understand the nature of this activity. Resource extraction 

tied to coal mining has lost significant jobs in the region as coal faces increased 

regulatory restrictions and becomes more costly compared to other sources of energy. 

At the same time, new technologies have made hydraulic fracturing (“fracking”) of 

natural gas a more competitive source of energy, spurring economic growth elsewhere 

in Appalachia. So, it is not enough to know the nature of the functional specialization; it 

is also important to understand the competitive conditions that specialization faces, in 

terms of the business cycle, the industry growth cycle, and the technology cycle. The 

data in the web tool provide only a starting point for exploring those conditions.

Examining the economy of a county in isolation can be misleading. It is critical to view a 

county’s profile in the context of its neighbors. A specialized county in a more diverse 

regional economy is in a very different situation than an undiversified county in a region 

of little diversity. While McDowell is highly concentrated in natural resource extraction 

activities, it is located in a broader, more diverse region that offers more alternative 

work opportunities for its residents.

One can examine the broader geographic region by selecting the tabs showing the 

diversity metrics for the combined counties in McDowell County's commuter shed 

and/or development district (see Figure 6). Doing so, one can see that the functional 

diversity of the region’s local development district (the Region 1 Planning and 

Development Council) is in the 78th percentile, much higher than the county’s own level. 

Selecting the ‘Commuting Shed’ or ‘Development District’ tabs at the top of the 

Diversity Measures chart shows how a county compares to the other counties in its 

commuting shed or development district. Clicking on the name of those other counties 

directs the user to the ‘County Profile’ page for those counties. Clicking on the box on 

the right-hand side of the county name brings up the ‘Compare Counties’ page. Using 

these tools, one can quickly compare a county to its neighboring counties.

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

Returning to the McDowell County example, the Commuter Shed and Development 

District metrics suggest that it is heavily reliant on one functional specialization, yet the 

risks associated with such concentration may be mitigated by the diversity of the 

broader region. The radar charts (see Figure 7), which illustrate the proportion of total 

employment concentrated in each functional category, reveal the range of economic 

activities underway in each county and make it easy to compare with other jurisdictions.

Looking at the radar charts for neighboring counties begins to identify the opportunities 

that the broader region may offer to McDowell's workers. Figure 7 displays radar charts 

for McDowell County and neighboring Mercer County.8 Mercer County has a much more 

balanced economy with significant employment in distributive services, healthcare, and 

finance, insurance and real estate. This distribution produced more employment growth 

and higher wages for Mercer than the less balanced employment mix did for McDowell. 

Furthermore, Mercer’s balance of activities offered job opportunities to commuters 

from throughout the surrounding region.

This type of information can shape economic development planning and practices. It 

reveals the critical role that thinking regionally can play in understanding the local 

economy. It also illustrates that economic development practitioners must not only 

understand their locality’s existing specializations, but also think about supporting the 

8 It should be noted that the Tool only allows for one radar chart to be displayed at a 

time. Figure 7 was an image created to illustrate the differences between two counties.

Figure 6: Development district diversity scores by percentile ranking

Diversity scores 

by development 

district

Additional data 

about counties in 

the development 

district

12

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

development of new specializations. This insight should be coupled with additional data 

about detailed industry trends and projections to truly understand current and future 

economic prospects. In addition, practitioners can use qualitative case study analysis to 

better anticipate local, national and global trends that are critical to existing and 

prospective specialties. 

Since diversity is scale-dependent, these data can help guide strategy development. For 

low diversity counties located in low diversity regions, practitioners may seek to focus 

economic development efforts on creating new specializations. Practitioners developing 

strategies for low diversity counties located in high diversity regions might seek to 

increase linkages with their neighbors to provide increased employment and business 

development opportunities. To do so requires not only identifying the economic drivers 

found throughout a region, but also determining how those economic drivers sit within 

their broader global supply and demand chains. 

Figure 7: Radar Charts displaying proportion of employment by functional specialization

McDowell County, WV Mercer County, WV

The radar charts show 

functional specializations a 

proportion of total 

employment

Radar charts appear after clicking the “View Radar 

Chart” button on the detailed county profile page

Links to additional 

county data

13
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

QUESTION 3: WHAT KIND OF ASSET IS MY LOCAL WORKFORCE? 

Discussions of economic diversity often focus on the mix of industries and companies in 

a place. Yet practitioners increasingly understand that one of their most important 

economic assets is the local workforce. Corporations considering a new facility focus on 

the availability and quality of labor. Quality is defined on more dimensions than just 

educational attainment; businesses are also concerned about the adaptability of local 

labor. So, it follows that one key aspect of economic diversity is how prepared the local 

workforce is to shift into sectors, master new technologies, and learn new skills.

The occupational diversity metrics provide a way for practitioners to better understand 

the nature of their workforce and the skills they have to offer. Occupational diversity 

measures how specialized or even the employment distribution is across 96 

occupational groups, such as agricultural workers, life scientists, secretaries and 

administrative assistants, and executives. The web tool provides several features that 

capture occupational diversity. The map on the Home page covers the entire ARC region 

and links are provided for state and county maps.

County Profile pages provide users with more information about a county's occupational 

diversity and structure. As demonstrated above, the bar chart on the County Profile 

page shows the county's diversity scores in terms of its percentile rankings. The third 

bar in the diversity bar chart reflects the county’s occupational diversity.

EXPLORING OCCUPATIONAL DIVERSITY: A SCENARIO

To explore the concept of occupational diversity, consider Tioga County, New York. 

Looking at Tioga County’s occupational mix reveals just how unique the county is when 

compared with the rest of the Appalachian region. Access this information by selecting 

the ‘Occupation Diversity’ tab at the top of the map on the Home page. The 

occupational diversity map shows that Tioga County has a relatively high level of 

occupational diversity.

We can further narrow our focus by using the occupational knowledge cluster filter on 

the Home page (Figure 8). Occupational knowledge clusters are 12 categories of 

occupations, where those occupations were grouped based on similarities in the types 

of work done (and the knowledge required for that work) in multiple detailed 

occupations. Using the Occupational Knowledge Cluster filter found at the bottom of the 

map, and then clicking the ‘Update Map’ button, identifies the primary knowledge 

cluster of each county.

Tioga County is one of only a few counties where the largest knowledge cluster is 

engineering, architecture, and natural resource sciences. The reason is the presence of a 

large Lockheed Martin facility. Other counties within the Appalachian region have 

similarly unique assets, such as Montgomery County, Virginia (home to Virginia Tech), 

Roane County, Tennessee (the location of Oak Ridge National Laboratory), and Madison

County, Alabama (the site for the U.S. Space and Rocket Center). 

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

Users are better able to dig deeper into Tioga County’s occupational profile by looking 

at its County Profile page. Figure 9 shows that the county's industrial diversity (the left 

bar in the bar chart) is in the 46th percentile of counties nationwide, but in terms of its 

occupational diversity, it is in the 98th percentile. We can infer that Tioga’s workforce 

has a relatively broad base of skills even if its employment is concentrated within an 

industrial base that lacks diversity.

One can begin to better understand this imbalance by looking at the distribution of the 

county’s employment by industry functions and occupational knowledge clusters. The 

data show (see Figure 10) that a large proportion of Tioga County's employment falls 

within one functional category—engineering-intensive manufacturing. This one category

accounted for over 3,000 jobs within the county, or as shown in the industry function

employment radar chart, 17.1 percent of total employment. Moreover, this 

employment exists within less than 10 establishments. Job losses in any of those 

establishments (which includes the county’s largest—Lockheed Martin) could 

significantly disrupt the county's economy.

Figure 8: The knowledge cluster filter can be used to find counties with similar occupational 

strengths

Home page filter to show 

counties within similar primary 

occupational knowledge clusters

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

However, these data do not necessarily reflect what Tioga County workers actually do. 

To better understand that dynamic, users might consider the ‘Occupational Knowledge 

Cluster Employment’ data found at the bottom of the left-hand column on the ‘County 

Profile’ page. This table shows county employment in each knowledge cluster. By 

clicking on the ‘View Radar Chart’ button at the bottom of the table, users can see a 

visual representation of the distribution of knowledge cluster employment within the 

county.

Figure 10 shows the knowledge cluster data and radar chart for Tioga County, NY. It 

demonstrates that the occupational diversity shown in Tioga County's workforce may 

represent its secret weapon in offsetting the risks associated with so much employment 

concentrated in a single specialty. The data show that the county has over 1,000 

workers in five different knowledge clusters—semi-skilled service; skilled and semiskilled labor and machine operation; legal, clerical and administrative; management and 

finance; and construction and specialized mechanics. This balance indicates that the 

county’s workforce may possess the capacity to move into a broader array of activities.

Figure 9: Occupational diversity by percentile ranking on the detailed County Profile page

Occupational 

diversity by 

percentile 

ranking

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

Data on occupational specialization can inform future workforce development policy 

making and directly influence economic development planning. If regional economic 

developers are better aware of the basic components of their workforce, they are in a 

better position to strategize about how to use those assets to leverage other potential 

industry specializations. Such information can also help practitioners target economic 

development efforts to other industries that tap similar skill sets. Understanding

knowledge clusters for places with a diverse range of skills can help define future 

possibilities while for regions with a narrow range of skills, training and education to 

broaden the talent pool may be the best strategy.

Figure 10: Knowledge cluster data and radar charts

Knowledge 

cluster data

Knowledge cluster 

radar chart

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

QUESTION 4: HOW IS MY COUNTY DOING RELATIVE TO SIMILAR 

COUNTIES ELSEWHERE? 

Practitioners may use data in a variety of ways, but they seldom have meaning unless 

they are compared with something else. Earlier in this guide, we noted that analysts can 

use the web tool to compare with the nation, the state, and neighboring counties. 

During the planning process, and in efforts to design programs or monitor programs, 

practitioners may find value in comparing their place to others as a way of giving some 

perspective on how well their county is doing.

However, which counties are most appropriate for comparisons? How do we find places 

that have similar economic structures or face similar challenges that would serve as 

more appropriate comparators?

One of the most useful features of the Appalachian Economic Diversity Web Tool is that 

it provides means of identifying useful and appropriate comparison counties. The tool 

provides two primary methods for identifying potential comparator counties. First, the 

filters available on the Home page map allow users to identify other counties within the 

ARC region or within their state that have similar economic conditions, functional 

specializations, or knowledge clusters. Users can build a list of comparator counties that 

have similar characteristics and thus potentially similar economic development priorities 

or challenges.

Second, the County Profile page of the web tool reports other counties in the U.S. that 

are most similar to a given study county in terms of key economic, social, demographic, 

and locational characteristics. Similarity was measured through a statistical analysis of 

16 different variables measuring population, geographic size, income and earnings, 

industrial structure, educational attainment, and poverty:

• Per capita Income (source: U.S. Bureau of Economic Analysis, Local Area 

Personal Income and Employment, 2011)

• Sources of income (source: U.S. Bureau of Economic Analysis, Local Area 

Personal Income and Employment) 

o Percent farm income

o Percent income from dividends, interest and rents

o Percent proprietors income 

• Population characteristics (source: U.S. Census Bureau) 

o Total population

o Percent of the population (age 25+) with at least a high school diploma

o Percent of the population in poverty

• Geographic considerations (source: U.S. Census Bureau) 

o Land area in square miles

o Distance from a large (one million plus) urban area

• Percent of earnings by industry (source: Economic Modeling Specialists 

International) 

o Share of mining earnings

o Share of capital-intensive manufacturing earnings

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

o Share of engineering-intensive manufacturing earnings

o Share of finance, insurance and real estate earnings

o Share of corporate management earnings

o Share of government earnings

o Share of knowledge-intensive business services earnings

Not every U.S. county can be strongly paired with one or more other U.S. counties; 

some counties are simply too unique. Moreover, in some cases, data were not sufficient 

to draw valid comparisons. For the vast majority of U.S. counties, however, it is possible 

to produce a measure of similarity that not only matches each county with comparators, 

but also assesses the strength of the match.

The web tool highlights the strength of each match with a color coding system. Strongly 

similar county pairs are coded as “green” while weak ones are coded in red. This 

strength of pairing is very important because there are a few counties so unique (such 

as New York County, or Manhattan) that they have few truly meaningful comparators. In 

these instances, the team identified comparators, but they are likely to be weaker and 

much more likely to compare well on only a few of the economic, demographic, and 

location characteristics like per capita income or population size.

IDENTIFYING BENCHMARK COUNTIES: A SCENARIO

Consider Oktibbeha County in Mississippi. Oktibbeha is the home of Mississippi State 

University (MSU) and, not surprisingly, has a functional specialization in higher 

education. The importance of MSU to the county’s economy makes Oktibbeha

somewhat unique. As a result, it might make more sense to benchmark Oktibbeha to

counties with dominant college towns rather than against neighboring counties or other 

Mississippi counties. However, finding the right university-driven economies will require 

additional research. At first blush, one might imagine Oxford, Mississippi (home of the 

University of Mississippi) or Tuscaloosa, Alabama (location of the University of 

Alabama), but how similar are those places to Oktibbeha? What if we want to explore 

how the county compares with other Appalachian counties or other university towns?

Much like the earlier example that used the Home page map filters to identify counties 

with similar knowledge clusters, practitioners can use a different filter on the Home 

page map to focus first on filtering counties by their functional specialization (see Figure 

11). Among those that the web tool reveals include Pickens County, SC (Clemson 

University), Watauga County, NC (Appalachian State University), Knox County, TN 

(University of Tennessee), and Montgomery County, VA (Virginia Tech). These counties 

are all dominated by a public university, a characteristic each shares with Oktibbeha 

County. They could serve as a good first cut of potential comparator counties, but how 

can we find counties that may be a closer fit than these?

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

What if we are interested in seeking comparison counties with the strongest similarity 

ratings? These may be located anywhere in the U.S. and additional inputs will be 

needed. Beginning with the individual county profiles, we have additional information 

readily at hand to help identify five other counties with the most similar economic and 

social characteristics. Figure 12 illustrates how those data might be used. Our 

comparison found that Athens County, OH, another Appalachian county (home to Ohio 

University), as a potential comparator. Under this scenario, the tool also generates 

Watauga County, NC (Appalachian State University) again. The tool identified three

other moderately similar counties, all home to medium-sized universities, including 

Isabella County, MI (Central Michigan University), Lincoln Parish Louisiana (Grambling 

University and Louisiana Tech), and Jackson County, IL (Southern Illinois University).

These five counties represent the most similar in the U.S. to Oktibbeha County in its 

overall demographic, economic, and location characteristics and therefore represent a 

first point of exploration in identifying benchmark counties. Of course, to a 

knowledgeable practitioner, it may be politically unwise to exclude Lafayette County, 

MS (University of Mississippi) or Tuscaloosa County, AL (University of Alabama), but 

Figure 11: The functional specialization filter can help identify similar counties

The functional 

specialization 

filter on the 

home page can 

help identify 

counties with 

similar 

specializations 

20
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

these data will help explain why those comparisons may be more aspirational than the 

comparisons with more empirically similar counties.

The web tool’s findings should not be the only inputs used in selecting comparison

counties. Policy makers, practitioners, and researchers selecting similar places to 

compare should also explore whether the counties being considered are pursuing 

similar economic development strategies or targeting similar industries. Sometimes, this 

may be more important than whether the existing economies of other areas are similar. 

Taking such factors into account will likely require a combination of quantitative and 

qualitative research.

Figure 12: Comparing Counties with Similar Characteristics 

Similar counties 

can be found on 

the detailed 

county profile 

page

The color 

indicates the 

strength of the 

similarity

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

DIVERSITY AND ECONOMIC DEVELOPMENT PLANNING

Diversity and economic diversification have several major implications for local 

economic development planning and policy making:

• Growth occurs through specialization;

• Diversification strategies help assess risks and capture opportunities;

• Economic diversity reflects the what, how, and why of local economic development;

• Diversity is also influenced by where economic activity is located;

• Fundamental strategic planning embraces diversification.

GROWTH OCCURS THROUGH SPECIALIZATION

A diverse set of economic activities is often tied to more economic stability. However, 

growth typically results when a region is leveraging a strength that gives it a means of 

out-competing other regions. Thus, practitioners should employ the web tool to assess 

whether the county or its region (1) has a strong mix of multiple competitive 

specializations or (2) relies too much on one or two major specializations. Furthermore, 

the web tool may also help to assess whether a diverse economy is the result of multiple 

specialized strengths or simply the absence of specialty. Lack of all specialization 

typically implies little activity that offers a distinctive competitive advantage. It is an 

important reason why some of the most diverse economies are also those in slow 

decline.

DIVERSIFICATION STRATEGIES HELP ASSESS RISKS AND CAPTURE

OPPORTUNITIES

A highly specialized economy may grow rapidly if core driver industries can expect 

robust demand for their goods and services, but the economy may face known or even 

unknown threats that could significantly disrupt key industries. In this environment, 

economic development practitioners have a key role in assessing and understanding 

potential “risks” associated with the current economic structure, as well as identifying 

competitive strengths that could offer prospects for future growth. The web tool 

provides a basis for scanning for both opportunities and risks. The tool may also help to 

identify ways to focus economic development efforts around attracting new companies, 

expanding existing firms, forming new enterprises, or investing in related institutions or 

infrastructure. The web tool also may help in crystalizing local leaders’ understanding of 

the risks tied to being overly specialized and reveal ways to manage the region’s 

portfolio better, not necessarily by divesting in driver industries (a political impossibility 

at best), but by ensuring that investments in retaining those industries do not crowd out 

the resource needs of emerging sectors.

ECONOMIC DIVERSITY REFLECTS THE WHAT, HOW, AND WHY OF LOCAL 

ECONOMIC DEVELOPMENT

The research team’s analysis revealed that practitioners should consider economic 

diversity beyond the traditional terms of the variety of industries or the absence of a 

22
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

single dominant enterprise. In this respect, economic diversity reflects what the place 

“makes” (its private sector firms and other employers); what its residents “do” (the skills 

and capabilities of its workforce); and the county’s role as an element of the broader 

national or global economy (its economic “function”). The web tool uses a variety of 

new metrics to describe economic diversity in each of those terms through the concepts 

of industrial diversity, functional diversity, and occupational diversity. Those distinctions 

are important because they reflect different ways a county or its surrounding region can 

develop a competitive advantage or develop a more risk-tolerant economy.

DIVERSITY IS ALSO INFLUENCED BY WHERE ECONOMIC ACTIVITY IS LOCATED

This guide has stressed the context of a county’s situation in assessing its economic 

diversity. A key aspect of that context is the county’s place in a larger global economy. 

For most local industries, distant industries, consumers, and markets determine current 

and future competitive needs. Local industries that serve multiple stable markets are 

less prone to volatility and risk. Furthermore, a local county that is quite specialized may 

also be tightly linked to a more diverse regional economy that helps to provide greater 

stability and alternative options even if there is volatility in the county’s core industry. 

The web tool can provide only limited understanding of the global consumer markets, 

but it can be very useful in assessing the linkages between the local county and its 

surrounding region. For those non-diverse counties in diverse regions, practitioners 

should heed the call for greater regional thinking in local planning efforts.

FUNDAMENTAL STRATEGIC PLANNING EMBRACES DIVERSIFICATION

Diversification strategies follow the same principles as a well-designed and wellbalanced economic development strategy. Such strategies emphasize the use of 

research and analysis to maximize the use of local and regional resources or assets.

Economic diversification also focuses on a broad array of strategic techniques. The web 

tool can be valuable in identifying counties with highly diverse economies as a first step 

in identifying those regions that have been intentional in promoting diversity as a core 

strategic goal. With an understanding of which counties are the best comparators, 

practitioners may employ the tool to find those counties that are most likely to have 

replicable experiences.

VALUE TO THE PLANNING PROCESS

Planning involves three major steps focused on developing, translating, and 

implementing a plan. Within each of those steps, there are a number of planning 

activities that the web tool may be used to inform. Figure 13 illustrates that planning 

process, including nine “elements” or sets of activities tied to each of the major steps in 

the process.9

9 More information about the planning process outlined in Figure 13 can be found in 

Creating Regional Coopetition: A Data-Driven Approach to Strategic Economic Planning: 

23

 
A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

Figure 13: The Strategic Economic Development Planning Framework

Source: (Council for Community and Economic Research, 2011)

The first phase of an economic development planning process involves developing the 

strategy. During this phase, economic development practitioners must identify the 

challenges and opportunities that face the county or its surrounding region, explore 

potential competitive advantages, and seek out ways to leverage advantages or 

ameliorate weaknesses. The web tool, with its basic profile information organized to 

reveal industrial, functional, and occupational specializations and to draw appropriate 

comparisons, is a convenient source for creating a basic understanding of a county (and 

its surrounding region).

The basic economic data in the web tool can be used to understand if the given county 

and region faces over-specialization in specific industries, functional economic 

categories, occupations, or knowledge areas. The data—and the insights drawn from 

them—may be used to target opportunities based on the revealed relative strengths. 

Furthermore, the tool can help identify other places with similar characteristics to aid 

benchmarking and comparisons of economic development practices and strategies.

The second phase of the planning process involves translating the strategy into action. 

In this phase, economic development practitioners must identify a specific series of 

tactics that can help to move the strategy forward. A critical element of this phase is 

pinpointing metrics that might ultimately be used in assessing progress. The web tool 

can be particularly helpful in providing insights about which economic or workforce data 

may be most useful for assessing the county’s economic performance as well as its 

development.

The third phase in an economic development planning exercise entails implementing 

the action plan. Implementation also involves developing new iterations of the plan 

based on real-life experience during the plan’s execution. This stage involves monitoring 

county economic performance as well as strategy implementation outcomes. 

Comparing the county’s performance with that of other counties can be particularly 

Develop (or 

Create) the 

Strategy

Translate the 

Strategy to 

Action

Implement the 

Action Plan

Motivate Action

Assess Options

Analyze & Diagnose

Prioritize & Choose

Organize Action

Marshal Resources

Create Metrics

Coordinate Partners

Manage Activities

 

 

 

3

steps

9

elements

 

 

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

The third phase in an economic development planning exercise entails implementing 

the action plan. Implementation also involves developing new iterations of the plan 

based on real-life experience during the plan’s execution. This stage involves monitoring 

county economic performance as well as strategy implementation outcomes. 

Comparing the county’s performance with that of other counties can be particularly 

useful in ascribing credit for strategies that may be implemented in one place but not in 

another.

In summary, the web tool provides a valuable instrument for policymakers and 

practitioners seeking to understand the importance of diversity and specialization in 

defining their county’s economic risks and opportunities. The tool provides an approach 

to examining diversity using data rather than anecdotes and it also provides a 

quantitative method for identifying benchmark counties and regions. Appropriate 

benchmarks ultimately help leaders better assess more precisely how well their local 

economic development efforts are doing.

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A PRACTITIONER'S GUIDE FOR PLANNING AND ANALYSIS

BIBLIOGRAPHY

Commisson, A. R. (2013, August). Distressed Designation and County Economic 

Status Classification System, FY 2007 – FY 2014. Retrieved from 

Appalanchia Regional Commission: 

http://www.arc.gov/research/SourceandMethodologyCountyEconomic

StatusFY2007FY2014.asp

Council for Community and Economic Research. (2011). Creating Regional 

Coopetition: A Data-Driven Approach to Strategic Economic Planning: 

Training Guidebook. Cornwall, ON: Economic Developers Council of 

Ontario.

University of Illinois, Regional Economics Applications Laboratory; Center for 

Regional Economic Competitiveness; RUPRI Center on 

Entrepreneurship. (October 2013). Economic Diversity in Appalachia.

Washington, DC: Appalachian Regional Commission.

26
Statistical Portrait of Economic Diversity in 

Appalachia

February 2014

Prepared for the Appalachian Regional Commission
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

CONTENTS

List of Tables...................................................................................................................... iv

List of figures ..................................................................................................................... vi

About this Report ............................................................................................................. vii

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

1-1. Research goals......................................................................................................... 1

1-2. Summary of findings and report organization ........................................................ 2

1-3. Background on economic diversity ......................................................................... 2

2. Data and Methods.......................................................................................................... 6

2-1. Base data and methods for calculating economic diversity.................................... 6

2-2. Measuring industrial diversity................................................................................. 8

2-3. Measuring functional diversity .............................................................................10

2-4. Measuring occupational diversity .........................................................................12

2-5. Measuring knowledge diversity ............................................................................13

2-6. Geographic scales and aggregations.....................................................................14

2-7. Analyzing functional county roles in the regional economy ................................. 19

2-8. Presentation of metrics.........................................................................................21

3. Urban places enjoy a natural advantage in economic diversity ...................................23

3-1. Economic diversity across urban and rural counties.............................................23

3-2. Diversity metrics and the mechanics of urban and rural diversity disparities......36

3-3. Lessons for practice...............................................................................................39

4. Spatial and Industrial legacies of economic diversity ...................................................41

4-1. Spatial distribution of economic diversity.............................................................41

4-2. Spatial distribution of functional specializations..................................................42

4-3. Functional specializations and their differential impacts on diversity..................45

4-4. Lessons for practice...............................................................................................49

5. Diversification through job loss and specialization through job gain...........................51

5-1. Trends and components of diversification............................................................51

5-2. Lessons for practice...............................................................................................53

6. Comprehensive examination of economic diversity can reveal economic strengths and 

weaknesses.......................................................................................................................55

6-1. Industrial diversity of employment and earnings.................................................55

6-2. Establishments size and diversity..........................................................................62

6-3. Occupational diversity...........................................................................................67

ii
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

6-4. Lessons for practice...............................................................................................69

7. functional and regional context can shed light on economic opportunities and threats

..........................................................................................................................................72

7-1. Functional and knowledge specializations in counties and commuting sheds.....73

7-2. Economic diversity in counties and commuting sheds .........................................79

7-3. Regional linkages and functional economic roles.................................................83

7-4. Lessons for practice...............................................................................................89

8. References....................................................................................................................90

9. Detailed documentation...............................................................................................92

iii
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

LIST OF TABLES 

Table 2-1: Functional Categories with Selected Industry Examples................................. 11

Table 2-2: Illustrative Description of Knowledge Clusters................................................14

Table 2-3: States by U.S. Census Regions and Divisions...................................................15

Table 2-4: Summary Results of Principal Components Analysis of Functional County 

Roles ................................................................................................................................. 20

Table 3-1: Industrial Diversity Statistics for Counties by County Character, 2012 ...........25

Table 3-2: Functional Diversity Statistics for Counties by County Character, 2012..........26

Table 3-3: Occupational Diversity Statistics for Counties by County Character, 2012 .....27

Table 3-4: Knowledge Diversity Statistics for Counties by County Character, 2012.........28

Table 3-5: Industry-based Unrelated Variety Statistics for Counties in Appalachia by 

County Character, 2012....................................................................................................38

Table 3-6: Industry-based Related Variety Statistics for Counties in Appalachia by County 

Character, 2012 ................................................................................................................38

Table 4-1: U.S. Counties by Region and County Character, 2010.....................................41

Table 4-2: Level of Industrial Diversity in U.S. Counties by U.S. Census Region, Controlled 

for County Character, 2012 ..............................................................................................42

Table 4-3: Level of Industrial Diversity in Appalachian Counties by Sub-region, Controlled 

for County Character, 2012 ..............................................................................................42

Table 4-4: Percent of Appalachian Counties with Select Functional Specializations, by 

Sub-region, 2012 ..............................................................................................................45

Table 4-5: Employment and Establishment Size Statistics by U.S. County Functional 

Specializations, 2012 ........................................................................................................46

Table 4-6: NAICS Industry Sectors by Functional Economic Groupings............................47

Table 4-7: Level of Industrial Diversity in Appalachian Counties with Select Functional 

Specializations, Controlled for County Character, 2012...................................................48

Table 4-8: Level of Functional Diversity in Counties with Select Functional 

Specializations, Controlled for County Character, 2012...................................................49

Table 5-1: U.S. and Appalachian Region Economic Activity and Diversity Trends, 2009-

2012..................................................................................................................................51

Table 5-2: Trends and Components of County Diversification in the U.S. and Appalachia 

by Significance of Change in Industrial Diversity, 1999-2009 and 2009-2012..................53

Table 6-1: Level of Industrial Diversity in Appalachia by County and Economic Status, 

Controlled for County Character, 2012 ............................................................................57

Table 6-2: Level of Ratio of Industry-based Earnings Diversity to Employment Diversity in 

Appalachia by County and Economic Status, 2012........................................................... 59

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 6-3: Illustrative Examples of the Ratio between Industry-based Employment and 

Earnings Diversity in Appalachia, 2012.............................................................................60

Table 6-4: Level of Estimated Average Establishment Size by County and County 

Character, 2011 ................................................................................................................63

Table 6-5: Level of Estimated Average Establishment Size in Appalachia by County and 

Economic Status, Controlled for County Character, 2011 ................................................64

Table 6-6: Illustrative Examples of Average Establishment Size in Appalachia, 2012 ......66

Table 6-7: Level of Occupational Diversity in Appalachia by County and Economic Status, 

Controlled for County Character, 2012.............................................................................67

Table 6-8: Illustrative Examples of Occupational Diversity in Appalachia, 2012 ..............69

Table 7-1: Functional Specializations with Most Extra jobs in Appalachia by County and 

Economic Status, 2012......................................................................................................74

Table 7-2: Functional Specializations with Most Extra Jobs in Appalachia by County 

Commuting Shed and Economic Status, 2012 ..................................................................76

Table 7-3: Level of Knowledge Diversity in Appalachia by County and Economic Status, 

Controlled for County Character, 2012.............................................................................77

Table 7-4: Largest Knowledge Cluster Specializations by County and Economic Status, 

Controlled for County Character, 2012.............................................................................78

Table 7-5: County Commuting Flow Statistics by Level of County and Commuting Shed 

Diversity, 2006-2010.........................................................................................................80

Table 7-6: Level of Industrial Diversity in Appalachia by County and Commuting Shed and 

Economic Status, 2012......................................................................................................81

Table 7-7: Level of Functional Diversity in Appalachia by County and Economic Status, 

2012 ..................................................................................................................................82

Table 7-8: Summary of Functional Specialization and Commuting Characteristics by 

County Functional Economic Types..................................................................................84

Table 7-9: Functional Economic Roles in Appalachian Counties by Economic Status, 2012

..........................................................................................................................................88

Table 9-1: NAICS 6-digit Industries by Functional Industry Categories.............................92

Table 9-2: Standardized Scores and Ranks of Industrial, Functional, Occupational, and 

Knowledge Diversity for Appalachian Region Counties..................................................103

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

LIST OF FIGURES 

Figure 2-1: Appalachian Sub-regions................................................................................16

Figure 2-2: ARC’s County Economic Status Designations, Fiscal Year 2013......................17

Figure 3-1: Industry-based Employment Diversity in U.S. Counties, 2012 .......................24

Figure 3-2: Industrial Diversity in Appalachian Counties, 2012........................................29

Figure 3-3: Industrial Diversity in Appalachian Counties, Controlled for County Character, 

2012..................................................................................................................................30

Figure 3-4: Functional Diversity in Appalachian Counties, 2012 ......................................31

Figure 3-5: Functional Diversity in Appalachian Counties, Controlled for County 

Character, 2012 ................................................................................................................32

Figure 3-6: Occupational Diversity in Appalachian Counties, 2012..................................33

Figure 3-7: Occupational Diversity in Appalachian Counties, Controlled for County 

Character, 2012 ................................................................................................................34

Figure 3-8: Knowledge Diversity in Appalachian Counties, 2012 .....................................35

Figure 3-9: Knowledge Diversity in Appalachian Counties, Controlled for County 

Character, 2012 ................................................................................................................36

Figure 3-10: Level of Industry-based Unrelated and Related Variety in Appalachian 

Counties, Controlled for County Character, 2012 ............................................................39

Figure 4-1: Functional Specializations with the Most Extra Jobs in U.S. Counties, 2012..44

Figure 5-1: Change in Industrial Diversity and Employment in the Appalachian Region, 

2009-2012.........................................................................................................................52

Figure 6-1: Distribution of Industry-based Employment Diversity for U.S. Counties, 2012

..........................................................................................................................................56

Figure 6-2: Distribution of the Ratio of Industry-based Earnings Diversity to Employment 

Diversity for U.S. Counties, 2012 ......................................................................................58

Figure 6-3: Level of Average Establishment Size and Industrial Diversity in Appalachia, 

Controlled for County Character, 2011/2012...................................................................65

Figure 6-4: Levels of Industrial and Occupational Diversity in Appalachia, 2012.............68

Figure 7-1: Functional Specializations by Counties in Appalachia, 2012 ..........................73

Figure 7-2: Functional Specializations in Appalachia’s Commuting Sheds, 2012 .............75

Figure 7-3: Level of Industrial Diversity for Counties and their Commuting Sheds in the 

Appalachian Region, 2010 ................................................................................................80

Figure 7-4: Level of Functional Diversity for Counties and their Commuting Sheds in 

Appalachia, 2012 ..............................................................................................................83

Figure 7-5: Functional Roles in Appalachia’s Rural & Mixed Rural Counties, 2012 ..........87

Figure 7-6: Functional Roles in Appalachia’s Urban & Mixed Urban Counties, 2012.......87

vi
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

ABOUT THIS REPORT

This report is one of four documents prepared as part of an extensive analysis of 

economic diversity for the Appalachian Regional Commission by the University of Illinois 

at Urbana-Champaign’s Regional Economics Applications Laboratory and the Center for 

Regional Economic Competitiveness, with assistance from the RUPRI Center for Rural 

Entrepreneurship and EntreWorks Consulting. Project leaders were Edward Feser, Ken 

Poole, Mark White, and Geoffrey Hewings, with Troy Mix serving as Project Manager.

Authors of this report were Troy Mix and Edward Feser. 

In addition to this report, the documents include a summary report (Economic Diversity 

in Appalachia: Statistics, Strategies, and Guides for Action), a guide to the web tool (A 

Practitioner's Guide for Planning and Analysis with the Appalachian Economic Diversity 

Web Tool), and a detailed synopsis of case studies (Case Studies in Economic 

Diversification in Appalachia All reports associated with the project can be found here: 

http://economicdiversityinappalachia.creconline.org/Report/. 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

1. INTRODUCTION 

Economic diversity merits attention for regional development practitioners working in 

Appalachia. Many of Appalachia’s rural, distressed regions have been historically 

dependent on a few dominant industries, such as manufacturing, mining, and forestry, 

and considerable research points toward a connection between this dependence and 

negative economic outcomes (Bradshaw, 1992; Freudenburg & Wilson, 2002; Stedman, 

Patriquin, & Parkins, 2011). Urban locales in Appalachia have also been associated with 

a lack of economic diversity. For example, the economies in regions surrounding 

Youngstown, Ohio and Pittsburgh, Pennsylvania were long propelled by large, verticallyintegrated steel producers. 

Prompted in part by Appalachia’s legacy of low economic diversity and the lack of 

practical guidance for pursuing diversification strategies, the Appalachian Regional 

Commission (ARC) sponsored research aimed at better understanding economic 

diversity in Appalachia and supporting the development of strategies aimed at 

diversifying regional economies. Begun in November 2012, this effort consisted of three 

major components—1) developing a quantitative understanding of economic diversity 

in Appalachia; 2) studying contemporary regional development strategies and activities 

to identify best practices for economic development related to economic diversity; and 

3) offering guidance for practitioners to use the data and best practice findings to 

implement successful and appropriate development initiatives in their own 

communities. 

This technical report presents detailed findings from the project component aimed at 

building a comprehensive, quantitative understanding of economic diversity across 

Appalachia. The remainder of this introductory chapter provides an overview of the 

goals particular to this component of the research, summarizes generalized findings 

from this effort and outlines the approach used to present these findings in this report, 

and reviews previous research on economic diversity.

1-1. RESEARCH GOALS

By presenting a multi-dimensional, quantitative picture of economic diversity across 

Appalachia and the United States, this project component aims to accomplish the 

following:

1. Frame questions useful for understanding the challenges to and opportunities 

for development in Appalachia;

2. Offer evidence of how concepts such as economic diversity and diversification 

manifest themselves in Appalachian counties and regions;

3. Provide regional development practitioners and scholars with tools for 

assessing and tracking indicators of economic diversity and using these data to 

inform strategic development initiatives.

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

To accomplish these goals, this technical report provides empirical evidence related to 

the spatial distribution of economic diversity; the associations among economic 

diversity and other indicators of regional economic conditions such as urban or rural 

character, industry mix, regional linkages, and economic outcomes; and the mechanics 

of how economic diversification and specialization occur at the county level. 

1-2. SUMMARY OF FINDINGS AND REPORT ORGANIZATION

Understood as a formal metric of regional economies, economic diversity refers to the 

number of categories of economic activity in a region and the evenness of the 

distribution of activity across those categories (Malizia & Ke, 1993). While 

commonsense notions of diversity suggest simple connections between economic 

diversity and stability or growth, the overall finding of this report is that the simple 

understanding of economic diversity fails to provide a comprehensive accounting for the 

distribution of economic diversity across the U.S. and the relationships among diversity, 

economic outcomes, and other measures of regional economic activity.

The following generalized findings expand on this overall finding and provide the basis 

for organizing the empirical investigation of economic diversity found in chapters 3-7:

1. Urban places enjoy a natural advantage in economic diversity;

2. Spatial differences in industrial legacy influence the distribution of economic 

diversity;

3. County-level economies tend to experience diversification via employment 

declines and specialization via employment gains;

4. Comprehensive examinations of economic diversity can reveal regional 

economic strengths and weaknesses; and

5. Functional and regional context can shed light on economic opportunities and 

threats.

1-3. BACKGROUND ON ECONOMIC DIVERSITY

Well-known admonitions such as “don’t put all your eggs in one basket” and “make sure 

you have a lot of legs to stand on” lend support to the commonsense notion that 

diversity provides practical benefits in daily life. Applications of this general proposition 

include the working professional that seeks to balance career advancement with 

personal fulfillment; the business that launches a new product line to access additional 

revenue sources; and the portfolio manager that advises a mixed investment strategy 

aimed at striking an appropriate balance between risks and gains. 

The appeal of economic diversity for regional development draws on similar ideas about 

the stability and growth benefits that often accompany diversification. Regional 

development practitioners have long advanced economic diversification as a desirable 

goal for the purported benefits it can provide in reducing regional exposure to economic 

downturns and opening up potential avenues for economic growth. Considerable 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

scholarship has accompanied this practical focus, with research aimed primarily at 

devising and evaluating methods for measuring regional economic diversity and testing 

the relationships among economic diversity and regional growth and stability (Attaran, 

1986; Conroy, 1975; Dissart, 2003; Frenken, Van Oort, & Verburg, 2007; Jackson, 1984; 

Mack, Grubesic, & Kessler, 2007; McLaughlin, 1930). 

This section reviews the literature on economic diversity, with a focus on assessing the 

evidence for major claims associated with this concept and evaluating methods used to 

measure economic diversity. 

1-3A. EVIDENCE OF ECONOMIC DIVERSITY’S VALUE FOR STABILITY

Scholars and practitioners advance the notion that economic diversity can reduce the 

impact of economic shocks on a region’s employment. Dissart (2003) refers to economic 

diversity as an “averaging process: the greater the variety of industries in a region, and 

the more dispersed the regional employment among these industries, the less likely a 

region is to suffer severe...economic decline” (p. 424). Put differently, diverse regions 

are expected to be more stable since “their fortunes are not tied to the fortunes of a 

few industries” (Chinitz, 1961, p. 281). 

There is considerable, though not unequivocal, empirical support for a positive 

relationship between regional economic diversity and stability. In a review of economic 

diversity literature since 1930, Dissart (2003) reported that the majority of 

approximately 40 studies have found a positive relationship between the diversity and 

stability of regional economies, and that larger economies tend to be both more diverse 

and more stable than smaller economies. Using an entropy measure of economic 

diversity, Malizia and Ke (1993) found a relationship between increased U.S. 

metropolitan area diversity and both less unemployment and more employment 

stability. Using the portfolio variance approach to measuring diversity, Conroy (1975)

also reported a significant, positive relationship between metropolitan area diversity 

and economic stability. In a recent study of employment in regions of the Netherlands, 

Frenken et al. (2007) found a negative relationship between the diversity of regional 

employment across major industry sectors (i.e., unrelated variety) and the growth of

unemployment in those regions.

In one example of a contrary study, Attaran (1986) found no relationship between 

economic diversity and employment stability across U.S. states. While Hammond and 

Thompson (2004) reported a negative relationship between economic diversity and 

employment volatility, they presented other findings that questioned the wisdom of 

pursuing stability policies. In particular, they found that increased local spending on 

education and increased educational attainment had a significant, positive impact on 

employment volatility—likely due in part to the increased mobility that tends to 

accompany a more educated workforce (Hammond & Thompson, 2004, pp. 537-539). 

This finding highlights a potential tradeoff between policies that seek economic stability 

and policies that seek to improve long-term growth rates but may exacerbate regional 

employment volatility, such as investments to improve education. 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Hammond and Thompson (2004) also stressed the differences in employment volatility 

between metropolitan and nonmetropolitan regions, with employment in metropolitan 

regions about 40 percent more stable than it is in nonmetropolitan regions. The 

separate examination of metropolitan and nonmetropolitan regions also revealed 

differences in the diversity characteristics that served as significant explanations for 

stability. For example, Hammond and Thompson (2004) found that for nonmetropolitan 

regions industrial specialization was not a significant predictor of stability, though 

employment in mining and manufacturing did have a significant, positive relationship to 

volatility. For metropolitan regions, industrial specialization had a significant, positive 

relationship to volatility, while mining and manufacturing employment was insignificant. 

1-3B. EVIDENCE OF ECONOMIC DIVERSITY’S VALUE FOR GROWTH 

At least three logics connect the concept of economic diversity to growth. First, the 

presence of more industries in a region creates opportunity for growth in support 

services (e.g., accounting and law firms) that increase the incentive for firms to locate 

and expand in a region. Second, the diversification of regional activities within a given 

sector can directly and indirectly affect the regional growth of employment. For 

example, a community dominated by the production of an agricultural commodity may 

enjoy immediate employment growth as the economy diversifies through the addition 

of industries aimed at processing these commodities. Later, the increased household 

incomes associated with the addition of this processing activity might result in increased 

employment in local-serving industries such as retail trade and personal services 

(Watkins, 1963). Third, the diversity of industries in a region may increase growth 

through innovation by improving “opportunities to interact, copy, modify, and 

recombine ideas, practices and technologies across industries” (Frenken et al., 2007, p. 

687). 

The empirical evidence supporting economic diversity’s relationship to growth is 

decidedly more mixed than it is for diversity’s relationship to stability. In the words of 

Dissart (2003), “the evidence regarding the relationship between economic diversity and 

employment growth is less conclusive [and]...research on the relationship between 

economic diversity and income levels and growth yields contradictory results” (p. 434). 

For example, Wagner and Deller (1998) found a positive relationship between economic 

diversity and growth in per capita incomes, while Attaran (1986) found a negative 

relationship between these variables. Refining the notion of the type of diversity that is

important for economic growth, Frenken et al. (2007) reported a significant positive 

relationship between the diversity of employment by industry within major economic 

sectors (i.e., related variety) and employment growth.

1-3C. EVIDENCE OF THE DANGERS OF WRONG SPECIALIZATION

While economic diversity is often measured in an industry-blind manner, many of the 

studies cited in this section present findings that stress the differential impacts of 

certain specializations. Notably, Malizia and Ke (1993) and Hammond and Thompson 

(2004) found that employment in mining had about as much impact on decreasing 

economic stability as did industrial specialization more generally. Employment in 

4 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

durable goods manufacturing—an industry thought to suffer from similar cyclical 

downturns—has also been studied for its relationship to economic stability. 

Considerable attention has been paid to the negative economic impacts of specialization 

in some extraction-based industries. Freudenburg and Wilson (2002) reviewed 

approximately 300 studies on this topic, finding that about half the studies reported 

negative economic impacts from mining employment, with mixed or neutral findings 

reported from the other studies. Where positive impacts were found, they tended to 

relate mining employment to income growth, not regional employment growth. In 

related work, Auty (2000) reported that, since the 1960s, developing, resourceabundant countries have experienced slower growth than relatively resource-poor 

countries have. Papyrakis and Gerlagh (2007) tested this relationship in a developed 

country context and found that resource-abundant states in the United States 

experienced slower growth than less endowed states did. 

Literature on the development of staple-based economies has sought to explain the 

stunted growth that often accompanies resource-based industries (Watkins, 1977). This 

literature advances the “staple trap” model for explaining the vicious economic cycle 

that keeps resource-dependent regions from diversifying. One iteration of this model 

specifies that, in regions with significant natural resources, industrialization is delayed 

since significant profits can be enjoyed through resource extraction; less urbanization 

occurs; a less skilled workforce results; and government intervention is called upon to 

create jobs and protect industries—reducing the competitiveness of extractive 

industries in the process (Auty, 2000, 2001). 

1-3D. APPROACHES TO MEASURING ECONOMIC DIVERSITY

Common economic diversity measures include the national averages, ogive, entropy, 

and Herfindahl indexes; the durable goods index; and portfolio variance (Dissart, 2003; 

Jackson, 1984; Mack et al., 2007). The national averages, ogive, entropy, and Herfindahl 

indices compare regional industrial structure to identified referents to measure 

diversity. The national averages index compares the share of a region’s employment in 

each sector to the same measure at the national level and sums the differences, with 

greater sums equating to greater than average specialization. Ogive and entropy indices 

are generally calculated by summing the differences between the shares of regional 

employment in each sector and the share of employment in each sector if employment 

were equally distributed among sectors. 

The remaining two measures make more explicit connections between diversity and 

stability. The durable goods index uses the percentage of regional employment or 

income in durable goods sectors as a measure of diversity, with high income elasticities 

of demand for these goods presumably capturing the vulnerability of a region to 

demand shocks. The portfolio variance method equates a region’s economic activity 

with a financial portfolio. Portfolio risk (e.g., employment instability) is determined by 

weighting each sector according to its share of total employment and summing the 

employment variances for each industry and employment covariances between each 

industry pair over a selected time period. 

5 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

For this study, the entropy measure serves as the basic metric of economic diversity. 

This measure has the advantage of assuming no particular relationship between 

diversity and stability or growth. Compared to other measures that make no particular 

assumptions about diversity’s value, the entropy measure results in more observable 

variation across counties and allows for the decomposability of regional diversity as 

described in Chapter 2. 

2. DATA AND METHODS

This chapter reviews the data and procedures used to complete the economic diversity 

analysis. In particular, the following sections focus on the measurement of economic 

diversity based on industries, economic functions, occupations, and workforce 

knowledge; the geographies used to calculate and aggregate measures of diversity; and 

the linkages among counties based on commuting and functional economic ties.

2-1. BASE DATA AND METHODS FOR CALCULATING ECONOMIC DIVERSITY

2-1A. ESTIMATES OF COUNTY EMPLOYMENT AND EARNINGS BY INDUSTRY

Most of the diversity calculations conducted for this report use county employment 

estimates acquired from Economic Modeling Specialists International (EMSI). The 

particular datasets used were “complete” employment estimates for 2009 and the third 

quarter of 2012, along with “covered” employment estimates for 2009 and 2012.1 Each 

dataset provides an individual row of data for each six-digit North American Industrial 

Classification System (NAICS) industry within a county, with the rows detailing the 

estimated employment and earnings in the industry and county in question. Based on 

the Bureau of Labor Statistics’ Quarterly Census of Employment and Wages, the covered 

employment dataset contains estimates for jobs covered by federal or state 

unemployment insurance systems. In addition to earnings and employment estimates 

by industry, the covered employment dataset provides estimates of the number of 

establishments by industry and county, although these establishment estimates are for 

2011. For this report, the covered employment dataset was only used to conduct 

analyses that relied upon establishment estimates. 

The complete employment dataset includes all employment in the covered dataset 

along with wage-and-salary employment exempt from unemployment insurance 

coverage—such as military and railroad employment and employment as a real estate 

or insurance agent—and self-employment that accounts for all or a portion of an 

individual’s income. For both datasets, EMSI uses proprietary algorithms and a variety of 

data sources from the Bureau of Economic Analysis, U.S. Census Bureau, and Bureau of 

Labor Statistics to produce estimates of employment that do not suppress employment 

and earnings numbers for any county or industry. With a considerable number of 

employment statistics suppressed to prevent the release of confidential firm 

1 See http://www.economicmodeling.com/data/ for more information on these 

datasets.

6 

 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

information, these datasets allow for a more complete analysis of county employment 

than allowed for by standard federal data releases (Isserman & Westervelt, 2006). 

2-1B. ENTROPY MEASURE OF ECONOMIC DIVERSITY

The entropy measure of diversity was used to calculate industry-, function-, occupation-, 

and knowledge-based measures of economic diversity across U.S. counties and a variety 

of other geographies (Malizia & Ke, 1993). These metrics were calculated according to 

formula 1: 

(1)

where there are i=1 to k industries and pi

is the share of economic activity (e.g., 

employment or earnings) in the ith industry. The products of industry shares of 

economic activity and the natural log of the inverse industry shares of economic activity 

are summed to arrive at the final entropy index measurement. The index has a 

minimum value of 0 when all economic activity is within one industry, and the value 

increases as the number of industries increases and the distribution of economic activity 

across these industries becomes more equal. 

Where entropy measures were calculated for non-county geographies (e.g., the United 

States as a whole or individual states), the employment data were summed by industry 

and the geography in question before the entropy calculation was performed. Unless 

otherwise noted, economic diversity statistics cited in this report were calculated based 

on the entropy values of individual counties located within a geography or aggregation 

of interest; they do not represent the calculation of entropy based on all aggregated 

economic activity within a given geography. For example, the average entropy by ARC 

sub-region represents the mean value of all county entropy values within each subregion, not the calculation of entropy across all economic activity in the sub-regions. 

2-1C. CALCULATING DIVERSITY-RELATED METRICS

Several measures are reported or cited in this document that are not diversity metrics, 

but provide context to these measures; in particular, correlations, average 

establishment sizes, and average earnings per job. The details of these calculations are 

briefly described in this sub-section.

Correlations

All correlation coefficients (r) presented in this report are Pearson coefficients. The 

coefficients are all significant at the p < 0.001 level.

7 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Average establishment size

Average establishment size calculations rely upon the establishment estimates provided 

in EMSI’s covered employment datasets. For average establishment sizes by industry, 

the sum of total employment across that industry is divided by the sum of the number

of establishments in the industry. For average establishment sizes by county, the sum of 

total employment across the county is divided by the sum of the number of

establishments in the county. 

Earnings per job

All earnings per job values draw from EMSI’s complete employment datasets. For 

average earnings per job by industry, the sum of total earnings across that industry is 

divided by the sum of employment in the industry. For average earnings per job by 

county, the sum of total earnings across the county is divided by the sum of total 

employment in the county.

2-2. MEASURING INDUSTRIAL DIVERSITY

2-2A. BASE DIVERSITY OF EARNINGS AND EMPLOYMENT 

The entropy measure serves as the base metric of industry-based economic diversity, 

which this report commonly refers to as industrial diversity. Measures were calculated 

for both earnings and employment, with earnings and employment by six-digit NAICS 

industry, respectively, serving as the share of total economic activity (pi

) specified in 

formula 1.

2-2B. DIVERSITY AT TWO SCALES OF INDUSTRIAL AGGREGATION

The entropy measure can be decomposed based on the hierarchical classification 

systems used to categorize the data being analyzed for diversity (Attaran, 1986; Frenken 

et al., 2007). In the case of this analysis, the hierarchical NAICS, which nests more 

specific industry classifications (e.g., four- or six-digit classifications) within larger 

aggregations of related industries (e.g., two- or three-digit classifications), provides a 

vehicle for decomposing the entropy measure of economic diversity.

Practically, unrelated variety measures the diversity of economic activity across major 

industry sectors that are relatively “unrelated” from one another compared to the 

similarity of six-digit industries within the same three-digit sector. Related variety 

measures the diversity of employment within a county’s three-digit sector 

specializations. Together, these measures can be used to examine the distribution of a 

diversity factor thought to be related to economic stability—unrelated variety or 

diversity across broad industry sectors—and another factor thought to be related to 

prospects for economic growth—related variety or the diversity of a county’s sectoral 

specializations (Frenken et al., 2007). 

8 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

To decompose industrial diversity for this analysis, unrelated and related variety were 

calculated according to formulas 2 through 5:

(2) 

where a subset of six-digit industries i fall into a three-digit sector G and the three-digit 

share of total employment, Pg, is equivalent to the sum of the subset, six-digit industry 

shares, Pi

. 

(3)

Unrelated variety (UV) is equivalent to the sum of the products of the three-digit 

industry shares and the natural logarithm of the inverse three-digit industry shares. In 

this analysis, unrelated variety equals the entropy measure of economic diversity 

calculated at a three-digit level of industrial aggregation. 

(4) 

Related variety (RV) equals the sum of the entropy of six-digit industries within each 

three-digit sector (Hg) multiplied by the three-digit share of total employment (Pg), 

where

(5)

the entropy of six-digit industries within three-digit sectors (Hg) is equivalent to the sum 

of the products of the share of a six-digit industry as a portion of three-digit sector 

employment and the natural logarithm of the inverse share of a six-digit industry as a 

portion of three-digit sector employment.

Calculated at the six-digit level of industrial aggregation, the entropy measure of 

industrial diversity is equal to the sum of unrelated variety—entropy measured at the 

three-digit NAICS level—and related variety—the weighted sum of six-digit industries 

within three-digit sectors. 

9 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

2-2C. ANALYZING CHANGES IN DIVERSITY

This analysis relies upon 2009 and 2012 complete employment estimates from EMSI and 

1999 suppression-adjusted employment data prepared by Isserman and Westervelt 

(2006) to calculate changes in economic diversity. Diversity changes were examined for 

the periods 2009-2012 and 1999-2009. For the 1999-2009 analysis, the industries 

included with the 1999 dataset were used as the baseline for comparison when 

accounting for differences between the Isserman and Westervelt (2006) and EMSI 

datasets. Raw changes and percent changes in entropy measures were calculated for 

the noted time periods. 

2-3. MEASURING FUNCTIONAL DIVERSITY

A region’s economic function or functions represent the collection of broad economic 

activities that the region’s workforce and firms engage in. Practically, functions can be 

identified by grouping industries together into categories that are broadly similar on 

factors such as inputs, outputs, and/or the technological or skill requirements necessary 

to perform the work customary to these industries. Grouping industries according to 

function, rather than simply accepting the NAICS industry categories, can help to 

broadly characterize the economic roles a county plays in its region; provide insight into 

the economic relationships and similarities counties have with other regions; identify 

factors that make regions comparatively better fits for certain economic activities; and 

speak to the broader economic and demographic forces that are likely to impact a 

county’s economic prospects.

2-3A. CREATING FUNCTIONAL INDUSTRY CLASSIFICATIONS

One purpose of a functional industry classification is to broadly define the types of work 

that are prevalent in a region. For example, Thompson and Thompson (1987) suggest 

grouping industries and occupations into functional classes to identify regional 

specializations in “routine work, precision operations, central management, research 

and development, and entrepreneurship” (p. 558). In an examination of the rise of 

services as a proportion of employment, Noyelle (1983) advanced a functional 

classification system for services “based on the type of outputs (intermediate or final 

outputs) and the institutional setting under which services are provided (private, public, 

or nonprofit sectors)” (p. 282). Lawrence (1984) classified manufacturing industries on 

the basis of the primary end use of the product (e.g., intermediate goods; consumer 

durables; producer durables; consumer nondurables) and the necessary inputs to the 

industry (e.g., research and development expenditures; scientists and engineers; capital-

, labor-, and resource-intensive).

10
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

This analysis draws primarily from the work of Lawrence (1984) and Noyelle (1983) to 

categorize industries according to functional types. In an effort to focus on the 

economic base of counties, industries that often serve local populations, such as retail 

trade, personal services, doctor’s offices, local government, and construction, were 

excluded from the analysis of functions. Eleven functional categories were delineated 

and Table 2-1 lists the category titles and selected examples of industries within each 

class. The complete list of six-digit industries included within each classification appears 

as Table 9-1. 

Table 2-1: Functional Categories with Selected Industry Examples

Functional industry category

NAICS code and title

Agriculture & resource extraction

11 Agriculture, Forestry, Fishing and 

Hunting 

21 Mining Quarrying, and Oil and Gas 

Extraction

Capital-intensive manufacturing

311 Food manufacturing 312 Beverage and tobacco product 

manufacturing 313 Textile mills

314 Textile product mills 315 Apparel manufacturing 316 Leather and allied product 

manufacturing

321 Wood Product Manufacturing 322 Paper Manufacturing 323 Printing and Related Support 

Activities

327 Nonmetallic Mineral Product 

Manufacturing 331111 Iron and steel mills 332116 Metal stamping

333414 Heating equipment (except warm 

air furnaces) manufacturing

335212 Household vacuum cleaner 

manufacturing 336111 Automobile manufacturing

Corporate management & administration

551111 Offices of Bank Holding 

Companies

551114 Corporate, Subsidiary, and 

Regional Managing Offices 561110 Office Administrative Services

Distributive services

22 Utilities 42 Wholesale trade 48-49 Transportation and warehousing

517110 Wired telecommunication 

carriers 517410 Satellite telecommunications 518 Data processing, hosting, and related srevices

Engineering-intensive manufacturing

324110 Petroleum refineries 325110 Petrochemical manufacturing 331311 Alumina refining

332994 Small arms manufacturing 333291 Paper industry machinery 

manufacturing

333314 Optical instrument and lens 

manufacturing

334 Computer and Electronic Product 

Manufacturing

335121 Residential electric lighting 

fixture manufacturing 336411 Aircraft manufacturing

Finance, insurance & real estate

52 Finance and insurance 53 Real estate and rental and leasing

Government

901149 U.S. postal service 901199 Federal civilian, except U.S. 

postal service

902999 State government, excluding 

education and hospitals

92 Public administration

Healthcare

621511 Medical laboratories 622110 General medical and surgical 

hospitals

622210 Psychiatric and substance abuse 

hospitals

623110 Nursing care facilities 902622 Hospitals, state government 903622 Hospitals, local government

Higher education

611310 Colleges, universities, and 

professional schools

902612 Colleges and universities, state 

government

903612 Colleges and universities, local 

government

Knowledge-intensive business services

541110 Offices of Lawyers 541330 Engineering services 541810 Advertising agencies

Media, entertainment & recreation

511 Publishing Industries (except 

Internet)

512 Motion Picture and Sound Recording 

Industries 515 Broadcasting (except Internet)

519 Other Information Services 71 Arts, Entertainment, and Recreation 721120 Casino hotels

Data sources: North American Industry Classification System; Economic Modeling Specialists International (EMSI)

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

2-3B. CATEGORIZING COUNTIES BASED ON FUNCTIONAL INDUSTRY 

SPECIALIZATION

The concept of extra jobs quantifies specializations in terms of the absolute number of 

jobs employed in a particular category above or below the national average. To assign a 

single functional industry specialization to all counties, an extra jobs value was 

calculated for the groups of industries comprising each functional category in each 

county. The functional category with the largest number of extra jobs was assigned as 

the functional industry specialization for the county in question.

For each county, extra jobs were calculated for each functional category as shown in 

formula 6:

(6) 

where Eic is employment in the functional category of interest (i) for a county (c), Ec is 

total employment in the county of interest, Ein is the nation’s employment in the 

functional category of interest, and En is total national employment. 

2-3C. CALCULATING FUNCTION-BASED ECONOMIC DIVERSITY

The entropy measure was used to calculate function-based economic diversity, which 

this report commonly refers to as functional diversity. The metric was calculated for 

each county with employment by functional category serving as the share of total 

economic activity (pi

) specified in formula 1.

2-4. MEASURING OCCUPATIONAL DIVERSITY

Understanding what a region “does”—in addition to what a region “makes”—can help 

an analyst to better gauge the adaptability and suitability of a region to shocks and 

opportunities (Feser, 2003; Thompson & Thompson, 1987). In part, knowing what a 

region “does” requires data on the occupations of workers employed in the region’s 

industries and the skills required to perform those occupations. This section reviews the 

methods used to estimate county-level employment by occupations. These occupational 

employment estimates are then used as the basis for the calculation of an occupationbased measure of economic diversity.

Occupational diversity uses the 96 minor occupational groups defined in the Bureau of 

Labor Statistics’ 2000 Standard Occupational Classification as the units of analysis in the 

calculation of entropy.2 According to the Bureau of Labor Statistics, occupations are 

grouped based on similarity of “work performed, skills, education, training, and 

credentials.” Example occupational groups include agricultural workers, life scientists, 

secretaries and administrative assistants, and top executives. To estimate employment 

by minor occupational grouping, data from the Bureau of Labor Statistics’ national 

2 See http://www.bls.gov/soc/2000/socguide.htm

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Staffing Pattern Matrix were used to translate county employment by industry data to 

county-level employment by occupation.

2-4A. CALCULATING OCCUPATION-BASED ECONOMIC DIVERSITY

The entropy measure was used to calculate occupation-based economic diversity, which 

this report commonly refers to as occupational diversity. The metric was calculated for 

each county with employment by minor occupational group serving as the share of total 

economic activity (pi

) specified in formula 1.

2-5. MEASURING KNOWLEDGE DIVERSITY

To estimate the diversity of workforce knowledge at the county level, occupation-based 

knowledge clusters were derived and employment across these clusters provided the 

basis for an additional entropy calculation. Knowledge clusters are 12 groups of 

occupations categorized based on similarities in the type and level of knowledge 

required to work in these professions. Feser (2003) details the procedures used to 

identify these clusters. Again, the Bureau of Labor Statistics’ national Staffing Pattern 

Matrix was used to translate county employment by industry data to county-level 

employment by knowledge cluster. Employment in government industries, including 

military employment, is not accounted for by these knowledge clusters. Table 2-2 lists 

the 12 knowledge clusters and provides examples of common occupations and average 

education or training levels associated with them. Similar to the functional 

specializations detailed in sub-section 2-3b, knowledge cluster specializations were 

identified for each county based on the knowledge cluster with the most extra jobs in a 

particular county.

2-5A. CALCULATING KNOWLEDGE-BASED ECONOMIC DIVERSITY

The entropy measure was used to calculate knowledge-based economic diversity, which 

this report commonly refers to as knowledge diversity. The metric was calculated for 

each county with employment by knowledge cluster serving as the share of total 

economic activity (pi

) specified in formula 1.

13
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 2-2: Illustrative Description of Knowledge Clusters

2-6. GEOGRAPHIC SCALES AND AGGREGATIONS

2-6A. BASE GEOGRAPHIES

Metrics for this analysis are reported for or summarized by geographies including all 

U.S. counties; counties in Appalachian states and the formal ARC region; ARC subregions; and U.S. Census Regions and Divisions. 3,142 county equivalents were used for 

14
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

the U.S. counties geography. For metrics reported for and summarized by Appalachian 

geographies, independent cities in Virginia were combined with the nearest county to 

create a combined county dataset with 420 counties in the formal ARC region, 1,070 

counties in the 13 Appalachian states, and 3,113 counties across the United States. 

Table 2-3 lists the states included within the U.S. Census Regions and Divisions used for 

summary purposes in this report. Figure 2-1 depicts the 13 Appalachian states, the 

boundary of the formal ARC region, and the five ARC sub-regions as revised in 

November 2009. This analysis also summarized diversity metrics by ARC’s county 

economic status classification system, which classifies counties in one of five categories 

based on unemployment rates, per capita income, and poverty rate (see Figure 2-2).3

Table 2-3: States by U.S. Census Regions and Divisions

3 See 

http://www.arc.gov/research/SourceandMethodologyCountyEconomicStatusFY2007FY2

014.asp for details on the source and methodology for the ARC economic status 

designations

U.S. Census Region

States

Northeast region

New England Division

Connecticut Maine Massachusetts

New Hampshire Rhode Island Vermont

Middle Atlantic Division

New Jersey New York Pennsylvania

Midwest region

East North Central Division

Illinois Indiana Michigan

Ohio Wisconsin

West North Central Division

Iowa Kansas Minnesota

Missouri Nebraska North Dakota

South Dakota

South region

South Atlantic Division

Delaware District of Columbia Florida

Georgia Maryland North Carolina

South Carolina Virginia West Virginia

East South Central Division

Alabama Kentucky Mississippi

Tennessee

West South Central Division

Arkansas Louisiana Oklahoma

Texas

West region

Mountain Division

Arizona Colorado Idaho

Montana Nevada New Mexico

Utah Wyoming

Pacific Division

Alaska California Hawaii

Oregon Washington

Data source: U.S. Census Bureau, 2010

15

 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 2-1: Appalachian Sub-regions

16
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 2-2: ARC’s County Economic Status Designations, Fiscal Year 2013

2-6B. DETERMINING URBAN AND RURAL CHARACTERISTICS

To examine the differences in economic diversity across urban and rural counties, we 

adopted the urban-rural typology method suggested in Isserman (2005). This method 

required the classification of counties as one of four county characters—Urban, Mixed 

Urban, Mixed Rural, or Rural—based on the population density of the counties and the 

relative size of urban and rural areas within the counties. To complete the classification, 

we used U.S. Census 2010 data on total population, rural population, urbanized area 

and urban cluster population, and total urban population by county. Census information 

on land area by county was used to calculate population density. Finally, the Census 

2010 Urban Area to County Relationship File Layout was used to determine the 

population of portions of urban areas located completely within individual counties.4

This file splits all urban areas based on county boundaries and reports the population 

associated with each portion.

4 See http://www.census.gov/geo/maps-data/data/ua_rel_layout.html for a description 

of the Census 2010 Urban Area to County Relationship File Layout contents.

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

The Isserman (2005, p. 475) typology classifies counties according to the following 

criteria:

Rural county: (1) The county’s population density is less than 500 

people per square mile, and (2) 90 percent of the county population is 

in rural areas or the county has no urban area with a population of 

10,000 or more. 

Urban county: (1) The county’s population density is at least 500 

people per square mile, (2) 90 percent of the county population lives 

in urban areas, and (3) the county’s population in urbanized areas is at 

least 50,000 or 90 percent of the county population.

Mixed rural county: (1) The county meets neither the urban nor the 

rural county criteria, and (2) its population density is less than 320 

people per square mile. 

Mixed urban county: (1) The county meets neither the urban nor the 

rural county criteria, and (2) its population density is at least 320 

people per square mile. 

2-6C. DETERMINING COMMUTING LINKAGES

In most U.S. counties, workers, firms, and consumers depend upon employment, 

shopping, and service opportunities that lie both within and outside their home county. 

While there are many potential regions that could be defined to approximate the 

multiple economic relationships among places (e.g., firm-to-firm; worker-to-employer; 

consumer-to-store), the analysis of commuting patterns provides one method for 

defining inter-county economic relationships. 

Using 2006-2010 county-to-county commuting flow data prepared by the U.S. Census 

Bureau, we defined commuting sheds for each U.S. county.5

 A county’s commuting 

shed includes all those counties that account for a significant share of the journey-towork commuting flow headed toward or away from that county. Journey-to-work data 

from the U.S. Census Bureau’s 2006-2010 American Community Survey were used to 

determine membership in a commuting shed. Specifically, counties are included in a 

commuting shed if they account for at least five percent of the worker flow toward or 

away from the county in question. Commuting sheds range in size from one to nine 

counties in size, including the county of interest. In order to calculate measures of 

commuting shed diversity, employment by industry data for all counties in a commuting 

shed were combined.

5 See http://www.fhwa.dot.gov/planning/census_issues/ctpp/ for information on 

Census Transportation Planning Products

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

2-7. ANALYZING FUNCTIONAL COUNTY ROLES IN THE REGIONAL ECONOMY

To understand the functional economic roles of counties in their regions, we conducted 

a principal components factor analysis to identify several latent variables that can be 

used to characterize functional economic types. The 25 variables for this analysis were

total employment for counties and their commuting sheds; extra employment in each of 

the defined functional categories for counties and their commuting sheds (see Table 2-

1); and counties’ net commuting flows expressed as a percentage of the number of 

workers residing in those counties. Collectively, these variables express counties’ 

economic roles in their regions as a function of their economic specializations, the 

economic specializations present in their commuting regions, and the commuting 

relationships between counties and their commuting sheds. 

Using the PROC FACTOR procedure available in the SAS statistical software package, 

these variables were subjected to a principal components analysis, with a varimax 

rotation used to aide interpretation of the resulting factors. Eigenvalues, scree plots, 

and the proportion of variance explained by each factor also helped with interpretation, 

and the ultimate selection of factors.6 After several test runs of the analysis, the 

decision was made to conduct separate factor analyses for mixed rural or rural counties 

and mixed urban or urban counties, with counties with total employment greater than 

400,000 included in the mixed urban or urban analysis.7

 

After examining the derived factors, we selected a final set of nine factors that explain 

approximately two-thirds of the variance of the 25 input variables across the 2,759 U.S. 

counties in the mixed rural or rural grouping. A final set of seven factors explain nearly 

80 percent of this same variance across the 354 U.S. counties in the mixed urban or 

urban groupings. These factors were then used to score individual counties based on 

their values on the input variables, resulting in each county having loading values for 

each of the factors derived for their county character type. Higher loading values 

indicate similarity between the distribution of an individual county’s variables and a 

derived factor. While each county has loadings for each factor derived for its county 

character type, counties were assigned to groups based on their highest loading value.

For example, Los Angeles County, California has the highest loading value among U.S. 

counties for both the media and entertainment centers and transportation and 

distributive services centers factors. However, Los Angeles was classified as a media and 

entertainment center since its loading value for this factor was higher than that for 

transportation and distributive services centers. These groupings can be interpreted as 

functional types of county roles in regional economies, and they are used in this report 

to examine the distribution of diversity characteristics and economic outcomes across 

6 See http://www.ats.ucla.edu/stat/sas/library/factor_ut.htm for an overview of the use 

of SAS for factor analysis.

7 This decision allowed for the analysis of counties such as Clark County, Nevada (the 

home of Las Vegas) alongside urban counties, even though the low density of 

development in these counties resulted in it being classified as Mixed Rural for other 

purposes.

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

county types. The final list of derived factors appears in Table 2-4, with each of these 

factors discussed in more detail within section 7-3.

Table 2-4: Summary Results of Principal Components Analysis of Functional 

County Roles

Counties 

analyzed

Factor #

Rural or Mixed Rural counties

1 Bedroom communities to midsize centers 4.13 16.5 16.5

2 Agriculture & resource extraction centers 2.65 10.6 27.1

3 Rural destinations & advanced services districts 2.40 9.6 36.7

4 Bedroom communities to corporate and 

distributive services centers

1.85 7.4 44.1

5 Corporate and distribution outposts 1.47 5.9 50.0

6 Rural manufacturing centers 1.28 5.1 55.1

7 Rural government districts 1.25 5.0 60.1

8 Town and gown communities 1.13 4.5 64.6

9 Rural medical centers 1.03 4.1 68.7

Urban or Mixed Urban counties

1 Satellite cities and suburbs 6.81 27.2 27.2

2 Corporate and financial centers 3.53 14.1 41.4

3 Government centers and suburbs 3.17 12.7 54.1

4 Media and entertainment centers 1.91 7.6 61.7

5 Higher education and medical complexes 1.50 6.0 67.7

6 Advanced manufacturing districts 1.21 4.8 72.5

7 Transportation and distributive services centers 1.13 4.5 77.0

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Derived as detailed in Statistical Portrait of Economic Diversity in Appalachia section 2-7

Factor interpretation Eigenvalue

% of total 

variance 

explained

% of 

cumulative 

variance 

explained

20
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

2-8. PRESENTATION OF METRICS

The diversity metrics calculated for this analysis cannot be easily interpreted unless 

counties are compared relative to one another or the overall distribution of county 

diversity values. To accomplish these comparisons and allow for interpretation, diversity 

measures were standardized and classified according to the procedures described in this 

section.

2-8A. DATA STANDARDIZATION 

For each diversity measure calculated on a county-by-county basis, data standardization 

required three steps. First, the diversity measure was calculated, resulting in a raw 

diversity score for each county. Second, the mean, raw diversity value was calculated for 

each measure by summing the raw diversity values across all counties and dividing by 

the number of counties. Third, each county’s raw diversity value was divided by the 

mean, raw diversity value, resulting in a standardized value for each county. 

Standardized values can be interpreted as follows:

• A standard value (X) less than 1.0 is (1 – X) * 100 percent less diverse than the 

mean county diversity value (e.g., a standard value of 0.67 is 33 percent less 

diverse than the mean diversity).

• A standard value of 1.0 is equivalent to the mean county diversity value

• A standard value (X) greater than 1.0 is (X - 1) * 100 percent more diverse than 

the mean county diversity value (e.g., a standard value of 1.25 is 25 percent

more diverse than the mean diversity).

2-8B. Z-SCORES CLASSIFICATION

While standardized diversity scores serve as a simple indicator of the relationship of a 

county’s diversity to the average diversity, z-scores provide information on the 

relationship of a value to the mean and the value’s placement relative to the 

distribution of diversity (or another measure, such as average establishment size) across 

all counties. Z-scores for each value and diversity measure were calculated as follows:

• Calculate the mean (x̅) and standard deviation (s) for a particular diversity 

measure

• For each diversity value (xi), calculate the difference (di

) between the value and 

the mean value (di = xi- x̅) 

• Calculate the z-score for each county’s diversity value (zi

) to equal the quotient 

of the difference between the county’s value and the mean value and the 

standard deviation (zi = di

/s) 

21
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

While the distributions of the diversity values vary by measure, and none of the 

measures have a perfect normal distribution, z-scores can be used to provide a 

shorthand classification of individual values into groups with high, low, or about average 

values. Z-scores were classified into groups as follows:

• Very high (zi >= 2)

• High (1 <= zi < 2)

• Above average (0 < zi < 1)

• Below average (0 > zi > -1)

• Low (-1 >= zi > -2)

• Very low (zi

 <= -2)

In addition to the six groupings noted above, above average and below average values 

were grouped and reported together as about average values in several instances. To 

control for county character, z-scores were also calculated according to the distribution 

of diversity scores across each of the four county character categories. Each county was 

then classified on the very low-very high scale according to its z-score based on the 

distribution of values in the same county character grouping. Where county characterbased z-scores were used to classify counties, tables or maps are identified by text 

indicating the displayed values control for county character.

2-8C. RANKING APPALACHIAN COUNTIES BASED ON ECONOMIC DIVERSITY

Table 9-2 lists the standardized industrial, functional, occupational, and knowledge

diversity values for each county in Appalachia. These counties are arranged in alphabetic 

order, and each standardized value is accompanied by a rank value based on the 

particular county’s standing relative to other Appalachian counties for that particular 

diversity metric. Counties ranked as #1 are the most diverse for a particular measure, 

while those ranked #420 are the least diverse. 

22
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

3. URBAN PLACES ENJOY A NATURAL ADVANTAGE IN ECONOMIC 

DIVERSITY

The nature of economic diversity measures and the typical characteristics of urban 

regions result in the tendency for urban regions to achieve higher economic diversity 

measures than their rural and less urban counterparts do. This chapter provides 

evidence for this tendency by reporting the distribution of economic diversity across 

urban and rural counties and decomposing diversity metrics to reveal the factors 

contributing to variation in diversity across urban and rural places. 

3-1. ECONOMIC DIVERSITY ACROSS URBAN AND RURAL COUNTIES

Industry-, function-, occupation-, and knowledge-based economic diversity measures 

vary significantly across counties according to their urban and rural characteristics. In 

this section, the composite measures of county character discussed in sub-section 2-6b. 

are used to examine the distribution of economic diversity across classifications of 

counties with similar urban or rural characteristics. 

3-1A. DISTRIBUTION OF ECONOMIC DIVERSITY ACROSS URBAN AND RURAL 

COUNTIES

While the distribution of industrial diversity is not normal, classifying counties according 

to their distance in standard deviations from the mean diversity provides a useful 

grouping of counties based on their relative diversity values. This classification, as 

depicted in Figure 3-1, makes one pattern abundantly clear—low and very low diversity 

counties are more likely to be located in the rural interior of the country than they are in 

the more densely populated coastal regions. For example, the states in the West North 

Central Census Division—Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, 

and South Dakota—contain 141 counties with a diversity score more than one standard 

deviation below the mean, while the states in the New England Census Division—

Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont—

don’t contain any counties with low or very low diversity scores.

23
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-1: Industry-based Employment Diversity in U.S. Counties, 2012 

24
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Classifying counties according to their urban or rural character presents an alternative 

perspective for understanding economic diversity. As listed in Table 3-1, the average 

industrial diversity increases as counties become more urban. While rural counties are 

not uniformly less diverse than their urban counterparts, their maximum diversity 

approximates the mean diversity for urban counties. This difference is even more 

pronounced in Appalachia, where the maximum diversity for rural counties is roughly 

equivalent to the mean of mixed urban counties. From an industry-based perspective, 

the average Appalachian county is more diverse than the average U.S. county—a 

relationship that also applies to Appalachian counties when they are compared to their 

peers in each of the four county character designations. 

Table 3-1: Industrial Diversity Statistics for Counties by County Character, 

2012 

Region

Number of 

county 

equivalents Minimum

25th 

percentile Mean Median

75th 

percentile Maximum

United States 3,142 0.23 0.93 1.00 1.01 1.08 1.24

County character, 2010

Urban 195 0.80 1.10 1.14 1.17 1.19 1.24

Mixed Urban 182 0.84 1.09 1.12 1.14 1.17 1.23

Mixed Rural 1,017 0.59 1.03 1.06 1.07 1.11 1.21

Rural 1,748 0.23 0.89 0.94 0.95 1.01 1.14

Appalachian counties 420 0.74 0.95 1.02 1.02 1.09 1.20

County character, 2010

Urban 5 1.14 1.15 1.17 1.17 1.18 1.20

Mixed Urban 29 0.95 1.11 1.13 1.14 1.17 1.20

Mixed Rural 146 0.89 1.03 1.07 1.08 1.11 1.20

Rural 240 0.74 0.92 0.97 0.98 1.02 1.13

Standardized industrial diversity statistics, 2012

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 

2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Standardized values represent ratio to 

U.S. mean 3. County character designated using U.S. Census 2010 data and Isserman (2005) typology

25
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 3-2 lists statistics for standardized functional diversity across urban and rural 

counties in the U.S. and Appalachia. No clear pattern in functional diversity is evident 

from these data, though the least diverse counties continue to be those with a rural 

character. The average county in Appalachia has a higher functional diversity level than 

the average county in the United States, although the average urban county in 

Appalachia is less diverse than the U.S. average.

Table 3-2: Functional Diversity Statistics for Counties by County Character, 

2012

Region

Number of 

county 

equivalents Minimum

25th 

percentile Mean Median

75th 

percentile Maximum

United States 3,142 0.16 0.94 1.00 1.03 1.09 1.20

County character, 2010

Urban 195 0.72 1.06 1.08 1.09 1.13 1.20

Mixed Urban 182 0.76 1.05 1.08 1.10 1.13 1.20

Mixed Rural 1,017 0.41 1.03 1.07 1.09 1.13 1.20

Rural 1,748 0.16 0.88 0.94 0.97 1.04 1.20

Appalachian counties 420 0.62 0.98 1.03 1.05 1.10 1.18

County character, 2010

Urban 5 0.90 0.96 0.99 1.00 1.02 1.06

Mixed Urban 29 0.89 1.04 1.06 1.06 1.10 1.15

Mixed Rural 146 0.81 1.04 1.08 1.09 1.13 1.17

Rural 240 0.62 0.96 1.01 1.02 1.08 1.18

Standardized functional diversity statistics, 2012

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated across eleven functional categories defined per Statistical Portrait of Economic 

Diversity in Appalachia section 2-3 2. Standardized values represent ratio to U.S. mean 3. County character 

designated using U.S. Census 2010 data and Isserman (2005) typology

26
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

The relationship between increasing urbanity and increasing occupational diversity is 

not as strong as it is in the case of industrial diversity, though occupational diversity

does tend to increase as counties become more urban. As shown in Table 3-3, the 

minimum floor for Urban and Mixed Urban occupational diversity exceeds that of all the 

other groupings across the U.S. and Appalachia. Occupational diversity is considerably 

less variable across counties than is industrial diversity, with most counties registering 

occupational diversity measures that fall very close to average. As discussed in subsection 3-2a., diversity metrics increase as the number of categories containing 

employment increases. With occupational diversity measuring employment across 96 

categories of occupations, it is not surprising that the variance of occupational diversity

is less than the variance of industrial diversity which measures employment across 1,110 

six-digit NAICS industries. 

Table 3-3: Occupational Diversity Statistics for Counties by County Character, 

2012 

Region

Number of 

county 

equivalents Minimum

25th 

percentile Mean Median

75th 

percentile Maximum

United States 3,142 0.68 0.99 1.00 1.01 1.02 1.04

County character, 2010

Urban 195 0.93 1.02 1.02 1.03 1.03 1.04

Mixed Urban 182 0.95 1.01 1.02 1.02 1.03 1.04

Mixed Rural 1,017 0.76 1.01 1.02 1.02 1.02 1.04

Rural 1,748 0.68 0.97 0.99 1.00 1.01 1.04

Appalachian counties 420 0.89 1.00 1.01 1.01 1.02 1.04

County character, 2010

Urban 5 1.02 1.04 1.04 1.04 1.04 1.04

Mixed Urban 29 0.98 1.02 1.03 1.03 1.04 1.05

Mixed Rural 146 0.96 1.02 1.03 1.03 1.04 1.05

Rural 240 0.86 0.99 1.00 1.00 1.02 1.05

Standardized occupational diversity statistics, 2012

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated across 96 minor occupational groups in 2000 Standard Occupational Classification, 

Bureau of Labor Statistics 2. Bureau of Labor Statistics' national Staffing Pattern Matrix used to estimate 

employment by minor occupational groups 3. Standardized values represent ratio to U.S. mean 4. County 

character designated using U.S. Census 2010 data and Isserman (2005) typology

27
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Comparing Appalachia to the U.S., the average county in Appalachia is slightly more 

diverse than the average U.S. county from an occupation-based perspective. There is 

also considerably less variance in occupational diversity across Appalachian counties 

than there is for U.S. counties as a whole. Compared to occupational diversity, the 

statistics for knowledge diversity demonstrate considerably less variance across all 

counties (see Table 3-4). Once again, the average Appalachian county is more diverse 

from a knowledge-based perspective than is the average U.S. county. This pattern holds 

across each of the four county character types, and the minimum floor for knowledge 

diversity is considerably higher than the minimum values found for this measure within 

which of the four types. 

Table 3-4: Knowledge Diversity Statistics for Counties by County Character, 

2012

While economic diversity is not always higher in more urban locations, the relationship 

between diversity level and urban character is strong enough to suggest the need to 

consider urban and rural counties separately when examining the relative diversity 

standings of counties. Figures 3-2 to 3-9 present two maps each for industry-, function-, 

occupation-, and knowledge-based economic diversity. The first of each pair of maps 

presents the diversity level of counties with all counties considered in the same 

distribution and counties qualitatively ranked based on their distance in standard 

deviations from the mean diversity level across U.S. counties. The second map in each 

pair presents qualitative rankings of county diversity based on considering each county 

in one of four distributions based on the county character type of the county in 

question. Counties are then qualitatively ranked according to their standing within the 

Region

Number of 

county 

equivalents Minimum

25th 

percentile Mean Median

75th 

percentile Maximum

United States 3,142 0.77 0.99 1.00 1.00 1.02 1.10

County character, 2010

Urban 195 0.87 1.00 1.01 1.02 1.03 1.09

Mixed Urban 182 0.94 0.99 1.01 1.01 1.02 1.05

Mixed Rural 1,017 0.84 0.99 1.01 1.01 1.02 1.10

Rural 1,748 0.77 0.98 0.99 1.00 1.01 1.10

Appalachian counties 420 0.90 0.99 1.01 1.01 1.02 1.09

County character, 2010

Urban 5 1.00 1.02 1.02 1.02 1.02 1.03

Mixed Urban 29 0.98 0.99 1.01 1.00 1.02 1.04

Mixed Rural 146 0.90 0.99 1.01 1.01 1.02 1.09

Rural 240 0.94 0.99 1.01 1.01 1.02 1.08

Standardized knowledge diversity statistics, 2012

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated across 12 knowledge clusters; see Feser (2003) for details on knowledge cluster 

derivation 2. Bureau of Labor Statistics' national Staffing Pattern Matrix used to estimate employment by 

knowledge cluster 3. Standardized values represent ratio to U.S. mean 4. County character designated using U.S. 

Census 2010 data and Isserman (2005) typology

28
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

distribution of counties of the same character type (e.g., comparing rural counties to 

rural counties and urban counties to urban counties). 

While the differences between these pairs of maps are not widespread, there are many 

occasions when the diversity level for a county considered relative to all counties is 

upgraded or downgraded when that county is considered only in relation to its peers 

with the same county character type. While comparing counties to all other counties is 

not necessarily inappropriate, the nature of diversity metrics, and its susceptibility to 

influence from characteristics associated with urban counties, suggests that comparing 

counties to their county character type peers presents a more realistic assessment of 

diversity conditions in a county.

Figure 3-2: Industrial Diversity in Appalachian Counties, 2012

29
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-3: Industrial Diversity in Appalachian Counties, Controlled for 

County Character, 2012 

30
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-4: Functional Diversity in Appalachian Counties, 2012

31
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-5: Functional Diversity in Appalachian Counties, Controlled for 

County Character, 2012

32
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-6: Occupational Diversity in Appalachian Counties, 2012 

33
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-7: Occupational Diversity in Appalachian Counties, Controlled for 

County Character, 2012

34
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-8: Knowledge Diversity in Appalachian Counties, 2012

35
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-9: Knowledge Diversity in Appalachian Counties, Controlled for 

County Character, 2012

3-2. DIVERSITY METRICS AND THE MECHANICS OF URBAN AND RURAL 

DIVERSITY DISPARITIES 

Diversity metrics seek to measure the actual diversity of economic activity in a region, 

but they must rely upon classifications of employment by type of economic activity that 

may over- or underemphasize the actual differences between categories. This section 

evaluates the degree to which the classification schemes that form the basis of 

economic diversity measures influence the resulting differences in diversity across urban 

and rural counties. In particular, the average number of industry sectors by county 

character is determined and the role of related variety in determining the variance of 

industrial diversity is examined. 

3-2A. INDUSTRY SECTORS BY URBAN AND RURAL COUNTIES

Industrial diversity measurements are influenced in part by the number of six-digit 

industries that a county has employment in. In 2012, the average county had 

employment in 587 six-digit industries, with this value ranging from 35 industries in 

36
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Loving County, Texas (diversity = 2.27) to 1,090 industries in Los Angeles County, 

California (diversity = 5.52). The number of industries varies significantly across the 

categories of county character, with Urban counties averaging employment in 974 

industries, Mixed Urban averaging 931 industries, Mixed Rural counties averaging 850 

industries, and Rural counties averaging employment in only 630 industries. 

3-2B. RELATED VARIETY AS A PRIMARY SOURCE OF VARIANCE IN 

INDUSTRIAL DIVERSITY

Industrial diversity can be decomposed into two components—unrelated variety that 

measures the diversity of employment across 3-digit NAICS industries and related 

variety that measures the sum of the diversity of employment of six-digit NAICS 

industries within each 3-digit industry weighted by the share each 3-digit industry 

comprises of total employment. With considerably more six-digit industries than 3-digit 

industries in most counties, 2012 related variety values range from standardized values 

of approximately 0.3 to 2.9, while unrelated variety values range from approximately 

0.7 to 2.8.

37
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

As shown in Tables 3-5 and 3-6, both unrelated and related variety values tend to 

increase as the level of county urbanity increases. However, the average standardized 

difference between the related variety values of urban and rural counties (0.64) is 

significantly greater than the average difference between the unrelated variety values 

of these county types (0.08). These statistics also demonstrate that the urban advantage 

in diversity is more fully expressed in the case of related variety, as each successive rung 

of urbanity has higher values at the 25th percentile, median, mean, 75th percentile, and 

maximum than the lower levels of urbanity do. The rankings for unrelated variety 

statistics do not follow this same pattern, though the mean at each higher level of 

urbanity exceeds or meets the value found at the preceding level.

Table 3-5: Industry-based Unrelated Variety Statistics for Counties in 

Appalachia by County Character, 2012 

Table 3-6: Industry-based Related Variety Statistics for Counties in 

Appalachia by County Character, 2012

Region

Number of 

county 

equivalents Minimum

25th 

percentile Mean Median

75th 

percentile Maximum

Appalachian counties 420 0.78 0.99 1.02 1.03 1.07 1.13

County character, 2010

Urban 5 1.03 1.07 1.07 1.07 1.09 1.09

Mixed Urban 29 0.99 1.06 1.07 1.07 1.09 1.12

Mixed Rural 146 0.90 1.04 1.06 1.06 1.09 1.13

Rural 240 0.78 0.96 0.99 1.01 1.03 1.11

Standardized industry-based unrelated variety statistics, 2012

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Unrelated variety uses 3-digit NAICS aggregations 2. Standardized values represent ratio to U.S. mean 

3. County character designated using U.S. Census 2010 data and Isserman (2005) typology

Region

Number of 

county 

equivalents Minimum

25th 

percentile Mean Median

75th 

percentile Maximum

Appalachian counties 420 0.43 0.83 1.00 0.99 1.15 1.65

County character, 2010

Urban 5 1.36 1.48 1.51 1.51 1.53 1.65

Mixed Urban 29 0.72 1.25 1.33 1.41 1.46 1.54

Mixed Rural 146 0.73 1.02 1.12 1.12 1.22 1.44

Rural 240 0.43 0.75 0.87 0.87 0.98 1.29

Standardized industry-based related variety statistics, 2012

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Unrelated variety uses 6-digit NAICS aggregations 2. Standardized values represent ratio to U.S. mean 

3. County character designated using U.S. Census 2010 data and Isserman (2005) typology

38
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 3-10 further illustrates the higher variability of industry-based related variety 

when compared to unrelated variety. This cross tabulation of unrelated and related 

variety levels reveals that 346 of the 420 Appalachian counties have an unrelated variety 

value that is about average. In the case of related variety, 312 counties have a related 

variety value that is about average.

Figure 3-10: Level of Industry-based Unrelated and Related Variety in 

Appalachian Counties, Controlled for County Character, 2012

3-3. LESSONS FOR PRACTICE 

3-3A. BE AWARE OF THE IMPACT OF CLASSIFICATION ON THE 

CALCULATION OF DIVERSITY

The default approach to measuring industrial diversity treats every separation of 

industries into different categories as equally important. In reality, there may be few 

significant differences between industries such as “Mens and Boys’ Cut and Sew Apparel 

Contractors (NAICS 315211) and “Womens, Girls’, and Infants’ Cut and Sew Apparel 

Contractors” (NAICS 315212) where factors such as industry inputs and workforce skill 

requirements are concerned. A diversity metric that accounts for slight industry 

39
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

differences, such as those in this example, may be artificially inflating economic diversity 

in particular regions.

Blind measurement of economic diversity without consideration of the relevant degree 

of differences among industries can result in an obscured picture of diversity, and might 

offer little practical value for benchmarking economic progress and informing strategy. 

Practitioners might pursue two potential approaches for reducing the adverse influence 

of classification schemes on diversity metrics. First, practitioners could revise existing 

industry classification systems to be useful for the purposes of exposing key differences 

between industries and thus accurately measuring diversity. At a minimum, such an 

approach might involve a practitioner deciding to use a more aggregated level of 

industry classification (e.g., 3-digit NAICS rather than six-digit NAICS) for the calculation 

of diversity.

A second approach might involve practitioners measuring the admittedly problematic 

and often ill-defined concept of diversity in multiple ways. This triangulation approach 

to understanding economic diversity is essentially the approach used for this analysis. By 

measuring diversity on industry, function, occupation, and knowledge bases, this 

approach aims to provide particularized understandings of economic diversity that 

eschew a single county diversity score in favor of multiple measurements that can be 

more readily applied to understanding the variety of economic development 

opportunities and threats facing localities.

3-3B. CONSIDER THE FRONTIERS OF DIVERSITY WHEN BENCHMARKING 

COUNTIES AGAINST EACH OTHER

If the aim is to have a high diversity score, then diversity metrics are certainly biased in 

favor of larger, more urban communities. The chief technical underpinning of this 

tendency is the positive relationship that exists between the number of industry sectors 

in a region and that region’s diversity level. In smaller places, the maximum diversity is 

limited by the natural tendency for there to be fewer industry sectors present. 

Practitioners should consider the natural bias of diversity for larger places and 

benchmark regional diversity to regions similar in size and urban population 

characteristics. This approach will allow for the identification of real differences in 

economic diversity that are not primarily due to size disparities. Benchmarking against 

similar size and character counties will allow practitioners to identify realistic goals for 

economic progress, though it may still be beneficial for comparisons to be made with 

larger places in order to track progress on more ambitious, transformative economic 

development goals.

40
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

4. SPATIAL AND INDUSTRIAL LEGACIES OF ECONOMIC DIVERSITY

Significant regional differences in economic diversity levels exist across the U.S. and 

Appalachia. In part these differences reflect the previously noted relationship between 

urbanization and diversity—with more urbanized counties of the north and northeast 

exhibiting higher diversity levels than their less densely populated counterparts in the 

southern and western portions of the United States. However, even when differences in 

urban and rural character are controlled for, spatial patterns in the distribution of 

economic diversity persist. This chapter provides evidence of these spatial patterns—

and offers potential explanations for their existence—by reporting the spatial 

distribution of economic diversity, examining the spatial distribution of functional 

specializations, and investigating the characteristics of these specializations that 

contribute to differences in overall diversity levels.

4-1. SPATIAL DISTRIBUTION OF ECONOMIC DIVERSITY

Based on the distribution of counties by county character across U.S. regions and 

Appalachia sub-regions, it should be little surprise that diversity differences based on 

urbanity carry over to diversity differences based on location. As listed in Table 4-1, 

Urban counties represent just over five percent of the 2,925 counties in the Midwest, 

South, and West Census Regions, while they comprise approximately 20 percent of the 

217 counties in the Northeast region. Rural counties, on the other hand, account for 

nearly 60 percent of the counties in the Midwest, South, and West regions, while 

representing about a quarter of the counties in the Northeast. In Appalachia, the 

proportion of Rural counties by sub-region ranges from a third of counties in the 

Northern sub-region to approximately 85 percent of counties in the Central sub-region. 

Table 4-1: U.S. Counties by Region and County Character, 2010

Regional designation

Number of county 

equivalents Rural Mixed Rural Mixed Urban Urban

U.S. Census region

Northeast 217 56 83 34 44

Midwest 1,055 666 305 46 38

South 1,423 792 456 83 92

West 447 234 173 19 21

Appalachian sub-region

Northern 86 28 47 10 1

North Central 63 43 17 3 0

Central 82 70 12 0 0

South Central 85 43 35 6 1

Southern 104 56 35 10 3

Data sources: U.S. Census Bureau, 2010; Appalachian Regional Commission, 2009

Notes: 1. County character designated using U.S. Census 2010 data and Isserman (2005) typology

Number of counties by county character

41
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Even when controlling for county character when considering the distribution of 

diversity, the regions with more rural counties tend to exhibit lower diversity levels (see 

tables 4-2 and 4-3). No very low diversity counties are located in the Northeast Census 

Region, while this category ranges from approximately four to six percent of the 

counties in the other regions. In Appalachia, the Central and North Central sub-regions

are the most rural settings, based on the county character indicator. Low diversity 

counties account for a larger percentage of the counties in these sub-regions (9.8 and 

7.9 percent, respectively) than they do in the other sub-regions, although no very low 

diversity counties are situated in these sub-regions.

Table 4-2: Level of Industrial Diversity in U.S. Counties by U.S. Census 

Region, Controlled for County Character, 2012

Table 4-3: Level of Industrial Diversity in Appalachian Counties by Subregion, Controlled for County Character, 2012

4-2. SPATIAL DISTRIBUTION OF FUNCTIONAL SPECIALIZATIONS

The fact that significant differences in diversity persist even when county urban and 

rural characteristics are accounted for suggests that other significant factors contribute 

to the regional distribution of diversity. One potential explanation for these differences 

is that comparative economic advantages enjoyed by regions make certain locations 

more suitable for extensive activities related to particular economic functions. For 

Northeast Midwest South West Total

Number of county equivalents 217 1,055 1,394 447 3,113

% Very high diversity 0.0 0.0 0.0 0.2 0.0

% High diversity 35.0 11.9 7.0 18.6 12.3

% Above average diversity 44.7 46.7 46.1 33.3 44.4

% Below average diversity 12.9 29.4 33.5 28.9 30.0

% Low diversity 7.4 8.2 9.5 13.4 9.5

% Very low diversity 0.0 3.8 3.9 5.6 3.8

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Levels based on standard deviations from mean

U.S. Census Region Level of industrial diversity by 

county

Level of industrial diversity by 

county Northern

North 

Central Central

South 

Central Southern Total

Number of county equivalents 86 63 82 85 104 420

% High diversity 39.5 6.4 3.7 11.8 11.5 15.0

% Above average diversity 40.7 44.4 43.9 51.8 54.8 47.6

% Below average diversity 14.0 41.3 42.7 29.4 28.9 30.5

% Low diversity 4.7 7.9 9.8 5.9 2.9 6.0

% Very low diversity 1.2 0.0 0.0 1.2 1.9 1.0

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Levels based on standard deviations from mean

Appalachian sub-region

42
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

example, significant mineral resources and forest stands can make some regions more 

likely than others to house counties dominated by natural resources-based industries. 

While significant employment in natural resources industries is not synonymous with a 

lack of economic diversity, related factors such as the topography associated with 

mineral resource deposits and the large land areas consumed by activities such as 

agriculture and forestry may contribute to challenges associated with attracting and 

sustaining a diversity of other industries in many of these regions. 

Apart from situations of industries dependent on the natural resources present in a 

region, particular industries may benefit more than others from economies of scale that 

makes a regional focusing of individual firm activities beneficial. Likewise, firms in 

certain industries may seek out the benefits of agglomeration economies when making 

site selection decisions. For example, a computer software company may choose to 

locate in Silicon Valley to benefit from networking opportunities and shared labor pools

made possible by a large, existing contingent of other high-technology firms. Whether 

regional specialization develops through particular industries targeting their location 

due to the limited availability of required natural resources or individual firms choosing 

to focus activities to reap positive externalities, such activities may contribute to 

regional differences in diversity that arise from unequal distribution of natural, human, 

and institutional resources. 

43
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

If regional functional specializations are not evenly distributed across U.S. and 

Appalachian counties, then this would lend some support to an explanation for 

differences in diversity based on differing regional resources and industry and firm 

requirements and business practices. Figure 4-1 depicts the uneven distribution of 

functional specialization across U.S. counties. For example, functional specializations in 

agriculture and resource extraction are found disproportionately in midwestern and 

western states, while specializations in capital-intensive manufacturing appear 

disproportionately in the eastern U.S. (see Figure 7-1 for Appalachian counties in more 

detail).

Figure 4-1: Functional Specializations with the Most Extra Jobs in U.S. 

Counties, 2012

44
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 4-4 can be used to observe several notable regional groupings of functional 

specializations in Appalachia. Mining communities in the North Central and Central subregions contribute to considerably higher than average percentages of counties in these 

sub-regions with functional specializations in agriculture & resource extraction. The 

North Central sub-region also contains a higher than average percentage of counties 

with functional specializations in government. A higher than average percentage of 

counties in the Northern, South Central, and Southern sub-regions have functional 

specializations in capital-intensive manufacturing. 

Table 4-4: Percent of Appalachian Counties with Select Functional 

Specializations, by Sub-region, 2012

4-3. FUNCTIONAL SPECIALIZATIONS AND THEIR DIFFERENTIAL IMPACTS 

ON DIVERSITY

For functional specializations to contribute to measurable differences in diversity, the 

specializations must vary from one another on at least one of several potential factors 

that would result in differential diversity impacts. This section reviews the evidence for 

two factors that might lead functional specializations to systematically impact regional 

diversity levels—1) differences in the average share of employment accounted for by 

functional specializations and 2) differences in the number of industry sectors contained 

within a functional specialization. This section concludes by examining the distribution 

of economic diversity levels across county functional specializations. 

4-3A. AVERAGE SHARE OF EMPLOYMENT IN FUNCTIONAL 

SPECIALIZATIONS

In addition to being influenced by the number of categories of economic activity in a 

region, economic diversity levels increase as the evenness of economic activity across 

Functional specialization Northern

North 

Central Central

South 

Central Southern Total

Number of county equivalents 86 63 82 85 104 420

Agriculture & resource extraction 27.9 61.9 72.0 31.8 19.2 40.2

Capital-intensive manufacturing 34.9 4.8 14.6 42.4 54.8 32.9

Corporate management & administration 1.2 0.0 0.0 0.0 0.0 0.2

Distributive services 4.7 3.2 0.0 1.2 7.7 3.6

Engineering-intensive manufacturing 4.7 4.8 1.2 5.9 3.9 4.1

Finance, insurance & real estate 1.2 1.6 0.0 0.0 5.8 1.9

Government 4.7 11.1 3.7 0.0 1.9 3.8

Healthcare 9.3 6.4 6.1 5.9 1.9 5.7

Higher education 8.1 3.2 2.4 7.1 3.9 5.0

Knowledge-intensive business services 0.0 0.0 0.0 2.4 1.0 0.7

Media, entertainment & recreation 3.5 3.2 0.0 3.5 0.0 1.9

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Functional specializations defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3

Appalachian sub-region

45
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

industry categories increases. If particular functional specializations tend to account for 

a disproportionately high or low percentage of regional employment then their 

presence might reasonably be expected to influence regional diversity levels. Table 4-5 

provides statistics on the average characteristics of functional specializations across all 

U.S. counties. Agriculture and resource extraction accounts for an average of 18 percent 

of a county’s total employment when it is a county’s functional specialization. Capitalintensive manufacturing, knowledge-intensive business services, and distributive 

services rank second, third, and fourth, respectively, behind agriculture and resource 

extraction for the largest percentage of employment accounted for when this function is 

a county’s specialization.

Table 4-5: Employment and Establishment Size Statistics by U.S. County 

Functional Specializations, 2012

While the data on establishments in Table 4-5 are not diversity metrics, they do offer 

insight into the dependence on individual firms that tends to accompany particular 

functional specializations. For example, where engineering-intensive manufacturing, 

healthcare, or higher education serve as a county’s functional specialization they 

account for an average of fewer than two percent of the establishments in their 

counties. At the same time, these few establishments are significantly larger than the 

average establishment size across all industries in these counties. Whether these large 

Functional specialization

Number of 

county 

equivalents

Percent of 

county 

employment

Percent of total 

establishments

Establishment 

size (employees)

Ratio of 

establishment 

size to overall 

county average

Agriculture & resource 

extraction 1,518 18.0 7.8 448 23.8

Capital-intensive 

manufacturing 656 15.1 5.2 484 22.3

Corporate management & 

administration 14 7.8 1.6 1,808 73.4

Distributive services 111 14.2 11.8 201 9.2

Engineering-intensive 

manufacturing 111 10.8 1.5 1,721 78.3

Finance, insurance & real 

estate 147 14.0 8.9 225 11.7

Government 195 12.0 5.5 739 31.9

Healthcare 123 9.0 0.8 3,212 155.9

Higher education 151 11.7 0.3 17,201 734.5

Knowledge-intensive 

business services 43 14.6 15.2 195 7.7

Media, entertainment & 

recreation 73 11.6 4.9 460 25.5

Notes: 1. Functional specializations defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3

Functional specialization averages

Data source: Estimated employment and establishments by industry, Economic Modeling Specialists 

International (EMSI), 2012

46
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

establishments leave a region particular vulnerable may depend on an assessment of 

the volatility of these industries. In this case, though there has been recent clamoring 

about a “higher education bubble,” healthcare and higher education have historically 

been seen as more stable industries (Cronin & Horton, 2009). Indeed, Silicon Valley was 

not built on a culture of stability, but many regional development practitioners would 

surely risk instability for the potential growth and prosperity associated with this 

successful IT district. Any specialization can be considered through a similar lens, with 

dependence bringing the potential for regions to “win big” while also exposing them to 

the risk of industry-focused economic downturns.

4-3B. INDUSTRIES BY FUNCTIONAL SPECIALIZATION

Much as urban counties tend to house employment in more industry sectors than their 

rural counterparts do, some functional specializations include more industries than 

others. In both cases, the presence of more industry groupings creates a built in 

advantage for achieving high diversity when certain functional specializations are 

present. Table 4-6 lists the number of six-digit NAICS industries that comprise each of 

the functional groupings of industries used for determining county functional 

specialization. With the number of industries by functional grouping ranging from 3 to 

328, some functional specializations seem to be considerably more likely than others to 

contribute to high diversity scores. For example, the 328 industries associated with 

capital-intensive manufacturing create an increased possibility for high diversity relative 

to the three industries associated with the government functional grouping.

Table 4-6: NAICS Industry Sectors by Functional Economic Groupings

Functional economic grouping

Agriculture & resource extraction

Capital-intensive manufacturing

Corporate management & 

administration

Distributive services

Engineering-intensive manufacturing

Finance, insurance & real estate

Government

Healthcare

Higher education

Knowledge-intensive business services

Media, entertainment & recreation

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Functional groupings defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3

144

Number of 6-digit NAICS industries in functional grouping

46

328

4

143

55

3

41

54

3

8

47
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

4-3C. AVERAGE ECONOMIC DIVERSITY BY FUNCTIONAL SPECIALIZATION

Differences among functional specializations on factors such as the average percentage 

of total employment accounted for by the specialization and the number of industries 

within the functional grouping appear to translate to differences in economic diversity 

across counties with different functional specializations. Table 4-7 displays the 

distribution of industrial diversity across counties with a specialization in one of the five 

most prevalent functional specializations. When controlling for county character, 

counties specialized in capital-intensive manufacturing—a functional grouping with a 

large number of industry sectors—have the greatest tendency to have an above average 

industrial diversity value. Counties specialized in higher education and healthcare are 

the most likely to have low industrial diversity values. In the case of these 

specializations, the small number of industries contained in each grouping may partially 

account for these low industrial diversity values.

Table 4-7: Level of Industrial Diversity in Appalachian Counties with Select 

Functional Specializations, Controlled for County Character, 2012

Level of industrial diversity 

by county

Agriculture & 

resource 

extraction

Capitalintensive 

manufacturing Healthcare

Higher 

education

Engineeringintensive 

manufacturing Total

Number of county 

equivalents 169 138 24 21 17 420

% High 13.6 18.1 12.5 4.8 5.9 15.0

% Above average 47.3 52.9 50.0 28.6 52.9 47.6

% Below average 33.7 23.2 29.2 47.6 41.2 30.5

% Low 5.3 4.4 8.3 19.1 0.0 6.0

% Very low 0.0 1.5 0.0 0.0 0.0 1.0

Selected county functional specializations

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Levels based on standard deviations from mean 

3. Functional specializations defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3

48
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Counties specialized in healthcare and higher education have the highest proportion of 

their membership with high functional diversity values (see Table 4-8). Each of these 

functions accounts for an average of approximately ten percent of total county 

employment in the counties where it is the most significant specialization, allowing 

sufficient room for other functional groupings to account for significant employment as 

well. At the same time, the large establishments that often accompany higher education 

specializations may contribute to the significant proportion of higher education 

specialized counties with a low level of functional diversity. 

Table 4-8: Level of Functional Diversity in Counties with Select Functional 

Specializations, Controlled for County Character, 2012

4-4. LESSONS FOR PRACTICE

4-4A. NATURAL, HUMAN, AND INSTITUTIONAL RESOURCES INFLUENCE 

REGIONAL SPECIALIZATIONS AND CAN AFFECT DIVERSITY LEVELS

Some places are a better fit for certain economic activities than other places. This notion 

of comparative advantage has long been part of the economist’s toolbox for explaining 

regional differences and the essential lessons of this idea can contribute to 

understanding regional diversity. The advantage of a place for particular industries 

might come from the presence of certain natural resources, the existence of a 

workforce with the requisite skills, or the presence of a finance and business support 

services network that has long catered to the needs of a particular industry sector. In 

addition to these resources benefitting incumbent firms, existing industry specializations 

may grow as these advantages attract new, related firms to the region.

While regional resources impact economic specializations, they also indirectly influence 

economic diversity in a place since functional specializations tend to vary on fronts such 

as percent of total regional employment, the diversity of skillsets required by their 

workforces, and the number of industries that form a cohesive, functional economic 

group—all factors that can significantly influence industry-, occupation-, function-, and 

Level of functional 

diversity by county

Agriculture & 

resource 

extraction

Capitalintensive 

manufacturing Healthcare

Higher 

education

Engineeringintensive 

manufacturing Total

Number of county 

equivalents 169 138 24 21 17 420

% High 11.8 13.8 41.7 33.3 29.4 15.5

% Above average 63.3 52.2 54.2 33.3 64.7 55.2

% Below average 18.9 26.8 4.2 19.1 5.9 22.1

% Low 4.7 6.5 0.0 14.3 0.0 6.2

% Very low 1.2 0.7 0.0 0.0 0.0 1.0

Selected county functional specializations

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated across eleven functional categories 2. Levels based on standard deviations from 

mean 3. Functional specializations defined per Statistical Portrait of Economic Diversity in Appalachia section 2-

3

49
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

knowledge-based economic diversity. For the practitioner interested in understanding 

regional diversity, wise places to start are to investigate the resources that make a 

region attractive or unattractive to particular industries and examine the diversityrelated characteristics of a county’s functional specializations. 

4-4B. BENCHMARKING TO COUNTIES WITH SHARED FUNCTIONAL 

SPECIALIZATIONS CAN MAKE FOR HONEST AND USEFUL COMPARISONS

Just as it makes sense to benchmark a county’s diversity to counties with similar urban 

or rural characteristics, benchmarking to the right places in terms of similar functional 

specialization can help to make realistic comparisons that at least partially account for 

differences in diversity due to the structure of counties’ economic specializations. 

Comparing several counties with similar specializations but very different characteristics 

related to factors such as economic performance and the tenure of the economic 

specialization can also help to assess the trajectory of a region’s development and might 

provide opportunities for gaining policy insights from the experiences of peer counties.

4-4C. INDUSTRY MIX MIGHT INFLUENCE THE ABILITY FOR REGIONS TO 

REACT TO SHOCKS AND OPPORTUNITIES

Dependence naturally creates some difficulty in adjustment. To the degree that counties 

are dependent on particular industries or groupings of industries, the economic diversity 

characteristics of these industries may make regional adjustment to economic shocks or 

opportunities more difficult. This difficulty might manifest itself in a few forms. Most 

generally, the larger the portion of a region’s economy that is connected to a particular 

industry (e.g., through employment or earnings), the more likely that a shock to this 

industry will negatively affect the region. More specifically, low occupational diversity

might make it difficult to seize opportunities since the workforce may lack the necessary 

skills or experiences to pursue employment in a wide variety of fields. 

Industrial specializations might also stamp counties with institutional legacies that make 

adaptation more or less likely. For example, Thompson and Thompson (1987) cite the 

example of Midwestern automobile cities and their “struggle...with the legacy of high 

wages for low skills, wrung from...oligopoly and union power” (p. 548). By contrast, 

Chinitz (1961) speculated that the independent and entrepreneurial nature of New York 

City’s apparel sector made risk-taking activity more common—and, by extension, 

allowed for greater adaptive capacity—in that location than it did in Pittsburgh, where 

the steel industry was dominated by large corporations.

Examining a county’s overall diversity levels, the diversity characteristics associated with 

its specializations, and the impact of prevalent specializations on the supply of financial 

and human capital in a region might help practitioners to better understand and work 

toward enhancing a region’s capacity for economic adaptation.

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

5. DIVERSIFICATION THROUGH JOB LOSS AND SPECIALIZATION 

THROUGH JOB GAIN

Diversification is generally assumed to be a positive economic process that brings 

significant value, particularly as long-run benefits of stability are achieved. However, 

there is little empirical evidence of how economic diversification occurs. This chapter 

uses employment estimates from 1999, 2009, and 2012 to examine diversification 

trends across the U.S. as a whole, U.S. counties, and Appalachian counties. These trends 

are further analyzed to reveal the factors associated with economic diversification or 

specialization. Finally, these findings on diversification are translated to lessons for 

economic development practitioners.

5-1. TRENDS AND COMPONENTS OF DIVERSIFICATION

According to the entropy index of industrial diversity, the United States became slightly 

less diverse from an employment perspective between 2009 and 2012 (see Table 5-1). 

In the United States, total employment over this time period increased by 

approximately five million jobs. At the same time, earnings increased at a rate more 

than double the rate of change in employment. This increase in earnings was 

accompanied by a very slight increase in the entropy metric of industry-based earnings 

diversity across the United States. Industry-based employment and earnings diversity 

increased very slightly in Appalachia between 2009 and 2012, while approximately 

400,000 jobs and $35 billion in earnings were added to the Appalachian economy over 

this timeframe.

Table 5-1: U.S. and Appalachian Region Economic Activity and Diversity 

Trends, 2009-2012

Economic activity measure 2009 2012 Percent change

United States

Employment

Total employment 174 million 179 million 3.21%

Entropy index 5.643 5.637 -0.10%

Earnings

Total earnings $8.3 trillion $9.0 trillion 7.34%

Entropy index 5.602 5.604 0.03%

Appalachian Region

Employment

Total employment 12.4 million 12.8 million 2.67%

Entropy index 5.595 5.596 0.02%

Earnings

Total earnings $488 billion $521 billion 6.77%

Entropy index 5.575 5.582 0.12%

Notes: 1. Entropy calculated at 6-digit NAICS aggregations

Data source: Estimated county employment and earnings by industry, Economic Modeling Specialists 

International (EMSI), 2009 & 2012

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 5-1 displays the spatial distribution of significant changes in industrial diversity 

(i.e., those changes in diversity greater than one standard deviation from the mean 

change) and changes in employment between 2009 and 2012 in Appalachian counties. 

The sea of green counties throughout Appalachia in this map reveals that the dominant 

trend over this time was one of only very slight changes to economic diversity. Where 

significant changes did occur, counties were most likely to experience either jobs gains 

associated with a significant decrease in industrial diversity or job losses associated with 

a significant increase in industrial diversity. 

Figure 5-1: Change in Industrial Diversity and Employment in the Appalachian 

Region, 2009-2012

Where significant changes to diversity did occur, decreases in diversity (i.e., 

specialization) tended to be associated with significant increases in employment levels, 

while increases in diversity tended to be associated with either decreases in 

employment levels or relatively smaller employment increases. For example, the 31 

counties in Appalachia that experienced a significant decrease in industrial diversity

between 2009 and 2012 had an average increase in employment levels of 5.7 percent

(see Table 5-2). Over the same time period, the 27 Appalachian counties that 

experienced a significant increase in industrial diversity had an average decrease in 

52
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

employment levels of 3 percent. In the case of changes between 1999 and 2009, 

counties that experienced increases in diversity averaged significantly smaller rates of 

employment increase than did counties that experienced decreases in diversity. One of 

the more notable recent examples of diversification and decline occurred in Clinton 

County, Ohio where DHL closed a major distribution hub that had employed 7,000 

people. This employment loss contributed to one of the largest increases in county-level 

diversity recorded between 2009 and 2012. Similarly, within Appalachia a large increase 

in diversity occurred in Whitfield County, Georgia—home to a declining carpet 

manufacturing industry.

Table 5-2: Trends and Components of County Diversification in the U.S. and 

Appalachia by Significance of Change in Industrial Diversity, 1999-2009 and 

2009-2012

5-2. LESSONS FOR PRACTICE

5-2A. GROWTH TENDS TO COMPLEMENT EXISTING INDUSTRIES, 

AMENITIES, OR RESOURCES

True industrial recruitment coups are rare. Most growth in employment, whether by 

existing businesses or new establishments, tends to complement the existing economic 

base in a region while drawing upon regional assets. The 1992 location of a BMW 

assembly plant in Greenville-Spartanburg provides an illustrative example of growth 

building on existing assets. While $130 million in state and local government incentives 

served as the popular explanation for BMW’s decision, this focus belied the importance 

53
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

of other factors. Since the late 1950s, the region had consciously sought to compensate 

for its declining, domestic textiles industry by attracting foreign investment—focusing 

first on manufacturers of equipment for the textiles industry and later on a broad 

assortment of industries ranging from chemicals to automotive supplies. By the time of 

BMW’s site selection decision, the Greenville-Spartanburg region had become one of 

the nation’s per-capita leaders in attracting foreign investment, with German companies 

including Michelin, Bosch, and Bertelesmann AG calling the region home (New York 

Times, 1992; Saporito & Solo, 1992). In addition to creating a friendly business 

environment for foreign investment, state and regional leaders had made significant 

investments in infrastructure, particularly a regional airport, and technical education 

and workforce training programs that proved attractive to BMW (Eichel, 1992; Kanter, 

2003). The well-known and much heralded Research Triangle Park in North Carolina 

serves as an additional example of an economic development success that resulted from 

at least fifty years of public policy effort—much to the consternation of economic 

developers seeking to easily replicate the success enjoyed in that region (Feldman & 

Desrochers, 2003). 

County employment growth proceeds less from filling in gaps in a region’s economic 

portfolio than it does from building on existing strengths (Frenken et al., 2007; Wagner 

& Deller, 1998). From the perspective of firms making location and expansion decisions, 

a region’s existing portfolio of industries can signal strengths or weaknesses on site 

selection criteria such as infrastructure capacity, workforce skills, and quality of life. For 

economic developers, these and other site selection criteria can be thought of as 

regional competencies. These competencies can be exploited by encouraging the 

expansion of existing and related industries through attraction and retention activities. 

Activities might also be aimed at enhancing existing strengths or addressing weaknesses 

in the current set of regional competencies. Fundamental, intentional changes to a 

region’s economy tend to result from an incremental process. Attracting truly new 

industries to a region can be a hard sell since regional conditions may be less than ideal 

for success in many industries. Further, a region’s competencies cannot be changed 

substantially in short order. For example, specialized training programs might be used to 

quickly ready workers for opportunities in particular industries, but changes that result 

in the opportunity for attracting wholly new industry sectors may only result from 

orchestrating a long, coordinated effort amongst entities such as governments, the 

business community, school districts, universities, and community colleges. 

5-2B. SHORT-RUN BENCHMARKING OF ECONOMIC DIVERSITY MAY 

CONFUSE EASILY MEASURABLE PROGRESS WITH DESIRABLE ECONOMIC

OUTCOMES

Distressed counties in Appalachia tend to be less diverse than the more prosperous 

counties in the Region. However, the statistics presented in this chapter indicate that, in 

many instances, diversification may not be an appropriate short-run goal. Empirically, 

short-run diversification is more likely to be associated with slow growth or employment 

declines than is economic specialization. This undesirable diversification is likely 

unintentional, with higher diversity measures resulting from often significant 

54
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

employment losses in particular industries effecting a more even distribution of 

employment across all industries. Building on the previous lesson in this chapter, a 

logical explanation for a specialization’s association with higher growth rates is that 

conditions already exist to support related activities in a particular region so more of 

these industries may be brought into the region in a relatively quick fashion. If the 

location of truly new activities in a region is rare, then the rapid, large-scale location of 

truly new activities in a region is even rarer. Lacking significant evidence of the success 

that can be won in a particular region, individual firms are likely to practice wait-and-see 

approaches as industries gain a foothold in new territories.

If economic diversity is to be used as a measure for tracking and benchmarking a 

region’s economy, then it should be considered in combination with other indicators 

such as total employment levels, unemployment and poverty rates, and the growth or 

decline of particular industries. This multidimensional approach can tell a developer 

much more about the outcomes and desirability of changes to a region’s economy than 

a single diversity measure can.

6. COMPREHENSIVE EXAMINATION OF ECONOMIC DIVERSITY CAN 

REVEAL ECONOMIC STRENGTHS AND WEAKNESSES

The call to avoid putting all your eggs in one basket presents only limited value for 

economic development practice. This maxim captures the idea that diversity can yield 

value in the form of economic stability and increased, long-term growth rates, but it 

shines little attention on the actual industries and firms that comprise a region’s 

economic base—the proverbial eggs that most directly impact outcomes related to 

wages in a region and the prospects for current and future growth rates. 

Mark Twain (1894, p. 197) offered an alternative perspective on this familiar quote—

“put all your eggs in one basket and—WATCH THAT BASKET.” As opposed to the more 

commonly cited quote, this version does not imply that there is anything inherently 

dangerous about specialization. For economic development, this version suggests an 

approach that does not simply aim for an even distribution of economic activity across 

industries—the implications being that the exact industries in question matter for 

economic outcomes and detailed knowledge of a region’s economy can provide insights 

about existing strengths and weaknesses.

This chapter presents what can be learned through a detailed examination of regional 

diversity measures—an exercise in carefully watching the regional basket of economic 

activities. In particular, several measures of diversity are examined to determine their 

relationships to one another and economic outcomes in a region. 

6-1. INDUSTRIAL DIVERSITY OF EMPLOYMENT AND EARNINGS

In the preceding chapters, industrial diversity has referred to the distribution of regional 

employment across NAICS-defined industrial groupings. This approach treats each 

industry classification as equivalent for the purposes of calculating diversity measures, 

with regions “rewarded” with a higher score when employment is distributed equally 

55
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

across all these classifications. An alternative way to consider industrial diversity is by 

examining the distribution of earnings across these same industry classifications. This 

approach weights employment in each industry according to the income earned by 

employees in these industries. 

This section presents information on industrial diversity of employment and earnings 

across the U.S. and Appalachia, with particular attention paid to the relationships 

between these two measures and these measures and regional economic outcomes. In 

order to illustrate how this diversity manifests itself in concrete settings, the section 

concludes with a discussion of examples of industrial diversity in Appalachian counties. 

6-1A. INDUSTRIAL DIVERSITY OF EMPLOYMENT

In 2012, industrial diversity of employment in the U.S. ranged from a low entropy 

measure of 1.02 in Chattahoochee County, GA to a high measure of 5.58 in Orange 

County, CA in 2012. Between these extremes, and as depicted in Figure 6-1, most 

counties (71 percent) have diversity measures which are less than one standard 

deviation from the mean diversity value of 4.51. More diversity observations are located 

above the mean value than below it, but no counties have a diversity measure greater 

than two standard deviations above the mean and 108 counties have a diversity 

measure greater than two standard deviations below the mean—reflecting the 

presence of very low diversity outliers without very high diversity counterparts.

Figure 6-1: Distribution of Industry-based Employment Diversity for U.S. 

Counties, 2012

56
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

In addition to the relationships among employment diversity, urban characteristics, and 

industry mix discussed in chapters 3 and 4, economic outcomes in Appalachia appear to 

have some relationship to diversity. Table 6-1 cross-tabulates the level of diversity in 

Appalachian counties versus economic status in those counties, while controlling for the 

different distributions of diversity in each county character type. Across the five 

classifications of economic status, distressed counties are most likely to have a low 

diversity measure and least likely to have a high diversity measure. Approximately 56 

percent of distressed counties in Appalachia have an employment diversity level of 

below average or low, with this combination accounting for no more than 40 percent of 

counties in the other economic status groups. As county economic status proceeds to 

the attainment level, counties generally become more likely to have a high diversity 

level and less likely to have a low diversity level. 

Table 6-1: Level of Industrial Diversity in Appalachia by County and Economic 

Status, Controlled for County Character, 2012

The general trend of increasing diversity along with improved economic status does not 

hold universally. For example, the few very low diversity counties in Appalachia are 

classified as either at-risk or transitional—not distressed. Additionally, the competitive 

classification has a larger percentage of low diversity counties than is present in the 

transitional classification. The few examples of high diversity counties in the distressed 

and at-risk classifications also run counter to the general pattern of higher diversity 

being associated with improved economic status. 

6-1B. INDUSTRIAL DIVERSITY OF EARNINGS

Industrial diversity of earnings has a high positive correlation with industrial diversity of 

employment across U.S. counties.8 As a result, the distribution of earnings diversity 

levels across economic status categories is largely equivalent to the distribution of 

employment diversity levels across the same groupings. There are differences between 

the two measures, however. For example, earnings tend to be more concentrated 

across industries than employment is, resulting in average earnings diversity being lower 

than average employment diversity.

8 r = 0.95 at p<0.001

Level of industrial diversity by 

county Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% High diversity 3.1 9.1 22.6 25.0 33.3 15.0

% Above average diversity 40.8 51.5 49.5 41.7 33.3 47.6

% Below average diversity 46.9 29.3 23.6 25.0 33.3 30.5

% Low diversity 9.2 8.1 3.4 8.3 0.0 6.0

% Very low diversity 0.0 2.0 1.0 0.0 0.0 1.0

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Levels based on standard deviations from mean

Economic Status

Data sources: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

57

 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

As shown in Figure 6-2, employment diversity exceeds earnings diversity in all but a 

small percentage of U.S. counties. In the hypothetical situation of equal employment 

across all industries in a high employment diversity county, a lower diversity of earnings 

would result from total earnings differences across these industries—reflecting wage 

differentials.

Figure 6-2: Distribution of the Ratio of Industry-based Earnings Diversity to 

Employment Diversity for U.S. Counties, 2012

58
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

For economic developers, job quality is often equated with salaries or wages paid. 

Examining the ratio of earnings diversity to employment diversity in a particular region 

might provide insight on the concentration of earnings relative to the concentration of 

employment across industries—potentially offering the first clues to an over- or underdependence on high or low paying jobs. Table 6-2 cross-tabulates the level of the ratio 

of earnings to employment diversity in Appalachian counties versus the economic status 

of these counties. Similar to the case with employment diversity and economic status, 

distressed counties are the least likely to have an above average or high ratio between 

earnings and employment diversity, and they are the most likely to have a low or very 

low ratio between these measures. While the likelihood for high ratios between these 

measures tends to increase as economic status improves, there are many fewer 

counties with high ratios than there are counties with high employment diversity 

measures. Excepting the three attainment counties, counties in the other classifications 

are more likely to have a low or very low ratio between these measures than they were 

to have a low employment diversity measure. 

Table 6-2: Level of Ratio of Industry-based Earnings Diversity to Employment 

Diversity in Appalachia by County and Economic Status, 2012

Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% High ratio 3.1 5.1 8.7 16.7 0.0 6.7

% Above average ratio 43.9 60.6 56.3 50.0 100.0 54.5

% Below average ratio 30.6 20.2 25.5 16.7 0.0 25.0

% Low ratio 18.4 9.1 7.7 8.3 0.0 10.5

% Very low ratio 4.1 5.1 1.9 8.3 0.0 3.3

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Levels based on standard deviations from mean

Economic Status Level of ratio of industry-based 

earnings to employment 

diversity by county

Data sources: Estimated county employment and earnings by industry, Economic Modeling Specialists 

International (EMSI), 2012

59
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

6-1C. ILLUSTRATIVE EXAMPLES OF INDUSTRY-BASED EARNINGS AND 

EMPLOYMENT DIVERSITY IN APPALACHIA

Without further investigation of conditions in an individual county, the explanations for 

particularly high or low ratios between earnings and employment diversity—and 

economic conditions accompanying these differences—can remain unclear. Table 6-3 

lists the names and basic characteristics of several counties in Appalachia that had 

either high, low, or very low ratios between earnings and employment diversity in 2012. 

While the general trend depicted in Table 6-2 holds, multiple economic statuses are 

represented in each of the groupings of ratio levels. Functionally, these counties pursue 

a variety of specializations from agriculture & resource extraction to engineeringintensive manufacturing.

Table 6-3: Illustrative Examples of the Ratio between Industry-based 

Employment and Earnings Diversity in Appalachia, 2012

All the high ratio counties have a higher standardized earnings diversity value than their 

standardized value for employment diversity. In Calhoun County, West Virginia, there is 

significant employment and earnings in the “Crude Petroleum and Natural Gas 

Ratio of earnings 

diversity to 

employment 

diversity

County

High

Calhoun, WV 0.81 0.77 3,736 Agriculture & resource 

extraction

Distressed Rural

Holmes, OH 1.18 1.12 27,052 Capital-intensive 

manufacturing

Transitional Rural

Bath, VA 0.80 0.75 2,686 Healthcare Competitive Rural

Low

Mineral, WV 0.87 0.96 10,886 Engineering-intensive 

manufacturing

Transitional Rural

Tompkins, NY 0.88 0.94 62,688 Higher education Transitional Mixed Rural

Martin, KY 0.76 0.86 3,761 Agriculture & resource 

extraction

Distressed Rural

Very low

Montour, PA 0.70 0.84 22,915

Corporate 

management & 

administration

Competitive Rural

Boone, WV 0.67 0.85 10,169 Agriculture & resource 

extraction

Transitional Rural

McDowell, WV 0.73 0.83 7,284 Agriculture & resource 

extraction

Distressed Rural

Data sources: Estimated county employment and earnings by industry, Economic Modeling Specialists 

International (EMSI), 2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Functional specializations defined per Statistical 

Portrait of Economic Diversity in Appalachia section 2-3 3. Levels based on standard deviations from mean 

4. County character designated using U.S. Census 2010 data and Isserman (2005) typology 

County characteristics

Standardized 

industrybased 

earnings 

diversity

Standardized 

industrybased 

employment 

diversity

Employment 

estimate 

(2012)

Functional 

specialization

Economic 

status

County 

character

60
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Extraction” industry.9 However, since average earnings in this industry are significantly 

lower than they are in many of other industries in the county, “Crude Petroleum and 

Natural Gas Extraction” accounts for approximately a third of total employment but only 

18 percent of total earnings in the county. While Bath County, Virginia is classified as 

competitive and Calhoun is distressed, they share similar earnings and employment 

diversity characteristics. For Bath, the high ratio between these measures results from 

substantial employment in the hospitality industry10 that pays significantly less on 

average than employment in the county’s second largest industry by employment—

“General Medical and Surgical Hospitals”11—does. Finally, Holmes County, Ohio 

contains employment across a wide diversity of manufacturing industries, with the 

mixture of mid- and high-paying jobs across these industries making earnings diversity 

even higher than employment diversity in this case. 

Standardized employment diversity exceeds standardized earnings diversity in all the 

low and very low ratio example counties listed in Table 6-3. Mineral County, West 

Virginia is home to many high-paying jobs associated with the Allegany Ballistics 

Laboratory12—resulting in a concentration of earnings by industry relative to 

employment by industry and a low ratio between the two diversity measures. High 

levels of employment associated with Cornell University and Ithaca College in Tompkins 

County, New York13—and the relatively high earnings associated with these jobs—result 

in a similarly low ratio between these diversity measures. Finally, employment 

associated with coal mining14 and a federal penitentiary15 in Martin County, Kentucky 

accounts for approximately one quarter of total employment in the county. The high 

pay in these industries relative to other employment in the county results in these 

industries accounting for just over half of total earnings in the county and a low ratio 

between earnings and employment diversity.

Montour County, Pennsylvania is a competitive county with low employment diversity 

and very low earnings diversity. These diversity conditions result most directly from the 

large proportion of county employment comprised by jobs related to the corporate 

headquarters of a regional medical center, and the even larger proportion of earnings 

that can be attributed to this center. Boone and McDowell counties in West Virginia 

represent two additional examples of regions with a very low ratio between earnings 

and employment diversity. In both these counties, relatively high-paying jobs in the 

9 NAICS 2007, 211111

10 NAICS 2007, 721110

11 NAICS 2007, 622110

12 e.g., Guided Missile and Space Vehicle Propulsion Unit and Propulsion Unit Parts 

Manufacturing (NAICS 2007, 336415)

13 Colleges, Universities, and Professional Schools (NAICS 2007, 611310)

14 Bituminous Coal and Lignite Surface Mining (NAICS 2007, 212111); Bituminous Coal 

Underground Mining (212112); Support Activities for Coal Mining (213113)

15 Federal government, civilian, excluding Postal Service (EMSI NAICS code 901199; see 

http://www.economicmodeling.com/2012/05/03/emsi-data-2012-1-final/) 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

mining industry account for a significant percentage of employment and earnings. From 

an economic outcomes perspective, Boone ranks as a transitional county while 

McDowell—which has a slightly lower employment diversity—ranks as a distressed 

county. 

6-2. ESTABLISHMENTS SIZE AND DIVERSITY 

The size of establishments in a region can affect conditions such as the vulnerability of a 

regional economy to periodic downturns and the supply of entrepreneurs, as well as 

being a factor that can affect the economic diversity of a place. Rationales for the 

impact of establishment size are similar to those of economic diversity’s impact. For 

example, the loss of any one large establishment in a region could have a significant, 

negative impact just as overdependence on one industry sector can leave a region 

exposed to economic downturns. When employment is spread more evenly across 

establishments, or industries, any one downturn may be offset by the continued vitality 

of other activities. In relation to entrepreneurship, large establishments may stifle the 

supply of entrepreneurs by limiting opportunity for individuals to access the managerial 

skills, capital, and experience necessary to successfully start and expand a business 

(Chinitz, 1961; Watkins, 1963). 

This section presents information on the average size of establishments across 

Appalachia and the relationships among establishment size, industrial diversity, and 

economic performance. The section concludes with a discussion of example Appalachian 

counties that illustrate various establishment size and diversity conditions. 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

6-2A. ESTABLISHMENT SIZE, GEOGRAPHY AND ECONOMIC OUTCOMES

The average establishment size by county in 2011 varied significantly across geography 

and county character. As listed in Table 6-4, in 2011, counties in Appalachia were about 

15 percent more likely than counties across the U.S. to have a larger than average 

establishment size. In Appalachia, rural counties were less than half as likely as other 

counties to have an above average establishment size. Eighty percent or more of Mixed 

Rural, Mixed Urban, and Urban counties in Appalachia had an above average 

establishment size.

Table 6-4: Level of Estimated Average Establishment Size by County and 

County Character, 2011

U.S. counties

Appalachian 

counties Rural Mixed Rural

Mixed 

Urban Urban

Number of county equivalents 3,113 420 240 146 29 5

% Very small 0.2 0.0 0.0 0.0 0.0 0.0

% Small 14.2 2.9 5.0 0.0 0.0 0.0

% Below average 39.2 37.6 56.3 13.7 6.9 20.0

% Above average 33.2 45.2 34.2 64.4 44.8 20.0

% Large 10.9 12.6 3.8 19.2 44.8 60.0

% Very large 2.4 1.7 0.8 2.7 3.5 0.0

Notes: 1. County character designated using U.S. Census 2010 data and Isserman (2005) typology 2. Levels based 

on standard deviations from mean

Appalachian counties by county character

Level of estimated average 

establishment size by county

Data source: Estimated employment and establishments by industry, Economic Modeling Specialists 

International (EMSI), 2012

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

To correct for differences in establishment size associated with the different size 

distributions across county character types, the remaining data presented on 

establishment size and diversity controls for county character. As evidenced in Table 6-

5, Appalachia’s distressed and at-risk counties were more likely than counties in any 

other economic status grouping to have an above average establishment size when 

controlling for county character. With many of Appalachia’s distressed counties located 

in rural areas, this suggests that Appalachia’s rural counties tend to have larger than 

average establishment sizes compared to their counterpart counties across the United 

States. By contrast, Appalachia’s transitional, competitive, and attainment counties 

were more likely to have below average establishment sizes. 

Table 6-5: Level of Estimated Average Establishment Size in Appalachia by 

County and Economic Status, Controlled for County Character, 2011

Level of estimated average 

establishment size by county Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% Small 0.0 1.0 3.4 8.3 33.3 2.4

% Below average 33.7 33.3 37.0 33.3 33.3 35.2

% Above average 48.0 48.5 45.7 50.0 33.3 46.9

% Large 18.4 15.2 11.5 0.0 0.0 13.6

% Very large 0.0 2.0 2.4 8.3 0.0 1.9

Economic Status

Data source: Estimated employment and establishments by industry, Economic Modeling Specialists International 

(EMSI), 2012

Notes: 1. Levels based on standard deviations from mean

64
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

6-2B. ILLUSTRATIVE EXAMPLES OF ESTABLISHMENT SIZE AND DIVERSITY 

IN APPALACHIA

Across U.S. counties in 2011, there was a slight negative correlation between 

establishment size and industrial diversity, suggesting that as establishment size 

increases industrial diversity tends to decrease.16 

Figure 6-3: Level of Average Establishment Size and Industrial Diversity in 

Appalachia, Controlled for County Character, 2011/2012

Figure 6-3 displays the distribution of average establishment size and industrial diversity 

across Appalachian counties. While the majority of Appalachian counties (266 of 420) 

exhibit about average levels of both establishment size and industrial diversity, there 

are numerous instances that speak to the negative correlation between establishment 

size and diversity. Eleven counties had a large or very large establishment size and a low 

or very low level of diversity. There were also 141 instances of counties having an about 

average level on one characteristic and a more extreme level on the other characteristic.

16 r = -0.06 at p < 0.001

65

 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Brief explanations of the examples listed in Table 6-6 can shed further light on the 

conditions existing in Appalachia’s counties across a variety of average establishment 

sizes. Centre County, Pennsylvania and Roane County, Tennessee serve as examples of 

counties with very large average establishment sizes. Both counties have low or very 

low levels of employment diversity, are classified in the transitional economic status 

grouping, and have more than a quarter of their employment in a single, relatively highpaying establishment. Centre County is home to the main campus of Penn State 

University, while Roane County hosts research and development employment related to 

Oak Ridge National Laboratory.

Table 6-6: Illustrative Examples of Average Establishment Size in Appalachia, 

2012

Both the listed examples of large average establishment size counties are classified as 

rural and distressed. In Macon County, Alabama, employment connected to the 

Tuskegee National Forest and Tuskegee University accounts for approximately 25 

percent of jobs in the county and nearly 50 percent of total earnings. Outside of 

employment in Macon County’s top two industries (i.e., higher education and 

government), the average earnings per job does not reach $30,000. Perry County, 

Kentucky has an above average level of diversity, but several large mining 

establishments account for approximately 15 percent of employment and 30 percent of 

earnings in the county. 

Cherokee County, Georgia and Grayson County, Virginia have small average 

establishment sizes and above average levels of employment diversity. Cherokee is a 

Level of estimated 

average establishment 

size, 2011

County

Very large

Centre, PA Low 96,347 Higher education Transitional Mixed Rural

Roane, TN Very low 23,000 Knowledge-intensive 

business services

Transitional Mixed Rural

Large

Macon, AL Below average 7,780 Higher education Distressed Rural

Perry, KY Above average 15,688 Agriculture & resource 

extraction

Distressed Rural

Small

Cherokee, GA Above average 75,293 Finance, insurance & real 

estate Competitive Mixed Urban

Grayson, VA Above average 4,353 Agriculture & resource 

extraction

At-Risk Rural

Data sources: Estimated employment and establishments by industry, Economic Modeling Specialists 

International (EMSI), 2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Functional specializations defined per Statistical 

Portrait of Economic Diversity in Appalachia section 2-3 3. Levels based on standard deviations from mean 

4. County character designated using U.S. Census 2010 data and Isserman (2005) typology 5. Levels controlled 

for county character

County characteristics

Level of industrybased 

employment 

diversity

Employment 

estimate 

(2012)

Functional specialization

Economic 

status

County 

character

66
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

competitive county located in metropolitan Atlanta. Reflecting a common pattern in 

many rural and exurban counties, the local school system represents the county’s 

largest employer with many small establishments in retail trade and finance, insurance 

& real estate sectors. Cherokee depends heavily on the surrounding region for economic 

opportunities, with more than 40,000 resident workers commuting outside the county 

for work. Grayson is an at-risk county in the Blue Ridge Mountains of rural southwestern 

Virginia. Many residents commute elsewhere for work and employers include the local 

school system, a few manufacturers, service and retail establishments serving the local 

population, and small-scale tourism-related establishments. 

6-3. OCCUPATIONAL DIVERSITY

The predominant workforce skillsets in a region can limit or expand opportunities for 

economic development. This section presents information on occupational diversity 

across Appalachia and its relationships with economic outcomes and industrial diversity. 

The section concludes with a discussion of example Appalachian counties that illustrate 

various occupational and industrial diversity characteristics. 

6-3A. OCCUPATIONAL DIVERSITY AND ECONOMIC OUTCOMES

The distribution of occupational diversity across the economic status groupings of 

Appalachian counties is similar to that of industrial diversity. As listed in Table 6-7, 

distressed counties are the most likely to have a below average or lower level of 

occupational diversity, followed closely by transitional and at-risk counties. More than 

80 percent of counties in the competitive and attainment classifications have above 

average or higher levels of occupational diversity. 

Table 6-7: Level of Occupational Diversity in Appalachia by County and 

Economic Status, Controlled for County Character, 2012

Level of occupational diversity 

by county Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% High 1.0 5.1 7.2 8.3 0.0 5.2

% Above average 69.4 70.7 65.9 75.0 100.0 68.3

% Below average 21.4 17.2 22.1 16.7 0.0 20.5

% Low 7.1 5.1 3.9 0.0 0.0 4.8

% Very low 1.0 2.0 1.0 0.0 0.0 1.2

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Appalachian counties by economic status

Notes: 1. Entropy calculated across 96 minor occupational groups in 2000 Standard Occupational 

Classification, Bureau of Labor Statistics 2. Bureau of Labor Statistics' national Staffing Pattern Matrix used to 

estimate employment by minor occupational groups 3. Levels based on standard deviations from mean

67
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

While there is a significant positive correlation between industrial- and occupational 

diversity, there are instances where the two values vary considerably from one 

another.17 As shown in Figure 6-4, most Appalachian counties have about average levels 

of industrial and occupational diversity. However, there are almost 80 counties that 

have an about average level in one diversity measure and a more extreme level in the 

other measure. 

Figure 6-4: Levels of Industrial and Occupational Diversity in Appalachia, 

2012

17 r = 0.76 at p < 0.001

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

6-3B. ILLUSTRATIVE EXAMPLES OF OCCUPATIONAL DIVERSITY IN 

APPALACHIA

As before, descriptions of examples—listed in Table 6-8—can provide further insight on 

the conditions associated with various levels of occupational diversity in Appalachian 

counties. Beaver County, Pennsylvania has high levels of occupational and industrial 

diversity. Beaver County is a transitional, mixed urban county that is part of the 

Pittsburgh metropolitan area. The county houses a diverse array of employment across 

industries and occupations—with none of the industries accounting for more than five 

percent of total employment in the county. 

Table 6-8: Illustrative Examples of Occupational Diversity in Appalachia, 2012

Montour County, Pennsylvania has below average levels of occupational and industrial

diversity. A competitive county, Montour County has a pronounced workforce 

specialization in occupations related to medical science & health services. Hancock 

County, is a distressed, rural counties with low levels of occupational and industrial 

diversity. Hancock contains a very small economy with employment concentrated in 

occupations related to skilled and semi-skilled labor and machine operation.

6-4. LESSONS FOR PRACTICE

6-4A. DIVERSITY CAN SIGNAL ECONOMIC STRENGTH OR WEAKNESS

While economic diversity tends to be greater in those counties that are more 

prosperous, the examples discussed in this chapter demonstrate that this is not a 

universal truth. Examining diversity from employment, earnings, and occupational 

perspectives can provide more insight into the strengths and weaknesses of a county’s 

economy than examining a single diversity metric can. In many urban areas, 

Level of occupational 

diversity

County

High

Beaver, PA High 72,341 Capital-intensive 

manufacturing

Transitional Mixed Urban

Below average

Montour, PA Below average 22,915 Healthcare Competitive Rural

Low

Hancock, TN Low 1,680 Agriculture & resource 

extraction

Distressed Rural

Data sources: Estimated employment and establishments by industry, Economic Modeling Specialists 

International (EMSI), 2012

Notes: 1. Entropy calculated across 96 minor occupational groups in 2000 Standard Occupational 

Classification, Bureau of Labor Statistics 2. Bureau of Labor Statistics' national Staffing Pattern Matrix used to 

estimate employment by minor occupational groups 3. Levels based on standard deviations from mean 4. County 

character designated using U.S. Census 2010 data and Isserman (2005) typology 5. Functional specializations 

defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3 6. Levels controlled for county 

character

County characteristics

Level of 

industrial 

diversity

Employment 

estimate 

(2012)

Functional specialization Economic 

status

County 

character

69
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

employment diversity exists in the form of business services that allow the development 

of numerous industrial specializations. While this can also be the case in more rural 

areas, diversity in these settings can also signal the lack of a significant, competitive 

industrial specialization within the region. In one setting diversity can provide a strength 

in the form of multiple potential avenues for growth and hedges against shocks. In 

another setting diversity can signal a small amount of employment in many industries—

none of which have been able to gain the traction necessary to achieve significant 

growth.

Considered with employment diversity, earnings diversity can offer insight into the 

prosperity that a region’s mix of industries provides. A ratio of earnings diversity to 

employment diversity that is higher than average can signal specializations in relatively 

low paying jobs, since this situation makes earnings across sectors more equal than 

employment across sectors. A ratio of earnings diversity to employment diversity that is 

lower than average can signal specializations in high paying jobs—effectively making

earnings more concentrated than employment. Occupational diversity can provide 

similar signals, with a diverse county potentially indicating that there are just about as 

many low-skill jobs as high-skill (and often high-wage) jobs. Low occupational diversity

might indicate a specialized workforce to support a competitive, high-wage industry, 

though it may also indicate a dearth of high-skill jobs. 

For practitioners, a comprehensive consideration of diversity metrics can help to 

understand whether economic diversity is a strength or weakness for a particular 

region. In almost all cases, proper interpretation of the clues that diversity metrics offer 

requires an examination of a region’s industry mix. This further investigation can help 

economic developers to uncover whether high diversity scores signify a competitive 

economy that can support a wide range of economic activities, or a struggling economy 

that lacks the critical mass of skills or support services to encourage growth on many 

fronts. Likewise, low diversity scores might signal an overdependence on a volatile, lowpaying industry, or an economy that enjoys the benefits of depth in related workforce 

skillsets and industries that make the region’s firms highly competitive. 

6-4B. CHARACTERISTICS OF ECONOMIC DEPENDENCE MATTER FOR 

STABILITY AND GROWTH 

Higher than average establishment sizes and low diversity metrics related to earnings 

and employment can signal economic dependence on a few sectors or firms. Addressing 

whether this dependence is good or bad for a region’s economic development requires 

a closer look at the firms and industries that make up the economy. The concepts of 

stability and growth can be useful for guiding the type of additional analysis that might 

help practitioners understand the nature of economic dependency in their regions. 

From a stability perspective, large firms are, on average, less likely to dissolve than small 

firms are. They may also have the resources and diverse product lines necessary to 

weather downturns while avoiding layoffs. On the other hand, large firms with remote 

ownership may be more likely to relocate than are small, locally-owned firms that may 

be more invested in the community in a variety of ways. The danger of relocation also 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

depends on the degree to which firms depend upon relatively scarce geographic 

features or human, institutional, or natural resources. 

The dominant industries in a region can also affect stability. For example, farming and 

mining industries heavily affected by commodity prices are generally more likely to 

exhibit greater volatility in employment and earnings than are traditionally more stable 

sectors such as higher education or government. Industries that depend primarily on 

local demand, such as secondary education, healthcare, and many personal services and 

retail sectors, may be more likely to maintain at least a minimum level of local 

employment than are those industries dependent on external demand sources. 

From a growth perspective, small firms are, on average, more likely than large firms to 

experience rapid expansion or contraction of employment and earnings. Industries also 

possess very different growth potentials. Industries such as healthcare, education, and 

government may be guided primarily by demographic imperatives, while industries 

dependent on continued research and development can experience explosive growth as 

new products create new demand. For regional development practitioners, the 

prospects of capturing growth locally may be just as important a consideration as the

prospects of firm growth more generally. From this perspective, some large firms may 

have more local growth potential than small firms, particularly if the firms pursue a 

diverse array of activities. Development efforts to grow the regional economy might 

focus on encouraging firms to diversify their local presence—potentially spurred on by 

cost savings that accompany their consolidation. Similarly, practitioners might seek to 

attract industries related to existing firms in an effort to both increase regional 

employment and enhance the competitiveness of local firms. 

To characterize economic dependency and stability in their region, practitioners might 

investigate factors such as:

• the location of firm ownership

• the competitive prospects of individual firms and industries

• financial and business support services available to small and large firms in the 

region

• the prospects for diversification within existing firms and existing industries

• the sourcing policies of existing and potential firms in the region

• the workforce skills, infrastructure, and capital required for diversification of 

existing firms and industries 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

7. FUNCTIONAL AND REGIONAL CONTEXT CAN SHED LIGHT ON 

ECONOMIC OPPORTUNITIES AND THREATS

Regional economies do not respect the artificial lines drawn to better understand them. 

The availability and format of regional data tends to bias analysis of regional economies 

to NAICS-defined industries and county-level geography. While this focus can yield 

considerable insights, it tends to undercut alternative methods for understanding 

regional economies. In particular, analyses that consider the functional and regional 

context of county-level economic activities can introduce concepts related to functionand geography-based economic connections into understandings of local economies. 

Adopting a functional perspective to understanding regional economies involves 

grouping industries into several large classifications—e.g., agriculture and resource 

extraction, capital-intensive manufacturing, and higher education—based on factors 

such as shared inputs, similar outputs, and/or similar technological or skill requirements 

necessary to perform the work customary to these industries. These functional 

groupings allow for the consideration of a county’s economic connections to broad 

economic forces that may not be apparent when examining only the specific industries 

resident to a county. Similarly, grouping occupations into knowledge clusters that 

require similar types and levels of knowledge may reveal regional competencies or 

weaknesses that a more specific examination of occupations can gloss over.

A regional perspective acknowledges that few counties rely only upon the economic 

activity that takes place within their formal boundaries. Workers commute elsewhere to 

work, households shop and purchase services outside their counties, and firms in 

counties draw from neighboring counties, and beyond, to populate their workforces. 

The regional context can provide insights into the surrounding economic strengths 

individual counties can draw from, as well as acknowledging the weaknesses that prevail 

in the regional economy. 

This chapter seeks to understand what can be learned through considering the 

functional and regional context of counties. Functional and knowledge cluster

specializations and function-, industry-, and knowledge-based economic diversity are 

examined for counties and their commuting sheds, with a particular emphasis on the 

association between these measures and economic outcomes (see Chapter 2 for details 

on these designations and geographies). The chapter concludes with an examination

and discussion of functional county roles that jointly consider the industrial 

compositions of counties and their regions and the commuting connections among 

counties and their regions. 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

7-1. FUNCTIONAL AND KNOWLEDGE SPECIALIZATIONS IN COUNTIES AND 

COMMUTING SHEDS

7-1A. FUNCTIONAL SPECIALIZATIONS

As noted in section 4-2, functional specializations are not distributed evenly across 

Appalachia and the United States. Figure 7-1 depicts the functional specializations of 

counties across Appalachia. Central Appalachia consists of many counties with an 

agriculture & resource extraction functional specialization, with relatively few counties 

with a specialization in one of the manufacturing functions. Counties with 

manufacturing specializations tend to cluster in Pennsylvania and Ohio and south of 

West Virginia. The remaining specializations are spread throughout the region, with few 

easily identifiable functional clusters.

Figure 7-1: Functional Specializations by Counties in Appalachia, 2012

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

A county’s functional specialization appears to have a relationship to economic 

outcomes. As shown in Table 7-1, just over 65 percent of distressed counties and 45 

percent of at-risk counties had a functional specialization in agriculture & resource 

extraction. By contrast, no distressed or at-risk counties had functional specializations in 

finance, insurance & real estate, corporate management & administration, or 

knowledge-intensive business services. 

Table 7-1: Functional Specializations with Most Extra jobs in Appalachia by 

County and Economic Status, 2012

Functional specialization Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

Agriculture & resource extraction 65.3 45.5 27.9 16.7 0.0 40.2

Capital-intensive manufacturing 23.5 40.4 35.1 8.3 33.3 32.9

Corporate management & 

administration

0.0 0.0 0.0 8.3 0.0 0.2

Distributive services 1.0 1.0 4.8 16.7 33.3 3.6

Engineering-intensive manufacturing 1.0 5.1 5.3 0.0 0.0 4.1

Finance, insurance & real estate 0.0 0.0 2.9 8.3 33.3 1.9

Government 2.0 3.0 4.8 8.3 0.0 3.8

Healthcare 3.1 3.0 8.2 8.3 0.0 5.7

Higher education 4.1 0.0 7.7 8.3 0.0 5.0

Knowledge-intensive business services 0.0 0.0 1.0 8.3 0.0 0.7

Media, entertainment & recreation 0.0 2.0 2.4 8.3 0.0 1.9

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Functional specializations defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3

Appalachian counties by economic status

74
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

By mapping the functional specializations of county commuting sheds across counties in 

Appalachian states, Figure 7-2 depicts a more regionalized picture of functional 

economic activity. While an individual county’s functional specialization only accounts 

for employment within that county, the functional specialization of a county’s 

commuting shed accounts for employment in all those counties identified as that 

county’s major commuting partners. Neighboring counties are much more likely to 

share the same commuting shed functional specialization than they are to share the 

same functional specialization for employment within their own boundaries. For 

example, Penn State University and other colleges and universities in central 

Pennsylvania create an identifiable hub of higher education activity based on 

commuting sheds, while the apparent regional impacts of this activity were minimized 

when examining functional specializations on a county-by-county basis. Similarly, the 

prevalence of finance, insurance & real estate and knowledge-intensive business 

services specializations demonstrate the often far-flung impacts of cities, such as 

Atlanta, Birmingham, New York City, and Philadelphia on their surrounding regions. 

Figure 7-2: Functional Specializations in Appalachia’s Commuting Sheds, 2012

Further, the significant number of corporate management & administration 

specializations in southwestern Ohio and northern Kentucky also demonstrate the 

75
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

impact of Cincinnati’s industry mix on the region. The lack of manufacturing 

specializations in central Appalachia continues to be apparent when considering 

functional specializations from a commuting shed perspective. The regional importance 

of government and recreation-related employment does become more apparent when 

viewed from a commuting shed perspective, however. The commuting shed perspective 

on functional specializations also suggests that many counties in Appalachia rely on 

economic activities that occur outside the ARC regional boundaries. For example, New 

York City is part of the commuting shed for Monroe County in northeast Pennsylvania. 

The knowledge-intensive business services specializations emerging from Atlanta in 

northwestern Georgia seem to demonstrate a similar phenomenon of significant activity 

crossing the ARC boundary.

Table 7-2 presents a cross-tabulation of county commuting shed functional

specializations by economic status. Distressed counties are most likely to be situated in 

a commuting shed with a functional specialization of capital-intensive manufacturing. 

An above average percentage of distressed counties have a commuting shed functional 

specialization in either agriculture & resource extraction or higher education. The 

knowledge-intensive business services and healthcare functions comprise an above 

average percentage of commuting shed specializations for competitive counties in 

Appalachia.

Table 7-2: Functional Specializations with Most Extra Jobs in Appalachia by 

County Commuting Shed and Economic Status, 2012

Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

Agriculture & resource extraction 22.5 26.3 13.5 0.0 0.0 18.1

Capital-intensive manufacturing 40.8 38.4 35.1 8.3 0.0 36.2

Corporate management & 

administration

1.0 0.0 1.0 0.0 0.0 0.7

Distributive services 3.1 1.0 4.3 0.0 66.7 3.6

Engineering-intensive manufacturing 1.0 4.0 3.4 0.0 0.0 2.9

Finance, insurance & real estate 1.0 1.0 4.3 0.0 33.3 2.9

Government 5.1 6.1 5.3 16.7 0.0 5.7

Healthcare 6.1 6.1 12.0 16.7 0.0 9.3

Higher education 19.4 13.1 14.4 33.3 0.0 15.7

Knowledge-intensive business services 0.0 3.0 5.8 25.0 0.0 4.3

Media, entertainment & recreation 0.0 1.0 1.0 0.0 0.0 0.7

Appalachian counties by economic status

Notes: 1. Functional specializations defined per Statistical Portrait of Economic Diversity in Appalachia section 2-3 

2. Commuting sheds defined per section 2-6

Data source: U.S. Census journey-to-work data, American Community Survey, 2006-2010; Estimated employment by 

industry, Economic Modeling Specialists International (EMSI), 2012

Commuting shed functional specialization

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

7-1B. KNOWLEDGE DIVERSITY AND KNOWLEDGE CLUSTER 

SPECIALIZATIONS

Thompson & Thompson (1987) argue that the quality and skills of a region’s workforce 

can help to ensure the “adaptability and mobility of human resources, and thereby 

[provide] perhaps the only sure road to security and opportunity”—much as other 

scholars posit that economic diversity can bring about stability and growth for a region’s 

economy (p. 560). The distribution of knowledge diversity across the economic status 

groupings of Appalachian counties reflects an increased tendency for Distressed and AtRisk counties to have either very high or very low levels of diversity (see Table 7-3). 

While these extremes of diversity are limited to a select few Appalachian counties, the 

tendency for relatively moderate values of knowledge diversity in counties with the 

highest economic status levels (i.e., in Competitive and Attainment counties) seems to 

indicate that positive economic performance depends on both not having employment 

overly concentrated in a few clusters of knowledge types and education levels and not 

having an overly balanced distribution across these knowledge clusters.

Table 7-3: Level of Knowledge Diversity in Appalachia by County and 

Economic Status, Controlled for County Character, 2012

Level of knowledge diversity 

by county Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% Very high diversity 3.1 0.0 0.5 0.0 0.0 1.0

% High diversity 9.2 19.2 18.3 25.0 0.0 16.4

% Above average diversity 46.9 48.5 45.7 50.0 66.7 46.9

% Below average diversity 31.6 24.2 26.9 8.3 33.3 26.9

% Low diversity 9.2 7.1 8.2 16.7 0.0 8.3

% Very low diversity 0.0 1.0 0.5 0.0 0.0 0.5

Economic Status

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated across 12 knowledge clusters; see Feser (2003) for details on knowledge cluster 

derivation 2. Bureau of Labor Statistics' national Staffing Pattern Matrix used to estimate employment by 

knowledge cluster 3. Levels based on standard deviations from mean

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Examining workforce specializations at the county level can shed further light on the 

relationship between knowledge diversity and economic performance. Based on a 

comparison with the share of employment made up by these occupations nationally, a 

full 70 percent of ARC counties have their largest knowledge cluster specialization in the 

Skilled, semi-skilled labor & machine operation cluster of occupations (see Table 7-4).18

Distressed and At-Risk counties are about ten percent more likely to have this cluster as 

their largest specialization, with the general trend reflecting a tendency for counties 

with higher economic status to have larger portions of their employment in service 

occupations. In part this tendency serves as evidence of the presence of higher skill and 

higher wage jobs (e.g., Medical science & health services) in counties of strong economic 

performance.19 Above average levels of service employment in these counties also 

reflect the demand for retail and personal services (e.g., Semi-skilled service) created by 

higher personal incomes.20

Table 7-4: Largest Knowledge Cluster Specializations by County and Economic 

Status, Controlled for County Character, 2012

18 Common professions in this cluster include janitors, laborers, landscapers and 

groundskeepers, farmworkers, and welders. On average, these jobs require less than 1 

year of post-secondary education—ranking 11th among the 12 knowledge clusters.

19 Common professions in this cluster include nurses, nursing aides, physicians, dental 

assistants, and pharmacists. On average, these jobs require just over 2.5 years of postsecondary education—the 5th highest average among the 12 knowledge clusters.

20 Common professions in this cluster include retail salespersons, customer service 

representatives, waiters and waitresses, stock clerks, child care workers, and food 

preparation workers. On average, these jobs require less than 1 year of post-secondary 

education—ranking 9th among the 12 knowledge clusters.

Knowledge cluster 

specialization Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

Skilled, semi-skilled labor 

& machine operation 82.7 81.8 62.5 16.7 66.7 70.5

Medical science & health 

services 8.2 4.0 11.5 16.7 0.0 9.1

Construction & specialized 

mechanical 2.0 7.1 7.2 25.0 0.0 6.4

Semi-skilled service 0.0 2.0 9.6 25.0 33.3 6.2

Education, counseling & 

therapy 0.0 3.0 5.8 0.0 0.0 3.6

Economic Status

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. See Feser (2003) for details on knowledge cluster derivation 2. Bureau of Labor Statistics' national 

Staffing Pattern Matrix used to estimate employment by knowledge cluster with specializations defined as detailed 

in Statistical Portrait of Economic Diversity in Appalachia section 2-5

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

7-2. ECONOMIC DIVERSITY IN COUNTIES AND COMMUTING SHEDS

For economic development purposes, practitioners may need to exploit opportunities or 

address threats related to industry sectors, workforce knowledge, or functional 

economic roles that span formal, jurisdictional boundaries. From the perspective of 

individual firms, understanding the region’s competencies in terms of industries, 

knowledge, and functional relationships can assist the effort to make informed location 

and expansion decisions. This section individually reviews the connections among 

county-level industry- and functional diversity, the commuting shed counterparts of 

these diversity measures, and the association between these measures and economic 

outcomes. 

7-2A. INDUSTRIAL DIVERSITY

Across U.S. counties, there is a moderate, positive correlation between industrial

diversity for counties and the same measure for county commuting sheds.21 Figure 7-3 

displays the distribution of county and commuting shed industrial diversity across 

Appalachia. The majority of counties have about average industrial diversity levels for 

both the county and commuting shed. Table 7-5 lists the commuting characteristics for 

each of the seven cross-tabulated categories of county and commuting shed diversity. 

On average, counties that have high industrial diversity and about average values for 

their commuting shed have the largest net commuting flows. Counties with about 

average industrial diversity values and high values for their commuting shed have the 

smallest net commuting flows, followed closely by counties that have a high diversity 

value along with their commuting shed. These findings suggest that more diverse 

economies attract workers from surrounding counties, while individuals in less diverse 

counties rely upon accessing jobs and exploiting business opportunities available in 

more diverse counties. 

21 r = 0.54 at p < 0.001

79

 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 7-3: Level of Industrial Diversity for Counties and their Commuting 

Sheds in the Appalachian Region, 2010

Table 7-5: County Commuting Flow Statistics by Level of County and 

Commuting Shed Diversity, 2006-2010

Number of 

county equivalents

Minimum Average Maximum

Both high 6 -13,163 -2,043 24,819

High / About average 41 -42,031 547 96,208

About average / High 9 -11,992 -4,412 1,547

Both about average 314 -38,183 -1,762 34,053

About average / Low or Very Low 16 -4,679 154 5,509

Low or Very Low / About Average 28 -2,736 -613 5,081

Both Low or Very Low 6 -928 45 1,291

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Commuting sheds defined in Statistical Portrait of Economic 

Diversity in Appalachia section 2-6 3. Levels based on standard deviations from mean

Level of industrial diversity for county and 

commuting shed

Net commuting flow statistics

Data source: U.S. Census journey-to-work data, American Community Survey, 2006-2010; Estimated employment by 

industry, Economic Modeling Specialists International (EMSI), 2012

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 7-6 cross-tabulates the levels of county and commuting shed diversity across the 

five economic status levels in Appalachia. Distressed and at-risk counties are more likely 

than average to have a low level of industrial diversity for the county and about average 

level of diversity for the commuting shed. By contrast, competitive and attainment 

counties are more likely than average to have a high level of county diversity and about 

average level of commuting shed diversity. In combination with the commuting 

characteristics shown in Table 7-5, these findings suggest that high diversity counties 

tend to serve as centers that attract commuters from less economically diverse

surrounding areas. On average, these economic centers tend to enjoy higher levels of 

economic status than do the less diverse surrounding counties.

Table 7-6: Level of Industrial Diversity in Appalachia by County and 

Commuting Shed and Economic Status, 2012

Level of industrial diversity by 

county and commuting shed Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% Both high 0.0 2.0 1.9 0.0 0.0 1.4

% High / About average 0.0 1.0 15.9 41.7 66.7 9.8

% About average / High 0.0 1.0 3.9 0.0 0.0 2.1

% Both about average 73.5 81.8 74.5 41.7 33.3 74.8

% About average / Low or 

very low 9.2 3.0 1.9 0.0 0.0 3.8

% Low or very low / About 

average 14.3 9.1 1.9 8.3 0.0 6.7

% Both low or very low 3.1 2.0 0.0 8.3 0.0 1.4

Appalachian counties by economic status

Data source: U.S. Census journey-to-work data, American Community Survey, 2006-2010; Estimated employment 

by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Entropy calculated at 6-digit NAICS aggregations 2. Commuting sheds defined in Statistical Portrait of 

Economic Diversity in Appalachia section 2-6 3. Levels based on standard deviations from mean

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

7-2B. FUNCTIONAL DIVERSITY

Distressed and at-risk counties in Appalachia have higher than average percentage of 

counties with below average, low, or very low functional diversity values. As listed in 

Table 7-7, those counties with a transitional status are most likely to have a high or 

above average functional diversity value. While most of the twelve competitive counties 

have an above average or high functional diversity value, there are four competitive 

counties that have a below average or low diversity value. Two of the attainment 

counties have a below average value for functional diversity, with the third county 

having an above average value. The lack of a clear, consistent pattern in these data 

further suggests functional diversity can have multiple impacts. For example, high 

functional diversity may indicate the presence of several competitive specializations, 

though it may also indicate about equal employment across several functions with none 

of these functions able to attain competitive stature. Further, low functional diversity

may indicate the presence of a large, competitive specialization or dependence on a 

large, potentially volatile specialization.

Table 7-7: Level of Functional Diversity in Appalachia by County and 

Economic Status, 2012

Level of functional diversity by 

county and commuting shed Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

% Both high 0.0 1.0 1.4 0.0 0.0 1.0

% High / About average 5.1 13.1 20.2 8.3 0.0 14.5

% About average / High 0.0 2.0 1.9 0.0 0.0 1.4

% Both about average 78.6 68.7 72.6 83.3 100.0 73.6

% About average / Low or 

very low 5.1 6.1 1.9 0.0 0.0 3.6

% Low or very low / High

0.0 1.0 0.0 0.0 0.0 0.2

% Low or very low / About 

average 10.2 6.1 1.9 8.3 0.0 5.0

% Both low or very low 1.0 2.0 0.0 0.0 0.0 0.7

Appalachian counties by economic status

Data source: U.S. Census journey-to-work data, American Community Survey, 2006-2010; Estimated employment 

by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: Entropy calculated across eleven functional categories defined per Statistical Portrait of Economic Diversity 

in Appalachia section 2-3 3. Commuting sheds defined in Statistical Portrait of Economic Diversity in Appalachia 

section 2-6 4. Levels based on standard deviations from mean

82
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 7-4 depicts the combinations of functional diversity for counties and commuting 

sheds across Appalachia. 309 of the 420 Appalachian counties have county and 

commuting shed values for functional diversity that are about average, though there are 

instances of significant variation between county and commuting shed values. For 

instance, there are 103 counties that have one about average value and a more extreme 

(i.e., high or low) value for functional diversity at either the county or commuting shed 

scale. 

Figure 7-4: Level of Functional Diversity for Counties and their Commuting 

Sheds in Appalachia, 2012

7-3. REGIONAL LINKAGES AND FUNCTIONAL ECONOMIC ROLES

Commuting characteristics and the functional specializations of a county and its 

commuting shed can be used to define a county’s economic role in its region. This 

section reports the results of an analysis of county and regional specializations and 

commuting patterns conducted according to the methods detailed in section 2-7. These 

results can be used to understand counties’ roles within their regional and national 

economy in ways that an examination of individual county or commuting shed 

83
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

characteristics cannot—providing an additional lens for considering the economic 

opportunities and threats that face a county.

7-3A. DEFINING COUNTY ROLES IN THE REGIONAL ECONOMIC HIERARCHY

Table 7-8 lists the derived functional types of counties along with the number of 

counties, the average largest functional specializations of counties and commuting 

sheds, average commuting characteristics, and the county with the largest factor 

loading in each category. 

Table 7-8: Summary of Functional Specialization and Commuting 

Characteristics by County Functional Economic Types

County types analyzed

Functional types

Rural or Mixed Rural counties

Agriculture & resource 

extraction centers

568 Agriculture & 

resource extraction

Agriculture & 

resource extraction

3.5 Loving, TX

Bedroom communities to 

corporate & distributive 

services centers

108 Agriculture & 

resource extraction

Distributive 

services -29.3 Chambers, TX

Bedroom communities to 

midsize centers

368 Capital-intensive 

manufacturing

Capital-intensive 

manufacturing

-28.5 Monroe, MI

Corporate & distribution 

outposts

381 Agriculture & 

resource extraction

Distributive 

services

-13.8 Benton, AR

Rural destinations & 

advanced services districts

375 Finance, insurance 

and real estate

Finance, insurance 

and real estate

-19.0 Washoe, NV

Rural government districts 283 Government Government -22.4 Sangamon, IL

Rural manufacturing centers 289 Capital-intensive 

manufacturing

Capital-intensive 

manufacturing

-5.7 Tazewell, IL

Rural medical centers 228 Healthcare

Media, 

entertainment & 

recreation

4.0 Hinds, MS

Town and gown communities 159 Higher education Higher education -4.1 Centre, PA

Urban or Mixed Urban counties

Advanced manufacturing 

districts 48

Engineeringintensive 

manufacturing

Knowledgeintensive business 

services

-9.7 Santa Clara, CA

Corporate and financial 

centers 39 Finance, insurance and real estate

Finance, insurance 

and real estate

27.1 New York, NY

Government centers and 

suburbs

68 Government Government 4.9 Washington, 

D.C.

Higher education and 

medical complexes

70 Higher education Healthcare 5.7 Suffolk, MA

Media and entertainment 

centers 45

Media, 

entertainment & 

recreation

Media, 

entertainment & 

recreation

-17.7 Los Angeles, CA

Satellite cities and suburbs 38 Finance, insurance 

and real estate

Finance, insurance 

and real estate

-20.1 Putnam, NY

Transportation and 

distributive services centers 46

Distributive 

services

Distributive 

services 3.6 Cook, IL

Notes: 1. Derived as detailed in Statistical Portrait of Economic Diversity in Appalachia section 2-7

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012; U.S. 

Census journey-to-work data, American Community Survey, 2006-2010

# of U.S. 

counties

County functional 

specialization with 

highest # of average 

extra jobs

Commuting shed 

functional 

specialization with 

highest # of average 

extra jobs

Average net 

commuting 

flow as % of 

resident 

workers

County most 

representative 

of functional 

type

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

The average net commuting flow characteristics indicate that the vast majority of 

counties do not function as the economic center of their regions—in other words, most 

counties lose more workers via out-commuting than they gain through in-commuting. 

Rural or mixed rural counties in the bedroom community categories tend to be the most 

dependent on economic opportunities outside their borders, with urban or mixed urban 

satellite cities and suburbs similarly dependent on activity outside their borders. The 

nature of dependency varies considerably across these types. For example, bedroom 

communities to corporate & distributive services centers tend to be dependent on large 

and sometimes distant urban centers, while bedroom communities to midsize centers 

tend to be dependent on smaller, relatively proximate centers with specializations in 

activities such as healthcare, higher education, and manufacturing. Satellite cities are 

usually large, urban or suburban communities in their own right with tight economic 

linkages to some of the largest urban centers in the United States.

The most common functional types among rural or mixed rural counties include rural 

destinations and advanced services districts, agriculture & resource extraction centers, 

rural manufacturing centers, and rural government districts. While agricultural and 

resource extraction tends to be common in rural areas, there are also many rural 

destinations and advanced services districts. As a group, this category includes many 

counties that rely on their historical role as an outdoor recreation or general 

entertainment destination. Members of this grouping contain casino destinations such 

as Reno, Nevada and Tunica, Mississippi, Lake Tahoe, numerous ski and golf resorts, 

communities surrounding Branson, Missouri and Nashville, Tennessee, and a wide 

variety of state and national parks and outdoor activities. Agriculture and resource 

extraction centers encompass a wide variety of counties focused on activities related to 

farming, forestry, and mining. Rural manufacturing centers and rural medical centers 

are among the most likely types of rural and mixed rural counties to be the central 

players in their regions from a commuting standpoint. Corporate & distribution outposts 

recognize the instances when firms locate significant headquarters or trade-related 

activities in rural areas—with Bentonville, Arkansas, the headquarters city of WalMart, 

representing the extreme example of this case. Finally, town and gown communities 

house major universities including Penn State, the University of Illinois, and Clemson 

University and tend to be the centers of significant economic activity in their regions.

Media and entertainment centers include many of the nation’s largest centers of 

entertainment, such as Los Angeles and Las Vegas. Corporate and financial centers 

include New York City and San Francisco. Many of the urban and suburban counties 

surrounding these major centers are grouped together as satellite cities and suburbs. 

While many of the government centers and suburbs are situated around Washington, 

D.C., communities around state capitals and military installations also find membership 

in this group. Higher education and medical complexes include U.S. cities with major 

healthcare or higher education sectors, such as Boston, Philadelphia, Baltimore, Ann 

Arbor, St. Louis, Pittsburgh, and Cincinnati. Distributive services centers include urban 

areas with significant employment related to transportation, trade, and utilities, such as 

Houston, Chicago, Atlanta, and Tulsa. Finally, advanced manufacturing districts include 

some of the nation’s most recognizable high-tech manufacturing regions, such as 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Greater Seattle’s aviation industry, Silicon Valley’s computer hardware and software 

industries, and suburban Boston’s biotech industries. 

7-3B. FUNCTIONAL ECONOMIC ROLES AND ECONOMIC OUTCOMES IN 

APPALACHIA

The distribution of the functional roles of Appalachian counties reflects many of the 

patterns visible from examining the distribution of functional specializations in 

Appalachia (see Figure 7-1). For example, there are relatively fewer counties with 

manufacturing-related roles in central Appalachia than there are in the northern and 

southern parts of the region. Compared to depictions of county functional 

specializations, the functional county roles shown in Figures 7-5 and 7-6 more neatly 

reveal the relationships among counties than a look at individual county functional 

specializations can. The bedroom community categories present the clearest case of 

counties that are particularly dependent on nearby centers for their economic 

livelihoods. Functional roles can also suggest economic peers for communities to 

benchmark their economic development activities against. For example, Allegheny 

County, PA (which includes the city of Pittsburgh) might track its progress and gain 

insights from examining the activities in other counties included in the higher education 

and medical complex grouping. 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Figure 7-5: Functional Roles in Appalachia’s Rural & Mixed Rural Counties, 2012

Figure 7-6: Functional Roles in Appalachia’s Urban & Mixed Urban Counties, 2012

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 7-9 cross-tabulates functional county types in Appalachia against the five 

economic status groupings. The numerous economic outcomes resulting from each 

functional type suggests that this classification system does not capture important 

variables for economic performance. For example, the simple assignment of counties to 

one functional type belies the fact that counties can play multiple functional roles to 

different degrees. Both the multiplicity of roles and the degree to which counties play 

these roles may be important variables for understanding potential economic 

performance and identifying peer counties and regions.

Table 7-9: Functional Economic Roles in Appalachian Counties by Economic 

Status, 2012

County functional economic role, 2012 Distressed At-Risk Transitional Competitive Attainment Total

Number of county equivalents 98 99 208 12 3 420

Rural or Mixed Rural counties

Agriculture & resource extraction 

centers 11.2 18.2 8.2 0.0 0.0 11.0

Bedroom communities to corporate 

& distributive services centers 27.6 25.3 20.2 0.0 0.0 22.4

Bedroom communities to midsize 

centers 3.1 2.0 0.0 0.0 0.0 1.2

Corporate & distribution outposts 24.5 6.1 3.9 25.0 33.3 10.0

Rural destinations & advanced 

services districts 9.2 7.1 7.2 16.7 33.3 8.1

Rural government districts 7.1 11.1 9.1 8.3 0.0 9.1

Rural manufacturing centers 10.2 19.2 16.8 0.0 0.0 15.2

Rural medical centers 2.0 8.1 9.6 0.0 0.0 7.1

Town and gown communities 5.1 2.0 11.5 16.7 0.0 7.9

Urban or Mixed Urban counties

Advanced manufacturing districts 0.0 0.0 1.4 0.0 0.0 0.7

Corporate and financial centers 0.0 0.0 0.5 0.0 0.0 0.2

Government centers and suburbs 0.0 0.0 1.9 8.3 0.0 1.2

Higher education and medical 

complexes 0.0 0.0 3.9 16.7 0.0 2.4

Media and entertainment centers 0.0 0.0 1.9 0.0 0.0 1.0

Satellite cities and suburbs 0.0 0.0 1.0 8.3 0.0 0.7

Transportation and distributive 

services centers 0.0 1.0 2.9 0.0 33.3 1.9

Appalachian counties by economic status

Data source: Estimated employment by industry, Economic Modeling Specialists International (EMSI), 2012

Notes: 1. Derived as detailed in Statistical Portrait of Economic Diversity in Appalachia section 2-7

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

7-4. LESSONS FOR PRACTICE

7-4A. CONSIDERING FUNCTIONAL ROLES AND THE SCOPE OF ECONOMIC 

DEVELOPMENT ACTION

The range of possible economic development actions is large enough that taking options 

off the table can be a valuable first step for practitioners. Considering functional roles 

can help to focus efforts by clarifying the feasibility of short- and long-term economic 

development goals and suggesting peer regions that may offer lessons for the region in 

question. As a check on the feasibility of goals, a region’s functional roles can be 

considered more permanent than its industry composition. For example, short-term 

efforts are unlikely to reverse the flow of commuters within a region or make a county a 

significant tourist destination overnight, but these might make for appropriate—if 

aspirational—long-term goals that suggest a strategic collection of actions in the shortand long-term. Identifying peer regions with similar functional roles can help to 

benchmark development efforts and suggest beneficial economic development actions. 

Practitioners might also identify the aspirational function of their region and examine 

the development history of regions with similar functions to assess potential strategies 

for progressing toward the region’s aspirations. 

7-4B. PURSUING REGIONAL PARTNERSHIPS 

For most communities, there is at least some value to be found in the pursuit of regional 

partnerships for economic development. The examination of functional county roles can 

help to clarify the necessity and potential content of these arrangements. In order to 

compensate for local gaps in factors such as workforce skills or infrastructure, individual 

communities might seek to highlight their ties to other communities in their region that 

play different functional roles. In the same vein, business recruitment or cluster 

strategies may be more successful if they highlight the region’s resources, not just those 

of individual communities. For example, a bedroom community with close economic ties 

to a regional work center may find it in their interest to cooperate with the work center 

and other surrounding counties in funding transit improvements. As part of regional 

branding efforts, individual communities and the region as a whole may benefit by 

highlighting the functional distinctions among the component jurisdictions of the region. 

For example, a campaign that highlights the vibrant urban centers, peaceful bedroom 

communities, and outdoor recreation opportunities in a region might be attractive to a 

diverse workforce and, thus, attractive to employers seeking to provide employees with 

a high quality of life. 

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STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

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9. DETAILED DOCUMENTATION

Table 9-1: NAICS 6-digit Industries by Functional Industry Categories

Functional industry category

NAICS code and title

Agriculture and resource extraction 

111000 Crop production 212221 Gold Ore Mining

112000 Animal production 212222 Silver Ore Mining

113110 Timber Tract Operations 212231 Lead Ore and Zinc Ore Mining

113210 Forest Nurseries and Gathering of Forest 

Products

212234 Copper Ore and Nickel Ore Mining

113310 Logging 212291 Uranium-Radium-Vanadium Ore Mining

114111 Finfish Fishing 212299 All Other Metal Ore Mining

114112 Shellfish Fishing 212311 Dimension Stone Mining and Quarrying

114119 Other Marine Fishing 212312 Crushed and Broken Limestone Mining and 

Quarrying

114210 Hunting and Trapping 212313 Crushed and Broken Granite Mining and 

Quarrying

115111 Cotton Ginning 212319 Other Crushed and Broken Stone Mining and 

Quarrying

115112 Soil Preparation, Planting, and Cultivating 212321 Construction Sand and Gravel Mining

115113 Crop Harvesting, Primarily by Machine 212322 Industrial Sand Mining

115114 Postharvest Crop Activities (except Cotton 

Ginning)

212324 Kaolin and Ball Clay Mining

115115 Farm Labor Contractors and Crew Leaders 212325 Clay and Ceramic and Refractory Minerals 

Mining

115116 Farm Management Services 212391 Potash, Soda, and Borate Mineral Mining

115210 Support Activities for Animal Production 212392 Phosphate Rock Mining

115310 Support Activities for Forestry 212393 Other Chemical and Fertilizer Mineral Mining

211111 Crude Petroleum and Natural Gas Extraction 212399 All Other Nonmetallic Mineral Mining

211112 Natural Gas Liquid Extraction 213111 Drilling Oil and Gas Wells

212111 Bituminous Coal and Lignite Surface Mining 213112 Support Activities for Oil and Gas Operations

212112 Bituminous Coal Underground Mining 213113 Support Activities for Coal Mining

212113 Anthracite Mining 213114 Support Activities for Metal Mining

212210 Iron Ore Mining 213115 Support Activities for Nonmetallic Minerals 

(except Fuels) Mining

Capital-intensive manufacturing 

311111 Dog and Cat Food Manufacturing 326113 Unlaminated Plastics Film and Sheet (except 

Packaging) Manufacturing

311119 Other Animal Food Manufacturing 326121 Unlaminated Plastics Profile Shape 

Manufacturing

311211 Flour Milling 326122 Plastics Pipe and Pipe Fitting Manufacturing

311212 Rice Milling 326130 Laminated Plastics Plate, Sheet (except 

Packaging), and Shape Manufacturing

311213 Malt Manufacturing 326140 Polystyrene Foam Product Manufacturing

311221 Wet Corn Milling 326150 Urethane and Other Foam Product (except 

Polystyrene) Manufacturing

311222 Soybean Processing 326160 Plastics Bottle Manufacturing

311223 Other Oilseed Processing 326191 Plastics Plumbing Fixture Manufacturing

311225 Fats and Oils Refining and Blending 326192 Resilient Floor Covering Manufacturing

311230 Breakfast Cereal Manufacturing 326199 All Other Plastics Product Manufacturing

311311 Sugarcane Mills 326211 Tire Manufacturing (except Retreading)

311312 Cane Sugar Refining 326212 Tire Retreading

311313 Beet Sugar Manufacturing 326220 Rubber and Plastics Hoses and Belting 

Manufacturing

311320 Chocolate and Confectionery Manufacturing 

from Cacao Beans

326291 Rubber Product Manufacturing for 

Mechanical Use

311330 Confectionery Manufacturing from 

Purchased Chocolate

326299 All Other Rubber Product Manufacturing

311340 Nonchocolate Confectionery Manufacturing 327111 Vitreous China Plumbing Fixture and China 

and Earthenware Bathroom Accessories 

Manufacturing

92
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

311411 Frozen Fruit, Juice, and Vegetable 

Manufacturing

327112 Vitreous China, Fine Earthenware, and Other 

Pottery Product Manufacturing

311412 Frozen Specialty Food Manufacturing 327113 Porcelain Electrical Supply Manufacturing

311421 Fruit and Vegetable Canning 327121 Brick and Structural Clay Tile Manufacturing

311422 Specialty Canning 327122 Ceramic Wall and Floor Tile Manufacturing

311423 Dried and Dehydrated Food Manufacturing 327123 Other Structural Clay Product Manufacturing

311511 Fluid Milk Manufacturing 327124 Clay Refractory Manufacturing

311512 Creamery Butter Manufacturing 327125 Nonclay Refractory Manufacturing

311513 Cheese Manufacturing 327211 Flat Glass Manufacturing

311514 Dry, Condensed, and Evaporated Dairy 

Product Manufacturing

327212 Other Pressed and Blown Glass and 

Glassware Manufacturing

311520 Ice Cream and Frozen Dessert Manufacturing 327213 Glass Container Manufacturing

311611 Animal (except Poultry) Slaughtering 327215 Glass Product Manufacturing Made of 

Purchased Glass

311612 Meat Processed from Carcasses 327310 Cement Manufacturing

311613 Rendering and Meat Byproduct Processing 327320 Ready-Mix Concrete Manufacturing

311615 Poultry Processing 327331 Concrete Block and Brick Manufacturing

311711 Seafood Canning 327332 Concrete Pipe Manufacturing

311712 Fresh and Frozen Seafood Processing 327390 Other Concrete Product Manufacturing

311811 Retail Bakeries 327410 Lime Manufacturing

311812 Commercial Bakeries 327420 Gypsum Product Manufacturing

311813 Frozen Cakes, Pies, and Other Pastries 

Manufacturing

327910 Abrasive Product Manufacturing

311821 Cookie and Cracker Manufacturing 327991 Cut Stone and Stone Product Manufacturing

311822 Flour Mixes and Dough Manufacturing from 

Purchased Flour

327992 Ground or Treated Mineral and Earth 

Manufacturing

311823 Dry Pasta Manufacturing 327993 Mineral Wool Manufacturing

311830 Tortilla Manufacturing 327999 All Other Miscellaneous Nonmetallic Mineral 

Product Manufacturing

311911 Roasted Nuts and Peanut Butter 

Manufacturing

331111 Iron and Steel Mills

311919 Other Snack Food Manufacturing 331112 Electrometallurgical Ferroalloy Product 

Manufacturing

311920 Coffee and Tea Manufacturing 331210 Iron and Steel Pipe and Tube Manufacturing 

from Purchased Steel

311930 Flavoring Syrup and Concentrate 

Manufacturing

331221 Rolled Steel Shape Manufacturing

311941 Mayonnaise, Dressing, and Other Prepared 

Sauce Manufacturing

331222 Steel Wire Drawing

311942 Spice and Extract Manufacturing 331312 Primary Aluminum Production

311991 Perishable Prepared Food Manufacturing 331314 Secondary Smelting and Alloying of 

Aluminum

311999 All Other Miscellaneous Food Manufacturing 331315 Aluminum Sheet, Plate, and Foil 

Manufacturing

312111 Soft Drink Manufacturing 331316 Aluminum Extruded Product Manufacturing

312112 Bottled Water Manufacturing 331319 Other Aluminum Rolling and Drawing

312113 Ice Manufacturing 331411 Primary Smelting and Refining of Copper

312120 Breweries 331419 Primary Smelting and Refining of Nonferrous 

Metal (except Copper and Aluminum)

312130 Wineries 331421 Copper Rolling, Drawing, and Extruding

312140 Distilleries 331422 Copper Wire (except Mechanical) Drawing

312210 Tobacco Stemming and Redrying 331423 Secondary Smelting, Refining, and Alloying of 

Copper

312221 Cigarette Manufacturing 331491 Nonferrous Metal (except Copper and 

Aluminum) Rolling, Drawing, and Extruding

312229 Other Tobacco Product Manufacturing 331492 Secondary Smelting, Refining, and Alloying of 

Nonferrous Metal (except Copper and Aluminum)

313111 Yarn Spinning Mills 331511 Iron Foundries

313112 Yarn Texturizing, Throwing, and Twisting 

Mills

331512 Steel Investment Foundries

313113 Thread Mills 331513 Steel Foundries (except Investment)

313210 Broadwoven Fabric Mills 331521 Aluminum Die-Casting Foundries

93
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

313221 Narrow Fabric Mills 331522 Nonferrous (except Aluminum) Die-Casting 

Foundries

313222 Schiffli Machine Embroidery 331524 Aluminum Foundries (except Die-Casting)

313230 Nonwoven Fabric Mills 331525 Copper Foundries (except Die-Casting)

313241 Weft Knit Fabric Mills 331528 Other Nonferrous Foundries (except DieCasting)

313249 Other Knit Fabric and Lace Mills 332111 Iron and Steel Forging

313311 Broadwoven Fabric Finishing Mills 332112 Nonferrous Forging

313312 Textile and Fabric Finishing (except 

Broadwoven Fabric) Mills

332114 Custom Roll Forming

313320 Fabric Coating Mills 332115 Crown and Closure Manufacturing

314110 Carpet and Rug Mills 332116 Metal Stamping

314121 Curtain and Drapery Mills 332117 Powder Metallurgy Part Manufacturing

314129 Other Household Textile Product Mills 332211 Cutlery and Flatware (except Precious) 

Manufacturing

314911 Textile Bag Mills 332213 Saw Blade and Handsaw Manufacturing

314912 Canvas and Related Product Mills 332214 Kitchen Utensil, Pot, and Pan Manufacturing

314991 Rope, Cordage, and Twine Mills 332311 Prefabricated Metal Building and Component 

Manufacturing

314992 Tire Cord and Tire Fabric Mills 332312 Fabricated Structural Metal Manufacturing

314999 All Other Miscellaneous Textile Product Mills 332313 Plate Work Manufacturing

315111 Sheer Hosiery Mills 332321 Metal Window and Door Manufacturing

315119 Other Hosiery and Sock Mills 332322 Sheet Metal Work Manufacturing

315191 Outerwear Knitting Mills 332323 Ornamental and Architectural Metal Work 

Manufacturing

315192 Underwear and Nightwear Knitting Mills 332410 Power Boiler and Heat Exchanger 

Manufacturing

315211 Men's and Boys' Cut and Sew Apparel 

Contractors

332420 Metal Tank (Heavy Gauge) Manufacturing

315212 Women's, Girls', and Infants' Cut and Sew 

Apparel Contractors

332431 Metal Can Manufacturing

315221 Men's and Boys' Cut and Sew Underwear and 

Nightwear Manufacturing

332439 Other Metal Container Manufacturing

315222 Men's and Boys' Cut and Sew Suit, Coat, and 

Overcoat Manufacturing

332510 Hardware Manufacturing

315223 Men's and Boys' Cut and Sew Shirt (except 

Work Shirt) Manufacturing

332611 Spring (Heavy Gauge) Manufacturing

315224 Men's and Boys' Cut and Sew Trouser, Slack, 

and Jean Manufacturing

332612 Spring (Light Gauge) Manufacturing

315225 Men's and Boys' Cut and Sew Work Clothing 

Manufacturing

332618 Other Fabricated Wire Product 

Manufacturing

315228 Men's and Boys' Cut and Sew Other 

Outerwear Manufacturing

332710 Machine Shops

315231 Women's and Girls' Cut and Sew Lingerie, 

Loungewear, and Nightwear Manufacturing

332721 Precision Turned Product Manufacturing

315232 Women's and Girls' Cut and Sew Blouse and 

Shirt Manufacturing

332722 Bolt, Nut, Screw, Rivet, and Washer 

Manufacturing

315233 Women's and Girls' Cut and Sew Dress 

Manufacturing

332811 Metal Heat Treating

315234 Women's and Girls' Cut and Sew Suit, Coat, 

Tailored Jacket, and Skirt Manufacturing

332812 Metal Coating, Engraving (except Jewelry and 

Silverware), and Allied Services to Manufacturers

315239 Women's and Girls' Cut and Sew Other 

Outerwear Manufacturing

332813 Electroplating, Plating, Polishing, Anodizing, 

and Coloring

315291 Infants' Cut and Sew Apparel Manufacturing 332911 Industrial Valve Manufacturing

315292 Fur and Leather Apparel Manufacturing 332912 Fluid Power Valve and Hose Fitting 

Manufacturing

315299 All Other Cut and Sew Apparel 

Manufacturing

332913 Plumbing Fixture Fitting and Trim 

Manufacturing

315991 Hat, Cap, and Millinery Manufacturing 332919 Other Metal Valve and Pipe Fitting 

Manufacturing

315992 Glove and Mitten Manufacturing 332996 Fabricated Pipe and Pipe Fitting 

Manufacturing

94 
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

315993 Men's and Boys' Neckwear Manufacturing 332998 Enameled Iron and Metal Sanitary Ware 

Manufacturing

315999 Other Apparel Accessories and Other Apparel 

Manufacturing

332999 All Other Miscellaneous Fabricated Metal 

Product Manufacturing

316110 Leather and Hide Tanning and Finishing 333298 All Other Industrial Machinery Manufacturing

316211 Rubber and Plastics Footwear Manufacturing 333414 Heating Equipment (except Warm Air 

Furnaces) Manufacturing

316212 House Slipper Manufacturing 333415 Air-Conditioning and Warm Air Heating 

Equipment and Commercial and Industrial 

Refrigeration Equipment Manufacturing

316213 Men's Footwear (except Athletic) 

Manufacturing

335211 Electric Housewares and Household Fan 

Manufacturing

316214 Women's Footwear (except Athletic) 

Manufacturing

335212 Household Vacuum Cleaner Manufacturing

316219 Other Footwear Manufacturing 335221 Household Cooking Appliance Manufacturing

316991 Luggage Manufacturing 335222 Household Refrigerator and Home Freezer 

Manufacturing

316992 Women's Handbag and Purse Manufacturing 335224 Household Laundry Equipment 

Manufacturing

316993 Personal Leather Good (except Women's 

Handbag and Purse) Manufacturing

335228 Other Major Household Appliance 

Manufacturing

316999 All Other Leather Good and Allied Product 

Manufacturing

335921 Fiber Optic Cable Manufacturing

321113 Sawmills 335929 Other Communication and Energy Wire 

Manufacturing

321114 Wood Preservation 336111 Automobile Manufacturing

321211 Hardwood Veneer and Plywood 

Manufacturing

336112 Light Truck and Utility Vehicle Manufacturing

321212 Softwood Veneer and Plywood 

Manufacturing

336120 Heavy Duty Truck Manufacturing

321213 Engineered Wood Member (except Truss) 

Manufacturing

336211 Motor Vehicle Body Manufacturing

321214 Truss Manufacturing 336212 Truck Trailer Manufacturing

321219 Reconstituted Wood Product Manufacturing 336213 Motor Home Manufacturing

321911 Wood Window and Door Manufacturing 336214 Travel Trailer and Camper Manufacturing

321912 Cut Stock, Resawing Lumber, and Planing 336312 Gasoline Engine and Engine Parts 

Manufacturing

321918 Other Millwork (including Flooring) 336322 Other Motor Vehicle Electrical and Electronic 

Equipment Manufacturing

321920 Wood Container and Pallet Manufacturing 336330 Motor Vehicle Steering and Suspension 

Components (except Spring) Manufacturing

321991 Manufactured Home (Mobile Home) 

Manufacturing

336340 Motor Vehicle Brake System Manufacturing

321992 Prefabricated Wood Building Manufacturing 336350 Motor Vehicle Transmission and Power Train 

Parts Manufacturing

321999 All Other Miscellaneous Wood Product 

Manufacturing

336360 Motor Vehicle Seating and Interior Trim 

Manufacturing

322110 Pulp Mills 336370 Motor Vehicle Metal Stamping

322121 Paper (except Newsprint) Mills 336391 Motor Vehicle Air-Conditioning 

Manufacturing

322122 Newsprint Mills 336399 All Other Motor Vehicle Parts Manufacturing

322130 Paperboard Mills 336510 Railroad Rolling Stock Manufacturing

322211 Corrugated and Solid Fiber Box 

Manufacturing

336611 Ship Building and Repairing

322212 Folding Paperboard Box Manufacturing 336612 Boat Building

322213 Setup Paperboard Box Manufacturing 336991 Motorcycle, Bicycle, and Parts Manufacturing

322214 Fiber Can, Tube, Drum, and Similar Products 

Manufacturing

336992 Military Armored Vehicle, Tank, and Tank 

Component Manufacturing

322215 Nonfolding Sanitary Food Container 

Manufacturing

336999 All Other Transportation Equipment 

Manufacturing

322221 Coated and Laminated Packaging Paper 

Manufacturing

337110 Wood Kitchen Cabinet and Countertop 

Manufacturing

95
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

322222 Coated and Laminated Paper Manufacturing 337121 Upholstered Household Furniture 

Manufacturing

322223 Coated Paper Bag and Pouch Manufacturing 337122 Nonupholstered Wood Household Furniture 

Manufacturing

322224 Uncoated Paper and Multiwall Bag 

Manufacturing

337124 Metal Household Furniture Manufacturing

322225 Laminated Aluminum Foil Manufacturing for 

Flexible Packaging Uses

337125 Household Furniture (except Wood and 

Metal) Manufacturing

322226 Surface-Coated Paperboard Manufacturing 337127 Institutional Furniture Manufacturing

322231 Die-Cut Paper and Paperboard Office 

Supplies Manufacturing

337129 Wood Television, Radio, and Sewing Machine 

Cabinet Manufacturing

322232 Envelope Manufacturing 337211 Wood Office Furniture Manufacturing

322233 Stationery, Tablet, and Related Product 

Manufacturing

337212 Custom Architectural Woodwork and 

Millwork Manufacturing

322291 Sanitary Paper Product Manufacturing 337214 Office Furniture (except Wood) 

Manufacturing

322299 All Other Converted Paper Product 

Manufacturing

337215 Showcase, Partition, Shelving, and Locker 

Manufacturing

323110 Commercial Lithographic Printing 337910 Mattress Manufacturing

323111 Commercial Gravure Printing 337920 Blind and Shade Manufacturing

323112 Commercial Flexographic Printing 339911 Jewelry (except Costume) Manufacturing

323113 Commercial Screen Printing 339912 Silverware and Hollowware Manufacturing

323114 Quick Printing 339913 Jewelers' Material and Lapidary Work 

Manufacturing

323115 Digital Printing 339914 Costume Jewelry and Novelty Manufacturing

323116 Manifold Business Forms Printing 339920 Sporting and Athletic Goods Manufacturing

323117 Books Printing 339931 Doll and Stuffed Toy Manufacturing

323118 Blankbook, Looseleaf Binders, and Devices 

Manufacturing

339932 Game, Toy, and Children's Vehicle 

Manufacturing

323119 Other Commercial Printing 339941 Pen and Mechanical Pencil Manufacturing

323121 Tradebinding and Related Work 339942 Lead Pencil and Art Good Manufacturing

323122 Prepress Services 339943 Marking Device Manufacturing

324121 Asphalt Paving Mixture and Block 

Manufacturing

339944 Carbon Paper and Inked Ribbon 

Manufacturing

324122 Asphalt Shingle and Coating Materials 

Manufacturing

339950 Sign Manufacturing

324191 Petroleum Lubricating Oil and Grease 

Manufacturing

339991 Gasket, Packing, and Sealing Device 

Manufacturing

324199 All Other Petroleum and Coal Products 

Manufacturing

339992 Musical Instrument Manufacturing

325991 Custom Compounding of Purchased Resins 339993 Fastener, Button, Needle, and Pin 

Manufacturing

325998 All Other Miscellaneous Chemical Product 

and Preparation Manufacturing

339994 Broom, Brush, and Mop Manufacturing

326111 Plastics Bag and Pouch Manufacturing 339995 Burial Casket Manufacturing

326112 Plastics Packaging Film and Sheet (including 

Laminated) Manufacturing

339999 All Other Miscellaneous Manufacturing

Corporate management and administration

551111 Offices of Bank Holding Companies 551114 Corporate, Subsidiary, and Regional 

Managing Offices

551112 Offices of Other Holding Companies 561110 Office Administrative Services

Distributive services 

221111 Hydroelectric Power Generation 424820 Wine and Distilled Alcoholic Beverage 

Merchant Wholesalers

221112 Fossil Fuel Electric Power Generation 424910 Farm Supplies Merchant Wholesalers

221113 Nuclear Electric Power Generation 424920 Book, Periodical, and Newspaper Merchant 

Wholesalers

221119 Other Electric Power Generation 424930 Flower, Nursery Stock, and Florists' Supplies 

Merchant Wholesalers

221121 Electric Bulk Power Transmission and Control 424940 Tobacco and Tobacco Product Merchant 

Wholesalers

221122 Electric Power Distribution 424950 Paint, Varnish, and Supplies Merchant 

Wholesalers

96
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

221210 Natural Gas Distribution 424990 Other Miscellaneous Nondurable Goods 

Merchant Wholesalers

221310 Water Supply and Irrigation Systems 425110 Business to Business Electronic Markets

221320 Sewage Treatment Facilities 425120 Wholesale Trade Agents and Brokers

221330 Steam and Air-Conditioning Supply 481111 Scheduled Passenger Air Transportation

423110 Automobile and Other Motor Vehicle 

Merchant Wholesalers

481112 Scheduled Freight Air Transportation

423120 Motor Vehicle Supplies and New Parts 

Merchant Wholesalers

481211 Nonscheduled Chartered Passenger Air 

Transportation

423130 Tire and Tube Merchant Wholesalers 481212 Nonscheduled Chartered Freight Air 

Transportation

423140 Motor Vehicle Parts (Used) Merchant 

Wholesalers

481219 Other Nonscheduled Air Transportation

423210 Furniture Merchant Wholesalers 482110 Rail transportation

423220 Home Furnishing Merchant Wholesalers 483111 Deep Sea Freight Transportation

423310 Lumber, Plywood, Millwork, and Wood Panel 

Merchant Wholesalers

483112 Deep Sea Passenger Transportation

423320 Brick, Stone, and Related Construction 

Material Merchant Wholesalers

483113 Coastal and Great Lakes Freight 

Transportation

423330 Roofing, Siding, and Insulation Material 

Merchant Wholesalers

483114 Coastal and Great Lakes Passenger 

Transportation

423390 Other Construction Material Merchant 

Wholesalers

483211 Inland Water Freight Transportation

423410 Photographic Equipment and Supplies 

Merchant Wholesalers

483212 Inland Water Passenger Transportation

423420 Office Equipment Merchant Wholesalers 484110 General Freight Trucking, Local

423430 Computer and Computer Peripheral 

Equipment and Software Merchant Wholesalers

484121 General Freight Trucking, Long-Distance, 

Truckload

423440 Other Commercial Equipment Merchant 

Wholesalers

484122 General Freight Trucking, Long-Distance, Less 

Than Truckload

423450 Medical, Dental, and Hospital Equipment and 

Supplies Merchant Wholesalers

484210 Used Household and Office Goods Moving

423460 Ophthalmic Goods Merchant Wholesalers 484220 Specialized Freight (except Used Goods) 

Trucking, Local

423490 Other Professional Equipment and Supplies 

Merchant Wholesalers

484230 Specialized Freight (except Used Goods) 

Trucking, Long-Distance

423510 Metal Service Centers and Other Metal 

Merchant Wholesalers

485111 Mixed Mode Transit Systems

423520 Coal and Other Mineral and Ore Merchant 

Wholesalers

485112 Commuter Rail Systems

423610 Electrical Apparatus and Equipment, Wiring 

Supplies, and Related Equipment Merchant 

Wholesalers

485113 Bus and Other Motor Vehicle Transit Systems

423620 Electrical and Electronic Appliance, 

Television, and Radio Set Merchant Wholesalers

485119 Other Urban Transit Systems

423690 Other Electronic Parts and Equipment 

Merchant Wholesalers

485210 Interurban and Rural Bus Transportation

423710 Hardware Merchant Wholesalers 485310 Taxi Service

423720 Plumbing and Heating Equipment and 

Supplies (Hydronics) Merchant Wholesalers

485320 Limousine Service

423730 Warm Air Heating and Air-Conditioning 

Equipment and Supplies Merchant Wholesalers

485410 School and Employee Bus Transportation

423740 Refrigeration Equipment and Supplies 

Merchant Wholesalers

485510 Charter Bus Industry

423810 Construction and Mining (except Oil Well) 

Machinery and Equipment Merchant Wholesalers

485991 Special Needs Transportation

423820 Farm and Garden Machinery and Equipment 

Merchant Wholesalers

485999 All Other Transit and Ground Passenger 

Transportation

423830 Industrial Machinery and Equipment 

Merchant Wholesalers

486110 Pipeline Transportation of Crude Oil

423840 Industrial Supplies Merchant Wholesalers 486210 Pipeline Transportation of Natural Gas

423850 Service Establishment Equipment and 

Supplies Merchant Wholesalers

486910 Pipeline Transportation of Refined Petroleum 

Products

97
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

423860 Transportation Equipment and Supplies 

(except Motor Vehicle) Merchant Wholesalers

486990 All Other Pipeline Transportation

423910 Sporting and Recreational Goods and 

Supplies Merchant Wholesalers

487110 Scenic and Sightseeing Transportation, Land

423920 Toy and Hobby Goods and Supplies Merchant 

Wholesalers

487210 Scenic and Sightseeing Transportation, Water

423930 Recyclable Material Merchant Wholesalers 487990 Scenic and Sightseeing Transportation, Other

423940 Jewelry, Watch, Precious Stone, and Precious 

Metal Merchant Wholesalers

488111 Air Traffic Control

423990 Other Miscellaneous Durable Goods 

Merchant Wholesalers

488119 Other Airport Operations

424110 Printing and Writing Paper Merchant 

Wholesalers

488190 Other Support Activities for Air 

Transportation

424120 Stationery and Office Supplies Merchant 

Wholesalers

488210 Support Activities for Rail Transportation

424130 Industrial and Personal Service Paper 

Merchant Wholesalers

488310 Port and Harbor Operations

424210 Drugs and Druggists' Sundries Merchant 

Wholesalers

488320 Marine Cargo Handling

424310 Piece Goods, Notions, and Other Dry Goods 

Merchant Wholesalers

488330 Navigational Services to Shipping

424320 Men's and Boys' Clothing and Furnishings 

Merchant Wholesalers

488390 Other Support Activities for Water 

Transportation

424330 Women's, Children's, and Infants' Clothing 

and Accessories Merchant Wholesalers

488410 Motor Vehicle Towing

424340 Footwear Merchant Wholesalers 488490 Other Support Activities for Road 

Transportation

424410 General Line Grocery Merchant Wholesalers 488510 Freight Transportation Arrangement

424420 Packaged Frozen Food Merchant Wholesalers 488991 Packing and Crating

424430 Dairy Product (except Dried or Canned) 

Merchant Wholesalers

488999 All Other Support Activities for 

Transportation

424440 Poultry and Poultry Product Merchant 

Wholesalers

491110 Postal Service

424450 Confectionery Merchant Wholesalers 492110 Couriers and Express Delivery Services

424460 Fish and Seafood Merchant Wholesalers 492210 Local Messengers and Local Delivery

424470 Meat and Meat Product Merchant 

Wholesalers

493110 General Warehousing and Storage

424480 Fresh Fruit and Vegetable Merchant 

Wholesalers

493120 Refrigerated Warehousing and Storage

424490 Other Grocery and Related Products 

Merchant Wholesalers

493130 Farm Product Warehousing and Storage

424510 Grain and Field Bean Merchant Wholesalers 493190 Other Warehousing and Storage

424520 Livestock Merchant Wholesalers 517110 Wired Telecommunications Carriers

424590 Other Farm Product Raw Material Merchant 

Wholesalers

517210 Wireless Telecommunications Carriers 

(except Satellite)

424610 Plastics Materials and Basic Forms and 

Shapes Merchant Wholesalers

517410 Satellite Telecommunications

424690 Other Chemical and Allied Products 

Merchant Wholesalers

517911 Telecommunications Resellers

424710 Petroleum Bulk Stations and Terminals 517919 All Other Telecommunications

424720 Petroleum and Petroleum Products 

Merchant Wholesalers (except Bulk Stations and 

Terminals)

518210 Data Processing, Hosting, and Related 

Services

424810 Beer and Ale Merchant Wholesalers

Engineering-intensive manufacturing 

324110 Petroleum Refineries 333911 Pump and Pumping Equipment 

Manufacturing

325110 Petrochemical Manufacturing 333912 Air and Gas Compressor Manufacturing

325120 Industrial Gas Manufacturing 333913 Measuring and Dispensing Pump 

Manufacturing

325131 Inorganic Dye and Pigment Manufacturing 333921 Elevator and Moving Stairway Manufacturing

325132 Synthetic Organic Dye and Pigment 

Manufacturing

333922 Conveyor and Conveying Equipment 

Manufacturing

98
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

325181 Alkalies and Chlorine Manufacturing 333923 Overhead Traveling Crane, Hoist, and 

Monorail System Manufacturing

325182 Carbon Black Manufacturing 333924 Industrial Truck, Tractor, Trailer, and Stacker 

Machinery Manufacturing

325188 All Other Basic Inorganic Chemical 

Manufacturing

333991 Power-Driven Handtool Manufacturing

325191 Gum and Wood Chemical Manufacturing 333992 Welding and Soldering Equipment 

Manufacturing

325192 Cyclic Crude and Intermediate Manufacturing 333993 Packaging Machinery Manufacturing

325193 Ethyl Alcohol Manufacturing 333994 Industrial Process Furnace and Oven 

Manufacturing

325199 All Other Basic Organic Chemical 

Manufacturing

333995 Fluid Power Cylinder and Actuator 

Manufacturing

325211 Plastics Material and Resin Manufacturing 333996 Fluid Power Pump and Motor Manufacturing

325212 Synthetic Rubber Manufacturing 333997 Scale and Balance Manufacturing

325221 Cellulosic Organic Fiber Manufacturing 333999 All Other Miscellaneous General Purpose 

Machinery Manufacturing

325222 Noncellulosic Organic Fiber Manufacturing 334111 Electronic Computer Manufacturing

325311 Nitrogenous Fertilizer Manufacturing 334112 Computer Storage Device Manufacturing

325312 Phosphatic Fertilizer Manufacturing 334113 Computer Terminal Manufacturing

325314 Fertilizer (Mixing Only) Manufacturing 334119 Other Computer Peripheral Equipment 

Manufacturing

325320 Pesticide and Other Agricultural Chemical 

Manufacturing

334210 Telephone Apparatus Manufacturing

325411 Medicinal and Botanical Manufacturing 334220 Radio and Television Broadcasting and 

Wireless Communications Equipment Manufacturing

325412 Pharmaceutical Preparation Manufacturing 334290 Other Communications Equipment 

Manufacturing

325413 In-Vitro Diagnostic Substance Manufacturing 334310 Audio and Video Equipment Manufacturing

325414 Biological Product (except Diagnostic) 

Manufacturing

334411 Electron Tube Manufacturing

325510 Paint and Coating Manufacturing 334412 Bare Printed Circuit Board Manufacturing

325520 Adhesive Manufacturing 334413 Semiconductor and Related Device 

Manufacturing

325611 Soap and Other Detergent Manufacturing 334414 Electronic Capacitor Manufacturing

325612 Polish and Other Sanitation Good 

Manufacturing

334415 Electronic Resistor Manufacturing

325613 Surface Active Agent Manufacturing 334416 Electronic Coil, Transformer, and Other 

Inductor Manufacturing

325620 Toilet Preparation Manufacturing 334417 Electronic Connector Manufacturing

325910 Printing Ink Manufacturing 334418 Printed Circuit Assembly (Electronic 

Assembly) Manufacturing

325920 Explosives Manufacturing 334419 Other Electronic Component Manufacturing

325992 Photographic Film, Paper, Plate, and 

Chemical Manufacturing

334510 Electromedical and Electrotherapeutic 

Apparatus Manufacturing

331311 Alumina Refining 334511 Search, Detection, Navigation, Guidance, 

Aeronautical, and Nautical System and Instrument 

Manufacturing

332212 Hand and Edge Tool Manufacturing 334512 Automatic Environmental Control 

Manufacturing for Residential, Commercial, and 

Appliance Use

332991 Ball and Roller Bearing Manufacturing 334513 Instruments and Related Products 

Manufacturing for Measuring, Displaying, and 

Controlling Industrial Process Variables

332992 Small Arms Ammunition Manufacturing 334514 Totalizing Fluid Meter and Counting Device 

Manufacturing

332993 Ammunition (except Small Arms) 

Manufacturing

334515 Instrument Manufacturing for Measuring and 

Testing Electricity and Electrical Signals

332994 Small Arms Manufacturing 334516 Analytical Laboratory Instrument 

Manufacturing

332995 Other Ordnance and Accessories 

Manufacturing

334517 Irradiation Apparatus Manufacturing

332997 Industrial Pattern Manufacturing 334518 Watch, Clock, and Part Manufacturing

99
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

333111 Farm Machinery and Equipment 

Manufacturing

334519 Other Measuring and Controlling Device 

Manufacturing

333112 Lawn and Garden Tractor and Home Lawn 

and Garden Equipment Manufacturing

334611 Software Reproducing

333120 Construction Machinery Manufacturing 334612 Prerecorded Compact Disc (except Software), 

Tape, and Record Reproducing

333131 Mining Machinery and Equipment 

Manufacturing

334613 Magnetic and Optical Recording Media 

Manufacturing

333132 Oil and Gas Field Machinery and Equipment 

Manufacturing

335110 Electric Lamp Bulb and Part Manufacturing

333210 Sawmill and Woodworking Machinery 

Manufacturing

335121 Residential Electric Lighting Fixture 

Manufacturing

333220 Plastics and Rubber Industry Machinery 

Manufacturing

335122 Commercial, Industrial, and Institutional 

Electric Lighting Fixture Manufacturing

333291 Paper Industry Machinery Manufacturing 335129 Other Lighting Equipment Manufacturing

333292 Textile Machinery Manufacturing 335311 Power, Distribution, and Specialty 

Transformer Manufacturing

333293 Printing Machinery and Equipment 

Manufacturing

335312 Motor and Generator Manufacturing

333294 Food Product Machinery Manufacturing 335313 Switchgear and Switchboard Apparatus 

Manufacturing

333295 Semiconductor Machinery Manufacturing 335314 Relay and Industrial Control Manufacturing

333311 Automatic Vending Machine Manufacturing 335911 Storage Battery Manufacturing

333312 Commercial Laundry, Drycleaning, and 

Pressing Machine Manufacturing

335912 Primary Battery Manufacturing

333313 Office Machinery Manufacturing 335931 Current-Carrying Wiring Device 

Manufacturing

333314 Optical Instrument and Lens Manufacturing 335932 Noncurrent-Carrying Wiring Device 

Manufacturing

333315 Photographic and Photocopying Equipment 

Manufacturing

335991 Carbon and Graphite Product Manufacturing

333319 Other Commercial and Service Industry 

Machinery Manufacturing

335999 All Other Miscellaneous Electrical Equipment 

and Component Manufacturing

333411 Air Purification Equipment Manufacturing 336311 Carburetor, Piston, Piston Ring, and Valve 

Manufacturing

333412 Industrial and Commercial Fan and Blower 

Manufacturing

336321 Vehicular Lighting Equipment Manufacturing

333511 Industrial Mold Manufacturing 336411 Aircraft Manufacturing

333512 Machine Tool (Metal Cutting Types) 

Manufacturing

336412 Aircraft Engine and Engine Parts 

Manufacturing

333513 Machine Tool (Metal Forming Types) 

Manufacturing

336413 Other Aircraft Parts and Auxiliary Equipment 

Manufacturing

333514 Special Die and Tool, Die Set, Jig, and Fixture 

Manufacturing

336414 Guided Missile and Space Vehicle 

Manufacturing

333515 Cutting Tool and Machine Tool Accessory 

Manufacturing

336415 Guided Missile and Space Vehicle Propulsion 

Unit and Propulsion Unit Parts Manufacturing

333516 Rolling Mill Machinery and Equipment 

Manufacturing

336419 Other Guided Missile and Space Vehicle Parts 

and Auxiliary Equipment Manufacturing

333518 Other Metalworking Machinery 

Manufacturing

339112 Surgical and Medical Instrument 

Manufacturing

333611 Turbine and Turbine Generator Set Units 

Manufacturing

339113 Surgical Appliance and Supplies 

Manufacturing

333612 Speed Changer, Industrial High-Speed Drive, 

and Gear Manufacturing

339114 Dental Equipment and Supplies 

Manufacturing

333613 Mechanical Power Transmission Equipment 

Manufacturing

339115 Ophthalmic Goods Manufacturing

333618 Other Engine Equipment Manufacturing 339116 Dental Laboratories

Finance, insurance, and real estate

521110 Monetary Authorities-Central Bank 524127 Direct Title Insurance Carriers

522110 Commercial Banking 524128 Other Direct Insurance (except Life, Health, 

and Medical) Carriers

522120 Savings Institutions 524130 Reinsurance Carriers

522130 Credit Unions 524210 Insurance Agencies and Brokerages

100
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

522190 Other Depository Credit Intermediation 524291 Claims Adjusting

522210 Credit Card Issuing 524292 Third Party Administration of Insurance and 

Pension Funds

522220 Sales Financing 524298 All Other Insurance Related Activities

522291 Consumer Lending 525110 Pension Funds

522292 Real Estate Credit 525120 Health and Welfare Funds

522293 International Trade Financing 525190 Other Insurance Funds

522294 Secondary Market Financing 525910 Open-End Investment Funds

522298 All Other Nondepository Credit 

Intermediation

525920 Trusts, Estates, and Agency Accounts

522310 Mortgage and Nonmortgage Loan Brokers 525990 Other Financial Vehicles

522320 Financial Transactions Processing, Reserve, 

and Clearinghouse Activities

531110 Lessors of Residential Buildings and Dwellings

522390 Other Activities Related to Credit 

Intermediation

531120 Lessors of Nonresidential Buildings (except 

Miniwarehouses)

523110 Investment Banking and Securities Dealing 531130 Lessors of Miniwarehouses and Self-Storage 

Units

523120 Securities Brokerage 531190 Lessors of Other Real Estate Property

523130 Commodity Contracts Dealing 531210 Offices of Real Estate Agents and Brokers

523140 Commodity Contracts Brokerage 531312 Nonresidential Property Managers

523210 Securities and Commodity Exchanges 531320 Offices of Real Estate Appraisers

523910 Miscellaneous Intermediation 531390 Other Activities Related to Real Estate

523920 Portfolio Management 532120 Truck, Utility Trailer, and RV (Recreational 

Vehicle) Rental and Leasing

523930 Investment Advice 532411 Commercial Air, Rail, and Water 

Transportation Equipment Rental and Leasing

523991 Trust, Fiduciary, and Custody Activities 532412 Construction, Mining, and Forestry 

Machinery and Equipment Rental and Leasing

523999 Miscellaneous Financial Investment Activities 532420 Office Machinery and Equipment Rental and 

Leasing

524113 Direct Life Insurance Carriers 532490 Other Commercial and Industrial Machinery 

and Equipment Rental and Leasing

524114 Direct Health and Medical Insurance Carriers 533110 Lessors of Nonfinancial Intangible Assets 

(except Copyrighted Works)

524126 Direct Property and Casualty Insurance 

Carriers

Government 

901149 US Postal Service 902999 State Government, Excluding Education and 

Hospitals

901199 Federal Civilian, Except US Postal Service

Healthcare 

621511 Medical Laboratories 622310 Specialty (except Psychiatric and Substance 

Abuse) Hospitals

621512 Diagnostic Imaging Centers 623110 Nursing Care Facilities

622110 General Medical and Surgical Hospitals 902622 Hospitals, State Government

622210 Psychiatric and Substance Abuse Hospitals 903622 Hospitals, Local Government

Higher education 

611310 Colleges, Universities, and Professional 

Schools

903612 Colleges and Universities, Local Government

902612 Colleges and Universities, State Government

Knowledge-intensive business services

541110 Offices of Lawyers 541513 Computer Facilities Management Services

541191 Title Abstract and Settlement Offices 541519 Other Computer Related Services

541199 All Other Legal Services 541611 Administrative Management and General 

Management Consulting Services

541211 Offices of Certified Public Accountants 541612 Human Resources Consulting Services

541213 Tax Preparation Services 541613 Marketing Consulting Services

541214 Payroll Services 541614 Process, Physical Distribution, and Logistics 

Consulting Services

541219 Other Accounting Services 541618 Other Management Consulting Services

541310 Architectural Services 541620 Environmental Consulting Services

541320 Landscape Architectural Services 541690 Other Scientific and Technical Consulting 

Services

101
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Functional industry category

NAICS code and title

541330 Engineering Services 541711 Research and Development in Biotechnology

541340 Drafting Services 541712 Research and Development in the Physical, 

Engineering, and Life Sciences (except Biotechnology)

541350 Building Inspection Services 541720 Research and Development in the Social 

Sciences and Humanities

541360 Geophysical Surveying and Mapping Services 541810 Advertising Agencies

541370 Surveying and Mapping (except Geophysical) 

Services

541820 Public Relations Agencies

541380 Testing Laboratories 541830 Media Buying Agencies

541410 Interior Design Services 541840 Media Representatives

541420 Industrial Design Services 541850 Display Advertising

541430 Graphic Design Services 541860 Direct Mail Advertising

541490 Other Specialized Design Services 541870 Advertising Material Distribution Services

541511 Custom Computer Programming Services 541890 Other Services Related to Advertising

541512 Computer Systems Design Services

Media, entertainment, and recreation 

511110 Newspaper Publishers 711120 Dance Companies

511120 Periodical Publishers 711130 Musical Groups and Artists

511130 Book Publishers 711190 Other Performing Arts Companies

511140 Directory and Mailing List Publishers 711211 Sports Teams and Clubs

511191 Greeting Card Publishers 711212 Racetracks

511199 All Other Publishers 711219 Other Spectator Sports

511210 Software Publishers 711310 Promoters of Performing Arts, Sports, and 

Similar Events with Facilities

512110 Motion Picture and Video Production 711320 Promoters of Performing Arts, Sports, and 

Similar Events without Facilities

512120 Motion Picture and Video Distribution 711410 Agents and Managers for Artists, Athletes, 

Entertainers, and Other Public Figures

512131 Motion Picture Theaters (except Drive-Ins) 711510 Independent Artists, Writers, and Performers

512132 Drive-In Motion Picture Theaters 712110 Museums

512191 Teleproduction and Other Postproduction 

Services

712120 Historical Sites

512199 Other Motion Picture and Video Industries 712130 Zoos and Botanical Gardens

512210 Record Production 712190 Nature Parks and Other Similar Institutions

512220 Integrated Record Production/Distribution 713110 Amusement and Theme Parks

512230 Music Publishers 713120 Amusement Arcades

512240 Sound Recording Studios 713210 Casinos (except Casino Hotels)

512290 Other Sound Recording Industries 713290 Other Gambling Industries

515111 Radio Networks 713910 Golf Courses and Country Clubs

515112 Radio Stations 713920 Skiing Facilities

515120 Television Broadcasting 713930 Marinas

515210 Cable and Other Subscription Programming 713940 Fitness and Recreational Sports Centers

519110 News Syndicates 713950 Bowling Centers

519120 Libraries and Archives 713990 All Other Amusement and Recreation 

Industries

519130 Internet Publishing and Broadcasting and 

Web Search Portals

721120 Casino Hotels

519190 All Other Information Services 721211 RV (Recreational Vehicle) Parks and 

Campgrounds

711110 Theater Companies and Dinner Theaters 721214 Recreational and Vacation Camps (except 

Campgrounds)

Sources: North American Industry Classification System; Economic Modeling Specialists 

International (2012)

Note: Eleven functional categories defined per Statistical Portrait of Economic Diversity 

in Appalachia section 2-3 

102
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Table 9-2: Standardized Scores and Ranks of Industrial, Functional, 

Occupational, and Knowledge Diversity for Appalachian Region Counties

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Bibb County, AL 0.99 264 1.04 237 1.01 265 0.99 297

Blount County, AL 1.09 103 1.05 220 1.02 104 1.01 189

Calhoun County, AL 1.09 95 1.07 175 1.02 135 1.00 291

Chambers County, AL 1.02 208 1.03 244 1.02 74 0.99 304

Cherokee County, AL 0.99 267 1.01 285 0.99 338 0.98 381

Chilton County, AL 1.03 185 1.02 259 1.01 210 0.99 317

Clay County, AL 0.92 359 0.96 350 1.01 233 0.99 349

Cleburne County, AL 0.97 304 0.94 359 0.99 378 0.97 390

Colbert County, AL 1.11 60 1.13 53 1.02 120 1.00 290

Coosa County, AL 0.82 412 1.08 154 1.00 313 1.02 113

Cullman County, AL 1.11 56 1.11 81 1.01 218 1.01 193

DeKalb County, AL 1.09 105 1.07 168 1.01 206 0.99 322

Elmore County, AL 1.08 115 1.11 87 1.02 70 0.99 299

Etowah County, AL 1.09 98 1.10 117 1.02 168 1.00 235

Fayette County, AL 1.02 220 1.11 73 1.02 116 1.01 202

Franklin County, AL 0.97 305 0.94 361 0.99 355 0.97 393

Hale County, AL 0.94 325 1.00 299 1.01 259 0.98 385

Jackson County, AL 1.05 156 1.02 262 1.01 276 0.98 371

Jefferson County, AL 1.18 7 1.02 261 1.03 33 1.02 132

Lamar County, AL 0.98 281 1.13 42 1.00 286 1.01 198

Lauderdale County, AL 1.10 90 1.12 55 1.02 128 0.98 363

Lawrence County, AL 0.98 285 1.02 275 1.00 335 1.00 237

Limestone County, AL 1.11 74 1.10 119 1.02 181 1.00 255

Macon County, AL 0.85 396 0.98 322 1.00 299 0.95 414

Madison County, AL 1.09 99 1.00 290 1.03 14 1.04 15

Marion County, AL 1.03 192 1.07 179 1.01 186 1.00 293

Marshall County, AL 1.04 183 1.00 292 1.00 331 0.97 407

Morgan County, AL 1.14 36 1.06 190 1.03 57 1.01 200

Pickens County, AL 0.96 308 1.05 218 1.01 187 1.03 79

Randolph County, AL 0.98 287 1.06 192 0.99 356 0.99 345

Shelby County, AL 1.16 20 0.93 375 1.02 72 1.00 238

St. Clair County, AL 1.11 66 1.04 229 1.02 131 0.99 351

Talladega County, AL 1.04 178 1.01 284 1.00 336 0.98 374

Tallapoosa County, AL 1.02 218 1.10 109 1.02 171 1.00 243

Tuscaloosa County, AL 1.11 70 1.13 43 1.02 109 0.99 343

Walker County, AL 1.12 51 1.05 202 1.02 68 1.02 139

Winston County, AL 0.97 303 0.93 366 1.00 315 0.99 354

Banks County, GA 1.01 239 0.98 329 1.00 329 0.97 396

Barrow County, GA 1.11 71 1.04 232 1.02 152 1.00 252

Bartow County, GA 1.14 40 1.02 266 1.02 125 1.01 224

Carroll County, GA 1.09 101 1.13 38 1.03 50 1.02 135

Catoosa County, GA 1.11 75 1.01 286 1.01 223 0.99 332

Chattooga County, GA 0.97 301 1.02 279 1.00 325 0.98 386

Cherokee County, GA 1.14 35 0.93 367 1.02 129 0.99 341

Dade County, GA 1.03 204 1.05 214 1.03 65 1.00 229

Dawson County, GA 1.07 133 0.93 368 0.98 394 0.94 418

Douglas County, GA 1.14 34 1.00 301 1.01 207 0.99 352

Elbert County, GA 0.95 317 0.98 325 1.00 296 0.99 331

Fannin County, GA 1.06 138 0.91 378 1.00 293 0.99 302

Floyd County, GA 1.08 118 1.13 46 1.03 29 1.03 48

Forsyth County, GA 1.14 33 0.96 340 1.03 19 1.02 100

Franklin County, GA 1.06 147 1.10 106 1.02 145 1.01 196

Gilmer County, GA 1.02 227 1.02 278 1.00 305 0.97 394

Gordon County, GA 1.02 225 0.92 376 0.98 384 0.98 379

Gwinnett County, GA 1.20 2 0.90 389 1.02 160 1.00 251

Habersham County, GA 1.05 169 1.11 76 1.03 42 0.99 326

Hall County, GA 1.12 47 1.07 163 1.02 84 1.02 120

Haralson County, GA 1.02 226 0.98 328 1.02 137 1.00 261

Hart County, GA 1.01 232 1.08 157 1.03 41 1.01 205

103
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Heard County, GA 0.86 394 1.04 243 0.98 393 1.00 289

Jackson County, GA 1.07 128 1.05 208 1.01 270 0.98 362

Lumpkin County, GA 1.03 184 1.11 93 1.03 63 1.00 260

Madison County, GA 1.03 202 0.99 309 1.01 236 1.01 157

Murray County, GA 0.89 382 0.81 410 0.94 414 0.94 419

Paulding County, GA 1.11 57 0.89 393 1.01 217 0.99 325

Pickens County, GA 1.08 116 1.01 289 1.02 76 1.01 191

Polk County, GA 1.04 181 0.95 355 1.02 167 0.97 391

Rabun County, GA 1.03 203 0.97 337 1.00 291 0.98 382

Stephens County, GA 1.06 148 1.10 107 1.03 9 1.01 171

Towns County, GA 0.98 279 0.91 380 1.00 298 1.01 199

Union County, GA 1.05 164 0.97 331 1.01 280 1.00 278

Walker County, GA 1.03 205 0.96 343 1.01 219 0.98 378

White County, GA 1.10 77 1.06 191 1.02 178 0.99 321

Whitfield County, GA 1.00 252 0.90 390 0.95 406 0.98 380

Adair County, KY 0.93 346 1.09 124 0.99 370 1.03 78

Bath County, KY 0.86 393 0.91 379 0.94 412 0.95 415

Bell County, KY 1.00 253 1.07 169 1.01 274 1.00 233

Boyd County, KY 1.07 136 1.11 78 1.01 209 1.03 55

Breathitt County, KY 0.94 329 1.07 166 0.99 359 0.99 320

Carter County, KY 1.00 262 0.97 333 1.00 302 0.96 409

Casey County, KY 0.98 288 1.06 193 1.00 323 1.00 241

Clark County, KY 1.13 43 1.01 283 1.03 67 1.01 158

Clay County, KY 0.93 350 0.94 360 1.00 311 1.02 150

Clinton County, KY 0.81 413 0.90 387 0.95 411 0.95 413

Cumberland County, KY 0.94 332 0.98 321 0.99 342 1.02 147

Edmonson County, KY 0.83 410 0.84 406 0.93 419 0.96 410

Elliott County, KY 0.77 416 0.77 413 0.93 418 0.97 402

Estill County, KY 0.93 339 1.05 219 1.00 310 0.99 303

Fleming County, KY 0.96 311 1.02 273 0.97 400 0.99 338

Floyd County, KY 1.05 152 1.05 215 1.01 208 1.04 37

Garrard County, KY 1.03 189 0.98 318 0.99 345 1.00 242

Green County, KY 0.92 354 0.89 391 0.96 405 0.98 368

Greenup County, KY 1.01 240 1.05 203 1.02 146 1.03 54

Harlan County, KY 0.92 357 0.99 313 0.99 349 1.04 36

Hart County, KY 0.93 345 0.88 398 0.98 396 0.97 405

Jackson County, KY 0.90 372 1.00 298 0.99 337 1.00 280

Johnson County, KY 1.01 242 1.00 293 0.99 352 0.99 347

Knott County, KY 0.90 378 0.90 388 1.01 267 1.05 5

Knox County, KY 1.05 163 1.11 75 1.02 69 1.02 88

Laurel County, KY 1.08 122 1.02 267 1.01 237 1.01 220

Lawrence County, KY 0.96 312 0.93 371 1.00 333 1.02 118

Lee County, KY 0.92 360 0.98 326 1.01 196 1.04 16

Leslie County, KY 0.87 390 0.98 323 0.99 346 1.02 126

Letcher County, KY 0.95 322 0.94 363 0.99 368 1.02 105

Lewis County, KY 0.93 342 0.96 347 0.98 381 0.99 315

Lincoln County, KY 1.00 255 1.07 180 1.02 155 1.02 123

Madison County, KY 1.03 191 1.15 16 1.02 162 1.01 212

Magoffin County, KY 0.85 399 0.94 364 0.99 377 1.00 230

Martin County, KY 0.86 395 0.81 409 0.97 398 0.98 376

McCreary County, KY 0.90 377 0.96 348 1.01 242 0.98 384

Menifee County, KY 0.88 387 0.98 330 0.97 399 1.00 287

Metcalfe County, KY 0.91 370 0.87 402 0.96 404 0.97 399

Monroe County, KY 0.90 376 1.00 295 0.97 403 0.98 358

Montgomery County, KY 1.01 238 0.95 353 1.00 334 0.98 365

Morgan County, KY 0.89 379 0.99 307 0.98 382 0.99 330

Nicholas County, KY 0.84 405 0.87 403 0.94 416 1.00 248

Owsley County, KY 0.83 407 0.90 384 0.97 401 1.00 276

Perry County, KY 0.98 282 1.04 225 0.99 371 1.02 87

Pike County, KY 1.02 217 1.03 246 1.01 262 1.04 29

Powell County, KY 0.98 283 1.05 200 1.00 326 0.97 395

Pulaski County, KY 1.09 102 1.12 67 1.02 154 1.02 114

Robertson County, KY 0.74 420 0.79 411 0.89 420 0.95 417

104
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Rockcastle County, KY 0.93 341 1.04 240 0.99 351 1.02 134

Rowan County, KY 0.94 330 1.11 92 0.99 357 0.99 323

Russell County, KY 1.02 215 1.07 171 1.01 266 1.00 250

Wayne County, KY 0.97 299 0.98 320 1.01 225 0.99 337

Whitley County, KY 0.97 302 1.13 50 1.02 85 1.06 3

Wolfe County, KY 0.90 375 1.00 294 0.98 389 1.00 254

Allegany County, MD 1.08 114 1.12 66 1.01 197 1.00 271

Garrett County, MD 1.13 44 1.07 172 1.02 144 1.01 226

Washington County, MD 1.15 28 1.07 161 1.00 308 0.99 335

Alcorn County, MS 1.04 177 1.11 88 1.02 101 0.99 344

Benton County, MS 0.85 398 0.95 354 0.94 413 0.99 342

Calhoun County, MS 0.93 349 0.97 335 0.98 395 0.98 364

Chickasaw County, MS 0.85 401 0.82 408 0.98 385 0.97 388

Choctaw County, MS 0.93 351 1.03 249 1.02 81 1.05 6

Clay County, MS 1.02 214 1.06 185 1.02 127 1.01 188

Itawamba County, MS 0.99 266 1.06 197 1.01 230 0.99 316

Kemper County, MS 0.92 362 1.15 15 1.00 284 1.03 70

Lee County, MS 1.11 58 1.06 195 1.01 232 1.00 249

Lowndes County, MS 1.11 55 1.14 33 1.03 66 1.01 192

Marshall County, MS 1.01 244 1.10 112 1.02 124 1.02 115

Monroe County, MS 1.03 193 1.10 103 1.02 107 1.01 177

Montgomery County, MS 0.96 313 1.00 291 1.00 285 1.01 195

Noxubee County, MS 0.93 340 0.98 316 0.98 383 0.95 411

Oktibbeha County, MS 0.93 335 1.00 300 1.03 44 0.98 373

Panola County, MS 1.05 165 1.02 274 1.02 108 1.00 273

Pontotoc County, MS 0.84 403 0.71 417 0.98 386 0.98 360

Prentiss County, MS 0.98 277 1.10 99 1.01 203 1.00 282

Tippah County, MS 0.99 273 1.10 102 1.02 71 1.01 169

Tishomingo County, MS 1.03 201 1.05 217 1.02 111 1.00 232

Union County, MS 0.96 310 0.94 358 0.99 369 0.99 355

Webster County, MS 0.91 366 1.04 239 1.01 264 1.01 197

Winston County, MS 1.01 236 1.11 86 1.02 80 1.02 141

Yalobusha County, MS 0.92 353 0.95 356 0.99 350 0.98 366

Alexander County, NC 1.03 187 0.93 372 1.02 106 1.00 295

Alleghany County, NC 1.01 247 1.09 138 1.01 275 1.01 172

Ashe County, NC 1.05 151 1.11 83 1.01 251 1.01 161

Avery County, NC 1.06 150 1.05 205 1.01 248 1.01 201

Buncombe County, NC 1.18 11 1.11 82 1.03 27 1.02 94

Burke County, NC 1.13 42 1.09 128 1.03 30 1.02 121

Caldwell County, NC 1.11 64 1.07 167 1.02 89 1.01 227

Cherokee County, NC 1.07 132 1.08 156 1.02 130 1.03 74

Clay County, NC 1.02 210 0.95 357 1.00 327 0.98 375

Davie County, NC 1.08 111 1.05 213 1.02 140 1.00 253

Forsyth County, NC 1.15 30 1.06 183 1.03 38 1.02 84

Graham County, NC 0.94 331 0.91 381 1.01 279 0.98 367

Haywood County, NC 1.08 120 1.08 150 1.01 222 0.98 361

Henderson County, NC 1.15 27 1.10 104 1.03 45 1.01 156

Jackson County, NC 1.02 219 1.04 222 1.00 290 0.99 313

Macon County, NC 1.08 117 1.00 302 1.01 254 1.01 216

Madison County, NC 1.00 256 1.14 28 1.02 75 1.04 12

McDowell County, NC 1.02 224 1.11 79 1.03 12 1.03 64

Mitchell County, NC 1.01 235 1.12 59 1.02 147 1.04 20

Polk County, NC 1.04 175 1.03 247 1.02 105 1.03 66

Rutherford County, NC 1.10 92 1.15 18 1.03 32 1.01 168

Stokes County, NC 1.05 159 1.04 234 1.02 156 1.01 218

Surry County, NC 1.10 79 1.11 71 1.01 235 1.00 279

Swain County, NC 0.75 419 0.62 420 1.00 332 0.96 408

Transylvania County, NC 1.08 109 1.07 164 1.02 96 1.03 62

Watauga County, NC 1.03 190 1.04 228 1.00 306 0.98 383

Wilkes County, NC 1.06 145 1.05 216 1.02 172 0.99 300

Yadkin County, NC 1.02 212 1.04 227 1.00 301 0.99 356

Yancey County, NC 1.04 173 1.09 133 1.02 110 1.01 215

Allegany County, NY 0.99 269 1.12 58 1.03 40 1.04 31

105
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Broome County, NY 1.13 41 1.15 23 1.03 53 1.03 80

Cattaraugus County, NY 1.04 180 1.12 68 1.03 24 1.01 170

Chautauqua County, NY 1.14 32 1.16 9 1.03 39 1.01 181

Chemung County, NY 1.11 59 1.16 12 1.02 83 1.02 108

Chenango County, NY 1.08 119 1.09 127 1.03 20 1.03 72

Cortland County, NY 1.08 108 1.17 4 1.03 8 1.01 176

Delaware County, NY 1.07 134 1.18 1 1.04 1 1.04 13

Otsego County, NY 1.02 209 1.13 49 1.02 112 1.02 95

Schoharie County, NY 1.02 222 1.09 132 1.02 132 1.02 127

Schuyler County, NY 1.06 142 1.05 212 1.02 97 1.02 137

Steuben County, NY 1.08 123 1.11 90 1.04 2 1.04 11

Tioga County, NY 1.00 251 1.07 176 1.03 6 1.05 9

Tompkins County, NY 0.94 327 0.94 362 0.99 376 1.01 173

Adams County, OH 1.03 195 1.11 77 1.01 228 1.01 204

Ashtabula County, OH 1.16 21 1.13 51 1.02 91 1.02 110

Athens County, OH 0.95 320 0.96 341 1.01 244 1.00 277

Belmont County, OH 1.11 67 1.12 56 1.01 283 1.00 257

Brown County, OH 1.06 146 1.07 177 1.01 221 1.02 117

Carroll County, OH 1.10 84 1.10 100 1.01 192 1.01 225

Clermont County, OH 1.17 14 0.99 312 1.02 93 0.99 298

Columbiana County, OH 1.16 25 1.13 44 1.02 100 1.02 93

Coshocton County, OH 1.11 65 1.05 207 1.03 5 1.04 39

Gallia County, OH 1.00 250 1.12 65 1.01 227 1.04 25

Guernsey County, OH 1.10 83 1.13 45 1.02 134 1.02 111

Harrison County, OH 0.95 319 1.03 253 1.02 177 1.01 182

Highland County, OH 1.04 174 1.15 20 1.01 268 1.00 292

Hocking County, OH 1.06 140 1.09 135 1.02 163 0.99 339

Holmes County, OH 1.12 50 0.90 385 1.00 294 0.98 357

Jackson County, OH 0.97 295 0.96 342 1.00 322 1.00 265

Jefferson County, OH 1.05 154 1.14 26 1.02 136 1.04 18

Lawrence County, OH 1.04 179 1.08 142 1.01 189 0.99 309

Mahoning County, OH 1.19 5 1.09 122 1.02 133 1.00 263

Meigs County, OH 0.97 300 0.96 345 1.01 229 1.01 185

Monroe County, OH 0.89 384 0.88 396 1.01 240 1.04 17

Morgan County, OH 0.98 291 1.07 170 1.01 183 1.02 125

Muskingum County, OH 1.10 89 1.14 25 1.01 201 1.02 145

Noble County, OH 0.95 315 1.07 178 1.00 300 1.02 124

Perry County, OH 0.99 270 1.01 288 1.02 90 1.02 92

Pike County, OH 1.00 261 1.14 30 1.03 34 1.03 46

Ross County, OH 1.02 211 1.02 270 1.00 330 1.00 231

Scioto County, OH 1.03 198 1.08 147 1.01 278 1.01 163

Trumbull County, OH 1.15 29 1.05 211 1.01 239 1.00 268

Tuscarawas County, OH 1.17 16 1.14 36 1.02 126 1.00 236

Vinton County, OH 0.90 371 1.08 149 1.01 255 1.01 160

Washington County, OH 1.11 68 1.16 8 1.03 25 1.04 21

Allegheny County, PA 1.17 12 1.00 304 1.03 21 1.03 44

Armstrong County, PA 1.13 45 1.08 152 1.03 15 1.04 24

Beaver County, PA 1.17 18 1.08 144 1.04 4 1.02 85

Bedford County, PA 1.10 82 1.08 145 1.01 261 1.00 259

Blair County, PA 1.16 22 1.10 94 1.02 166 1.01 186

Bradford County, PA 1.09 93 1.08 146 1.01 184 1.03 43

Butler County, PA 1.19 4 1.09 121 1.03 48 1.02 106

Cambria County, PA 1.16 26 1.12 61 1.03 18 1.04 26

Cameron County, PA 0.85 397 0.85 405 0.95 408 0.95 412

Carbon County, PA 1.09 96 1.05 204 1.03 49 1.02 149

Centre County, PA 0.99 271 0.97 339 1.03 52 1.03 59

Clarion County, PA 1.08 126 1.11 72 1.03 58 1.03 77

Clearfield County, PA 1.12 49 1.11 89 1.01 194 1.01 167

Clinton County, PA 1.08 127 1.13 54 1.01 190 0.99 340

Columbia County, PA 1.12 48 1.09 139 1.01 215 0.99 311

Crawford County, PA 1.14 38 1.17 2 1.03 36 1.03 61

Elk County, PA 1.04 182 1.02 264 0.99 375 1.01 164

Erie County, PA 1.17 13 1.14 32 1.03 46 1.02 96

106
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Fayette County, PA 1.16 23 1.12 62 1.02 86 1.01 219

Forest County, PA 0.79 414 0.87 401 1.02 122 1.03 65

Fulton County, PA 0.91 365 1.02 265 0.99 366 1.02 146

Greene County, PA 0.93 338 0.88 395 0.99 373 1.03 42

Huntingdon County, PA 1.06 143 1.13 52 1.03 26 1.03 75

Indiana County, PA 1.11 62 1.10 105 1.02 77 1.02 90

Jefferson County, PA 1.10 76 1.10 95 1.02 79 1.03 58

Juniata County, PA 1.04 176 0.97 334 1.01 250 0.99 319

Lackawanna County, PA 1.18 9 1.10 120 1.03 60 1.02 97

Lawrence County, PA 1.15 31 1.10 97 1.03 31 1.03 71

Luzerne County, PA 1.19 6 1.07 165 1.02 148 1.01 183

Lycoming County, PA 1.18 10 1.16 10 1.03 54 1.02 131

McKean County, PA 1.08 125 1.12 69 1.03 10 1.04 32

Mercer County, PA 1.16 24 1.15 17 1.02 139 1.03 76

Mifflin County, PA 1.10 85 1.08 160 1.02 180 1.02 116

Monroe County, PA 1.11 69 1.10 118 1.01 185 0.99 328

Montour County, PA 0.84 406 0.78 412 0.98 380 1.05 7

Northumberland County, PA 1.14 39 1.10 115 1.02 82 1.02 129

Perry County, PA 1.05 168 1.02 260 1.00 328 0.99 310

Pike County, PA 1.07 129 0.91 382 1.01 271 0.97 401

Potter County, PA 1.05 155 1.11 91 1.03 23 1.04 22

Schuylkill County, PA 1.14 37 1.08 151 1.01 191 1.01 153

Snyder County, PA 1.05 158 1.04 231 1.01 273 0.99 348

Somerset County, PA 1.11 72 1.11 84 1.03 43 1.02 86

Sullivan County, PA 0.99 272 1.07 162 1.01 205 1.01 184

Susquehanna County, PA 1.08 113 0.97 336 1.01 193 1.03 60

Tioga County, PA 1.08 121 1.16 7 1.01 216 1.00 234

Union County, PA 1.01 248 1.12 63 1.01 238 1.03 53

Venango County, PA 1.10 87 1.17 3 1.02 176 1.01 166

Warren County, PA 1.07 130 1.09 140 1.03 51 1.04 34

Washington County, PA 1.20 3 1.13 39 1.03 22 1.02 103

Wayne County, PA 1.10 91 1.06 188 1.01 234 1.01 211

Westmoreland County, PA 1.20 1 1.11 74 1.03 62 1.02 140

Wyoming County, PA 0.99 263 0.98 314 0.99 361 1.00 274

Anderson County, SC 1.12 53 1.09 129 1.02 157 0.99 336

Cherokee County, SC 1.05 170 1.05 210 1.01 282 0.99 334

Greenville County, SC 1.16 19 1.06 182 1.03 11 1.02 109

Oconee County, SC 1.07 135 1.08 148 1.02 98 1.01 214

Pickens County, SC 1.06 144 1.14 29 1.02 113 0.99 353

Spartanburg County, SC 1.17 17 1.06 184 1.02 141 0.99 327

Anderson County, TN 1.07 131 0.99 311 1.02 117 1.04 28

Bledsoe County, TN 0.89 380 0.82 407 0.95 409 0.99 350

Blount County, TN 1.11 54 1.02 280 1.02 119 1.00 256

Bradley County, TN 1.12 52 1.14 27 1.03 17 1.02 128

Campbell County, TN 1.05 171 1.14 31 1.02 179 1.01 213

Cannon County, TN 0.99 275 1.03 251 0.99 367 1.02 102

Carter County, TN 1.08 124 1.15 21 1.01 214 1.01 206

Claiborne County, TN 0.99 265 1.10 114 1.03 47 1.02 89

Clay County, TN 1.00 260 1.06 194 1.00 312 1.01 194

Cocke County, TN 1.03 199 1.02 271 1.01 247 0.97 400

Coffee County, TN 1.06 139 1.04 230 1.03 37 1.04 27

Cumberland County, TN 1.10 88 1.09 134 1.02 158 1.01 223

DeKalb County, TN 0.93 343 1.02 268 0.98 387 1.00 294

Fentress County, TN 1.01 243 0.99 306 1.01 246 1.00 285

Franklin County, TN 1.03 207 1.15 13 1.02 87 1.03 45

Grainger County, TN 0.95 323 0.98 324 0.98 391 0.98 377

Greene County, TN 1.05 166 1.15 22 1.01 272 1.01 175

Grundy County, TN 0.97 306 0.96 346 1.01 241 1.00 264

Hamblen County, TN 1.11 73 1.10 110 1.01 277 1.00 240

Hamilton County, TN 1.18 8 1.05 201 1.03 55 1.02 107

Hancock County, TN 0.77 415 0.87 400 0.93 417 0.98 369

Hawkins County, TN 1.03 196 1.02 269 1.01 257 0.99 346

Jackson County, TN 1.03 188 0.99 308 1.00 309 1.00 267

107
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Jefferson County, TN 1.06 149 1.15 24 1.03 64 1.02 143

Johnson County, TN 0.94 334 1.09 126 0.99 362 1.00 262

Knox County, TN 1.17 15 1.04 233 1.02 114 1.02 151

Lawrence County, TN 1.05 153 1.07 181 1.00 292 1.00 269

Lewis County, TN 1.01 246 1.06 189 1.01 224 1.00 228

Loudon County, TN 1.05 157 1.04 238 1.01 256 1.00 266

Macon County, TN 1.01 228 1.03 252 1.00 314 1.00 286

Marion County, TN 1.02 213 1.08 155 1.01 253 0.99 329

McMinn County, TN 1.09 100 1.03 250 1.02 138 1.01 162

Meigs County, TN 1.03 186 0.98 315 1.01 252 1.00 272

Monroe County, TN 1.02 221 0.94 365 1.00 304 0.99 312

Morgan County, TN 0.98 286 1.03 258 1.03 59 1.04 35

Overton County, TN 0.95 314 1.04 236 1.00 307 1.01 155

Pickett County, TN 0.89 383 1.04 224 0.99 365 0.99 333

Polk County, TN 0.94 328 1.10 116 1.01 263 0.97 403

Putnam County, TN 1.08 110 1.12 57 1.01 198 0.98 359

Rhea County, TN 0.97 298 0.95 352 1.02 88 0.99 307

Roane County, TN 0.91 368 0.97 338 1.04 3 1.09 1

Scott County, TN 0.98 278 1.06 196 1.02 164 1.02 119

Sequatchie County, TN 1.00 257 1.01 287 1.01 195 0.97 397

Sevier County, TN 1.02 223 0.93 373 0.97 402 0.90 420

Smith County, TN 0.96 307 0.98 317 0.99 344 1.00 275

Sullivan County, TN 1.10 86 1.11 85 1.03 61 1.03 57

Unicoi County, TN 0.92 355 1.08 158 1.02 102 1.03 68

Union County, TN 0.97 297 1.03 254 0.99 353 0.97 389

Van Buren County, TN 0.90 373 1.04 241 0.99 348 1.02 138

Warren County, TN 1.05 161 1.03 248 1.01 281 1.01 222

Washington County, TN 1.09 97 1.14 35 1.01 202 0.99 308

White County, TN 1.02 216 1.09 131 1.01 260 0.99 301

Alleghany (+ Covington city) County, 

VA

1.01 233 1.04 221 1.02 170 1.02 104

Bath County, VA 0.75 418 1.09 136 0.99 341 0.98 370

Bland County, VA 0.82 411 1.04 223 0.99 360 1.02 81

Botetourt County, VA 1.09 106 1.00 305 1.02 99 1.02 144

Buchanan County, VA 0.95 318 0.91 383 0.99 374 1.04 30

Carroll (+ Galax city) County, VA 1.07 137 1.07 174 1.01 226 1.00 281

Craig County, VA 0.95 316 0.93 369 1.00 318 1.01 154

Dickenson County, VA 0.87 391 0.77 414 0.98 379 1.02 99

Floyd County, VA 1.03 200 1.04 226 1.01 199 1.01 174

Giles County, VA 0.93 336 1.10 111 1.02 95 1.02 83

Grayson County, VA 0.94 326 0.98 319 0.99 347 1.01 152

Henry (+ Martinsville city) County, VA 1.10 81 1.08 143 1.02 118 1.00 258

Highland County, VA 0.92 356 0.90 386 0.98 390 1.01 178

Lee County, VA 0.98 292 0.98 327 1.00 317 1.02 148

Montgomery (+ Radford city) County, 

VA

1.00 254 1.04 235 1.02 94 1.01 179

Patrick County, VA 0.99 274 1.11 80 1.02 174 1.04 38

Pulaski County, VA 0.97 296 1.10 98 0.98 397 0.97 406

Rockbridge (+ Buena Vista city + 

Lexington city) County, VA

1.05 160 1.11 70 1.03 7 1.02 112

Russell County, VA 1.01 241 1.00 296 1.01 220 1.03 56

Scott County, VA 0.92 358 1.09 141 0.99 340 0.99 296

Smyth County, VA 1.01 231 1.12 64 1.01 211 1.01 221

Tazewell County, VA 1.05 172 1.16 11 1.02 151 1.02 122

Washington (+ Bristol city) County, 

VA

1.12 46 1.17 5 1.02 103 1.00 283

Wise (+ Norton city) County, VA 0.98 289 1.07 173 1.00 320 1.02 91

Wythe County, VA 1.01 237 1.13 48 1.00 324 0.98 372

Barbour County, WV 0.97 293 1.03 256 1.02 121 1.05 10

Berkeley County, WV 1.06 141 1.05 199 1.02 149 1.01 187

Boone County, WV 0.85 402 0.74 416 0.95 410 1.00 244

Braxton County, WV 0.95 324 1.02 281 0.99 363 1.00 270

Brooke County, WV 0.99 268 1.06 187 1.02 150 1.03 52

108
STATISTICAL PORTRAIT OF ECONOMIC DIVERSITY IN APPALACHIA

Industrial Functional Occupational Knowledge

County, State

Std. 

Score Rank Std. Score Rank Std. Score Rank Std. Score Rank

Cabell County, WV 1.11 63 1.15 19 1.01 200 1.01 210

Calhoun County, WV 0.77 417 0.67 419 1.00 289 1.08 2

Clay County, WV 0.88 386 0.87 399 0.99 339 1.03 63

Doddridge County, WV 0.83 409 0.69 418 0.99 372 1.03 51

Fayette County, WV 1.01 230 1.13 41 1.01 231 1.00 288

Gilmer County, WV 0.87 392 0.93 370 1.03 28 1.05 8

Grant County, WV 0.98 284 1.03 255 1.01 243 1.03 73

Greenbrier County, WV 1.03 197 1.14 34 1.01 249 0.99 324

Hampshire County, WV 1.01 245 0.99 310 1.00 297 1.01 209

Hancock County, WV 0.95 321 1.05 198 1.01 269 0.98 387

Hardy County, WV 0.85 400 0.85 404 0.95 407 0.95 416

Harrison County, WV 1.09 107 1.09 125 1.03 16 1.03 67

Jackson County, WV 0.98 290 1.05 209 1.00 287 1.00 245

Jefferson County, WV 1.01 229 1.10 101 1.02 159 0.97 392

Kanawha County, WV 1.10 78 1.05 206 1.02 78 1.03 69

Lewis County, WV 0.93 337 0.96 351 1.01 258 1.02 130

Lincoln County, WV 0.93 344 0.93 374 1.01 213 1.03 49

Logan County, WV 0.98 276 1.09 123 0.99 343 1.01 217

Marion County, WV 1.09 94 1.10 113 1.02 115 1.01 208

Marshall County, WV 1.03 206 1.06 186 1.02 92 1.02 101

Mason County, WV 0.93 348 1.10 108 1.01 188 1.04 14

McDowell County, WV 0.83 408 0.89 392 0.94 415 1.00 284

Mercer County, WV 1.08 112 1.17 6 1.02 123 0.99 306

Mineral County, WV 0.96 309 1.15 14 1.02 73 1.04 23

Mingo County, WV 0.89 381 0.96 349 0.98 392 1.03 50

Monongalia County, WV 1.00 258 1.09 137 1.01 245 1.03 47

Monroe County, WV 0.93 352 1.02 282 1.00 316 1.03 41

Morgan County, WV 1.00 259 1.08 159 1.03 56 1.01 190

Nicholas County, WV 1.00 249 1.04 242 1.00 288 0.99 318

Ohio County, WV 1.05 162 1.12 60 1.02 161 1.00 247

Pendleton County, WV 0.92 363 1.00 297 0.98 388 1.01 180

Pleasants County, WV 0.94 333 1.09 130 1.03 13 1.04 19

Pocahontas County, WV 0.92 361 1.02 277 1.02 182 1.00 239

Preston County, WV 1.01 234 1.03 257 0.99 364 1.01 207

Putnam County, WV 1.11 61 1.00 303 1.02 153 1.02 142

Raleigh County, WV 1.09 104 1.13 47 1.02 165 1.02 98

Randolph County, WV 1.03 194 1.13 40 1.02 143 1.02 82

Ritchie County, WV 0.90 374 0.88 397 1.02 142 1.02 133

Roane County, WV 0.87 389 0.77 415 1.02 169 1.06 4

Summers County, WV 0.91 364 0.96 344 1.00 295 1.01 203

Taylor County, WV 0.93 347 1.02 276 1.00 319 0.99 314

Tucker County, WV 0.91 369 1.02 272 1.01 212 0.97 398

Tyler County, WV 0.88 388 1.03 245 1.01 204 1.03 40

Upshur County, WV 1.05 167 1.08 153 1.02 175 1.04 33

Wayne County, WV 0.97 294 1.02 263 1.03 35 1.01 159

Webster County, WV 0.88 385 0.97 332 0.99 358 1.02 136

Wetzel County, WV 0.98 280 1.10 96 0.99 354 0.97 404

Wirt County, WV 0.84 404 0.89 394 1.00 303 0.99 305

Wood County, WV 1.10 80 1.14 37 1.02 173 1.00 246

Wyoming County, WV 0.91 367 0.92 377 1.00 321 1.01 165

Data source: Estimated employment by industry, Economic Modeling Specialists 

International (EMSI), 2012

Note: Industrial, functional, occupational, and knowledge diversity calculated per 

Statistical Portrait of Economic Diversity in Appalachia Chapter 2

109