Sources of Regional Growth in Non-Metro Appalachia 

Vol. 1. Project Background and Prior Research 

on Economic Growth Paths 

Prepared for the Appalachian Regional Commission 

Prepared by: 

Economic Development Research Group, Inc. 

 Regional Technology Strategies, Inc. 

 MIT Dept. of Urban Studies & Planning

Revised 2007 
Vol.1 Project Background and Summary 

Sources of Growth in Non-Metro Appalachia 

SOURCES OF GROWTH PROJECT

The Sources of Growth project is part of a series of research efforts funded by the Appalachian 

Regional Commission to improve our understanding of factors affecting economic growth in 

rural and distressed areas. As stated in the Volume 1 Introduction, “the starting premise of 

this project is that there can multiple paths that an area can pursue in successfully enhancing 

job and income creation. They may build on natural resources, cultural resources, human 

resources, local amenities, institutional facilities or location advantages. The resulting 

direction of economic growth may involve manufacturing or supply chain development, 

resource extraction or tourism development, educational development or trade center 

development.” This research is intended to provide a basis of information that can ultimately 

be useful for enhancing the effectiveness of policies and tools aimed at improving the region’s 

economic development. 

Results of the Sources of Growth project are presented in a series of documents listed below. 

This document is Volume 1. 

• Executive Summary –synthesis of findings from all work products related to the 

study’s four main research components. 

• Volume 1, Project Background and Prior Research on Economic Growth Paths – 

study objectives, characteristics of non-metro Appalachian counties, classification of 

economic development growth paths, and synopsis of white paper findings on theory 

relating to economic development growth paths. 

• Volume 2, Case Studies of Local Economic Development Growth Processes –

findings related to growth paths as observed for selected case studies covering 

manufacturing industry specialization clusters, supply chain-based development, 

tourism-based development, advanced technology development, and diversification 

from resource-based economies. 

• Volume 3, Statistical Studies of Spatial Economic Relationships – findings from a 

series of econometric modeling and GIS-based analyses, focusing on roles of spatial 

adjacency, market access and transportation in determining economic growth and 

development of trade centers. 

• Volume 4, Tools for Economic Development & Study Conclusions – description of 

new and updated tools available to ARC and its Local Development Districts to assess 

economic development opportunities and potential directions for economic growth. 

• Appendices – (A) Spatial Analysis of Economic Health, (B) Economic Analysis of 

Hub-Spoke Relationships, (C) White Papers on Economic Growth Theories, (D) 

Literature Review of Empirical Studies on Spatial Influences in Economic 

Development 
Vol.1 Project Background and Summary 

Sources of Growth in Non-Metro Appalachia 

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i 

1 INTRODUCTION..........................................................................................................................1

1.1 BACKGROUND AND OBJECTIVE...............................................................................................1

1.2 STUDY COMPONENTS AND TEAM ROLES.................................................................................3

1.3 CLASSIFICATION OF APPALACHIAN COUNTIES ........................................................................6

1.4 CLASSIFICATION OF ECONOMIC GROWTH PATHS ..................................................................10

2 THEORY OF GROWTH PATHS..............................................................................................13

2.1 ECONOMIC DEVELOPMENT PROCESSES .................................................................................13

2.2 TRADE CENTERS ...................................................................................................................16

2.3 INDUSTRY AGGLOMERATION (CLUSTERS).............................................................................20

2.4 SUPPLY-CHAINS AND DISPERSAL ECONOMIES ......................................................................25

2.5 NATURAL AND CULTURAL ASSETS .......................................................................................29

2.6 LEARNING-BASED DEVELOPMENT ........................................................................................36

2.7 REFERENCES CITED...............................................................................................................45

3 IMPLICATIONS FOR RESEARCH AND PRACTICE..........................................................52
Vol.1 Project Background and Summary 

Sources of Growth in Non-Metro Appalachia page i

ACKNOWLEDGEMENTS

The Sources of Growth project involved a team of researchers including: 

• Economic Development Research Group, Inc. (EDRG) – Lisa Petraglia (Project 

Director), Glen Weisbrod and Teresa Lynch, with research support from Tyler 

Comings and Brett Piercy; 

• Regional Technology Strategies, Inc. (RTS) –Stuart Rosenfeld, Phil Psilos and Dan 

Broun. 

• Massachusetts Institute of Technology, Department of Urban Studies & Planning 

(MIT-DUSP) – Prof. Karen R. Polenske, Prof. Joseph Ferreira, Jr. and Ayman Ismail, 

with research support from Tan Zhijun, Isabelle Xin Li, 

Yi Xu and Leigh Graham. 

The project also benefited from the expertise of outside policy and research experts who 

reviewed various project documents, participated in meetings and provided technical 

guidance: Deb Markley (Co-Director of the Center for Rural Entrepreneurship, a Rural Policy 

Research Institute), Joseph Cortwright (Vice-President of Impresa Consulting), Ken Poole 

(Executive Director of ACCRA: The Council for Community and Economic Research), David 

Freshwater (Professor of Agricultural Economics and Public Policy at the University of 

Kentucky), David McGranahan and Luc Anselin (Professor. Dept. of Agriculture & Consumer 

Affairs and Regional Economics Applications Laboratory at the University of Illinois, 

Urbana- Champaign). 

Overall project direction and oversight was provided by Dr. Greg Bischak of the Appalachian 

Regional Commission, whose wide range of research experience served to focus the project 

team on the development of policy applications. Important insight and suggestions were also 

provided by officials of the Appalachian Regional Commission who participated in a day-long 

symposium with the project team, including Thomas Hunter (executive director of ARC, Ann 

Pope (federal co-chair of ARC) and Rick Peltz (alternate federal co-chair). In addition, Ken 

Wester and Jason Wang of ARC assisted the project team in collecting and assembling 

transportation and geographic data. 

Finally, the project team acknowledges the important role of prior ARC-funded research 

studies by Andrew Isserman, Ed Feser and Oleg Smirnov that provided a foundation for this 

project to build upon. 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 1

1 INTRODUCTION 

1.1 Background and Objective 

(A) Background: Growth Paths for Rural Economic Development 

The Appalachian Region spans many diverse local economies (across 410 counties in 

13 states), but is generally characterized by a greater degree of economic hardship and 

poverty than the nation as a whole. The Appalachian Regional Commission (ARC) 

was started specifically to help improve economic conditions in the region through a 

series of infrastructure and area development programs, accompanied by an active 

research program to help increase the effectiveness of those programs. 

It has become clear that the most distressed economic conditions are generally 

occurring in the more isolated and rural parts of Appalachia, and that targeted efforts 

are needed to address those conditions. At the same time, it has also become clear that 

“in-vogue” economic development strategies, which often focus on seeking largescale high-tech cluster development, are not necessarily appropriate or realistic for 

isolated, rural areas. Accordingly, the ARC embarked on a series of efforts to enhance 

our understanding of the alternative paths of growth that can be appropriate for rural 

areas, and ways that local development districts can move down those paths. The 

Sources of Growth project grew out of that effort. 

The starting premise of this project is that there can multiple paths that an area can 

pursue in successfully enhancing job and income creation. They may build on natural 

resources, cultural resources, human resources, local amenities, institutional facilities 

or location advantages. The resulting direction of economic growth may involve 

manufacturing or supply chain development, resource extraction or tourism 

development, educational development or trade center development. For any specific 

area, though, some growth paths are much more likely to succeed than others. So 

successful economic development becomes a matter of first understanding the possible 

growth paths that may be relevant for a region, then assessing the best directions and 

pursuing the necessary steps to make them succeed. 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 2

(B) Objectives: Building on Prior Research

The Sources of Growth project emerged as a logical (and much needed) step from 

regional growth research – much of it ARC sponsored - to understand factors affecting 

economic growth, persistent distress, and implications for local policy initiatives in 

Appalachia’s non-metro counties. It builds upon a program of prior ARC-funded 

research that has sought to explain why some parts of Appalachian have economically 

outperformed others and been more successful in moving out of economic distress, 

and what practically can be done to raise the economic well-being of communities 

long in need. Key prior ARC studies on these topics are listed in Exhibit 1-1 (table). 

Exhibit 1-1. Prior ARC-Funded Studies Pertaining to Economic Growth Patterns 

• Amenities and Rural Appalachian Growth (Deller, 2003) 

• An Assessment of the Economic Base of Distressed and Near-Distressed 

Counties in Appalachia (Smirnov and Smirnova, 2000) 

• An Assessment of Entrepreneurship in Local Appalachian Economies (CFED, 

1998) 

• An Assessment of Labor Force Participation Rates and Underemployment in 

Appalachia (Keystone Research Center, 2001) 

• The Economic Effects of the Appalachian Regional Commission (Isserman and 

Rephann, 1995) 

• Analysis of Business Formation, Survival and Attrition Rates of New and 

Existing Firms and Related Job Flows in Appalachia (Brandow Co., 2001) 

• The Appalachian Economy, Establishment and Employment Dynamics 1982-

1997: Evidence from the Longitudinal Business Database (Foster, 2003) 

• Exports, Competitiveness, and Synergy in Appalachian Industry Clusters, 

Rosenfeld, 1997 

• Birth and death of Manufacturing Plants and Restructuring in Appalachia’s 

Industrial Economy, 1963-1992, Jensen, 1998 

• Regional Technology Assets and Opportunities: The Geographic Clustering of 

High-Tech Industry, Science and Innovation in Appalachia, Feser and 

Goldstein, 2002 

• Core-Periphery Effects on Appalachian Regional Growth, Moore, 1994 

• Trends in National and Regional Economic Distress, 1960-2000, Wood, 2005 

• Building on Past Experiences: Creating a New Future for Distressed Counties, 

Glasmeier and Fuellhart, 1999. 

• Branch Plants and Rural Development in the Age of Globalization, Glasmeier 

et al, 1995 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 3

This new project has sought to address two limitations with existing research on 

sources of economic growth: (a) the literature features a multiplicity of theoretical 

approaches, with different perspectives for viewing the same growth phenomena; and 

(b) much of the current research is not accessible to practitioners, nor developed in 

ways that can directly help communities to pursue economic development. 

Accordingly, this project has generated a series of reports collectively aimed at 

fulfilling three core objectives: 

a) to span currently divergent lines of research on economic growth in order to 

build a broader understanding of factors that can facilitate economic 

development; 

b) to advance the state of data analysis concerning how spatial location and 

access may affect the economic growth of ARC counties; and 

c) to translate these activities into understandable findings and applications usable 

by practitioners. 

1.2 Study Components and Team Roles 

(A) Study Components 

The Sources of Growth project involved four research undertakings: 

(1) thematic “white papers” summarizing the distinguishing features of various 

economic development paths and the theories underlying them, 

(2) case studies of economic development paths occurring in various non-metro 

areas in Appalachia, 

(3) statistical studies of economic growth factors and the role of spatial 

relationships in Appalachia’s non-metro counties, 

(4) enhancement of tools for assessing local economic growth opportunities. 

The white papers reviewed existing theories and literature in the fields of regional 

science and economics to describe the mechanisms that affect the nature of a local 

area economy and how further economic growth occurs. They examined the 

following forms of local and regional economic development: industry clusters, trade 

centers, supply chain and dispersal economies, resource-dependent, natural asset and 

learning-based economic development. They were also reviewed and discussed by an 

expert panel at a day-long symposium. This process provided an important foundation 

for identifying the different types of growth paths and the location factors determining 

their appropriateness for various areas. 

The case studies used in-person interviews with local business and government 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 4

officials, together with data analysis of economic trends, to provide insight into how 

the various growth paths have actually taken hold for selected local areas. These case 

studies also provided a basis for assessing how hypotheses concerning the form and 

evolution of growth paths matched up (or in some cases, did not match) with actual 

experiences of those communities. This element of the project thus provided an 

important basis for refining our understanding of how location factors can enhance, 

constrain or redirect the direction and degree of economic growth success. 

 

The statistical studies examined time-series data on changes in economic growth 

patterns and their relationship to spatial isolation, market access and transportation 

infrastructure. The reasons for this focus were: (1) recognition that while the various 

paths of economic growth served different markets, they all depended in some way on 

access; (2) that many of ARC’s programs have aimed to reduce isolation and improve 

access, and (3) the availability of relatively new analytic methods for examining 

spatial relationships among counties. 

The effort to enhance practical tools focused on upgrading the web-based Local 

Economic Assessment Package (LEAP) available for ARC’s Local Development 

Districts and other economic development agencies to assess economic opportunities 

and targets for business growth and attraction. Based on findings from the other study 

elements, additional data sources and analysis measures were identified for evaluating 

the relevance of economic growth paths for local areas. Some of those additional 

elements have now been implemented, while others are still planned. . 

(B) Study Team Process

Research Team. This research project was a joint effort of Economic Development 

Research Group, Inc. (EDRG), Regional Technology Strategies, Inc. (RTS) and the 

Department of Urban Studies & Planning at the Massachusetts Institute of Technology 

(MIT-DUSP). 

• EDRG managed the overall project, organized the one-day symposium and 

expert panel review processes; developed three of the case studies, conducted 

time series analysis of access impacts on economic growth, developed a set of 

growth path indicators for the Local Economic Assessment Package, and 

authored the summary documents on overall study findings. 

• RTS staff contributed to the classification of growth paths, developed a white 

paper on learning-based clusters, participated in the symposium and completed 

three of the case studies. 

• MIT-DUSP provided the core literature review and data set assembly, 

developed white papers on trade centers and resource-based economies, 

participated in the symposium, conducted statistical analysis of economic 

“hub-spoke” relationships, and also conducted spatial correlation analysis of 

county-level economic growth outcomes. 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 5

Expert Panel for Research and Policy. An expert panel also contributed to the study 

by reviewing the white papers developed by project team members to summarize the 

state of theory and research on economic development strategies and growth paths. A 

One-day symposium was then held to discuss the content of the white papers, their 

policy implications, and the priorities for further research. The symposium was 

attended by the expert panel, officials of ARC and the project team. The goal was to 

refine our understanding of how various growth paths actually evolve and how they 

can be encouraged in non-metro parts of Appalachia. (A summary of findings on 

economic development theory from the white papers is included in this volume, and 

additional material from the white papers appears in a separate Appendix volume.) 

The expert panel was comprised of: 

• Deb Markley - Co-Director of the Center for Rural Entrepreneurship, a Rural 

Policy Research Institute. 

• Joseph Cortwright – Vice-President of Impresa Consulting and former chief 

economic development staff for the Oregon Legislature. 

• Ken Poole - Executive Director of ACCRA: The Council for Community and 

Economic Research; 

• David Freshwater –Professor of Agricultural Economics and Public Policy at 

the University of Kentucky; formerly Program Manager of TVA Rural Studies 

Program 

• David McGranahan – Senior Economist at the US Dept of Agriculture’s 

Economic Research Service, specializing in rural development. 

Technical Modeling Expertise. Additional technical support for spatial modeling 

issues was provided by Luc Anselin, Professor Dept. of Agriculture & Consumer 

Affairs and Senior Research professor of the Regional Economics Applications 

Laboratory (REAL), University of Illinois, Urbana- Champaign. He provided advice 

on methods for investigating spatial influences on economic growth patterns, led a 

day-long seminar on GeoDA spatial analysis software, and provided comments on 

several elements of the MIT team’s spatial analysis findings. 

(C) Reports on Study Findings

Results of the Sources of Growth project are presented in a series of volumes: 

• Executive Summary –synthesis of findings from all of the project’s research 

components. 

• Volume 1, Project Background and Prior Research on Economic Growth 

Paths – study objectives, characteristics of non-metro Appalachian counties, 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 6

classification of economic development growth paths, synopsis of white paper 

findings on theory relating to economic development growth paths, and 

empirical literature review on spatial growth modeling studies. 

• Volume 2, Case Studies –findings related to growth paths as observed for 

selected case studies covering manufacturing industry specialization clusters, 

supply chain-based development, tourism-based development, advanced 

technology development, and diversification from resource-based economies. 

• Volume 3, Spatial Analysis – findings from a series of econometric and 

statistical modeling studies and GIS-based analyses, focusing on roles of 

spatial adjacency, market access and transportation in determining economic 

growth and development of trade centers. 

• Volume 4, Tools for Economic Development – description of new and 

updated tools available to ARC and its Local Development Districts to assess 

economic development opportunities and potential directions for economic 

growth. 

• Appendices – (A) Spatial Analysis of Economic Health, (B) Economic 

Analysis of Hub-Spoke Relationships, (C) White Papers on Economic Growth 

Theories 

1.3 Classification of Appalachian Counties 

The Appalachia Region is an area of 410 counties, spanning thirteen states. For 

purposes of this study, there are two key attributes that vary among the counties. They 

are: (1) level of urbanization and (2) level of economic distress. This study focuses on 

the economic development of non-metropolitan areas, which are the counties where 

the highest levels of economic distress have tended to occur. 

(A) Categories of Urbanization

The level of urbanization is defined in terms of a distinction between metropolitan and 

non-metro areas. Exhibit 1-2 (map) shows the location of metropolitan and non-metro 

counties within Appalachia. A “metropolitan area” is defined as a county or set of 

counties with an urban cluster having a population of 50,000 or more in which at least 

50% of the residents work also work in that area. The remaining counties are 

classified as “non-metro.” Altogether, the Appalachian region has 140 metropolitan 

counties (with a total 2000 census population of 14.1 million) and 270 non-metro 

counties (with a total population of 8.7 million,). 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 7

Exhibit 1-2 Metro and Non-Metro Classification of Appalachian Counties 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 8

The 270 “non-metro” counties are further subdivided into 92 “micropolitan areas” 

(each having a total population base of 10,000 to 49,999 with at least 25% of the 

workers residing within that area) and 178 “non-core” counties (also more formally 

referred to as OBSA – “Outside of Core-Based Statistical Areas”). Exhibit 1-3 (map) 

shows the location of the micropolitan and non-core counties. Altogether, the 

Appalachian region has 92 micropolitan counties (with a total population of 4.9 

million) and 178 non-core counties (with a total population of 3.8 million). 

Exhibit 1-3: Micropolitan and Non-Core Classification of Appalachian Counties 

Source: map generated by the MIT Multiregional Planning Research Group. 

Data Sources: 2004 Urban Influence Codes, Economic Research Services, U.S. Department of 

Agriculture; 2000 Cartographic Boundary Files, U.S. Census Bureau; 2004 National Highway 

Planning Network, Federal Highway Administration, U.S. Department of Transportation. 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 9

(B) Categories of Economic Performance

The level of economic performance of Appalachian counties is classified as one of 

four categories: “distressed,” “transitional,” “competitive” and “attainment.” Each 

year, the ARC updates its tracking of the economic performance of the region’s 

counties. Using a recent three-year moving average on the unemployment rate, percapita income levels and the Census poverty rate, thresholds are applied to create the 

four classes of economic performance. Exhibit 1-4 shows how the ARC economic 

performance categories are defined. 

Exhibit 1-4. Criteria for County Economic Performance Levels, FY 2005 

No. of 2000-2002

Economic Counties in Three-Year Average 2001 Per Capita 2000 Census

Level Appalachia Unemployment Rate "Market" Income Poverty Rate

Distressed 82 7.3% or more and $17,627 or less and 18.6% or more OR

twice U.S. 

poverty rate & 

qualify on one 

other indicator

[150% of U.S. 4.8%] [67% of U.S. $26,309] [150% of U.S. 12.4%] 

Competitive 20 4.8% or less and $21,047 - $26,308 and 12.4% or less 

[100% of U.S.] [80% of U.S. = $20,541] [100% of U.S.]

Attainment 8 4.8% or less and $26,309 or more and 12.4% or less

[100% of U.S.] [100% of U.S.] [100% of U.S.]

Transitional 300 All counties not in other classes. Individual indicators vary.

Sources: U.S. Department of Labor - Bureau of Labor Statistics, Local Area Unemployment Statistics 

(LAUS), 2000-2002 (employment data); U.S. Department of Commerce - Bureau of Economic Analysis, 

Regional Economic Information System (REIS), 2001 (income data); U.S. Department of Commerce - 

Bureau of the Census, 2000 (poverty data).

Exhibit 1-5 shows how the relationship between level of urbanization and level of 

economic performance. It is notable that nearly all of the counties with an economic 

performance rating of “attainment” or “competitive” are within metropolitan areas. 

Conversely, most of the counties with an economic performance rating of “distressed” 

are (non-core) rural areas. 

Exhibit 1-5. Relationship Between Urbanization and Economic Performance 

 

“Attainment” & 

“Competitive” 

“Transitional” “Distressed” All of 

Appalachia 

Urban Influence 

Codes (2003) 

# of 

counties 

Population 

(2000) 

# of 

counties

Population 

(2000) 

# of 

counties 

Population 

(2000) 

# of 

counties 

Population 

(2000) 

Metropolitan 26 5,229,995 104 8,552,415 10 359,457 140 14,141,867

Micropolitan 3 120,353 69 4,152,993 20 640,796 92 4,914,142

Non-Core (rural) 1 18,324 86 1,965,980 91 1,785,929 178 3,770,233

Grand Total 30 5,368,672 259 14,671,388 121 2,786,182 410 22,826,242

Data source: Economic Research Services, U.S. Department of Agriculture, 2003. 

http://www.ers.usda.gov/briefing/rurality/UrbanInf/
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 10

This project focuses specifically on the non-metro counties which account for nearly 

all of the under-performing areas. Exhibit 1-6 shows that distressed counties exist 

across all parts of Appalachia, though they are most strongly represented in the central 

part. Concerns have been raised in current regional growth research (Isserman 2005) 

that more important than a metro – non-metro county distinction would be 

classification distinguishing degrees of rurality at the sub-county level, since there are 

many cases of a county containing both a thriving urban area and poor rural 

communities. The USDA-ERS’ Beale Codes offer further gradations on county 

classifications based on population densities and whether or not an adjacency to a 

metro area exists. Those more complicated codes are used in the empirical analysis 

parts of this project, as described later (refer to Volume 3).

Exhibit 1.6 County Economic Performance Ratings by Geographic Region

North South Central Total

91 102 77 270

transitional 74 83 36 193

distressed 16 18 41 75

competitive 11 0 2

arc status

# of Non-metro Appalachian Counties

Appalachia's Major Region

1.4 Classification of Economic Growth Paths 

Exhibit 1-7 (schematic) illustrates five basic types of growth paths, along with the 

process for initially assessing their appropriateness for a given area, and later 

evaluating program efforts to pursue them. 

Exhibit 1-7. Types of Regional Growth Paths and their Use 

Assetbased 

Strategy

Performance Evaluation – gauging progress on a growth path

Given Local Conditions – Resources, Constraints and Opportunities

Decision Criteria on Most Appropriate Growth Path(s) to Pursue

Goal – Improved Economic Development

Supply 

Chain 

Strategy

Learningbased 

Strategy

Agglomeration 

Strategy

Trade 

Center 

Strategy
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 11

This research study began with a general articulation of specific growth processes that 

have been emerging in the regional science literature and shown some success in the 

applied economic development field. The initial study phase focused on refining our 

understanding of the select set of growth theories and seeing how well each could be 

adapted to address rural locations such as Appalachia. A set of white papers was 

developed and became the basis for holding a one-day symposium in Washington DC, 

with comments led by an expert panel. A synopsis of the key conclusions from these 

white papers is presented in Chapter 2. 

The specific economic growth paths examined included: trade center development, 

industry concentration clusters, dispersal economies (e.g. supply-chain development), 

resource-dependent growth, and asset-based growth (including both learning-based 

and natural amenity-based development). A brief description of each is provided in 

Exhibit 1-8 below. The theory behind these growth paths is discussed in the following 

chapter, and case study examples of them are provided in a separate report volume 

(refer to Volume 2). 

Exhibit 1-8. Definition of Five Major Classes of Economic Growth Paths 

Basis for County’s 

Economy Growth 

Description 

Trade Center Growth pattern emanating from a small urban cluster that 

provides goods and services to the exurban communities & 

rural hinterlands 

Agglomeration 

(e.g. cluster economy)

Growth resulting from geographic concentrations of 

interconnected businesses and institutions that enhance the 

productivity of the core industries. 

Supply-Chain 

(e.g. dispersal economy) 

Remote location is chosen over the central metropolitan 

area to host a node of economic activity (distribution or 

assembly) that is part of a larger (geographic) production 

chain. 

Natural Amenity or 

Cultural Assets 

Growth as a result of either quality-of-place attracting new 

households –or – efforts to actively develop & promote 

cultural, recreation, eco-tourism venues and their 

supporting visitor services. A variant exists based upon 

natural-resource assets that are tied to extractive activities 

such as mining, logging. 

Knowledge (Learning) 

Assets 

Growth opportunities leveraged from the collective 

knowledge embodied in the region, including social 

capital, technical applications / commercialization, 

institutional assets (educational and financial), 

entrepreneurial start-ups. 
Vol.1 Project Background and Summary Ch.1 Study Overview

 Sources of Growth in Non-Metro Appalachia page 12
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 13

2 THEORY OF GROWTH PATHS

A series of white papers were developed that reviewed existing literature to inform our 

understanding of the various bases for economic growth, including 

(a) trade centers, (b) industry concentration clusters, (c) supply chain and dispersal 

economies, (d) resource-dependent growth, and (e) asset-based growth. 

The white paper research process was designed to provide a better understanding of 

where and when a specific form of regional growth is most applicable and what 

characterizes such an economy; the potential to confuse/misidentify the economic 

growth process; whether multiple explanations of the growth process could represent 

an evolution of a region’s economy; why there may be exceptions to what growth 

theory prescribes and most important the implications for development policy to 

achieve success along any of these paths. This chapter provides a synopsis of the key 

findings from these papers and the subsequent symposium discussion. The issues 

raised here provide a basis for identifying key aspects to be addressed in any economic 

development strategy that pursues a specific growth direction. 

2.1 Economic Development Processes 

(A) Role of Basic Industries. Underlying essentially all economic development 

strategies is the concept of developing business activity that can bring a flow of 

spending into the target region, which in turn can generate income and associated jobs. 

Economic-base theory classifies all economic activity as either “basic” or “non-basic” 

(Berry and Garrison 1958, Klosterman 1990, Blumenfeld 1955). A basic sector is 

composed of local businesses and firms that produce goods or services for “export” to 

customers located outside of the local area, which thus generates the flow of spending 

into the region. Products based on natural resources (e.g., mining, logging or tourism), 

learning-based resources (e.g., major educational institutions or cultural attractions) 

and manufacturing centers (e.g., furniture or computer products) tend to be basic 

industries because they usually export most of their products to outside customers in 

response to national or international demands. The non-basic sector is then comprised 

of firms that operate and produce primarily for local consumption. Analysts consider 

most local retail and personal services to be non-basic economic activities. 

Using this classification, it then becomes clear that the means of strengthening and 

growing the local economy is to develop and enhance the basic sector. The basic 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 14

sector can be seen as the “engine” of a local economy, whereby development of firms 

that serve outside markets provides a basis for growing business investment and 

activity. Exports further fuel the economic growth of an area through “multiplier 

effects.” Revenues from exports trickle through the local economies as payments to 

local factors of production, land, labor, and capital. These, in turn, generate an 

economic multiplier in the form of a chain-reaction effect. Local industries buy inputs 

from local suppliers, which then pay local employees and buy further inputs from 

local suppliers, etc. Local industries pay salary or wages to local employees, who then 

buy local products, further stimulating local businesses, who pay their local 

employees, and so on. These multiplier effects are important in triggering economicgrowth, especially when the local economy is not developed enough to constitute a 

strong local demand; “priming the pump” in Keynesian terms (Berry and Garrison 

1958, Klosterman 1990).

(B) Confusion about Clusters. Perhaps no single concept has propagated as much 

interest or confusion in the economic development field as the concept of clusterbased economic development. The concept of cluster-based development took off in 

the field of economic development following the work of Michael Porter (1990). He 

described the advantage of developing interconnected networks of businesses, 

suppliers, and associated institutions in ways that can increase productivity and create 

“Sustainable Competitive Advantage” (SCA). 

However, in the sixteen years that followed, the meaning and interpretation of those 

concepts diverged between researchers and applied economic developers. Porter’s 

original work never claimed that clusters were restricted to individual locations or 

individual industries. However, to many economic developers the concept became 

simplified down to the popular dictionary definition of the word “cluster,” which 

implies a spatial concentration of a single item or type of activity in a single region. 

Going even further, some consultants further “dumbed down” the concept of cluster 

definition to economic base studies that simply generate a listing of the most 

prominent industries in a given study area. Those latter concepts are often of little use 

for achieving practical and effective economic development (Weisbrod and Piercy, 

2006). 

In fact, researchers have since clarified how the advantages of cluster dynamics can 

encompass concentrations of economic activity among places or industries or 

technologies or supply chains. This point is made clear in Exhibit 2-1, which shows 

Enright’s (2001) twelve dimensions that can describe a competitive cluster. Following 

that research perspective, we can view clusters broadly, as concentrations of 

interrelated companies and institutions of sufficient scale to generate external 

economies. Their location may be concentrated in a single community, spread 

throughout a broad region, or aligned along a corridor stretching for hundreds of 

miles. However, in all cases, they include competing firms, cooperating material and 

service suppliers, and associated institutions – all of which may do business with each 

other and share needs for common talent, technology and infrastructure. This 

definition encompasses the range of potential growth models for Appalachia, though 
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the reader should be aware that arguments about and refinements to this definition are 

many. 

Exhibit 2-1 Twelve Factors Describing a Competitive Cluster 

Dimension Types 

Geographic scope Localized, Dispersed 

Density (Number of firms) Dense, Sparse 

Breadth (horizontally related industries) Broad, Narrow 

Activity Base (activities in the value-added chain) Activity-Rich, Activity-Poor 

Depth (Range of vertically-related industries) Deep, Shallow 

Geographic Span of Sales Local, Regional, National, Global 

Strength of Competitive Position Leading in Region, Nation, World 

Stage of Development Embryonic, Emerging, Mature 

Technological Activities Users, Adapters, Generators 

Innovative Capacity (Ability to generate key 

innovation relevant to competitive advantage) 

High Innovation, Low Innovation 

Ownership Structure Local, National, Foreign 

Industrial Organization (Governance structures 

and relationships among firms) “All Ring - No Core”, “All Core - No Ring”, 

“Core-Ring with coordinating or leading firm 

Co-Ordination Mechanisms 

(Organization of inter-firm relationships) 

Spot markets, Short-term coalitions, LongTerm Relationships, Hierarchies 

Source: Enright (2001) 

Clusters are often, but need not necessarily be, defined around a specific industry 

sector, supplier-buyer network or industry supply chain. Some, such as 

semiconductors in Northern California, automobile manufacturing in and around 

Detroit, and furniture in Northeast Mississippi fit neatly within NAICS -based industry 

definitions. Other clusters are based on process technologies, such as the firms that 

produce plastic goods in the Naugatuck Valley of Connecticut. The largest users of 

plastics technology and skills, however, are Bic, Schick, and Lego, none of which is 

classified as a plastics company. Still other interdependencies that define clusters 

include supply chains, core technologies, proximity to natural resources, or 

distribution channels. Rocha (2002), in fact, outlines seven different intersections of 

geographical, industrial, inter-sectoral, and inter-organizational dimensions that have 

been used to create conceptual and operational definitions of clusters. 

A correct representation of clusters thus starts with a portrayal of core industries, 

suppliers of capital goods, direct inputs, and specialized services, as well as privatesector economic activities that are “induced” by the presence of core industries. It 

may also include associations or supporting institutions specific to the cluster, skill 

and education providers such as universities and community and technical colleges 

that contribute to the territory’s human capital stock, (and which may be public or 

private but are most frequently public institutions), and knowledge providers such as 

research institutions, technology diffusion organizations, and other providers of 

research and technology. 
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If we adopt this broad research-oriented concept of clusters, then it becomes clear that 

all of the growth paths examined in this study are variant forms of clusters. That 

includes trade centers, industry agglomerations, supply chains and dispersal 

economies, resource-dependent growth, and asset-based growth. However, if we 

adopt the more commonly used concept of clusters as viewed by practitioners, which 

defines clusters as the concentration of a single industry in a community or region, 

then only the “industry agglomerations” would be classified as traditional clusters. All 

other growth paths would then be classified as alternative economic growth strategies. 

2.2 Trade Centers

Trade Center Economic Growth. A pattern of economic growth and development 

emanating from a small urban cluster that provides goods and services to the exurban 

communities & rural hinterlands. Spending money flows from the outlying region 

into the trade center. 

(A) Overview of Trade Centers. A trade center can be defined as the urban nucleus 

(metropolitan or micropolitan) in a county or group of counties that plays a central 

role in the region’s economy and economic-growth. It typically has a number of key 

ingredients, such as business and office space, a community college, retail outlet, 

and/or medical, business and personal services. A trade center can be the core of 

major metropolitan area, but it can also be a small town (of 10,000 or more 

population) that serves residents of a multi-county rural region. The core county is 

then classified as a micropolitan center. Trade center-based economic growth 

depends on the development of “hub-spoke” travel and trade patterns that connect the 

core community with the outlying region that it serves. (See schematic of 

metropolitan and micropolitan area relationships in Exhibit 2-2.) 

Exhibit 2-2. Schematic of Spending Flows and Relative Locations of 

Micropolitan Areas in Non-Metro Rural Fringe 
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From an economic development policy viewpoint, the key questions are: (1) how do 

trade centers evolve over time as urban centers of retail trade and services for a 

surrounding hinterland, (2) what are the characteristics of a successful trade center, 

and (3) how can existing trade centers be leveraged as an agent for economic 

development. These questions are addressed through a discussion of the functional 

role of trade centers and a synthesis of theories drawn from the economic development 

and economic geography literature that help explain the role of trade centers as 

economic growth engines relevant in Appalachia. 

(B) The Functional Role of Trade Centers. The functional role of a trade center can 

be best understood by answering the following questions: (1) what functionally makes 

a trade center, (2) what are the hierarchies of trade centers and their roles, and (3) what 

are the complementary roles of other adjacent, proximate or otherwise interacting 

activity centers. 

The concept of trade centers is based on the highly simplified central-place model of 

Christaller and Lösch. The central-place model examines the interaction between a 

rural region that is dependant on activities requiring extensive land use, e.g., 

agriculture or mining, and an urban center that has significant economies of 

agglomeration, and is based on activities requiring higher density, e.g., trade or 

industry (Hoover 1997, Krugman 1995). 

Urban geographers identified typical geometric patterns that describe the way trade 

centers form with respect to the surrounding rural regions. They also defined 

hierarchies of trade centers that range from small towns that serve a rural surrounding 

area, to a larger city that serves a group of small surrounding towns, and so on. These 

hierarchies are influenced by three basic factors: transportation costs, market density, 

and scale or agglomeration economies (Hoover 1975). Most of these factors are based 

on an agrarian or industrial economy where the economy’s equilibrium is determined 

around the optimum physical delivery of goods from their origin to their final 

consumers. However, different patterns may evolve as a result of the current service 

economy, e.g., higher sprawl of urban activities. New factors may affect the evolution 

of trade centers, their distribution over space, and their functional role, e.g., the 

globalization of markets and the role of exports in economic development (see our 

later discussion of economic-base and import- substitution theories), and 

agglomeration and dispersion, including supply-chain theories. 

Based on these theories, it can be suggested that a trade center performs a critical 

functional role to its rural surrounding area. Hoover (1975, p. 129) illustrates a 

hierarchy of services that are typically performed by trade centers depending on their 

size and position in the hierarchy, and ranging from the “convenience services” to the 

“primary wholesale-retail” services. This hierarchical model may be extrapolated to 

other types of services, e.g., financial services ranging from a small bank branch, to a 

full banking service; or to educational institutions ranging from a primary school to a 

large regional university with research capabilities. However, the distribution and 

hierarchy of trade centers may have evolved from the simple “transportation-
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dependant” model that is based on proximity, to a more complicated model that 

incorporates the effects of services and technology (e.g., call centers scattered over 

space with no transportation cost and low investment requirements). 

(C) The Multiplier Effect of Trade Centers. The current definition of metropolitan 

and micropolitan areas reflects their linkages with the adjacent areas in the form of 

labor commuting, commodity flows, and shopping and recreational activities. Each of 

these linkages has a “multiplier effect” on the adjacent regions. For example, labor 

commuting to/from these centers to adjacent areas has a multiplier effect on the 

economy of the counties where the workers live. The size of the multiplier effect 

varies depending on the size of a region’s economy and the employment base, but 

analysts typically determine local multipliers of two or three (ERS 2005). Applying 

this multiplier of two or three to the 25 percent minimum-commuting requirement 

implies that 50 to 75 percent of the income in the adjacent counties where workers 

reside is connected to the central economy of the metropolitan or micropolitan area. 

This could be a direct relationship, through commuting to jobs located in the central 

county, or an indirect relationship, through services provided to local residents whose 

jobs are in the central county. 

(D) Adjacency and the Urban Influence of Trade Centers. Geography matters in 

economic development. A county’s geographic context has a significant effect on its 

economic growth and development through its size and access to larger economies. 

This access to larger economies, which represent the centers of trade, information, 

education, communication, labor, and finance, enables a smaller economy to connect 

to national and international marketplaces. Studies by Smirnov and Smirnova (2000) 

attempt to portray how areas can be classified as trade center “hubs” that export goods 

and services, and outlying areas that represent “spokes” importing goods and services 

from the hubs. 

The measurement of adjacency and urban influence has also been developed by ERS 

using a set of county-level, urban-influence categories. The 2003 urban-influence 

codes divide the 3,141 US counties into 12 groups based on their urbanization 

(large/small metropolitan, micropolitan, or noncore) and adjacency to large/small 

metropolitan, micropolitan, or none (see Exhibit 2-4). 

The urban influence codes define proximity based on physical adjacency. For 

example, there are 15 micropolitan areas that are adjacent to a large metropolitan area 

in Appalachia, with a total population of more than 1 million (~70 thousand 

inhabitants per town). Due to their location, it is likely that these trade centers’ 

economies are linked with the larger adjacent metropolitan area. In using these trade 

centers as triggers for economic growth to their surroundings areas, we can emphasize 

their functional and economic relationship with the larger metropolitan city. 

The urban influence codes also define 24 smaller micropolitan areas that are not 

adjacent to a large metropolitan area. They have a total population of ~ 900 thousand 

inhabitants (~37 thousand inhabitants per town). These trade centers are not connected 
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through geographic proximity to the larger metropolitan cities, and they may fall lower 

in the hierarchy. Analysts should consider other factors that may contribute to their 

connectedness when thinking of an economic-development strategy. For example, are 

these trade centers part of a supply chain? Are they nodes on a major transportation 

route (highway, airport or river)? Empirical studies described in the next chapter 

discuss how alternate measures of proximity may explain how different types of trade 

centers affect economic growth outcomes for Appalachia. 

Exhibit 2-4. Urban Influence Codes 

United States Appalachia 

Description counties Pop. 

(million)

Pop. 

density counties Pop. 

(million)

Pop. 

density

Metropolitan counties:

1 In large metro area of 1+ million residents 413 149.2 558 34 5.2 293

2 In small metro area of less than 1 million residents 676 83.4 132 106 9.0 182

 

Non-metropolitan counties:

3 Micropolitan adjacent to large metro 92 5.1 55 15 1.1 114

4 Non-core adjacent to large metro 123 2.4 27 17 0.4 52

5 Micropolitan adjacent to small metro 301 14.7 51 53 2.9 95

6 Non-core adjacent to small metro with own town 358 7.9 23 41 1.2 54

7 Non-core adjacent to small metro no own town 185 1.9 6 36 0.6 38

8 Micropolitan not adjacent to a metro area 282 9.1 27 24 0.9 78

9 Non-core adjacent to micro with own town 201 3.2 17 30 0.7 49

10 Non-core adjacent to micro with no own town 198 1.3 7 24 0.3 32

11 Non-core not adjacent to metro/ micro with own town 138 2.2 5 8 0.3 62

12 Non-core not adjacent to metro/micro with no own town 174 1.0 4 22 0.3 35

 Total 3,141 281.4 80 410 22.8 114

Source: Economic Research Services, U.S. Department of Agriculture. All population figures from year 

2000 Census. See http://www.ers.usda.gov/briefing/rurality/UrbanInf/. Calculations by MIT-DUSP. 

(E) Trade Centers as a Basis for Broader Economic Development. Economic-base 

theory provides an explanation of the role of metropolitan and micropolitan trade 

centers in the development of adjacent areas. The linkages of small adjacent counties 

to a large metropolitan or micropolitan economy provide it with access to a large 

external market for product and service “exports” (sales beyond the trade center itself). 

This market could be the local market in this adjacent urban region, or a national or 

international export market that is accessed through the network of firms and 

businesses in this area. In effect, the metropolitan or micropolitan area becomes an 

“export” market, or a channel to a larger export market for the adjacent economy. 

Similar to the role of export growth, the economic development strategy of “importsubstitution” also emphasizes the role of trade centers in local economic-growth. With 

adjacency to a large metropolitan or micropolitan area acting as a trade center, local 

industry in a proximal county could experience growth through an import-substitution 
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role, by providing a market for growth of locally-based suppliers. A small rural 

county that is not adjacent to a trade center does not have the size or scale that allows 

for local entrepreneurs to create local industries that substitute for imports. Adjacency 

to a larger trade center is thus a necessary but not sufficient condition to trigger this 

process. 

As trade centers ties together surrounding counties to comprise a larger market area, 

they can also provide a critical mass of labor force, training and/or commercial 

activity to make the area attractive for additional business activity. Building on the 

theories of “agglomeration” and “supply chains” (discussed later in this chapter), the 

trade center labor market can become a basis for directly growing industry clusters or 

growing suppliers to more distant industries. That can help explain the advantages of 

trade centers located in central places, along major transportation routes, or across 

industrial supply chains (physical or virtual). 

These opportunities together make the issue of access a critical one in Appalachia. 

Given the geography of the region, many of the rural counties have no adjacent urban 

trade center (metropolitan or micropolitan center), nor do they have effective access to 

one via the transportation network. Those counties tend to be the distressed ones. An 

obvious cause of this disconnectedness is the mountainous topography of the region. 

For that reason, the development of enhanced highway links, such as the Appalachian 

Development Highway System (ADHS), can become important in enhancing 

connectedness to trade centers. 

Case Studies. Examples of economic development based on trade centers are shown 

in Volume 2 focused on Pike County (KY) and Southwest North Carolina (Murphy). 

Measurement issues regarding the definition of a trade center are also discussed in the 

Volume 2 in the case examined for Scioto County (OH). 

2.3 Industry Agglomeration (Clusters)

Industry Agglomeration-Based Growth – Economic growth resulting from geographic 

concentrations of interconnected businesses and institutions that enhance the 

productivity of the core industries. 

 (A) Overview of Industry Clusters. An industry agglomeration cluster is a group of 

business enterprises and non-business organizations that benefit from belonging to the 

cluster by increasing their individual competitiveness. Binding the cluster together are 

“buyer-supplier relationships, or common technologies, common buyers or 

distribution channels, or common labor pools” (Enright 2001). Clusters are ultimately 

based on individual firm economic maximization functions. However, cluster analysts 

also recognize the role of trust and cooperation among cluster firms. They define nonbusiness organizations as “related and supporting institutions,” which are a critical 
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element in the success of the cluster. These organizations may include industry 

associations, universities, technical and community colleges with specialized 

industrial programs, economic-development agencies, or government industrialextension programs. Regional industry clusters are industry clusters that are 

concentrated geographically, where geographic proximity between member 

enterprises creates a competitive advantage for the industry and region (Enright 1996). 

(B) Functional Role of Industry Clusters. Michael Porter (1990), through his 

publication of The Competitive Advantage of Nations, revived policy interest in 

regional industry clusters as a source of national and regional competitive advantage. 

He identifies a key role for geographic proximity, which is largely consistent with the 

previous work by Isard (1956) on industrial-complex analyses. Porter’s clusters are 

also similar to the constellations of suppliers, producers, and other economic actors 

suggested by Darwent (1969). Exhibit 2-5 illustrates this interplay design, assembly 

supply, and educational activities. 

An even earlier antecedent is the work on agglomeration economics descending from 

Alfred Weber’s (1909) classical location theory formulation, and those descending 

from Alfred Marshall’s industrial districts formulation. These works have evolved 

into a more comprehensive theory of sectorally-based regional advantage through 

numerous iterations and refinements. The dual tenants that firms benefit from 

clustering with like firms, suppliers, and related institutions (1) through agglomeration 

economies or external economies of scale that reduce production, transportation, and 

coordination costs, and (2) through soft economies of learning and collaboration that 

speed innovation and product and process advancement (also Collective Efficiency).

Exhibit 2-5. Schematic of Agglomerated Economic Activities 
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(C) Basis for Development of Industry Clusters. The idea of why enterprises 

cluster in geographic space and how that influences regional economic-development 

finds its theoretical explanation in the literature in two basic theories, both of which 

cite externalities to explain why firms cluster: 

• industrial location theory that builds on both Weber and Hoover, where the 

benefits are called agglomeration economies; and 

• Marshall’s analysis of external “economies of scale” (agglomeration benefits) 

and their presence in “industrial districts.” 

Finally, it can be argued that a metropolitan or micropolitan trade center may also help 

a competitive industry to emerge by benefiting from economies of scale and links to 

national and international supply chains. Some industries rely on an urban nucleus to 

provide the basic elements required for a cluster to emerge. Industry clusters need 

infrastructure that supports them (e.g., labor and transportation for a manufacturing 

industry, or research centers and universities for a hi-tech industry). They also need 

access to transportation, telecommunication, and other necessary infrastructure. All 

these elements need an existing trade center that could act as an incubator for this 

cluster. Therefore the existence of a trade center can be a starting basis for later 

emergence of industry clusters. 

(D) Process Motivating Cluster Development. It is important to note that few (if 

any) clusters have been “created” through policy or program interventions. Cluster 

formation and growth has tended to be an organic process with varying degrees of 

influence of factors such as natural resource (raw material or energy supply) inputs, 

antecedent industries, “lead firms,” either headquarters or branch plants, and local or 

regional craft or skill traditions. More recently, greater attention has been given to 

clusters that are created or enhanced by the residential location preferences of skilled 

professionals, creative and artistic communities, and entrepreneurs (Kotkin, 2000). 

Examples of “reasons” that have motivated firms to cluster appear in Exhibit 2-6. 

Exhibit 2-6 Types and Examples of Cluster-Based Development 

"Reason" For Cluster Example Cluster(s) 

Product Hosiery, Catawba Valley, North Carolina 

Process Plastics, Naugatuck Valley, Connecticut 

Industry Supply Chain Auto suppliers, Central Kentucky 

Company Supply Chain Proctor & Gamble, Alexandria, Louisiana 

Technologies Optics & Imaging, Tucson, Arizona 

Skills/talent New Media, Manhattan 

Resources Log homes, Montana 

Location/Infrastructure Distribution: Hampton Roads, Virginia 

Creativity Writers, Livingston, Montana 

Lifestyle Software, Fairfield, Iowa 
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Firms may remain in a cluster long after the initial “reason” for choosing its location 

has become irrelevant, largely due to the development of one or another form of 

special expertise over time (Enright, 2001). As noted by Feser et.al. (2001), “in their 

ideal form, clusters are essentially the empirical manifestation of the mutually 

reinforcing influences of first-mover effects, conventional business agglomeration 

economies, localized technology spillovers, and geographical path dependence.” 

Numerous state and regional studies in the US have explored the “family trees” of 

clusters to identify the process by which they have evolved and grown. The number 

and scope of businesses in a cluster typically results from spinoffs and company 

formation subsequent to layoffs. These include efforts by the UC-Connect in San 

Diego, Maryland’s TEDCO, and the National Commission on Entrepreneurship. In 

addition, the presence of a ready base of customers, suppliers, and knowledge also 

tends to coincide with an environment that exhibits a high degree of support for new 

entrepreneurs with a well facilitated entrepreneurial process which is a key component 

of cluster growth. 

A concise summary of the types of benefits that firms access through operating in 

clustered configurations considers both the “hard” economies related to cost factors 

stemming from agglomeration efficiencies and “soft” economies that capture “higher 

order” dimensions related to learning and collective efficiency. (See Exhibit 2-7.) 

Exhibit 2-7. Advantages of Industry Agglomeration Clustering 

Type of Economies Specific Factors Present Benefits to Firms 

“Hard” Economies Supply Chains Reduced transaction costs 

 (Agglomeration) Labor Pools Higher levels of experience 

 Specialized Services More options, lower costs 

 R&D and Technology Quicker adoption 

 Capital Increased availability 

 

“Soft” Economies Association Collective influence 

(Collective Efficiency) Networking Economies of scale, learning 

 Tacit Learning Innovation 

 Knowledge Leaks Imitation 

 Labor Grapevines Better employment opportunity 

Of course, the line between these types of economies is somewhat fuzzy: specialized 

services may evolve due to the intentional or unintentional communication of multiple 

firms’ service needs, and it is often difficult to separate cost reduction and innovation 

when assessing why firms adopt new technologies or processes. 

(E) Implications for Policy in Appalachia. Industry agglomeration clusters in nonmetropolitan areas (and less favored regions in general) face specific challenges on a 

number of the dimensions outlined above. It is well known that the nation’s rural 
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manufacturing economy was largely seeded by branch plants seeking lower operating 

costs and contains many firms that suffer from isolation and less sophisticated 

management. When considered along Enright’s descriptive dimensions rural clusters 

are challenged by their low density, less advanced technology activities, lower 

innovative capacity, and limited activity base. As noted by Rosenfeld (2001), the 

types of businesses that tend to clusters in less favored regions are inclined to rely 

more on cluster characteristics that reduce costs than on those that accelerate 

innovation and learning. The characteristics of many less-favored regions—low levels 

of educational attainment, weak schools, little investment capital, weak connections to 

external markets, and poor physical and support infrastructures—strongly favor those 

clusters that are low-tech, traditional industries, based more on imitation than 

innovation. Those clusters are very susceptible to global competition. 

This appears to be particularly true in Appalachia. Bernard, et.al (2004), present six 

conclusions regarding Appalachian industries’ vulnerability to imports. They cite (1) 

accelerating growth in trade with low-wage partners such as China and India, 

particularly in non-capital or technology intensive industries, (2) the associated high 

probability of plant closure, employment loss, and output reduction resulting from the 

arrival of low-wage imports for a given sector, (3) the concentration of Appalachian 

manufacturing employment and output in industries that are highly exposed to these 

imports resulting from the Appalachian industry’s lower skill intensiveness and 

productivity, (4) an observed “more pronounced” impact of low-wage imports on 

shutdowns of Appalachian manufacturing plants than on plants in other US regions, 

(5) the forecasted rapid increase of low-wage imports in the coming decade, and 

finally, (6) low rates of entry and exit of Appalachian manufacturing industries 

indicating a tendency to be slow to adjust their product mix. (Bernard, et.al. 2004) 

This phenomena is not unique to Appalachia or to non-metropolitan regions in the 

U.S. Nearly all of the industry agglomeration clusters that have been studied in less 

favored or less developed regions consist of companies that use low levels of 

technology and require skills that can be learned on the job, where barriers to and 

costs of entry are low, and that require little if any investment in research and 

development. An overview of the regions in the European Union categorized by its 

Social Fund as “less favored” characterizes them as having “sectoral specialization in 

traditional industries with little inclination for innovation and predominance of small 

family firms with weak links to external markets” (Landabaso, Oughton, and Morgan, 

1999, Rosenfeld, 2001). 

Yet, in recent years, accelerated globalization has combined with restructuring of 

global manufacturing firms to produce rapid job loss in the United States’ 

manufacturing sector. The popular press attributes much of this phenomenon to 

China’s current ascendance as an industrial power, as a consumer marketplace, and as 

a low cost production platform (Engardio, 2004). According to the US-China 

Economic and Security Commission’s 2004 report, over 1.5 million jobs were shifted 

from the US to China in the 1989-2003 period. 
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For economic development professionals and researchers, the post-2001 period 

appears to be a “perfect storm” for the manufacturing sector. Several events have 

been at play: first, a jobless recovery from a brief recession of 2001-2002 has led US 

and global companies to restructure operations focused on cost saving and access to 

rapidly growing Asian markets (especially China); second, increased per-worker 

productivity stemming from automation and technology have reduced employment 

growth in many sectors; third, financial resources available among state and local 

governments to address industry competitiveness have been limited due to state budget 

shortfalls and other current federal spending priorities. Add to these factors two 

decades of trade liberalization which has hit some of the most vulnerable industries in 

the United States especially hard - furniture, textiles, and other traditional 

manufacturing sectors (all key employers in Appalachia) - when quota restrictions on 

imports have been lifted. 

Case Studies. Examples of manufacturing and industry agglomeration clusters are 

shown in Volume 2 focused on Chautauqua County (NY) and Monongalia County 

(WV). 

2.4 Supply-Chains and Dispersal Economies 

Supply Chain Development– Economic growth based on the development of 

businesses that are dispersed across a large distance but accessible to a single 

transportation corridor. This is typically a concentration of assembly, parts and 

distribution activities supporting a common set of industries. This arrangement takes 

advantage of “dispersal economies” that come from tapping different labor and 

material supplier markets, while serving a “just-in-time” supply chain made possible 

by transportation facilities. 

(A) Overview of Supply Chain Basis for Economic Growth. A supply chain is the 

network of producers, retailers, distributors, transporters, storage facilities and 

suppliers that participate in the production, delivery, assembly, and sale of a particular 

product. The supply-chain concept has its theoretical foundation in two sets of 

literature. First, the early regional development literature on industrial development 

and infrastructure planning, which deals with how firms make decisions on locating 

their activities based on the economies or diseconomies of dispersal over the supply 

chain. The second set of literature is based on logistics and supply-chain management 

in operations research, management and civil engineering, which deals with the 

optimization of the time and cost of managing the supply chain (Polenske, 2001). 

As supply transactions chains become more complex involving technological and 

logistical relationships between firms (e.g., firms connecting their inventory 

management systems, or firms creating long term preferred supplier networks), it 
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becomes important for a firm to be an integrated part of an established supply chain. 

Exhibit 2-8 illustrates the relationship between assembly, suppliers and distribution 

activities in a spatially-dispersed supply chain. 

Exhibit 2-8 Schematic of Dispersed Supply Chain Linkages 

(B) Dispersion of Business Location. Firms in different manufacturing sectors have 

different product characteristics, demand patterns, and require different service levels, 

so that they prefer different supply chains and logistic systems. Polenske (2003) 

developed the concept of “dispersion economies” to represent various cost and 

technology factors that are now causing some firms to move away (disperse) from 

concentrated centers of economic activity. 

A considerable amount of this dispersal occurs along supply chains. Glasmeier and 

Kibler (1996) examine the dispersing trend of wholesale and distribution industries in 

the United States. They find that locations of wholesale establishments and 

warehouses have shifted from urban areas to rural and adjacent suburban areas largely 

due to the technological improvements in inventory management, warehouse structure, 

as well as transportation deregulation, all of which are critical components of supplychain management. With dramatic advances in information technology, the expansion 

of globalization, and the decrease of transportation costs as a share in the total cost, 

some firms have larger scope and more flexibility in their supply-chain design. 

In terms of structural approaches in supply chain management, cost reduction by 

moving to lower labor-cost regions often outstrips increased delivery costs if 

transportation costs and duties are low. Additionally, improvement of the 

coordination mechanism makes it possible for an industry or a firm to access more 

sophisticated products and services at a greater distance with higher quality than 

before (Flaherty 1996). 
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(C) Dispersal through Organizational Networks and Transportation Corridors. 

Improved information flow and just-in-time transportation processes have facilitated 

industrial dispersal occur more rapidly than before. Teubal et al (1991) note that a 

network organization linking firms or economic agents represents an intermediate 

“system of governance” that lies between the firm and the market. Traditionally, there 

are different types of networks, including inter-firm networks, employment networks, 

social networks, and political networks. Analysts have viewed the scope of networks 

as extending from pure simple connections of similar characteristics to more complex 

relationships among all economic participants, including private firms, government 

agencies, universities, intermediary agencies, and communities (Harrison 1992). 

Linkages among those economic actors can occur at various levels, from local retail 

districts, which stay close to urban residential communities, to specialized auto-parts 

towns that serve regional auto-manufacturing factories, and to research and 

development (R&D) institutions that disseminate their newly developed technologies 

at state and world levels. Therefore, depending on the growth potential of an industry, 

patterns of activities, innovative capacity, and governmental structure, multilevel 

networks (local, regional, national, and international) lead to various dispersion 

tendencies of economic activities. In this case, policy makers should take into 

consideration the impacts of other economic players on firms’ location decision in 

addition to inter-firm networks. 

Auto parts industries are well represented in Appalachia and are also a source of 

income to the region, as their products are “exported” to the rest of the US and world. 

An important evolution in this industry over the past twenty years has been the 

evolution of parts manufacturing locations. While they were once located in the 

immediate vicinity of the assembly plants, today the plants are dispersed along several 

hundred miles of the “Auto Alley,” a manufacturing corridor along I-65 and I-75 (see 

Exhibit 2-9b). This firm location pattern was enabled by advanced information 

technologies that allowed just-in-time production processes to utilize parts plants 

located wherever they can reliably provide same-day delivery. 

Exhibit 2-9 shows the location of auto supply plants in and around the Appalachian 

Region. Exhibit 2-10 shows the key origins and port destinations of Appalachian auto 

parts that are exported beyond the US. Both maps illustrate the importance of 

highway corridors in enabling the growth of this industry in Appalachia. 

Case Study. An example of a dispersed supply chain is shown in Volume 2 focused 

on Alabama’s automotive assembly and parts economy. 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

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Exhibit 2-9 Maps of Dispersed Auto Assembly and Supplier Locations 

(A) AL Auto Assembly & Parts Plants (B) Southeast Auto Alley Corridors 

Exhibit 2-10 Major Flows of Auto Parts Exports from Appalachia 

(from state of origin to port of exit from the US) 

Note: key exit ports are Miami, Detroit and New York City 

Source: Jack Faucett Associates and Economic 

Development Research Group, 2004. 
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2.5 Natural and Cultural Assets 

Natural Resource, Amenity and Cultural-Based Development -- Economic growth 

based on the natural and human-made assets of an area. The traditional form of assetbased development has been based on natural-resource assets that are tied to extractive 

activities such as mining and logging. Other natural amenity and cultural resources 

may attract eco-tourism, vacation and retirement industries and their supporting visitor 

services, as well as attract entrepreneurs. 

(A) Overview of Asset-Based Development. The Appalachian Regional commission 

has defined assets as the natural, cultural, and structural assets, and “the hospitality, 

work ethic and can-do attitude of its residents.” Asset-based economic development 

practices have been implemented in communities throughout Appalachia since the 

1960s. It is useful to divide the basis for asset-based development into two groups: 

a) natural and cultural-based development, which depend on an area’s preexisting features (including physical features, amenities and cultural/historical 

attributes), and 

b) learning-based development, which depends on cultivating worker skills and 

capabilities (including entrepreneurship, education, and research/development 

activities). 

This section (2.5) focuses on group “a”. The next section (2.6) focuses on group “b”. 

As discussed below, the theory of asset-based development draws upon aspects of 

Ricardo’s comparative advantage theory, Alfred Marshall’s concept of the “industrial 

district” and Porter’s interpretation of theory on cluster formation. Some of the recent 

economic-geography studies on asset-based development in Europe also provide 

insight to this study. The cultivation of entrepreneurship, social-capital formation, and 

local-knowledge spillovers are the overarching drivers for asset-based growth. 

• Natural amenity-based development: Kusmin et al. (1996) indicate that 

traditionally, there is evidence that natural amenities are a factor contributing to 

population and employment change, hence regional economic development, in 

the United States. In the 1990s, the Economic Research Service (ERS) staff of 

the U.S. Department of Agriculture (USDA) conducted two studies, including a 

literature review and an empirical study to investigate factors that may have 

affected rural economic growth in the 1980s (Aldrich and Kusmin 1997). In the 

literature review, they identified temperature and precipitation as the two major 

factors facilitating rural economic growth. 

• Natural resource-based development: Land-based mineral and forest resources 

provided a comparative advantage that accounted for much of the initial 
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economic development of Appalachia in past centuries. However, natural 

endowments are sometimes regarded as a “curse” for long-term development. 

Today, those industries are seen as mature and in some cases declining sources 

of jobs. Most areas of Appalachia that have historically been dependent on those 

industries have been seeking to diversify their economic bases. Accordingly, the 

rest of this discussion focuses on the other four categories of asset-based 

development. 

• Culture-based development: Pratt (1997) defines cultural industries as products, 

performance, in the form of fine art and literature; their reproduction, as books, 

magazines, TV and radio programs, recordings and etc., and activities that link 

together art forms such as advertising. He includes also the production, 

distribution, and display processes of printing, and broadcasting, as well as 

museums, libraries, theatres, night clubs, and galleries. Andersson (1985) argues 

that there six key drivers to the growth of cultural industries, including (1) a 

sound financial basis, but without tight regulation; (2) basic original knowledge 

and competence; (3) an imbalance between need for cultural products and their 

actual provision as the new environment calls for new cultural products; (4) a 

diverse milieu; (5) good internal and external possibilities for personal transport 

and communications, and (6) an uncertainty about the future, which calls for 

creative change. In his overview article, Hall (1997) reviews other analysts’ 

work and stresses the importance of initial wealth effects and the randomness of 

the development. 

(B) Theoretical Foundation and Measurements. An asset-based strategy may have 

different effects on the economic upgrading of a region depending on the asset types. 

Tangible assets, such as coal or timber, may accelerate economic development only 

for a short period of time, but the development may not be sustainable, as shown by 

history in the Appalachian Region. However, smart use of the tangible assets can lift 

the region through the early growth stages and facilitate more sustainable growth if 

careful use is made of intangible assets, such as education or entrepreneurship. These 

intangible assets may be difficult to establish on a sustainable basis, but they are the 

backbone of healthy long-term economic development and link closely to the learningeconomy approach to development used in a number of northern European 

communities (Asheim 1996). 

An analyst can view an asset-based growth strategy as complementary to other growth 

strategies, and it often serves as a base for other development strategies. For example, 

the agglomeration of firms of a certain industry in a place is often decided by the 

availability of the labor force, which is highly related to educational requirements. In 

certain types of manufacturing industries may seek locations with lower-skilled 

workforce to avoid a wage premium. Another example is tourism: natural assets such 

as climate, topology, local culture, and geographic locations serve as a foundation for 

higher-level development strategies. In an environment of high bio-diversity, an ecotourism development strategy is more feasible than otherwise. 
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In terms of methods analysts use to measure the presence or maturity of asset-based 

growth patterns related to tangible assets, they often use many variations of the inputoutput analysis and the economic-base analysis, including mix-and-share analysis and 

location quotients (Broadberry 1998; OhUallachain 1991; Riefler 1979). In the case 

of some intangible assets, such as entrepreneurship or culture, case studies, e.g. assetmapping, can be the first step to investigate the presence of such assets. 

Researchers can conduct multiplier analyses for regional development planning, but 

they must interpret the results of such calculations cautiously. As an example, they 

should not necessarily encourage the sector with the largest direct economic impact to 

expand in a region for several reasons, including that the benefits may not be retained 

in the local area, large multipliers for a sector do not always imply a large multiplier 

for sub-industries within a sector, and there are often significant differences between 

the employment, income, and output multiplier effects for a given industry in a given 

region (Miernyk et al., 1970; Schaeffer 1998; Smirov-Smirova 2000). 

(C) Resource Extraction. One of the potential big traps in asset-based development 

is resource extraction in the name of competitive advantage, which can result in local 

poverty and boom-and-bust cycles. There are two issues here: the local multiplier of 

the ensuing development and overspecialization of the economy. 

In terms of the local multiplier of the industry, the development of the coal industry in 

the ARC region is a good case example. Duncan (1992) concluded in her book that 

although the result of fierce competition in the coal industry was cheap energy to fuel 

industrialization in the Northeast and Midwest, the costs were severe for miners and 

their families. In the twenty-first century, with rising oil prices, the hope of sp,e profit 

from coal has resurfaced. At least 94 coal-fired electric power plants—with the 

capacity to power 62 million American homes—are now planned across 36 states. 

One industry observer commented that "the situation has changed 180 degrees in the 

last year, so that we're almost back to the point where we were in the 1970s with a 

slew of coal-fired plants on the drawing board." (The Christian Science Monitor 2004) 

Currently, Eastern spot prices for coal are hitting peak levels. Some urgent buying of 

Eastern compliance coal on the spot market can run $65 per ton, compared with the 

mid-$20 range of a few years ago. Alan Stagg, head of the West Virgina-based Stagg 

Resource Consultants, said that the current situation reminded him of the coal boom of 

1974. He also remembers that it took decades to wring out the excess mine capacity 

that came online and cure many of the bad habits that resulted from that brief boom 

period. Stagg told the EUCI (Electric Utility Consultants Inc.) conference on volatile 

coal markets that he sees many parallels between then and now. (Power Daily, 2005) 

With the current reentry of investors into the mining industry in the Appalachian 

region, policy makers need to evaluate the costs and benefits of the mining industry to 

the health of local economy, especially in terms of overall stability and the portion of 

benefit accruing to the local communities. In the next phase of development, the 

Appalachian region needs to think carefully about how to build a strong, diversified, 

and resilient economy based on local-assets with the local communities as the chief 
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beneficiary. Coal and timber, undoubtedly, could play an important role in this 

development phase, but as policy makers design development strategies, they should 

emphasize ways in which the change and/or expansion of these sectors can help the 

region grow as well as become sustainable. 

 (D) Natural Amenity-Based Development: the Retirement Industry. Asset-based 

development is a development strategy with wide applicability. Policy-makers start 

from within the economy, understanding and cultivating the local strengths. A 

prevalent form in recent years has emphasized natural-amenities of a region. The 

retirement industry is based on local amenities, and typically has a low intensity of use 

of natural resources. The migrant retirees spend locally, and the income usually 

circulates within the local area. The spending also has a direct impact on high jobcreating industries, such as hospitality, construction, and health care. For example, as 

the top retiree destination Florida, mature residents, while making up one-third of the 

state’s population, account for about one-half of all income and consumer spending 

(The Destination Florida Commission 2002). 

Although the retirement industry already began to gain favor among regional planners 

during the late 1980s and the early 1990s, its significance is likely to increase 

markedly in the future when the baby-boom generation retires. In 1995, the U.S. 

Census projected that 25 million people (pre-boomers) were in the 50-59 group who 

are currently planning retirement, among whom 17 to 38 percent may move from their 

home states to retire (Reeder 1998). This would represent a large and growing market 

for retirement destinations. 

Researchers have identified both advantages and disadvantages of the retirement 

industry to local communities. On the one hand, according to the USDA research, the 

retirement industry manifests its benefits by “population growth, increased family 

incomes, greater economic diversification, and reduced unemployment rates.” 

Contrasting sharply with income stagnation or decline in most other rural areas in the 

1980s, the median income in rural retirement-destination counties (15% or more net 

immigration of those age 60 and over) increased by 4 %. On the other hand, not all 

retiree impacts are positive. Retiree attractions can result in undesirable congestion 

and environmental strain and drive up housing prices and property taxes. Many of the 

jobs created by retirees are low-wage service jobs, and retirees may require more of 

the public health services, which drains local public-financial resources. (Reeder 

1998) 

Many states have been actively promoting the retirement industry, adopting a wide 

variety of strategies. In Alabama, the State government has been an active agent for 

attracting retirees, including State marketing and retiree-related development 

investments. In Arkansas, the private sector, like real-estate associations, has taken 

the lead in developing a comprehensive attraction strategy. In South Carolina, new 

residential developments, including planned retirement communities, play a major role 

in attracting retirees. In North Dakota, the focus is on attracting former residents back 

into the community and filling existing vacant housing. In Washington, the state 
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chose the relatively inexpensive community self-help model. For example, Chelewah, 

population 2000, attracted 150 new residents, most of them retirees, in one year with a 

$10,000 promotion budget of distributing brochures and making videos. The 

marketing methods also vary from integration through tourism (North Carolina), to 

traditional marketing media, like newspapers, magazines, television, and radio 

(Alabama ), financial incentives like tax breaks (Michigan and Mississippi), and even 

word-of-mouth advertising (Idaho and North Dakota). (Reeder 1998) 

As summarized by Longino et al. (2005), there are three typical motivators behind the 

phenomenon of retiree migration: (a) move to warmer weather; (b) move down the 

metropolitan hierarchy to smaller cities and towns; and (c) move from higher to lower 

cost-of-living areas. Retirement migration has historically been concentrated in a 

relatively few states, but has shown tendency of seek out other locations. There are 

three challenges that Appalachia’s regional policy makers will face in order to develop 

along this path: first requires formulating a unique marketing position to win in an 

increasingly competitive retiree market; second involves building upon human-made 

amenities and natural amenities to make the latter even more attractive; third is 

knowing in advance what the long-term economic and environmental impacts are 

related to an established retiree industry. 

 (D) Recreation/Tourism Asset-based Development. In contrast to resource 

extraction, natural assets can also be utilized to develop a sustainable recreation and 

tourism sector. Conventionally, tourism builds on local natural assets, such as 

mountains and lakes; and plays an important role in economic development. The 

World Travel and Tourism Council estimates that travel and tourism is now the 

world's largest generator of jobs. In 1995, the industry provided direct and indirect 

employment accounting for 10% of the global work force and providing one in every 

nine jobs. Tourism is labor-intensive and provides immediate employment 

opportunities. Many tourism activities are within the reach of the small operator. As 

many of the natural beauties are not located in the city centers, but in the rural areas, 

tourism allows rural peoples to share in the benefits of tourism development, 

promoting more balanced and sustainable forms of development. 

Sustainable Tourism can be defined as the means to "... meet the needs of present 

tourists and host regions while protecting and enhancing opportunities for the future. It 

is envisaged as leading to management of all resources in such a way that economic, 

social and aesthetic needs can be fulfilled while maintaining cultural integrity, 

essential ecological processes, biological diversity and life support systems." (World 

Tourism Organization 1988, quoted by UNEP Report 2002 P2) Among the many 

forms of sustainable tourism, Ecotourism is one of the most prominent in recent years. 

Ecotourism is defined as a form of tourism whereby tourists travel to destinations 

where natural environment (flora and fauna) and cultural heritage are the primary 

attractions. Ecotourism emphasizes the support of the local economy and its 

indigenous atmosphere and the preservation of entire local ecosystems and promotion 

of the importance of conserving nature. 
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Natural assets and tangible assets are not necessarily the determining factors in 

recreation/tourism industry development. The importance of cultural heritage cannot 

be neglected in the development process. In the Appalachian region, “Cultural 

tourism is the type of ’asset-based development’ that can produce permanent jobs in 

the region, drawing on the region's music, history, environment and warmth of its 

people”, according to Governor Mark R. Warner and Anne B. Pope, federal co-chair 

of the Appalachian Regional Commission. As a joint effort by National Geographic 

and the Appalachian Regional Commission in 2005, more than 350 of Appalachia's 

top cultural tourism destinations are featured on a color map. Local music and crafts 

industry are important components of the cultural tourism industry. One of the ARC 

states, Virginia, ranks in the top 10 states in the nation as a cultural tourism 

destination. Cultural tourism is growing twice as fast as traditional tourism, and 

cultural tourists tend to spend more than others. (Richmond Times-Dispatch 2005) 

(E) Implications for Asset-Based Economic Development. From the discussion 

above, two important implications stand out for asset-based development: 

sustainability and local economy as the main beneficiary. 

Sustainability refers to sustaining the asset-based economic development without 

over-extracting the local resources, resulting in environmental deterioration. Success 

in asset-based economic development depends on long-term investment and a 

building-block process rather than a quick-fix approach. An important part of assetbased development is to build a foundation, such as infrastructure, for asset-based 

development and to enhance the local assets constantly instead of depleting them 

(ARC 2004). 

More importantly, how much of the benefit of the economic development can be 

retained and circulated in the community. Two of the most useful indexes are the 

local income multiplier and the local employment multiplier. As our earlier analysis 

exemplifies that coal mine workers suffered from low income when the mining 

business prospered. More questions should be asked for the sake of the real benefit of 

the local people. What is the quality of the created jobs? Are the jobs created at the 

expense of existing local jobs? How much lead time is there before the development 

can take off from the date of investment? To what extent do the extra jobs trigger 

multiplier benefits elsewhere in the ARC region? Local planners must fully explore 

these questions before undertaking the asset-based development initiatives.

On the execution level, asset-based development has two levels of implications for 

local policy, the industrial development level and the community revitalization level 

(Polenske 2001). On the industrial development side, policies should promote 

innovation and the evolution of an industrial network based on an evaluation of local 

assets. Perroux (1988) illustrated this point clearly by defining a growth pole as a set 

of economic activities that has the capacity to induce the growth of another set of 

economic activities in an innovative way. On the community-side, policies should 

focus more on building, appreciating, and mobilizing individual and community 

talents, skills, and assets rather than focusing on problems and needs. Also, the 
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development process is supposed to be led by the community rather than driven by 

external agencies. 

Asset-based development strategy has the potential to be central to the Appalachian 

regional development as the area has rich natural, cultural, and human assets 

“sleeping” in the mountains. Joint government-community initiatives in the region 

have the potential in increase opportunities for development to take off through 

various mechanisms. They may include education-based, entrepreneurship-based, 

resource-based, culture-based, or natural-amenity-based processes. 

A remaining issue for asset based development is access to customer markets. Such 

access issues hold whether the customers themselves travel to the region to obtain the 

products (e.g., tourism) or the products are delivered directly to the customers (e.g., 

wood products). Exhibit 2-11 illustrates the key issue of topography and 

transportation links, which can affect the markets available for access to/from a 

region’s fixed assets. 

Case Studies. Examples of natural and cultural asset-based economic development 

are shown in the Volume 2 case studies of Southeastern TN and Southwestern NC, 

and also discussed in the case study of Chautauqua County (NY). 

Exhibit 2-11 Role of Market Access and Topography in 

Asset-Based Development 
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2.6 Learning-Based Development 

Learning-Based or Knowledge Asset-Based Development. Growth opportunities 

leveraged from the collective knowledge embodied in the region, including social 

capital, technical applications / commercialization, institutional assets (educational 

and financial), entrepreneurial start-ups. 

(A) Overview of Learning-Based Economic Development. Forms of economic 

development that are based on knowledge and learning are focused on the 

development of business-related skills among the local workforce. They include: 

• Education-based development: Education institutions contribute to economic 

development through “research, creation of human capital through teaching, 

technology development and transfer, and co-production of a favorable 

milieu.” (Goldstein and Renault 2004) According to their research, among 

these drivers, the spillover of university research and technology creation 

contributes most to regional economic development. There are two types of 

education assets-based counties: (1) counties that are the sources of welleducated people due to the location of universities, and (2) counties that absorb 

well-educated people in their labor market. 

• Entrepreneurship-based development: In a market with perfect information, 

the development of entrepreneurship would not be necessary. In reality, 

entrepreneurship contributes to development by overcoming uncertainties, 

factor-market imperfections, and externalities by individual initiatives and 

skills (Leff 1979). The key drivers of entrepreneurship-based development 

include the overall quality of human resources in the area, the cultivation of an 

entrepreneurial culture, the establishment of property rights to protect profits 

gained from entrepreneurial activities, and the establishment of supporting 

institutions, such as financial agencies targeting small businesses. 

Currently there are two parallel streams of thought and research about learning-based 

economies, both dating back at least a century. “Human capital” theories are top 

down, driven by public institutions and public policy. “Learning region” theories are 

bottom up, driven by social norms, associational structures, and workplace 

organization. The more traditional and widely accepted human capital view of 

learning is tightly linked to research on education and training—human resource 

development (Ross and Rosenfeld, 1988). This line of research focuses on 

demonstrating the value of education, educational attainment, and skill development to 

regional or national economic outcomes. The research that correlates measures of 

educational attainment or achievement to economic outcomes, dates back to Horace 

Mann’s circulars, which asked business owners in Massachusetts to estimate the dollar 

value of educated workers to their profits. 
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Newer (or, more accurately, rediscovered) learning theories assume that the economic 

development of regions is linked to the informal knowledge that is embodied in and 

transmitted through the social and organizational structures of businesses, 

communities, and societies. This idea that access to the non-codified, or tacit, 

knowledge that resides in people’s heads and organizations’ routines drives innovation 

dates back to the beginning of the 20th century. Alfred Marshall attributed the success 

of industrial districts to the informal flow of ideas and information. This hypothesis is 

more resistant to quantification, and generally demonstrated with anecdotal evidence. 

Both of these lines of research of research are investigated as they affect and are 

affected by non-metro conditions and industry agglomeration. The first is based on 

traditional human capital theory and focuses on the individual. The second is based on 

“learning” theories as applied to people, companies, and places, and requires some 

store of social capital. Human capital assumes rationality and transparency; learning 

occurs through socially determined values and norms (Schuller, 1998). 

(B) Theoretical Basis on Human Capital Development. Relationships between 

human capital and economic development in rural areas have been acknowledged and 

thoroughly studied for decades. The importance of education to economic 

development in rural areas was a significant part of Roosevelt’s Carnegie Commission 

Report on Rural Life highlighted the importance of education to rural economies. 

Human capital theory presumes that the knowledge and skills of the work force are 

contributing factors to economic growth. In conventional econometric models, human 

resource development accounts for anywhere from 20 to 80 percent of growth. 

Increased skill and knowledge, when applied to work situations, leads to higher 

productivity and increased innovation, which is used to justify public expenditures on 

training and induce businesses to invest more in education and training. Some 

economists have shown that the contribution of knowledge and education to 

productivity far exceeds that of capital (Carnevale, 1983). This suggests to both 

governments and businesses that investments that increase the value of human capital 

produce higher rates of return than investments in physical capital, and therefore they 

would be wise to invest in education and training (Schultz, 1981). 

Modern human capital concepts developed by Schultz and Gary Becker and, with 

respect to agglomeration, by Paul Krugman provide a theoretical basis for the 

importance of human capital, and Ray Marshall, Eli Ginzberg, Sar Levitan and many 

others have provided a more practical set of principles for human resource 

development policy. Schultz’s research led to the additional finding that “the supply 

of entrepreneurial ability is definitely increased by additional schooling.” 

There are basically three ways that human capital plus the system that develops it 

contribute to non-metro economies. The first is direct, the impact of a more skilled 

and creative workforce. The second is induced, the impact of better education on the 

location choices of employees and employers. The third is contributory, the impact of 

education and training institutions and organizations as a source of employment and 

external revenues. 
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Incumbent and potential labor force. The more common means for assessing human 

capital is to estimate the scale and productivity of the workforce. Scale is measured in 

total numbers of people in the work force, diplomas, certificates, and degrees awarded, 

number completing relevant programs of study, and average levels of educational 

attainment in the population. The numbers of college graduates in the Appalachian 

counties of most ARC states is significantly below those in non-ARC counties (Haaga, 

2004). Occupational projections, however, suggest that about eighty percent of the 

work force over the next ten years will require some postsecondary education. 

In fact, one of the most serious human capital challenges for rural areas over the past 

century has been keeping youth, particularly the most educated youth, from leaving 

for urban amenities and better job opportunities. No one, however, has solved the 

persistent problem of rural out-migration. While educational attainments levels have 

been rising in the U.S. constantly, gains in metro counties far exceed gains in nonmetro counties, and non-metro non-adjacent counties fare the worst (Artz, 2003). 

Advocates for education and training argue that companies benefit from a more highly 

trained workforce but findings don’t fully support this hypothesis—at least for 

manufacturing. A study of the non-metro South in the 1980s found that a 10 percent 

increase in educational attainment resulted in a 3.8 percent increase in total 

employment—but a net loss in manufacturing employment (Rosenfeld et al, 1986). A 

review of the literature on plant locations conducted in 1994 concluded that 

“education levels of the local work force have not been important determinants of 

local employment growth in the rural areas of the United States (McGranahan, 1994). 

A more recent study on impacts of education discovered modest gains—that a five 

percent increase in share of population attending college in non-metro counties is 

associated with a 0.15 percent increased in annual income growth of $325 annually 

(Barkley, 2005). A concurrent study found that a one percent increase in high school 

completion rates among adults resulted in an additional $128/year per capita income. 

Business Decision-making. The historical finding of a weak relationship between 

education and traditional manufacturing is not really surprising, since traditional 

manufacturing has lower skill requirements and fewer requirements for technical 

expertise. Among rural manufacturers asked in 1996 to name the top five barriers to 

competitiveness, only those in the Southern region listed quality of primary and public 

schools, and there it was number five, well behind quality of labor, amenities, 

regulations, and taxes (Teixeira, 1998). 

But in today’s economy, with less labor intensive manufacturing and more knowledge 

based industry, conditions are very likely quite different. A recent USDA Economic 

Research Service study showed that the share of rural employment in rural low skilled 

jobs declined from 49.4 percent in 1980 to 42.2 percent in 2000 (still far above the US 

average of 35.5 percent). More of the decline was attributed to changes in skill needs 

due to technology within industries than to changes in industrial mix (Gibbs, 2003). 
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Direct Employment. The education and training institutions represent a large direct 

source of employment and, where concentrated, can constitute a sizable portion of 

total regional employment. About six percent of all employment in the United States 

is in the education sector, and the projected growth rate is almost 25 percent, which is 

67 percent above the overall national employment growth rate. In rural counties, the 

proportion working in education is usually even greater. Since most of the revenues 

are from state or federal sources, education is a value added industry from the local 

perspective. 

Agglomeration effects. Agglomeration has three impacts on human capital. The first 

is the effect first described by Alfred Marshall (1936) in industrial districts, that 

“workers by associating with one another teach one another." He argued that 

innovation is a collective experience and that "If one man starts a new idea, it is taken 

up by others and combined with suggestions of their own; and thus it becomes the 

source of further new ideas (Bellandi, 1988). Further, he hypothesized that association 

leads to learning. (Marshall, 1936). Marshall refers to processes of transmission of 

ideas that occur through inter-firm mobility of skilled workers, social institutions, and 

business organizations. 

Krugman later developed economic models to demonstrate Marshall’s theories that 

pooled markets for workers with specialized skills result in clusters. His model 

explains why the advantages associated with access to labor pools with specialized 

skills outweigh the disadvantages of potential poaching of employees by competitors 

(Krugman, 1992). Clusters should also benefit workers because they would be less 

dependent on fewer employers and also protected against fluctuations in demand. One 

study did indeed show that the presence of clusters (based on the most basic two-digit 

industry classifications) is associated with higher wages even after accounting for 

characteristics of workers (Bernat, 1998). 

A second agglomeration effect is in the increase in workforce development networks 

formed among companies with similar needs. A survey of 1,600 employers and 250 

community colleges in the rural U.S. found that employers rely heavily on networks. 

The author identified four structures for the networks: sole providers; hub-spoke, 

usually with a community-based organization (CBO) at the center; employer-centered 

networks; and sector- or cluster-oriented cluster networks (Green, 2003). The 

networks were most often industry specific (44 percent), community specific (38 

percent), and supply chain driven (26 percent). 

The third advantage of agglomeration is that the workforce is more likely to have 

learned special knowledge of the peculiarities of the structure and work environment 

common to the cluster, giving them context-specific skills they can apply more 

directly to the work environment of the cluster. This was the rationale behind the 

requirement that has been part of the federal Carl Perkins Act since 1984 to teach “all 

aspects of the industry,” that employees who understand the way their industry works 

are more productive and have more opportunities to advance. 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 40

(C) Learning and Industry Clusters. Learning has always been, and remains, one of 

the most fundamentals reasons for, and value of, regional agglomerations of like and 

related companies, or clusters. Technological advances in communications have not, 

according to most analysts, replaced the Informal learning across a sector, or cluster, 

has a long tradition in rural America, with roots in the Grange, the Farmers Alliance, 

and the populist movement—all of which intentionally facilitated the free exchange of 

agricultural knowledge throughout the industry. In non-agricultural settings, much of 

Agglomeration Effects. Alfred Marshall’s work focused on learning as a critical 

factor in industry agglomeration. Contemporary concepts of learning regions are 

included within the recent deluge of literature on industry clusters, districts, and 

networks, especially out of Europe. It includes learning ranging from 

informal/unintentional to structured/ intentional and from what Peter Maskell calls 

“local buzz” to “global pipelines.” Much of the technology transfer literature focuses 

on creating opportunities and building structures for knowledge spillover. 

One of the leading economic advantages of clusters is the opportunities for knowledge 

spillover and know-how trading. The disadvantages associated with leaking 

proprietary knowledge are outweighed by the advantages of learning about new 

technologies and techniques, through both formal and informal means. Von Hippel’s 

research on informal know how trading in the U.S. steel industry found that exchange 

among competitors is most effective when know bow is proprietary only by virtue of 

secrecy and when its value is too small to justify an explicit contract (Von Hippel, 

1987). However, “sharing activity is not captured as a transaction in the firm’s 

financial records and therefore it is not reported as economic activity in the standard 

economic statistics.”(Cater, 1989). Krugman agreed, writing that “knowledge flows 

[in contract to labor pooling] are invisible; they leave no paper trail by which they may 

be measured and tracked, and there is nothing to prevent the theorist from assuming 

anything about them that she likes.” 

Learning occurs in clusters in a number of ways, some of which fall under the rubric 

of “networks” and up and down “supply chains” and other organized forums for 

associative behavior,” through gatekeepers, which can be lead firms of institutions, 

and some of which fall under the less intentional and formal “social capital.” These 

can include participation in local associations, networks of firms, mobility of 

personnel among firms, informal social activities or via “gatekeepers (local 

institutions, lead firms, or community leaders). 

Maskell (2000) developed a “learning-based theory” of clusters in which he contended 

that learning is an explanation existence, internal organization and boundary 

definitions of the cluster. The cluster and learning theory literature leads to the 

hypotheses that the more similar and/or complementary the company, the more likely 

companies are to interact, watch, discuss, and compare solutions to similar problems, 

and learn from each other and that proximity increases the likelihood of interaction 

and learning among companies. Learning occurs through both formal structures, such 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

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as networks and associations and through informal social venues that depend on stocks 

of social capital. 

Networks, alliances, and associations. Four types of network arrangements have 

been found to facilitate learning but also to reduce the costs of training. One is an 

unintentional outcome—at least from, a policy perspective—of inter-firm 

collaboration for business purposes. One is the intentional formation of skills 

alliances among firms, which supplements formal human resource development with 

informal learning among members. Another is the top-down supply chain network, 

with information flowing from customer company/mentor, company to suppliers, but 

also back up the chain with the specialized knowledge of the suppliers and smaller 

companies. The last is the sector or cluster association that builds relationships of 

trust and provides venues for knowledge exchange. These networks are 

operationalized by supply chain associations, regional skills alliances, cluster 

associations or councils, or gatekeeper organizations. 

While most of the government strategies to encourage and support small and midsized businesses to work collaboratively through networks have targeted hard business 

outcomes, the companies themselves have been much more interested in learning as an 

outcome. Evaluations of network programs in the western region of the United States, 

Wales, and New South Wales in Australia all found that the highest ranked priority for 

company involvement in networks was learning. Michigan turned this into a state 

policy by funding Continuous User Improvement Networks of companies with similar 

interests. Similarly, the recent spurt of interest in forming cluster councils or 

associations has to do with sharing knowledge. 

• Supply chain learning associations - One form of inter-firm learning occurs 

though the supply chain learning and training networks, where original 

equipment manufacturers join with their suppliers or users to ensure that all 

have the skills required to meet efficiency and quality goals. This was the 

official innovation strategy for Wales, with supply chain associations formed 

around each of its multinational branch plants (Morgan, 1967). It’s important 

to bear in mind that knowledge chains are not simply captured by value chains 

compiled in input-output tables. Many of the companies in value chains are 

merely engaged in currency transactions while some companies not involved 

in currency transactions are engaged in knowledge transactions. 

• Regional skills alliances - Regional skill alliances (RSAs) are multi-employer 

worker training programs organized on the demand side of the labor market. 

They are by definition demand driven; they address employers’ training and 

skill development needs. An effective RSA gives each employer access to 

lower cost or higher quality training than would have been available to the 

individual firm. Broader-based RSAs include the public sector, education and 

training organizations, and frequently organized labor. The Southwestern 

Employers’ Training Consortium (Pittsburgh) links firms who have identified 

shared skill needs across industries and occupations. 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

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• Cluster associations - Cluster organizations that represent sectors or clusters, 

either formed by members spontaneously or by government agencies in 

response to cluster initiatives, are also venues for knowledge transfer. Some is 

transferred intentionally to benefit region collectively and some is transferred 

quietly, among colleagues and business partners and associates who expect that 

they will receive as much intelligence as they reveal. In an evaluation of four 

cluster associations in Washington and Minnesota (two in wood products, one 

in engineering, and one in crafts) members of the associations placed a much 

higher value on “access to information and learning” than they did on “hard” 

outcome such as new products or markets (Rosenfeld, 1996). 

• Gatekeepers - Within regions and clusters, certain lead firms, institutions, or 

specialized services function as gatekeepers and disseminators of knowledge 

and know how. In some clusters it’s the multi-nationals that are closer to 

global markets and new technologies. In other clusters it’s an institution—

usually a specific center or program faculty at a community college or 

university—that is responsible for generating and accumulating knowledge and 

know how and works with large numbers of companies. In still other regions, 

it’s a purchasing agent or exporter used by many firms or a sector based 

nonprofit. ARC sponsored an analysis of business intermediaries that fill this 

role but research was limited to the services provided, not as sources or 

disseminators of knowledge. 

Social capital and norms of reciprocity. Social capital has become a popular untraded asset of regions and assumed to influence economic development, despite the 

lack of any compelling studies. There have been, however, repeated observations on 

site that social capital produces learning and learning creates social capital—which in 

turn affects innovation and productivity (Maskell, 2001). A tight social fabric has 

been considered fundamental to the functioning of the classical Italian industrial 

districts. Brusco (1995) noted that “local know-how is passed on by doing things and 

seeing how other people do things through informal chit-chat” and workplace 

knowledge is rooted in places where “people are linked by the bonds of shared history 

or values...and where codes of behaviour, lifestyles, employment patterns and 

expectations are inextricably implicated in productive activity.” 

There are formal associations in Italian industrial districts. However, the social 

structure in northern Italy is embedded in the community and the associations appear 

to be valued more for their collective services than their contributions to social capital. 

In the United States, though, new urban centers lack the shared history and culture to 

form the same kinds of bonds that have supported the exchange of production-based 

knowledge in Italy. Further, as work becomes more knowledge based, the functions 

and skills become less transparent to the community at large. Therefore regions that 

want to build economic development policies around clusters try to create social 

settings that will encourage the learning that Brusco attributes to Italian industrial 

districts. In the U.S., much of the economic value of social capital may in fact be the 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 43

unintended consequence of something else—such informal chitchat at company 

bowling leagues. 

One form of social capital-based learning is the more general information that 

advantages the region without disadvantaging the firm. Those firms that are part of 

global pipelines have little to lose by sharing their knowledge, and strengthening their 

cluster may provide an advantage in the form of a recognized brand. The other social 

capital-based learning depends on reciprocity. 

Exhibit 2-2. Mechanisms for Learning 

Mechanism Units of 

Analysis

Form Constraints

Intra-firm Individuals Structured Resources and 

company policy 

Inter-firm 

Intentional

Networks & 

associations 

General & 

selective 

Time pressures and 

potential rivalry 

Inter-firm 

Unintentional

Clusters Unstructured Business isolation 

Casual Communities Unstructured Social isolation 

Perhaps the most widely cited researcher on social capital and clusters is Annalee 

Saxenian 1994). Her research on Silicon Valley led her to conclude that the "major 

purpose of these organizational structures was to facilitate the exchange of ideas and 

information." Entrepreneurs view social relationships and even gossip as a "crucial 

aspect of their business." "Entrepreneurs came to see social relationships and even 

gossip as a crucial aspect of their businesses....such informal communication was often 

of more value than more conventional but less timely forums such as industry 

journals." "In many cases, the flow of information between the two firms was 

continuous, occurring across different levels of the organization and different 

functional specializations." A more recent survey of 445 SMEs across Great Britain 

found that innovative companies were more likely to exchange information outside 

normal commercial relations, rate collaboration higher, and rate external information 

form other SMEs more highly than non-innovators (Cooke and Clifton, 2002). 

Limitations of social capital. While social capital bring economic benefits to regions, 

it can also restrict who has access to those benefits, and, if it becomes too inward 

directed and insular, be harmful to the region’s competitiveness. The social capital 

that serves a cluster does not automatically benefit all firms, people, and places 

equally. A report from the Organization for Economic Cooperation and Development 

hypothesizes that “the increasing importance of individual learning within the 

knowledge based economy produces new forms of social inequalities, through the 

intensification of the disadvantages experienced by those denied access to learning 

opportunities” (OECD, 2001). The Aspen Institute noted that cluster-based initiatives 

aimed at low-income populations are defined “not simply by absence of resources but 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 44

by the absence of marketplace relationships that can create opportunities of value to 

both participants and employers” (Clark and Dawson, 1995). Associations may have 

exclusionary guidelines. They may meet in places not easily accessible to everyone or 

operate internally as a “club” in which some insiders gain access to tacit knowledge 

while others do not. Tightly controlled associations can act as “gated communities” 

where those not considered part of the “business community” operate at a distinct 

disadvantage. 

Secondly, poorer and socially isolated regions and populations too often have 

insufficient access to benchmark practices, innovations, markets, and jobs outside of 

their region or neighboring regions. While social capital is the medium that transports 

information and accelerates imitation inside a cluster, competitiveness is highly 

dependent on new information and ideas outside the cluster. Successful regions have 

lead firms or associations that either attract or are part of global networks and markets 

and that employ people who are active in international professional associations and 

maintain extensive personal networks. 

(D) Implications for analyzing growth patterns. Efforts to build stronger 

economies in Appalachia since the establishment of the ARC have focused on human 

resource development. After infrastructure, nothing has received more attention or 

resources from the ARC. Human capital has long been a priority, and in support of a 

modern vocational education system the agency contributed to the construction of 

some 700 vocational-technical schools and community colleges in the region 

(Coulombe, 2004). 

However, the federal government is a small player in supporting public education and 

training (usually no more than about five percent), and the major burden falls on the 

state and local governments. The poorest ARC regions, which need good schools the 

most, have the lowest tax bases and are least able to keep youth in school and raise 

levels of human capital enough to support economic growth. Even with more money, 

diseconomies of scale and social and physical isolation make it difficult for many parts 

of the region to attract highly qualified teachers, provide specialized programs and 

services, and keep the highest performers and most talented graduates in the 

community. Therefore, the levels of education of adults in non-metro ARC counties 

are among the lowest in the nation. 

Decades of educational and school finance reform, the Internet, and innovative 

approaches plus the efforts of dedicated teachers and principals, and CBOs, have had 

positive results. Measures of human capital in rural areas have improved considerably 

and closed the gap with metro areas. But on average, they still fall well below those in 

the suburbs and cities, and the issues that keep rural areas behind haven’t changed 

much. They are: (a) limited financial resources, (b) inability to attract the best 

teachers. Higher pay and urban amenities attracts teachers to cities, (c) lack of school 

choices, and (d) out-migration of young adults. 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 45

Networking is more common in rural places than in more impersonal cities. The real 

challenge for Appalachia is access to external knowledge. The “local buzz’ is strong 

but the ”global pipelines” are weak. Rural places are generally more culturally 

homogeneous and have limited access to innovations, ideas, benchmarks, and market 

opportunities from other places, and major barrier to innovation and economic 

development. 

Case Studies. Examples of learning-based technology economic development based 

on technology and education centers are shown in the Volume 2 case studies of Pike 

County, KY and Monongalia, County, WV. 

2.7 References Cited 

(A) General Economic Development

Berry, Brian J. L. and William Garrison, 1958. “The Functional Bases of the Central Place 

Hierarchy,” Economic Geography, 34 (1958), pp. 145-54. 

Blumenfeld, Hans. 1955. “The Economic Base of the Metropolis.” Journal of the American 

Institute of Planners, Vol. 21, p. 114-32. 

Klosterman, Richard E. 1990. Community and Analysis Planning Techniques. Savage, 

Maryland: Rowmand and Littlefield Publishers, Inc. 

Porter, Michael. 1990. The Competitive Advantage of Nations. New York: The Free Press. 

Enright, Michael J. (2001) ‘Regional Clusters: What we know and what we should know.’ 

Paper prepared for the Kiel Institute International Workshop on Innovation Clusters and 

Interregional Competition. 

Rocha, Hector O., (2002) “Entrepreneurship and Development: The Role of Clusters.” Small 

Business Economic Journal, 2002 

Weisbrod, Glen and Brett Piercy., (2006) “New Tools for Economic Development Targeting 

and Strategy”, Economic Development Research Group, September 2006. 

(B) Trade Center Development

Alberta Bureau of Economic-development and Tourism. 1992. Communities on the Grow: 

Economic-development Bulletins. Edmonton, Canada. 

Cromartie, J. and Linda Swanson, 1996. “Defining Metropolitan Areas and the Rural-Urban 

Continuum: A Comparison of Statistical Areas Based on County and Sub-county 

Geography.” Washington, DC: Rural Development Perspectives, Vol. 11, No. 3, pp31-39, 

June 1996. 

DTI (U.K. Department of Trade and Industry). 2003. A Practical Guide to Cluster 

Development. http://www.dti.gov.uk/clusters/ecotec-report/download.html. 

Economic Research Services (ERS), U.S. Department of Agriculture. “Measuring Rurality: 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 46

Micropolitan Areas.” http://www.ers.usda.gov/Briefing/Rurality/MicropolitanAreas 

Economic Research Services (ERS), U.S. Department of Agriculture. “Measuring Rurality: 

Urban Influence Codes.” http://www.ers.usda.gov/briefing/rurality/UrbanInf 

Engardio, P, Roberts, D., (2004) ‘The China Price.’ Business Week. December 6. Internet 

Retrieval. 

Edward J. Feser and Malizia. Emil. 1999. Understanding local economic development. New 

Brunswick, NJ: Center for Urban Policy Research. 

Grabowski, Richard. 1994. “Import Substitution, Export Promotion, and the State in 

Economic-development.” Journal of Developing Areas, Vol. 28, p. 535-54. 

Hoover, Edgar M. 1975. An Introduction to Regional Economics. New York: Alfred A. 

Knopf. 

Huskey, Lee. 1987. “Import Substitution in Frontier Regions.” Ch. 3 in Developing 

America’s Northern Frontier, ed. Lane, Theodore. Lanham, Maryland: University Press of 

America. 

Isard, Walter. 1956. Location and Space Economy. New York: John Willey & Sons. 

Isserman, Andrew M. 2005. “In the National Interest: Defining Rural Correctly in Research 

and Policy.” Forthcoming in International Regional Science Review, October 2005. 

Krugman, Paul. 1995. Development, Geography, and Economic Theory. Cambridge, MA: The 

MIT Press. 

Markusen, Ann R., Yong-Sook Lee, and Sean DiGiovanna (Ed.). 1999. Second-Tier Cities: 

Rapid Growth beyond the Metropolis. Minneapolis, MN: University of Minnesota Press. 

Norton R.D. and J. Rees. 1979. The Product Cycle and the Spatial Decentralization of 

American Manufacturing. Regional Studies. 13:141-151. 

Office of Management and Budget. 1998. “Alternative Approaches to Defining Metropolitan 

and Nonmetropolitan Areas,” Federal Register, 63 (244): 70526-70561. 

Office of Management and Budget. 2000. “Standards for Defining Metropolitan and 

Micropolitan Statistical Areas,” Federal Register, 65 (249): 82228-82238. 

Perroux, Francois. 1950. Economic Space, Theory and Application. Quarterly Journal of 

Economics. 64 (1): 89-104. 

Perroux, Francois. 1988. "The Pole of Development's New Place in a General Theory of 

Economic Activity" in B. Higgins and D. Savoie (Eds.), Regional Economic Development: 

Essays in Honour of Francois Perroux. Boston: Unwin Hyman 

Persky, J., Ranney, D., and Wiewel, W. 1993. “Import substitution and local economicdevelopment.” Economic-development Quarterly, Vol. 7, p. 18-29. 

Polenske, Karen. 2001. “Competitive Advantage of Regional Internal and External Supply 

Chains.” In Regional Science Perspectives in Economic Analysis, Benjamin H. Stevens, 

edited by Michael L. Lahr and Ronald E. Miller, Amsterdam, The Netherlands: Elsevier 

Science, B.V., pp. 259-284. 

Polenske, Karen. 2003. Clustering in Space versus Dispersing over Space: Agglomeration 

versus Dispersal Economies, Innovation, Entrepreneurship, Regional Development, and 

Public Policy in the Emerging Digital Economy. Trollhatten, Sweden. 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 47

Polenske, Karen. 2003. Clustering in Space versus Dispersing over Space: Agglomeration 

versus Dispersal Economies, Innovation, Entrepreneurship, Regional Development, and 

Public Policy in the Emerging Digital Economy. Trollhaätten, Sweden. 

Porter, Michael. 1996. “Competitive Advantage, Agglomeration Economies, and Regional 

Policy,” International Regional Science Review, 19(1-2) (including comments by Ann 

Markusen and Porter’s response): 85-94. 

Ratcliffe, Michael. “Creating Metropolitan and Micropolitan Statistical Areas.” 

http://srdc.msstate.edu/measuring/ratcliffe.pdf 

Smirnov, Oleg, and Alena Smirnova. 2000. “An Assessment of the Economic Base of 

Distressed and Near-Distressed Counties in Appalachia: A Report to the Appalachian 

Regional Commission.” Appalachia Regional Commission. 

United States Census Bureau. “About Metropolitan and Micropolitan Statistical Areas,” 

http://www.census.gov/population/www/estimates/aboutmetro.html 

US Census Bureau. “Metropolitan and Micropolitan Statistical Areas.” 

http://www.census.gov/population/www/estimates/metroarea.html (accessed April 1, 2005). 

Williams, Colin C. 1994. “Local sourcing initiatives in West Yorkshire: an evaluation of 

their effectiveness.” Ch. 2 in Reinventing a Region, ed. Haughton, Graham and Whitney, 

David. Aldershot, United Kingdom: Avebury. 

(C) Industry Agglomeration and Dispersal Economies

Bernard, A., Jensen, J.B., Schott, P.K. ‘Assessing the Impact of Trade Liberalization on 

Import-Competing Industries in the Appalachian Region.’ 2004. 

Darwent D F, 1969, "Growth poles and growth centers in regional planning -- a review" 

Environment and Planning A 1(1) 5–32 

Feser, Edward, and Harvey Goldstein. August 2002. “Regional Technology Assets and 

Opportunities: The Geographic Clustering of High-Tech Industry,” Science and Innovation 

in Appalachia. 

Feser, E. J., Koo, J., Renski, H., and Sweeney, S. H. 2001. ‘Incorporating spatial analysis in 

applied industry cluster studies.’ 

Flaherty, M. Therese. 1996. Global Operations Management. New York: McGraw-Hill 

Company. 

Glasmeier, Amy K. and Jeff Kibler. 1996. "Power Shift: The Rising Control of Distributors 

and Retailers in the Supply Chain for Manufactured Goods," Urban Geography, Vol. 17, pp. 

740-757. 

Harrison, Bennett. 1992. Industrial Districts: Old Wine in New Bottles. Regional Studies. 

26(5): 469-83. 

Hoover, E. M. 1937. Location Theory and the Shoe and Leather Industries. Cambridge, MA: 

Harvard University Press. 

Isard, Walter. 1956. Location and Space Economy. New York: John Willey & Sons. 

Jack Faucett Associates and Economic Development Research Group. 2004. Analysis of 

Global Competitiveness of Selected Industries and Clusters in the Appalachian Region,

prepared for the Appalachian Regional Commission. 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 48

Kotkin, J. (2000). The New Geography: How the Digital Revolution is Reshaping the 

American Landscape. New York: Random House. 

Krugman, Paul. 1991. Increasing Returns and Economic Geography. Journal of Political 

Economy. 99(3): 483-499. 

Landabaso, M, Oughton, C and Morgan, K (2001) ‘Innovation Networks and Regional Policy 

in Europe', in Koschatzy, K, Kulicke, M and Zenker, A, Innovation Networks: Concepts and 

Challenges in the European Perspective , Physica-Verlag, Heidelberg and New York. 

Li, Yu, and Karen R. Polenske. 2004. “Measuring Dispersal Economies”. In 

Entrepreneurship, Spatial Industrial Clusters and Inter-Firm Networks. Trollhätten, 

Sweden: Universities of Trollhätten,/Uddevalla: 615-634 

Polenske, Karen R. 2001. “Competitive Advantage of Regional Internal and External Supply 

Chains.” Regional Science Perspectives in Economic Analysis. Edited by M.L. Lahr and 

R.E. Miller. New York: Elsevier. 

Polenske, Karen R. 2003. “Clustering in Space versus Dispersing over Space: Agglomeration 

versus Dispersal Economies,” Innovation, Entrepreneurship, Regional Development, and 

Public Policy in the Emerging Digital Economy. Trollhaätten, Sweden. 

Rosenfeld, Stuart A. 2001. “Networks and Clusters: The Yin and Yang of Rural Development. 

A report to the Federal Reserve Bank of Kansas City.” 

Teubal, Morris, Tamar Yinnon, and Ehud Zuscovitch. 1991. Networks and Market Creation. 

Research Policy. 20 (5): 381-392. 

Weber, Alfred. 1909. Theory of the Location of Industries. English translation: Chicago: The 

University of Chicago Press, 1929. 

(D) Natural and Cultural Asset-Based Development

Andersson,AÊ .E. 1985. “Creativity and Regional Development.” Papers of the [Regional 

Science] Association, 56: 5–20. 

Appalachian Regional Commission. 2004. “Appalachia: Turning Assets into Opportunities” 

Asheim, Björn T. 1996. "Industrial Districts as “Learning Regions”: A Conduction for 

Prosperity." European Planning Studies, 4(4): 379-400. 

Broadberry, Stephen 1998 “How Did the United States and Germany Overtake Britain? A 

Sectoral Analysis of Comparative Productivity Levels, 1870-1990” The Journal of 

Economic History Vol. 58, No. 2, pp. 375-407 

Christian Science Monitor. 2004 “America's New Coal Rush.” February 24. 

http://www.csmonitor.com/2004/0226/p01s04-sten.html 

Destination Florida Commission. 2002. Destination Florida Commission's final report with 

recommendations, http://www.ccfj.net/DestFlaFinRep.html 

Duncan, Cynthia M. 1992. “Persistent Poverty in Appalachia: Scarce Work and Rigid 

Stratification.” in Rural Poverty in America, edited by Cynthia M. Duncan, Auburn House 

Fingleton, Bernard, Danilo Igliori, Barry Moore, and Raakhi Odedra, forthcoming. 

“Employment Growth and Clusters Dynamics of Creative Industries in Great Britain.” In 

Geographies of Innovation, edited by Karen R. Polenske, publisher being selected) 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 49

Global Philanthropy and Foundation Building, 

http://www.synergos.org/globalphilanthropy/02/abcdoverview.htm 

Goldstein H., and C. Renault 2004. “Contributions of Universities to Regional Economic 

Development: A Quasi-experimental Approach.” Regional Studies: The Journal of the 

Regional Studies Association, 38 (7): 733 –746. 

Hall, Peter. 2000. “Creative Cities and Economic Development.” Urban Studies,37(4): 639–

649 

Kusmin, Lorin, D. Kusmin, J.M. Redman, and David W. Sears. 1996. Factors Associated with 

Rural Economic Growth: Lessons from the 1980s. Economic Research Service, United 

States Department of Agriculture–TB 1850. USDA: Washington, DC. 

Leff, N. H. 1979. Entrepreneurship and Economic Development: The Problem Revisited. 

Journal of Economic Literature, 17: 46-64. 

Longino and Bradley. 2003. “A First Look at Retirement Migration Trends in 2000, 

Gerontologist” 43: 904-907. 

Miernyk W. H., Shellhammer, K. L., Brown, D. M., Coccari, R. L., Gallagher, C. J., 

Wineman, W. H. 1970. Simulating Regional Economic Development, Heath Lexington 

Books, D.C. Heath and Company, Lexington, Massachusetts. 

Mytelka, Lynn K. 2001. “Mergers, Acquisitions, and Inter-firm Technology Agreements in 

the Global Learning Economy,” In The Globalizing Learning Economy, edited by Daniele 

Archibugi and Bengt-Åke Lundvall. Oxford, England: Oxford University Press:127-14 

OhUallachain, Breandan; Neil Reid. (1991) “The Location and Growth of Business and 

Professional Services in American Metropolitan Areas, 1976-1986.” Annals of the 

Association of American Geographers, Vol. 81, No. 2, pp. 254-270. 

Perroux, Francois. 1988. "The Pole of Development's New Place in a General Theory of 

Economic Activity" in B. Higgins and D. Savoie (Eds.), Regional Economic Development: 

Essays in Honour of Francois Perroux. Boston: Unwin Hyman 

Polenske, Karen R. 2001. “Taking Advantage of a Region’s Competitive Assets: An AssetBased Regional Economic-Development Strategy.” Entrepreneurship, Firm Growth, and 

Regional Development in the New Economic Geography. Trollhätten, Sweden: Uddevalla 

Symposium 2000: 527-544. 

Power Daily. 2005. “Industry Veterans See Potential Repeat of Coal Boom and Bust Cycle”, 

25 February. 

Pratt, A. C. 1997. The Cultural Industries Sector, Its Definition and Character from Secondary 

Sources on Employment and Trade, Britain 1984-91. Research Papers on Environmental 

and Spatial Analysis NO.41, London School of Economics 

Reeder. R. J. 1998 “Retiree-Attraction Policies for Rural Development.” Food and Rural 

Economics Division, Economic Research Service, U.S. Department of Agriculture. 

Agriculture Information Bulletin NO. 741. 

Richmond Times-Dispatch, 2005. “National Geographic puts Appalachia on the Map,” by 

Katherine Calos. March 22. 

Riefler, Roger F. 1979. “Nineteenth-Century Urbanization Patterns in the United States.” The 

Journal of Economic History. Vol. 39, No. 4 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 50

Schaffer, William A. 1998. A Survey of Regional Economic Models. Atlanta, GA: Georgia 

Institute of Technology: 48-83. 

http://www.rri.wvu.edu/WebBook/Schaffer/chap03.html#Heading25 

Smirnov, Oleg, and Alena Smirnova. 2000. An Assessment of the Economic Base of 

Distressed and Near-Distressed Counties in Appalachia: A Report to the Appalachian 

Regional Commission. Washington, DC: Appalachia Regional Commission. 

United Nations Environment Program. 2002. “International Year of Ecotourism 2002” 

Walker M., and Jourdan P. 2003. “Resource-based Sustainable Development: An Alternative 

Approach to Industrialisation in South Africa” Minerals and Energy—Raw Materials 

Report, Routledge, 18(3) 

(E) Learning-Based Development

Artz, Georgianne: “Rural Brain Drain: Is it reality?” Choices, 4th Quarter 2003. 

Barkley, David, Mark Henry, and Haizhen Li, “Does Human Capital Affect Rural Economic 

Growth? Evidence from the South,” The Role of Education, Mississippi State, MS: Southern 

Rural Development Center, 2005. 

Bellandi, 1988, p. 145 

Bernat Jr, G. Andrew. “Clusters and Rural Labor markets,” Bureau of Economic Analysis, 

U.S. Department of Commerce, Paper prepared for Southern Rural Labor Force Conference, 

October 1-2, 1998. 

Brusco, Sebastiano. “Global Systems and Local Systems,” International Seminar on Local 

Systems of Small Firms and Job Creation, Paris: Organization for Economic Cooperation 

and Development, June 1995. 

Carnevale, Anthony, 1983 

Carter, Ann. “Knowhow trading as economic exchange,” Research Policy, 18 (1989), 

155:163. 

Clark, Peggy and Steven L. Dawson, Jobs and the Urban Poor: Privately Initiated Sectoral 

Programs, Washington, DC: The Aspen Institute. 1995. 

Cooke, Phil and Nick Clifton, “Social Capital and Small and Medium Enterprise Performance 

in the United Kingdom,” paper for workshop on Entrepreneurship in the Modern SpaceEconomy, Tinbergen Institute, Amsterdam, June 2002. 

Coulombe, Serge, Jean-Francois Tremblay, and Sylvie Marchand, “International Adult 

Literacy Survey: Literacy scores, human capital and growth across fourteen OECD 

countries.” (Ottawa: Statistics Canada, 2004). 

Gibbs, Robert, Lorion Kusmin, and John Cromartrie, “Low-Skill Jobs: A Shrinking Share of 

the Rural Economy,” Amber Waves 2 (Issue 5, 2003), Economic Research Service. 

Green, Gary Paul. “Workforce Development Networks in Rural Areas of the United States,” 

SRDC Policy Series. September 2003. 

Haaga, John: Educational Attainment in Appalachia, (Washington, DC: Appalachian Regional 

Commission, May 2004). 

Krugman, Paul Geography and Trade, (Cambridge: MIT Press, 1992). 
Vol.1 Project Background and Summary Ch.2 Theories of Growth Paths

 Sources of Growth in Non-Metro Appalachia page 51

Marshall, Alfred. Principles of Economics. Eighth Edition. London: Macmillan, 1936). 

Maskell, Peter. “Towards a learning-based theory of the cluster,” paper presented at the World 

Conference on Economic Geography, Singapore, December 2000. 

Maskell, Peter “Knowledge creation and diffusion in geographic clusters,” International 

Journal of Innovation Management 5 (2, 2001): 213-237 

McGranahan, David: “Education and Rural Economic Development in the United States,” in J 

Norman Reid and Sara Mazie (Eds) Conceptual Frameworks for Understanding Rural 

Development, A report funded by the Aspen Institute, USDA, University of Guelph, and the 

Arkleton Trust, 1994 

Morgan, Kevin, “The Learning Region: Institutions, Innovation and Regional Renewal,” 

Regional Studies 31.5 (19967) 491:503. 

OECD, Innovative Clusters: Drivers of Innovation Systems, Paris: Organization for Economic 

Cooperation and Development, 2001. 

Rosenfeld, Stuart, Ed Bergman, and Sarah Rubin, After the Factories: Changing Employment 

Patterns in the Rural South, (Research Triangle Park, NC: Southern Growth Policies Board, 

December 1986). 

Rosenfeld, Stuart. Shotgun Weddings, Open Marriages, and Quickie Divorces: An Analysis of 

Education and Local Development in the United States. (Paris, France: Organization for 

Economic Cooperation and Development, 1980). 

Rosenfeld, Stuart. “Does cooperation enhance competitiveness? Assessing the impacts of 

inter-firm collaboration,” Research Policy 25(1996) 247:263 

Ross, Peggy and Stuart Rosenfeld, “Human Resource Policies and Economic Development,” 

in Rural Economic Development in The 1980’s: Prospects for the Future, (Washington, 

DC: US Department of Agriculture, Rural Development Research Report Number 69, 1988. 

Rupasingha, Anil, Thomas W. Ilvento, and David Freshwater, “Demand for Skills Training in 

the Rural South,” Staff Paper 00-02, TVA Rural Studies Program (Lexington: University of 

Kentucky Department of Agricultural Economics, February 2000). 

Saxenian, AnnaLee. Regional Advantage: Culture and Competition in Silicon Valley and 

Route 128, (Cambridge: Harvard University Press, 1994). 

Schuller, Tom. “Three Steps towards a learning society,” Studies in the Education of Adults

30 (Ni. 1 April, 1998), 11:20. 

Schultz, Theodor: Investing in People: The Economics of Population Quality, (Berkeley: 

University of California Press, 1981), p. 60. 

Teixeira, Ruy: “Rural and Urban Manufacturing Workers: Similar Problems, Similar 

Challenges,” ERS Agricultural Information Bulletin 736-02, January 1998. 

Thurow, Lester> Zero Sum Society: Distribution and the Possibilities for Economic Change, 

(New York: Basic Books, 1980), p. 56 

Von Hippel, Eric “Cooperation between rivals: Informal know-how trading,” Research Policy, 

16 (1987), 291:302. 

 
Vol.1 Project Background and Summary Ch.3 Spatial Dimension of Growth 

Sources of Growth in Non-Metro Appalachia page 52

3 IMPLICATIONS FOR RESEARCH 

AND PRACTICE

The background material presented in this volume represents just one small part of the 

Sources of Growth Study. However, the background research played an important 

role in defining the other parts of the study: 

The discussion of theory and research (in chapter 2 of this volume) identified five key 

categories of economic development growth paths – asset-based development, 

learning-based development, manufacturing agglomeration growth, dispersed supply 

chain growth and trade center growth. Those categories guided the selection of case 

studies discussed in the separate Volume 2 document. The case studies provided 

examples of the complexities involved in pursuing each of the five major classes of 

growth path strategies. They also showed examples of the types of institutional and 

policy actions required for those strategies, and factors affecting their success. 

The discussion of prior empirical research (in chapter 3 of this volume) also raised 

issues regarding the role of spatial location and access in affecting economic growth 

opportunities. This helped define the series of four research studies summarized in the 

separate Volume 3 document. Those statistical studies represent a step in a continuing 

process of research to further our understanding of the roles of spatial proximity to 

industry clusters and trade centers, the roles of transportation access improvements, 

and the impact of market scale on economic growth opportunities. 

Finally, the classification of major growth paths (in this volume), together with the 

cast studies and additional statistical studies (discussed in separate volumes) together 

served to define a series of tools and measures that can be of practical use for 

economic developers seeking to better target economic growth and business attraction 

opportunities. 
 
 

Sources of Regional Growth in Non-Metro Appalachia 

Vol. 2. Case Studies of Local Economic 

Development Growth Processes 

Prepared for the Appalachian Regional Commission 

Prepared by: 

Economic Development Research Group, Inc. 

 Regional Technology Strategies, Inc. 

Revised 2007 
 
Vol.2 Case Studies of Economic Growth Processes 

Sources of Growth in Non-Metro Appalachia 

SOURCES OF GROWTH PROJECT

The Sources of Growth project is part of a series of research efforts funded by the Appalachian 

Regional Commission to improve our understanding of factors affecting economic growth in 

rural and distressed areas. As stated in the Volume 1 Introduction, “the starting premise of 

this project is to go beyond the theory of comparative advantage to understand more 

concretely the multiple paths that an area can pursue in successfully enhancing job and income 

creation, and the effects of spatial linkages among communities in shaping these options. It is 

in this context that one can understand how communities may build on natural resources, 

cultural resources, human resources, local amenities, institutional facilities or location 

advantages. Furthermore, certain developmental path dependencies may shape the direction of 

economic growth may involve manufacturing or supply chain development, resource 

extraction or tourism development, educational development or trade center development.” 

This research is intended to provide a basis of information that can ultimately be useful for 

enhancing the effectiveness of policies and tools aimed at improving the region’s economic 

development. 

This is Volume 2 in a series of reports prepared as part of this project: 

• Executive Summary –synthesis of findings from all work products related to the 

study’s four main research components. 

• Volume 1, Project Background and Prior Research on Economic Growth Paths – 

study objectives, characteristics of non-metro Appalachian counties, classification of 

economic development growth paths, and a synopsis of white paper findings on theory 

relating to economic development growth paths. 

• Volume 2, Case Studies of Local Economic Development Growth Processes –

findings related to growth paths as observed for selected case studies covering 

manufacturing industry specialization clusters, supply chain-based development, 

tourism-based development, advanced technology development, and diversification 

from resource-based economies. 

• Volume 3, Empirical Studies of Spatial Economic Relationships – findings from a 

series of econometric modeling and GIS-based analyses, focusing on roles of spatial 

adjacency, market access and transportation in determining economic growth and 

development of trade centers. 

• Volume 4, Tools for Economic Development & Study Conclusions – description of 

new and updated tools available to ARC and its Local Development Districts to assess 

economic development opportunities and potential directions for economic growth. 

• Appendices – (A) Spatial Analysis of Economic Health, (B) Economic Analysis of 

Hub-Spoke Relationships, (C) White Papers on Economic Growth Theories, (D) 

Literature Review of Empirical Studies on Spatial Influences in Economic 

Development. 
Vol.2 Case Studies of Economic Growth Processes 

Sources of Growth in Non-Metro Appalachia 

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i 

1 INTRODUCTION..........................................................................................................................1

2 SCIOTO COUNTY, OH: REBUILDING AN ECONOMIC ENGINE.......................................5

2.1 INTRODUCTION........................................................................................................................5

2.2 REGIONAL PROFILE .................................................................................................................5

2.3 EVOLUTION OF PROGRESS .....................................................................................................10

2.4 CATALYSTS OF CHANGE........................................................................................................13

2.5 LESSONS LEARNED................................................................................................................18

2.6 INTERVIEWEES ......................................................................................................................19

3 CHAUTAUQUA COUNTY, NY: MANUFACTURING DIVERSIFICATION.......................21

3.1 INTRODUCTION......................................................................................................................21

3.2 REGIONAL PROFILE ...............................................................................................................21

3.3 EVOLUTION OF PROGRESS .....................................................................................................24

3.4 CATALYSTS OF CHANGE........................................................................................................33

3.5 LESSONS LEARNED................................................................................................................40

3.6 INTERVIEWEES ......................................................................................................................41

4 PIKE COUNTY, KY: EVOLUTION AS A TRADE CENTER..................................................43

4.1 INTRODUCTION......................................................................................................................43

4.2 REGIONAL PROFILE ...............................................................................................................43

4.3 EVOLUTION OF PROGRESS .....................................................................................................48

4.4 CATALYSTS OF CHANGE........................................................................................................52

4.5 LESSONS LEARNED................................................................................................................55

4.6 INTERVIEWEES ......................................................................................................................57

5 MARION & MONONGALIA CO, WV: HIGH-TECH INITIATIVE.....................................58

5.1 INTRODUCTION......................................................................................................................58

5.2 REGIONAL PROFILE ...............................................................................................................58

5.3 EVOLUTION OF PROGRESS .....................................................................................................66

5.4 CATALYSTS OF CHANGE........................................................................................................70

5.5 LESSONS LEARNED................................................................................................................71

5.6 INTERVIEWEES ......................................................................................................................72

6 SOUTHEAST TN & SOUTHWEST NC: RE-TOOLING ASSET-BASED GROWTH...........73

6.1 INTRODUCTION......................................................................................................................73

6.2 REGIONAL PROFILE ...............................................................................................................74

6.3 EVOLUTION OF PROGRESS .....................................................................................................79

6.4 CATALYSTS OF CHANGE........................................................................................................82

6.5 LESSONS LEARNED................................................................................................................87

6.6 INTERVIEWEES ......................................................................................................................88

7 ALABAMA: AUTO ALLEY ........................................................................................................90

7.1 INTRODUCTION......................................................................................................................90

7.2 REGIONAL PROFILE ...............................................................................................................90

7.3 EVOLUTION OF PROGRESS .....................................................................................................92

7.4 CATALYSTS OF CHANGE ........................................................................................................93

7.5 LESSONS LEARNED ................................................................................................................95

7.6 INTERVIEWEES ......................................................................................................................99
Vol.2 Case Studies of Economic Growth Processes 

Sources of Growth in Non-Metro Appalachia 

8.0 CONCLUSIONS – STRENGTH OF GROWTH PROCESSES..............................................100
Vol.2 Case Studies of Economic Growth Processes 

Sources of Growth in Non-Metro Appalachia page i

ACKNOWLEDGEMENTS

This volume was authored by Economic Development Research Group, Inc. (EDRG) and 

Regional Technology Strategies, Inc. (RTS). EDRG directed the overall research study and 

edited final documents. Staff of EDRG and RTS each developed half of the case studies. 

Altogether, the Sources of Growth project involved a team of researchers including: 

• Economic Development Research Group, Inc. (EDRG) – Lisa Petraglia (Project 

Director), Glen Weisbrod and Teresa Lynch, with research support from Tyler 

Comings and Brett Piercy; 

• Regional Technology Strategies, Inc. (RTS) –Stuart Rosenfeld, Phil Psilos and Dan 

Broun; 

• Massachusetts Institute of Technology, Department of Urban Studies & Planning 

(MIT-DUSP) – Prof. Karen R. Polenske, Prof. Joseph Ferreira, Jr., Ayman Ismail and 

Li Xin, with research support from Tan Zhijun, and Isabelle Yi Xu. 

The project also benefited from the expertise of outside policy and research experts who 

reviewed various project documents, participated in meetings and provided technical 

guidance: Deb Markley (Co-Director of the Center for Rural Entrepreneurship, a Rural Policy 

Research Institute), Joseph Cortwright (Vice-President of Impresa Consulting), Ken Poole 

(Executive Director of ACCRA: The Council for Community and Economic Research), David 

Freshwater (Professor of Agricultural Economics and Public Policy at the University of 

Kentucky), David McGranahan and Luc Anselin (Professor. Dept. of Agriculture & Consumer 

Affairs and Regional Economics Applications Laboratory at the University of Illinois, 

Urbana- Champaign). 

Overall project direction and oversight was provided by Dr. Greg Bischak of the Appalachian 

Regional Commission, whose wide range of research experience served to focus the project 

team on the development of policy applications. Important insight and suggestions were also 

provided by officials of the Appalachian Regional Commission who participated in a day-long 

symposium with the project team, including Thomas Hunter (executive director of ARC, Ann 

Pope (federal co-chair of ARC) and Rick Peltz (alternate federal co-chair). In addition, Ken 

Wester and Jason Wang of ARC assisted the project team in collecting and assembling 

transportation and geographic data. 

Finally, the project team acknowledges the important role of prior ARC-funded research 

studies by Andrew Isserman, Ed Feser and Oleg Smirnov that provided a foundation for the 

work in Volume 3 this project to build upon. 
Vol.2 Case Studies of Economic Growth Processes Introduction 

Sources of Growth in Non-Metro Appalachia page 1 

1 INTRODUCTION

Overview. This volume presents six case studies of local economic development in 

Appalachia. The study areas range from single counties to multi-county regions. The 

case studies document the local context and history of economic development in these 

areas, in order to illuminate the processes of economic growth and change that have 

been and are occurring there. 

All of the case studies focus on non-metro parts of Appalachia. They were selected to 

a range of locations and a range of economic growth paths, while also testing the 

usefulness of economic statistics and spatial linkages in illuminating the economic 

development situations actually occurring across the region. 

Selection of Case Study Locations. Since the Appalachian region spans north, south, 

and central locations in the US it was desirable to have case study representation in 

each of these three major regions. Also of interest would be to examine how a 

previously resource dependent, which has seen its prospects diminish, made the 

transition to reorient their economy (such as leveraging cultural assets). 

In addition to these two general priorities for case study definition and development, 

three specific outcomes from prior analysis studies were considered in identifying 

potential case study locations: 

• Based on their analysis of trade centers and “hub-spoke” economic 

relationships between counties, Smirnov-Smirnova (2000) identified a sereies 

of Appalachian counties with “hub” potential and others with “spoke” 

potential. Case studies could illuminate how some of these areas have actually 

been performing as trade center hubs or feeders to them. 

• Based on ARC’s recent time-series comparison (1960 & 2005) of Appalachia’s 

distressed counties, case studies focus on actions taken that helped some areas 

transition out of distress.

• Based on successful bids of two southern states to attract auto assembly plants, 

community stakeholders examine how economic development efforts have 

affected the extension of -chain and knowledge-based development processes 

into non-metro Appalachian counties. 

The following six locations represent the final selection for undertaking case study 

regarding sources of growth: 
Vol.2 Case Studies of Economic Growth Processes Introduction 

Sources of Growth in Non-Metro Appalachia page 2 

• Scioto County, OH has been at the center of a ring of distress. It exhibits a 

services-oriented type of economy. While N-S highway improvements along 

US 23 have helped improve access to E-W traffic along US 50, the lack of an 

interstate has hampered Scioto development and the county has been slow to 

advance into the possible trade center (hub) role that Smirnov’s analysis (2000) 

identified. The case study diagnoses the inertia and uncovers small positive 

steps now underway. 

• Chautauqua County, NY has been maintaining its transitional status despite 

continued adverse forces tied to structural adjustments around U.S. 

manufacturing. Now attraction of jobs in transportation equipment 

manufacturing is serving to anchor the regional economy along with impetus 

of HUD Renewal Community status, various enterprise zones, and attempts to 

diversify/foster entrepreneurial development around tourism. 

• Pike County, KY is the eastern-most county of a five county Local 

Development District that sits adjacent to the WV border. Pike County has 

managed to move from distressed to transitional status since 2003. However 

the four remaining counties in this mining-dependent LDD area have not faired 

the same. The case study explores reasons for Pike’s gradual success, the 

stalled spillover to its neighboring counties and transferable lessons to other 

mining-dependent areas of Appalachia. 

• Marion & Monongalia Counties, WV represent contrasts; Monongalia (home 

to Morgantown) is a metro county, while adjacent Marion County is a 

“micropolitan” area. Marion County had prior mining roots. This case study 

examines the development of a hi-tech initiative in these two counties with 

emphasis on the role of university-based research and commercialization and 

the extent to which Marion County is achieving diversification in its economy. 

• SE Tennessee/SW North Carolina are covered by two adjacent Local 

Development Districts that are connected by Appalachian Highway Corridor 

K. The case study traces economic development efforts to develop cultural 

and recreation tourism along Corridor K between Chattanooga, TN and 

Asheville, NC. 

• Alabama provides a state-level case study that traces how northern Alabama’s 

automotive-related manufacturing activity (initiated by attracting MercedesBenz to Tuscaloosa, followed by auto parts suppliers) is raising the economic 

prospects in Appalachian AL. 
Vol.2 Case Studies of Economic Growth Processes Introduction 

Sources of Growth in Non-Metro Appalachia page 3 

Exhibit 1-1 below itemizes the case study locations, the research focus for each case 

study, its location, its ARC-rated economic status and the extent of urbanization. 

Exhibit 1-1. Attributes of Case Study Areas 

Case Study Area Focus Loc Econ Status 

2005 

Type 

1. Scioto County, Ohio Potential economic hub 

(trade center) for services 

N Distressed Micropolitan 

2. Chautauqua County, NY Manufacturing cluster 

diversification; Tourism 

development 

N Transitional Micropolitan 

3. E. Kentucky (Big Sandy Area) Shift from mining, Medical 

technology; Trade center 

C Distressed Rural 

4. Monongalia and Marion, WV Learning-based devel.; 

High tech complex, 

C Transitional Micropolitan 

5. SE Tennessee and 

 SW North Carolina 

Recreation-based amenity; 

Trade center 

S Transitional Mixed 

6. Alabama Auto Alley Auto industry supply 

chain corridor 

S Mixed Mixed 

Collectively, these six case study areas span seven states. There are two case studies 

each in the northern (N), central (C) and southern (S) parts of Appalachia. Two are 

rated as substantially “distressed,” while three are rated as “transitional” and the 

remaining one is a mix of those two classes. All are located primarily outside of 

metropolitan areas, though three feature micropolitan centers, one is completely rural 

and the other two have a mixture of rural and micropolitan settings. 

The focus of these case studies covers all of the growth paths discussed in Volume 1, 

including trade centers, manufacturing and technology clusters (agglomerations), 

learning-based development, amenity-based development and supply chains. In 

addition, each case study addresses different examples of spatial linkages with 

neighboring communities and the role of metro and micropolitan areas. 

Organization of Case Studies. Each case study is structured to present the following: 

1) Introduction – explanation of why the case study was selected and the types of 

growth paths that it illustrates 

2) Profile – brief description of the area’s economy and its economic history: 
Vol.2 Case Studies of Economic Growth Processes Introduction 

Sources of Growth in Non-Metro Appalachia page 4 

• Composition of economy, 

• Special features or assets; 

• Labor markets: commuting, migration and education;

• Educational institutions: public and private schools; colleges, vocational 

training, 

• Entrepreneurship: self-employed, startups, special services. 

3) Evolution of Progress –how regional economy has been changing, related policy 

interventions and the effectiveness of strategies and actual outcomes: 

• History of interventions, basis for economy, changes over time, business 

recruitments and closures, supplier development, 

• Plans and strategies: types of plans and/or visions that were pursued 

• Degree to which any of the place-specific assets have been exploited 

• Resources: previous federal grants, subsidies, local foundations, etc. 

4) Catalysts of Change – the organizational structures and technical changes that 

support collaboration:

• Social capital: civic infrastructure, associations, non-profits, local leadership, 

education, external linkages; 

• Physical infrastructure: transportation, broadband and utility enhancement 

• Politics: strength and interest of state and federal representatives, political 

access, funding and tax policy 

• External factors: globalization, logistics technology advances 

5) Lessons Learned –findings that can be useful for application elsewhere: 

• Flexibility in response to unforeseen changes; 

• Role of key players and institutions in leading change. 

6) Interviewees – credits to business and economic development representatives 

who were interviewed (Note: all findings are interpretations of the report 

authors and not the responsibility of interviewees.) 

 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 5 

2 SCIOTO COUNTY, OH: REBUILDING 

AN ECONOMIC ENGINE

2.1 Introduction 

Scioto County, OH has been at the center of a ring of distress. It exhibits a servicesoriented type of economy. While N-S highway access improvements to US 23 have 

mitigated some of the effects of being bypassed by the interstate system Scioto County 

has been slow to advance into the possible trade center (hub) role that Smirnov’s 

analysis (2000) identified. The case study diagnoses the inertia and uncovers positive 

steps now underway. 

In the last half of the 20th century, Scioto County, Ohio went from being an industrial 

powerhouse to a community struggling to meet the challenges of the new economy. 

However, some recent up-ticks in growth suggest that the county is showing signs of 

progress as it adjusts to a new economic reality. Significantly, while neighboring Ohio 

counties have remained classified as distressed by the Appalachian Regional 

Commission, Scioto recently moved to a transitional designation. In addition, some 

spatial analysis suggests that the county could serve as a regional hub for surrounding 

counties, drawing outside consumers to the county for the purchase of goods and 

services. In addition, Scioto County has moved from a community heavily dependent 

on manufacturing to one in which services play a prominent role. 

2.2 Regional Profile 

Setting. Scioto County, population 79,195, sits in the far southern part of Ohio, just 

across the river from Kentucky. It is almost equidistant from the large metropolitan 

areas of Cincinnati and Columbus and about 45 minutes from Huntington, West 

Virginia, a mid-sized city. The county seat of Scioto is Portsmouth, which, with 

20,909 residents, is also by far the largest city in the county. (See Exhibits 2-1,2,3.) 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 6 

Exhibit 2-1 Scioto's Location Within Ohio 

Source: FedStats. 

Exhibit 2-2. Distance from Scioto County to Surrounding Metropolitan Cities 

Source: Microsoft Streets and Trips 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 7 

Exhibit 2-3. Scioto County - Detail Map 

The county has been steadily losing population, with a decline of 1.4% between 2000 

and 1990 (Exhibit 2-4). This is in comparison to the rest of Appalachian Ohio, which 

enjoyed a modest population growth of six percent over the same time period. Scioto 

County also has a smaller population than it did in 1950, declining by 4.5% since that 

date. 

Exhibit 2-4. Population Growth, 1970-2000 

Population 1970 1980 1990 2000 1970-80 1980-90 1990-2000 1970-2000

Scioto County 76,951 84,545 80,327 79,195 9.9% -5.0% -1.4% 2.9%

State of Ohio 10,652,017 10,495,445 10,847,115 11,353,140 -1.5% 3.4% 4.7% 6.6%

Source: US Census Bureau and EDR Group calculations.

Population % Change

Economic Overview. The stagnant or negative growth in the county can be directly 

traced to the changing economy of the region. For most of its history, the county was 

dependent on manufacturing. The county, and Portsmouth specifically, were seen as 

attractive places to locate and operate industry. Access to transportation was the 

driving factor in much of the industry location. With a location on the Ohio River, 

industry had easy access to transport their goods and receive important supplies like 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 8 

coal. As railroads became more important, the presence of an extensive rail network 

became beneficial for the county. 

Foremost in the development of the county in the first half of the 20th century was the 

steel industry. The community served as a focal point for the industry as Ohio took its 

place as a center of the national steel industry. As the industry declined due to 

globalization and mechanization so did the fortunes of the industry in Scioto County. 

By the early 1980s, the last steel manufacturer had left the county. 

The county’s other manufacturing industries suffered similar fates. For instance, 

Portsmouth had been a focal point for the shoe manufacturing industry in the US. But 

by the 1990s, there were basically no shoe manufacturers left in the US, much less in 

Scioto County. While the county remains the home to Mitchellace Inc., the only 

shoelace manufacturer in the country, the production of actual shoes has long ended. 

The production of bricks also ended leaving the county without a real export driven 

economic base. 

The decline of Scioto County’s manufacturing base is partly due to the fact that the 

particular sectors that the county specialized in were ones most vulnerable nationally. 

The county also suffered from a changing transportation environment. While the 

county once could rely on its prime access to the Ohio River and rail access to 

promote industrial advancement, it began to suffer as the highway became the 

economic driver. As one local resident put it: 

“First there was the river, and Scioto County thrived. Then there was the 

railroad and Scioto County thrived. Then came the interstate and Scioto 

County died.” 

Scioto County does not have an interstate within its borders and while the region’s 

best four lane North-South corridor US 23 has improved access to the East-West route 

of US 50 just north of Scioto, there are still accessibility issues, particularly with the 

bypass being built on the east side of the county. So due to both a general decline in its 

once prominent industries and a changing infrastructure environment, Scioto County 

no longer can be seen as a county whose economy is manufacturing dependent. 

Currently, 7.6 percent of the county’s workforce is employed in manufacturing. 

Instead, in a story similar to most of the country, the areas of economic specialization 

in the county are in the service industries. Exhibit 2-5 shows the percentage of county 

employment in various sectors. 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 9 

Exhibit 2-5. Percentage of Scioto County by Sector 

Industry 

Percent of Total 

 Employment 

Government & non NAICs 16.5% 

Food services & drinking places 8.7% 

Ambulatory health care 7.1% 

Admin support services 6.6% 

Hospitals 6.6% 

Construction 6.2% 

Nursing & residential care 4.8% 

Social assistance 3.2% 

Professional- scientific & tech svcs 2.5% 

Primary metal mfg 2.3% 

Source: IMPLAN data derived from BEA, 2002. 

The large percentage of Scioto County employment in the health care and social 

assistance sector is borne out in looking more closely at location quotients for the 

county. Examining sectors at a more detailed NAICS code reveals areas in which the 

county appears to have specialization relative to the United States as a whole. Exhibit 

2-6 shows the sectors in the county that have a location quotient greater than 1.2 and 

have more than 500 employees. (Note a location quotient above 1.2 means that the 

industry sector has a representation in the local area that is 20% or more above the 

national average for that industry.) 

Exhibit 2-6. Location Quotients of Scioto County Sectors 

Description 

Number of 

Employees 

Location 

Quotient 

Primary metal manufacturing 707 7.8 

Nursing & residential care 1,449 2.8 

Hospitals 2,001 2.8 

Ambulatory health care 2,162 2.1 

Social assistance 970 1.7 

Food svcs & drinking places 2,653 1.4 

General merch stores 688 1.3 

Admin support svcs 2,013 1.2 

Government & non NAICs 5,022 1.2 

Food & beverage stores 676 1.2 

Source: IMPLAN data derived from BEA, 2002. 

As the above chart shows, with the exception of primary metal manufacturing, Scioto 

County’s comparative strengths lie in retail and services. The concentration of 

employment in such sectors as food services and food stores would suggest that Scioto 

County might serve as a hub for these activities. In addition, health care shows up as 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 10 

being an industry in which the local economy has a high concentration. Hospitals, 

nursing and residential care, and ambulatory care all have extremely high location 

quotients. 

The center of Scioto County’s health care industry is Southern Ohio Medical Center 

(SOMC) in Portsmouth. SOMC is the largest medical center in Ohio, south of 

Columbus. It employs 2,100, making it the largest employer in the County. Later in 

this study, we will discuss the role that SOMC plays and could potentially play in the 

growth of the County. 

2.3 Evolution of Progress 

Economic Attainment. One reason for choosing Scioto County as a case study was its 

movement in economic status between ARC’s distressed and transitional categories. 

Exhibit 2-7 shows Scioto’s oscillation in economic status using the ARC categories of 

economic performance. The recent change in status can be attributed to the fact that 

Scioto’s three-year unemployment rate for 2001-2003, while relatively high at 7.8 

percent, was just below the critical ARC distressed threshold of 150 percent of the 

national unemployment rate during the three-year period of measurement. 

Exhibit 2-7. Scioto’s ARC Economic Status 

Period ARC Economic Attainment level 

1988-1992 Transitional 

1993-2004 Distressed 

2005-2006 Transitional 

Trade Center (Hub) Status. Another reason for choosing Scioto County as a case 

study was an earlier economic base study by Smirnov and Smirnova (2000), in which 

Scioto County was classified as a “Type 1 County” – one with both “a strong 

economic-base” and “strong local spatial linkages,” suggesting a likely regional trade 

center. That analysis of the industrial mix of the Scioto and surrounding counties 

concluded that residents and business in the more rural surrounding counties could be 

expected to utilize businesses in Scioto County. 

Further analysis, both quantitative and qualitative, indicated that despite the statistical 

suggestions that Scioto could serve as a regional hub, the reality is far different. 

Indeed, representatives from the Ohio Regional Development Commission saw 

surrounding counties such as Ross and Pike (both transitional counties) as more the 

regional centers than Scioto, although they believe the county has strong growth 

potential. Community leaders within Scioto also doubted the county’s current ability 

to serve as a regional hub, several calling Portsmouth “ a typical Wal-Mart Town,” 

offering little beyond that store as an attraction to those living outside. “We were once 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 11 

a hub, but we are no longer,” said one community leader. “People go to Ashland (WV) 

or Huntington (KY) for their services, especially retail.” 

Scioto County’s failure to serve as a regional hub manifests itself in several ways, as 

described in the following pages. 

Commuting patterns. If it were a true regional hub, Scioto County could be expected 

to draw workers to its firms on a daily basis. In fact, as Exhibit 2-8 shows, Scioto is 

actually a net exporter of workers, with about 2,700 employees commuting on a daily 

basis. Indeed the only county in which Scioto appears to out commute to is Greenup 

County, Kentucky, which lies just across the Ohio River. 

Exhibit 2-8. Scioto County Area Commuting Patterns 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 12 

Retail patterns. If Scioto County operated as a true regional hub, it would be expected 

that residents of surrounding counties would migrate to the area to satisfy their retail 

needs such as shopping and eating. The location quotients of retail establishments 

shown earlier in Exhibit 2-6 also suggest that the county is drawing individuals from 

surrounding counties. But it appears that this is more of a statistical quirk than any real 

proof that Scioto County or Portsmouth is acting as a regional hub. Indeed, most 

leaders and residents interviewed for the study reported that residents of the county 

were more likely to drive to other areas for destination shopping and dining. 

The main reason for Scioto County’s inability to attract retail customers is one of 

location. As shown earlier in Exhibit 2-2, Scioto is actually at the center of a set of 

counties that have much better metropolitan access. For instance, Pike County, to the 

north of Scioto, would be more likely drawn into the metropolitan sphere of 

Columbus. Similarly, Adams County, to the west, is drawn into Cincinnati’s service 

area. In addition, Scioto County does not have any large retail establishment such as a 

mall to draw shoppers. In fact, most interviewed residents of Scioto County 

themselves shopped at malls in Ashland, Kentucky or Huntington, West Virginia for 

their needs. 

Supplier-customer relationships. While retail and commuting patterns are critical, the 

interchange between businesses is even more important to an economy. If a county’s 

businesses draw their customers from surrounding locales, then they can be seen to 

serve as a regional hub. In the case of Scioto County that does not appear to be the 

case. The manufacturing firms that remain in Scioto County export their products on a 

national or larger regional basis than just surrounding counties. For example, 

Mitchellace in Portsmouth has a large contract with Kiwi, a company not located in 

Ohio. 

There is a small concentration of financial and other professional services but these 

tend to be more focused on the local market. Portsmouth is home to seven banks, five 

of which are nationally chartered. Again. conversations with local officials suggest 

that these banks do not draw heavily from surrounding companies but instead serve 

the relatively large Portsmouth market. 

One “industry” that does appear to attract regional usage is higher education. 

Portsmouth is home to Shawnee State University, a four-year institution that also 

offers two-year associate degrees. The university draws both students and employees 

from surrounding counties. For instance, 71 percent of Pike county residents who 

attend a four-year public university attend Shawnee State University. The university 

also employs a large number of employees who cross the bridge from Kentucky. The 

role of Shawnee State is discussed later in this paper. 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 13 

2.4 Catalysts of Change 

Responding to Growth Challenges. There are three areas of economic emphasis that 

merit closer examination. Each one of these categories represent ways in which Scioto 

County, either through intentional government intervention or through market forces, 

is attempting to grow its economy. This section of the paper examines: 

The Health Care Industry 

Higher Education 

Manufacturing 

For each of these categories, the ways in which growth is manifesting itself is 

discussed as well as the challenges pursuing such a strategy may pose to Scioto 

County. In addition, this section includes a discussion of how economic development 

agencies in the county and region are working to improve Scioto County. 

Health Care. As mentioned, Scioto County enjoys a high concentration of 

employment in the health care industry. While there are substantial numbers of private 

doctors’ offices, clinics and health care homes, the prime driver of this sector is 

Southern Ohio Medical Center located in Portsmouth. Located in Portsmouth, SOMC 

employs nearly 2,100 doctors, nurses and associated staff. The main hospital has more 

than 400 beds and the center owns several other health care offices and programs in 

the county. The hospital’s growth has been in part facilitated by a commitment to 

make SOMC a high-tech medical center. In the last few years, SOMC has added a 

cancer treatment facility as well as a cardiac care center. All told, the hospital has 

undergone $70 million in expansion in recent years. SOMC took its current form in 

1986, when several local health care facilities merged. The hospital operates as a nonprofit entity. SOMC’s impact on the economy comes primarily through direct 

employment. Doctor’s offices in the area can affiliate themselves with the hospital and 

at least seven do so. 

A major factor in the growth of the industry is the graying population of Scioto 

County. According to the 2000 Census, 14.9 percent of Scioto County’s residents are 

over 65 compared to 12.4 percent of the national average. The population is expected 

to get even older proportionally—31percent of the population is over 50 compared to 

27% of the US total. And SOMC is clearly geared to the local market: more than 80 

percent of patients come from Scioto County with the rest from surrounding counties 

including some from Kentucky. 

The population served by SOMC does present a problem for the hospital as a true 

economic growth strategy. The payer mix of the population is extremely dependent on 

government-assisted patients. Fully 75% of the patients use government insurance to 

pay for their services. This dependence reduces income for the hospital, as the hospital 

estimates it only collects a small portion of each dollar charged to these payers. 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 14 

One plus for the hospital has been its ability to attract and retain nurses and other staff. 

While rural locations can sometimes hurt hospitals, administrators report an extremely 

high retention rate. SOMC offers generous benefits including offering 100 percent 

tuition reimbursement for staff or their spouses to go back to school to become nurses. 

In addition to paying for school for staff, one of the reasons for the ability to attract 

employees are quality educational programs available locally. Shawnee State in 

particular provides a good source of nurses to SOMC. Students in the program are 

comfortable with Scioto County and are pleased with the opportunity to pursue 

employment in that area. 

Unfortunately attracting doctors to the area has been extremely difficult for SOMC. In 

a story repeated all over Rural America, SOMC relies heavily on foreign doctors who 

travel to the US on special visas or domestic doctors who are able to work off heir 

medical school loans by working in an economically disadvantaged regions. In either 

case, doctors that do come do not tend to stay long. Administrators report that the 

problem in keeping and attracting doctors tends to have more to with these personnel’s 

families comfort level with the community. Families often complain about the lack of 

cultural and shopping amenities. The results can often result in delays in implementing 

the high-tech facilities that the center rightly prides itself on. For instance, hospital 

administrators report that the state of the art cancer facility sat unused for a full year 

before doctors could be attracted to the site. 

To make up for the difficulty in attracting doctors, SOMC relies on different 

strategies. One is using osteopaths to perform many tasks. The hospital maintains a 

strong working relationship with Ohio University in Athens and their college of 

Osteopathic Medicine. This relationship allows graduates of that institution to provide 

a pipeline of qualified practitioners who understand life in small town Appalachian 

Ohio and who are committed to staying and working in a community such as 

Portsmouth. 

While the hospital is growing and continued expansion plans are in the work, there are 

limits to using SOMC, and indeed health care as an economic growth strategy. There 

is heavy competition in the region for medical services, and while SOMC is the largest 

facility in the surrounding counties, it is not the only one. Adena Health Systems in 

Pike County draws significant numbers of patients. In addition, for specialized 

medical care, patients are likely to journey to national medical centers in Cleveland 

and Cincinnati. Staff estimate that 70 percent of the “heart market” leaves the county. 

The fact that 80 percent of patients at SOMC come from Scioto suggests that new 

money is not being brought into the county. 

Higher Education. Shawnee State is clearly an institution that has the potential to 

impact the growth of Scioto County. Founded as a community college, Shawnee State 

became a university in 1986. The college has 3,800 students and college offers both 

four year degrees and associate degrees, making an interesting hybrid of a community 

college and a university. As a university, Shawnee State is increasing the numbers of 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 15 

students who live on campus. Currently, nearly 500 students a year reside in campus 

housing and plans are in the works to expand these numbers. 

The college possesses several especially strong programs. Of special import to 

Scioto’s “new” economy, is a strong allied health curriculum. As mentioned, Shawnee 

State provides a significant number of the nurses to SOMC and the hospital and the 

university have close relationships. In addition to offering traditional programs, 

Shawnee State also innovative programs that are drawing national attention. For 

instance, the university offers one of the nation’s only programs in gaming. This 

program trains students in creating and marketing video games. The program attracts 

students not only from around Ohio but from states all around the country. 

Shawnee State does represent one area where the notion of Scioto County serving as a 

hub is a reality. Exhibit 2-9 shows the percentage of college students from 

neighboring counties who attend Shawnee State. As it shows, Shawnee is by far the 

preferred destination for students in these counties. 

Exhibit 2-9. OH Public University Attendance at Shawnee State 

Scioto 80.1% 

Pike 70.8% 

Adams 61.7% 

Lawrence 27.9% 

Jackson 26.4% 

Ross 15.2% 

Gallia 14.5% 

 

The university not only welcomes these students it, like many universities, is an 

engine of innovation. Several companies have spun out of activities at the university. 

For example, Yost Engineering, one of the most successful firms in the County, was 

founded by a former professor at Shawnee State. In addition to this spin off potential, 

the college offers training to companies in the area in a variety of areas including: 

Management and Human resources training 

Customer service 

Office skills such as using Microsoft programs 

A targeted industry program providing training for 201 employees in the 2004-5 

school year. Training was delivered at companies such as Mitchellace, Inc. 

Sun Coke and Scioto Plastics. 

The university has made an impact in the cultural life of the community as a hole. The 

university is responsible for the Verne Riffe Center for the Arts, a 1,140-seat 

performing arts center on campus. The Center brings national musical and performing 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 16 

arts to the area. These amenities are critical in attracting and just as important retaining 

families and young people to the area. 

Using Shawnee State as a growth strategy, however, is not without its problems. 

Foremost among these is the fact that Portsmouth is not structured right now as a 

traditional college town, or certainly not one that can cater to a large residential 

population. Several local officials and residents pointed out the lack of typical college 

student hangouts as just one example of how Portsmouth lacked amenities that could 

serve students and benefit from the dollars that they often spend. For instance, there is 

not a pizza place or a college-focused bar within walking distance of campus. 

Some individuals interviewed doubted whether or not Portsmouth really even wants to 

be a “college town. “They don’t want to be an Oxford, Ohio. They don’t want to be an 

Athens, Ohio.” 

There recently have been heated battles over the location of student housing, 

suggesting that not all county residents are comfortable with embracing a residential 

student population in the area. 

Manufacturing: Returning to Scioto County’s Roots. While Scioto County’s 

industrial mix has changed dramatically, leaders in the community have not 

abandoned what once was the heart of the region’s economy: a strong manufacturing 

base. Several steps have been taken to encourage the location of new industry in the 

county and making sure that companies that remain can prosper. 

The prime example of the community’s focus is the effort to redevelop a plot of land 

to host Sun Coke, Inc., a large steel manufacturer. Through grants through an 

empowerment zone project, Scioto County worked to turn a brownfield site into an 

area that could host an industry that provided good, high paying jobs. The site is not 

only home to Sun Coke but is the future home of large retail establishments such as a 

“big box” (e.g. Wal-Mart). 

A key in the location of Sun Coke was a regional approach to securing land for the 

facility. For example, Ohio Valley Regional Development Corporation gave Scioto 

County funds to provide sewer access for the plant. And although the plant was 

located in the small municipality of Franklin Furnace, representatives from 

communities throughout the county were involved in the development of the project. 

Indeed, regional cooperation is key to much of the manufacturing development that is 

occurring in the area. Another prime example is the focus on a facility in neighboring 

Pike County. Pike County as home to a large uranium enrichment plant that employed 

hundreds of workers, the majority of whom came from Scioto County. Community 

leaders in a variety of counties are cooperating through an organization called the 

Southern Ohio Development Initiative (SODI), to devise ways to turn this brownfield 

into a viable industrial site. Recently, USEC, Inc. announced plans to build a large 

centrifuge plant that will employ more than 500 workers, providing opportunities for 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 17 

workers throughout the region, including Scioto County. Although the workers will 

not be employed within the county limits, the presence of a qualified workforce in 

Scioto County was critical to the decision to build the facility. 

A qualified and experienced manufacturing employment base is a strength of Scioto 

County. Sun Coke, for instance, reported that they were very pleased with the quality 

of employees that came to their facility once they opened. Other manufacturing firms 

may also be attracted by this strong cadre of qualified individuals when making site 

selection decisions. 

Another strength that Scioto County has tried to capitalize on is its continued access to 

rail and river transportation. Industries that rely on these two transportation modes 

could be drawn to the county to do business. However, according to the director of the 

port authority, significant improvement to the system of dams and locks along the 

river need to be made to take full advantage of the river’s potential draw. 

A more critical barrier to large-scale industrial/manufacturing growth is the lack of 

free engineering space. “We don’t have 200 acres to show people,” one community 

leader said, saying that brownfield redevelopment as in the case of Sun Coke, is the 

only real place that manufacturing firms can be expected to open new facilities. The 

reason for the lack of new development space has more to do with geography than an 

unwillingness to open land for development. The county is extremely hilly and the 

presence of two rivers (the Scioto and Ohio) means much available space lies in flood 

plains. Some available land is owned by Norfolk and Southern which is reluctant to 

sell the land unless a company that is extremely rail dependent would be willing to 

purchase the land. 

Formal intervention. Scioto County is fortunate to have a number of economic 

development organizations that are committed to its growth. Foremost among these is 

the Ohio Valley Regional Development Commission (OVRDC), the local 

development district, headquartered across the border in Pike County. OVRDC 

continues to work closely with local officials to see how more industry and commerce 

can be attracted to the county. For instance, OVRDC provided funding to the County 

to make infrastructure improvements to ensure that Sun Coke would come to the 

region. 

The Commission is not alone in trying to promote Scioto as part of a regional 

renaissance. SODI, mentioned in the previous section, is an example of an 

organization that is looking for regional solutions to vexing problems facing the 

county. Perhaps the greatest cooperation among economic development players came 

through the enterprise zone/empowerment zone project in the New Boston Area. As 

discussed, the project brought together a wide range of community leaders to help 

convert what previously was a Brownfield site into one that can attract industry. 

Leaders in several counties help build a small business incubator in Pike County that 

is utilized by residents from Scioto County. In addition, the Southern Ohio Growth 

Partnership acts as a regional chamber of commerce, serving businesses within a 30 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 18 

mile radius of Portsmouth. 

Indeed, much of the formal economic development efforts underway concentrate on 

both ensuring that existing manufacturing can prosper and helping to attract new 

businesses to the area. In terms of attracting new businesses, area leaders recognize the 

importance of helping firms compete in an era dependent on information exchange. 

The Scioto County Economic Development Office is active in HighTech Ohio, an 

initiative that highlights information and technology based companies in the county. 

While these efforts at improving Scioto County are impressive, there is not a 

comprehensive economic development strategy in place for the county that stresses a 

single economic sector or set of sectors. Rather efforts are made to make sure that the 

county continues to adjust as it moves away from being solely dependent on 

manufacturing to an economy that depends more heavily on the health care and 

educational sectors. 

2.5 Lessons Learned 

Scioto County represents an interesting model for those Appalachian counties that are 

struggling to reinvent themselves. In this case, Scioto County is trying to emerge from 

an economic downturn related to the loss of manufacturing jobs, particularly in the 

steel industry. Several lessons emerge for counties facing similar challenges. 

Switching to a service-based economy is difficult. Scioto County’s economic future 

is increasingly tied into the service-based industries—particularly health care and 

higher education. Any community attempting to refocus itself will encounter 

difficulties. One of the main challenges is in providing a workforce that is equipped to 

handle this change in direction. Those trained in working in a manufacturing plant 

can’t necessarily turn on a dime to work in a hospital or university. Often service jobs 

pay less than do manufacturing which means that the average wage in Scioto County 

may not grow as vibrantly as once expected. Creating better paying service jobs are a 

challenge but one that needs to be explored. Certainly Shawnee State’s strong allied 

health education program is one way to steer residents into higher paying medical 

professions. 

Manufacturing should not be ignored. Of course, Scioto County recognizes that 

concentrating solely on service industries is problematic. These types of businesses, as 

mentioned, pay lower wages and bring less outside money into a community. Thus, 

Scioto County has tried, when possible, to ensure that the County’s strong 

manufacturing base is not ignored. Programs such as the Sun Coke project and the 

regional cooperation around the former uranium enrichment plant allow the County to 

keep a foothold in manufacturing. 

Build upon existing assets. Scioto County recognizes that although it has undergone 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 19 

significant economic distress it does have something going for it. Foremost among 

these are access to rail and river transport. County leaders recognize that while the role 

of the Ohio River may not be what it once was, for certain industries it remains a 

critical component of trade. Industrial recruitment efforts promote the area’s river 

access—something that could draw more heavy industry to the county. 

The County is also recognizing that the more modern transportation choice of road 

access is a continued problem for the County. Accordingly, leaders are pushing for a 

by-pass that would provide better four-lane access through the County. The belief is 

that this will encourage development in the County and perhaps increase the County’s 

ability to attract consumers from neighboring areas. The proposal is not without its 

detractors of course. In particular, those who want to see a rebirth of downtown 

businesses worry that a bypass on the outskirts of town would further cripple retail 

traffic downtown. 

Creating a regional hub is problematic. This case study began with the hypothesis 

that Scioto County served as a regional hub for its neighbors. Ground truthing that 

belief revealed that the County does not draw significant business, consumers or 

workers from surrounding counties. The county faces special challenges due to the 

relative proximity of larger urban centers, which border Scioto County’s neighbors. 

This siphoning of periphery demand from an older, limited scale core economy to a 

larger, extra-regional metro area is an example of adverse spillover effect. Improved 

transportation access at the periphery and emanating away from the core facilitates 

this economic displacement. It is perhaps unrealistic to believe that Portsmouth could 

draw individuals away from the bright lights of Columbus or Cincinnati. What Scioto 

County can do, and has in many cases, is to make sure that it retains the local market 

within the county. For instance, SOMC ‘s expansion goals are to capture as much of 

the Scioto County patient population as possible. The cancer center at SOMC was put 

into place because more than 75 percent of cancer patients in Scioto County were 

traveling elsewhere for treatment. 

In any case Scioto County is looking regionally for solutions through cooperation with 

the Ohio Valley Regional Development Commission and other economic development 

entities in the area. Other efforts include an active program that promotes better 

internet access in Appalachian Ohio, a shared incubator space, and continued 

cooperation around the uranium enrichment plant. The leaders in the county recognize 

that if the County is truly to be reborn it is going to take more than just the residents of 

Scioto County it will take regional cooperation and linkages with Appalachian Ohio. 

2.6 Interviewees 

• Elizabeth Blevins, Community Relations Director, Southern Ohio Medical Center 

• Steve Carter, Director, Scioto County Economic Development Office 
Vol.2 Case Studies of Economic Growth Processes Scioto County 

Sources of Growth in Non-Metro Appalachia page 20 

• Craig Gilliand, Administrative Director of Financial Support and Facilities, 

Southern Ohio Medical Center 

• Jason Gillow, Research/Planning Supervisor, Ohio Valley Regional Development 

Commission 

• Steve Gregory, Office of Career Services, Shawnee State University 

• Jennifer Hanlon, Director of Community Development, City of Portsmouth, 

Sondra Hash, Managed Care Manager, Southern Ohio Medical Center 

John Hemmings, Assistant Director, Ohio Valley Regional Development 

Commission 

• Robert Huff, President, Southern Ohio Growth Partnership 

• Jim Kalb, Mayor, City of Portsmouth 

Virginia Moore, Director University Outreach Services, Shawnee State 

University 

Greg Simonton, Executive Director, Southern Ohio Diversification Initiative 

Jeff Spencer, Executive Director, Ohio Valley Regional Development 

Commission 

Bob Walton, Director, Scioto County Community Action Program/ Southern 

Ohio Port Authority 

Susan Warsaw, Director of Development, Shawnee State University
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 21

3 CHAUTAUQUA COUNTY, NY:

MANUFACTURING DIVERSIFICATION

3.1 Introduction 

Chautauqua County, NY has been maintaining its transitional status despite continued 

adverse forces tied to structural adjustments in U.S. manufacturing. Attraction of jobs 

in transportation equipment manufacturing is now serving to anchor the regional 

economy along with impetus of HUD Renewal Community status, various enterprise 

zones, and attempts to diversify/foster entrepreneurial development around tourism. 

Chautauqua County presents an interesting case study as a region in transition. For 

much of the twentieth century, a strong manufacturing sector that relied on an 

established transportation network, a well-trained blue collar workforce, and the 

county’s natural resources defined the region’s economy. Over the past twenty years, 

the manufacturing sector has declined sharply as a result of global competition, 

changing consumer tastes, and technological advances. In the face of significant job 

loss in the manufacturing sector, the County has transitioned, with some success, to a 

more diversified economy. It retained some of its manufacturing base through 

specialization and targeted economic development programs, and successfully 

expanded its base to include a growing tourist industry characterized by expansion in 

the service and retail sectors. The lessons learned by the County and its strategies for 

addressing its changing economy offer useful insights for other regions in Appalachia. 

3.2 Regional Profile 

Chautauqua County is located in far western New York State, directly south of 

Buffalo. It is bordered by Lake Erie and Pennsylvania to the west and Pennsylvania to 

the south. It is part of the Southern Tier West planning area along with the counties of 

Allegany and Cattaraugus. The county encompasses 1,062 square miles Exhibit 3-1. 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 22

Exhibit 3-1 Chautauqua County’s Location Within New York State 

Chautauqua is a rural county, with a population density of 131.6 persons per square 

mile. As shown in Exhibit 3-2, , the U.S. Census reports that 139,750 people lived in 

the County in 2000, a decrease of 5.1% since 1970. In comparison, New York State’s 

population increased by 4.1 percent over the same period. Census estimates for 2003 

indicate a continued decline in population of Chautauqua County to 137,645. 

Jamestown, with a population of approximately 31,000 people, is the County’s largest 

city. Dunkirk, located on the shores of Lake Erie, had a population of 13,131 in 2000 

and is the County’s only other city. The remainder of the County includes 27 towns 

and 15 villages. The Jamestown-Dunkirk-Fredonia area is the region’s designated 

micropolitan area. The region’s business and industry is concentrated around its two 

cities. Farmland and forests characterize much of the remainder of the County. 

Exhibit 3-2 Population Change, 1970-2000 - Chautauqua Co. and NY State 

Population 1970 1980 1990 2000 1970-80 1980-90 1990-2000 1970-2000

Chautauqua County 147,205 146,925 141,895 139,750 -0.2% -3.4% -1.5% -5.1%

New York State 18,236,967 17,558,072 17,990,455 18,976,457 -3.7% 2.5% 5.5% 4.1%

Source: US Census Bureau.

Population % Change

Historically, the geography, climate, and natural resources of Chautauqua County have 

shaped its economy. For over two hundred years, the rich soils have supported 

farming, and the microclimate along Lake Erie have proven ideal for growing grapes. 

The agricultural and viticulture industries in turn spawned a robust food processing 

industry in the County. The region boasts large areas of hard wood forests that have 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 23

supported the development of both wood products and furniture manufacturing 

industries. The primary and fabricated metals industries grew up along the Lake Erie 

Coast where the Great Lakes shipping lanes and rail road lines provided cheap and 

easy transportation access to the automobile manufacturing centers in Michigan. Tool 

and dye, machinery, and transportation equipment manufacturers located in the area to 

both support the metals industries and to have easy access to their products. 

These manufacturing industries remain important to the economic health of 

Chautauqua County, although the region has struggled in the face of plant closures and 

employment contractions. Exhibit 3-3 shows that manufacturing employment 

represented 31.3 percent of all jobs in the County in 1970. While manufacturing still 

accounts for 19.3 percent of the County’s employment, manufacturing jobs declined 

by over 24 percent between 1970 and 2000. 

Exhibit 3-3 Employment Change by Industry, Chautauqua County, 1970-2000 

1980 1990

EMPLOYMENT % of Tot. % of Tot.

Farm employment 3,070 5.1% 3,470 2,890 2,460 3.3%

Agricultural Services 280 0.5% 440 680 830 1.1%

Mining 30 0.1% 540 510 280 0.4%

Construction 2,290 3.8% 2,220 3,180 3,010 4.1%

Manufacturing 18,770 31.3% 18,120 15,400 14,190 19.3%

Transportation, Comm., PU 2,640 4.4% 2,320 2,460 2,840 3.9%

Wholesale Trade 1,830 3.1% 2,610 2,440 2,340 3.2%

Retail Trade 9,570 16.0% 10,290 12,780 13,240 18.0%

FIRE 3,710 6.2% 3,920 2,860 3,200 4.3%

Services 8,280 13.8% 13,190 17,730 21,100 28.6%

Government 9,480 15.8% 10,270 10,530 10,220 13.9%

Total 59,950 100.0% 67,390 71,460 73,710 100.0%

% CHANGE 1970-80 1980-90 1990-2000 1970-2000 1980-2000

Farm employment 13.0% -16.7% -14.9% -19.9% -29.1%

Agricultural Services 57.1% 54.5% 22.1% 196.4% 88.6%

Mining 1700.0% -5.6% -45.1% 833.3% -48.1%

Construction -3.1% 43.2% -5.3% 31.4% 35.6%

Manufacturing -3.5% -15.0% -7.9% -24.4% -21.7%

Transportation, Comm., PU -12.1% 6.0% 15.4% 7.6% 22.4%

Wholesale Trade 42.6% -6.5% -4.1% 27.9% -10.3%

Retail Trade 7.5% 24.2% 3.6% 38.3% 28.7%

FIRE 5.7% -27.0% 11.9% -13.7% -18.4%

Services 59.3% 34.4% 19.0% 154.8% 60.0%

Government 8.3% 2.5% -2.9% 7.8% -0.5%

Total 12.4% 6.0% 3.1% 23.0% 9.4%

Source: CEDDS Volume II, 2002 Woods and Poole Economics, Inc.; EDR Group

1970 2000
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 24

Exhibit 3-4 displays the industry mix (location quotients) by industry for Chautauqua 

County compared to both New York State and the nation. The location quotients 

confirm that the region retains a concentration of manufacturing jobs, particularly in 

the food processing, furniture, fabricated metals, and machinery manufacturing 

categories. The continued viability of these industries in the County has been dictated 

by their ability to adapt to global competition; the availability of a well-trained, loyal 

workforce; and efforts by the economic development community to provide assistance 

and incentives. Some have faired better than others. 

As the manufacturing sector has struggled and lost employment, the tourism industry 

has expanded in the County. The County’s bucolic landscape and natural features such 

as Lake Chautauqua have helped attract visitors, as have successful efforts to develop 

destination attractions within the County. Growth in the tourism sector has helped 

mitigate job loss in manufacturing, and in recent years, the economic development 

community has recognized that tourism can provide an alternative source of job 

growth for the workforce. 

3.3 Evolution of Progress 

Despite contractions in important manufacturing sectors, the county has had some 

success reinforcing existing industries such as primary and fabricated metals, 

transportation equipment, food processing and wood products. The region is also 

beginning to look to recreation and tourism assets to further diversify its economy. 

Manufacturing 

Primary and Fabricated Metals. The fabricated metals industry dominated the 

economy of Chautauqua County communities such as Dunkirk for several decades in 

the mid-1900s. Proximity to Detroit’s auto manufacturers provided by cheap water 

and rail transportation supported the development of the steel and related industries all 

along the shores of Lake Erie. 

Beginning in the early 1980s, international competition and resistance to the strong 

union workforce led to a sharp decline in the metals industries. Several businesses 

contracted or closed, including Roblin Steel and Alumax Extrusions. Empire Specialty 

Steel (formerly Al Tech Steel), which was once employed 800 people and was one of 

the largest steel manufacturers in the County, closed its doors in 2001 after several 

years of decline. Employment in the primary metals industry declined by 25.2 percent 

between 1997 and 2002, while employment in the fabricated metals sector declined by 

2.4 percent over the same time period. Reductions in these industries, and the impact 

of these reductions on the County’s local tool and dye and machinery manufacturers, 

account for a large percentage of the decline in the manufacturing sector in recent 

decades. 
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Exhibit 3-4. Industry Mix – Chautauqua County Compared with the State & U.S. 
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Despite the sharp decline in the metals industries, both the private and public sectors 

remain determined to retain these important components of the economic base. After 

the closure of Empire Specialty Steel, the state took possession of the facility and 

worked diligently with the local economic development community to find a buyer for 

the plant. In 2002, Universal Steel, a Pennsylvania-based company purchased the 

facility, and opened Dunkirk Specialty Steel on the site. The firm streamlined 

production, hired 100 employees, and is now producing steel in Dunkirk, although at a 

much reduced level. Some additional metal fabricators have managed to remain in 

operation in Chautauqua County by identifying specialty markets such as galvanized 

rebar. Dawson Metals, a locally owned firm, has developed a niche making steel doors 

for clients including the Mayo Clinic and US Senate. Another manufacturer has 

developed a specialty market producing metal door handles. In 2002, the fabricated 

metals industry still accounted for four percent of the County’s employment (2,645 

jobs), making it the fifth largest employment sector in the region (see Exhibit 3-5). 

Exhibit 3-5. Top Five Employment Sectors in Chautauqua County, 2002 

Transportation Equipment. The transportation equipment industry in Chautauqua has 

managed to remain viable, primarily as a result of continued expansion at two major 

employers. Cummins, Inc., an international manufacturer of diesel engines, 

consolidated its mid-west operations in 2002, shutting its plant in Indiana and 

expanding employment at its Lakeville site. The firm is now the fifth largest employer 

in the County with 1,020 employees, and is expected to increase employment to 1,250 

before the expansion is completed. The firm chose to expand operations in Jamestown 

because of the well-trained workforce, low energy rates ($0.04 per kilowatt hour) 

available through the municipal utility (the remainder of the County’s energy is 

provided by a private firm, and is more than double the cost of competing regions), 

and tax incentives available through the Greater Jamestown Empire Zone. 

Truck-Lite Co., Inc., a manufacturer of vehicle safety lighting, opened in Jamestown 

50 years ago. The firm, which is now a subsidiary of Penske, retains it headquarters 

and a manufacturing plant in Chautauqua County, in addition to several plants 

throughout the world. The firm has 550 employees in Chautauqua County. The 
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quality of the workforce and good union relationship are two important reasons why 

the firm remains in the County. It is located in the Greater Jamestown Empire Zone 

and has taken advantage of the employee tax credits available through the Empire 

Zone as it has expanded. Because of expansion at these two facilities, employment in 

the transportation equipment sector increased by 3.4 percent between 1997 and 2002. 

Food Processing. The County retains a strong food processing industry in the 

northern part of the County, even though today much of the raw product used in it 

manufacturing facilities is imported from out of state or abroad. The ability to quickly 

ship produce around the world for processing, as well as wage competition from other 

regions have created challenges for the food processing industry in Chautauqua. Kraft 

Foods closed its Chautauqua Operation, and within the past year, Welch’s 

significantly reduced its operations at its two facilities in the County. Despite these 

challenges, employment in the food products industry grew by 5 percent between 1997 

and 2002. Two off-label food processing companies, the locally-owned Cliffstar 

Corporation (635 employees) and the nationally-owned Carriage House Companies 

(793 employees) anchor these sectors in the County, producing products such as juice, 

ketchup, and peanut butter. The presence of these two off-label manufacturers proved 

important during the recent national economic downturn, as consumers increase their 

purchases of lower priced off-label goods when the economy falters. 

Other major food processors include Fieldbrook Foods (400 employees locally), the 

second largest producer of ice cream on the east coast. Fieldbrook Foods bought out 

the locally-owned Dunkirk Ice Cream Company in 1996. Another major food 

processing firm in the northern part of the region is Nestle Purina PetCare Company 

(270 local employees), which manufactures pet food in the Dunkirk area. Nestle 

Purina recently completed a $90 million expansion of its operations, and purchased 

more than 50 acres of land to accommodated future expansion plans. The Empire State 

Development Corporation has given a large grant to the firm to finance an electrical 

substation to reduce energy costs. 

In 1999, the County Industrial Development Agency invested in four industrial parks, 

including the Chadwick Bay Park in the Dunkirk-Sheridan Empire Zone. The County 

developed a spec building in the Park aimed at attracting businesses that support the 

food processing industry. In 2002, Grafco PET Packaging Technologies, which 

manufactures containers and bottles for the food processing industry, built a 120,000 

square foot manufacturing facility in the Chadwick Bay Industrial Park in close 

proximity to the existing food manufacturers. The firm located in Dunkirk to “better 

serve its northeastern and Canadian markets” and because of the well-trained 

workforce. The plant supplies both Carriage House and Cliffstar. The plant employs 

over 300 people. The economic development community believes that Grafco will be 

an important factor in retaining and growing the region’s food processing industry. 

Wood Products. The southern part of the County around Jamestown is noted for its 

hardwood forests, which supported vibrant wood products and furniture industries for 

many years. The wood products industry has faltered in recent years, decreasing 
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employment by 5.1 percent between 1997 and 2002. The decline in the County’s wood 

products industry is a result of changes in consumer tastes and competition from other 

regions with cheaper labor costs. Today, much of the high end furniture market, which 

once used the high quality hardwoods grown in Chautauqua County, now uses exotic 

woods from Asia and South America. Less expensive furniture is now manufactured 

using laminates and lesser quality woods grown elsewhere. 

Although the County’s furniture industry has faced strong competition from abroad 

(particularly Asia and the southern United States) and the closure of several plants 

including two Ethan Allen facilities, employment in the furniture and related products 

manufacturing sector has managed to remain stable (+0.8% between 1997 and 2002). 

The industry employs 2,783 people (4.2 percent of all jobs), ranking fourth among 

Chautauqua County industries. The importance of this industry to the region is 

underscored by its 19.16 location quotient compared to New York State, and 11.46 

compared to the nation. 

To compete, the County’s furniture manufacturers have developed niche markets. 

Bush Industries has moved away from using the high quality woods grown in the 

region to manufacturing pieces made from composites. The firm, which opened in 

Jamestown in 1959, is the third-largest employer in the County, with 1,249 employees 

in 2005. The firm has branch plants in several locations around the world. The locallyowned Crawford Furniture manufactures reproduction pieces in the Stickley style. 

Greco, a national firm, makes baby furniture, and Cold Craft manufactures conference 

furniture. The ability to identify and create specialty products has helped the furniture 

sector survive in the County, although according to the region’s economic 

development professionals, the industry continues to struggles to remain competitive. 

The decline in manufacturing employment in the County has certainly raised concerns 

about the economic future of the region. In particular, many of the jobs lost were in 

strongly unionized sectors that provided good wages for a skilled workforce. 

However, the ability of the County to retain and attract some new firms in its 

traditional industries through specialization and streamlining provides some 

encouragement that the region can maintain, through creative approaches and targeted 

economic development programs, a solid manufacturing sector. 

Tourism. Chautauqua County has mitigated job loss in the manufacturing sector 

through diversification into other sectors. In 1970, the service sector accounted for 

13.8 percent of jobs in the County, and retail jobs made up 16 percent of the 

employment base. In 2000, these industries accounted for 28.6 percent and 18 percent 

of the County’s jobs, respectively. Food service and drinking establishments employed 

4,874 people in 2002, ranking it second among employment sectors in the County. 

The strength of the retail and service sectors is attributable to growth in the tourism 

industry, which has tapped the region’s natural resources, landscape, and history 

create destinations and activities to attract tourists. 

The cornerstone of the County’s tourist attractions is the world-renowned Chautauqua 
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Institution (CI), founded in 1874 as a religious retreat on Lake Chautauqua. The CI is 

a unique community built on 750 acres of land. The gated community includes 1,200 

properties, including 300 year-round residents, hundreds of summer homes, a 160 

room hotel, and a 5,500 capacity amphitheater. Home values within the gates have 

soared and have anchored an escalating interest in summer homes in Chautauqua 

County. The CI is currently expanded, purchasing several acres of land adjacent to the 

facility and adding 32 housing lots. The Institution’s nine-week summer season 

features over 2,000 programs including lectures, theater, opera, symphonies, and other 

activities, which attract over 150,000 visitors (both day trippers and overnight guests) 

to the region. The CI is currently seeking to expand its draw beyond the summer 

season by marketing the facility for conferences, reunions and other events. 

The Peek’n Peak Resort and Conference Center is a four season destination that 

includes two golf courses and 27 trails of downhill and cross country skiing. Visitors 

come from surrounding states and Canada to use these facilities. The resort recently 

constructed 150 condominiums on-site. Golf Digest recently recognized the region as 

an outstanding golfing destination. Snowmobile clubs in the County also help promote 

winter tourism by maintaining hundreds of miles of trails, and summer tourist 

activities include fishing in Lake Erie and Chautauqua Lake. The grape growers and 

wine producers have developed a “wine trail” along Routes 5 and 20 near Lake Erie, 

which attracts visitors from surrounding states and Canada. The region is also 

attracting cyclists because of its rolling hills, scenic landscape and roads with wide 

shoulders. 

Chautauqua Lake has long drawn visitors from outside the County, including Ohio, 

Pennsylvania and Ontario. Attractions have included Bemus Point amusement park 

and numerous beaches along the shores of the Lake. The Lily Dale Assembly, a 

spiritual retreat on the eastern shores of the Lake attracts several thousand visitors 

during the summer. In recent years, interest in second homes around the Lake has 

increased, with real estate values increasing at a fast pace. The economic potential of 

the Lake area as a tourist attraction is demonstrated by the interest of at least one 

outside investor, who has purchased and opened four restaurants in the past few years. 

Attractions have also been built around the reputations of the region’s famous sons 

and daughters. Jamestown houses the Lucille Ball-Desi Arnaz Center and Museum, 

and hosts two Lucy-Desi festivals each year. The Roger Tory Peterson Institution of 

Natural History, the Robert Jackson Center, and the Fenton History Center also 

celebrate the lives and legacies of famous Jamestonians. Other attractions in 

Jamestown include the recently-completed ice arena, funded by the Gebbie 

Foundation. The arena successfully hosted the junior national ice skating 

championships in 2004, and expects to draw additional competitions in the future. A 

Best Western Hotel is under construction adjacent to the arena. 

The interest of the region’s economic development community in growing its tourism 

industry has emerged in recent years. Jamestown currently is preparing a downtown 

urban design plan that will recommend the city pursue the development of a tourist 
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attraction that can attract up to 100,000 people per year. The city is developing its 

river-walk, and has applied for federal transportation funds to redevelop its train 

station. In the Dunkirk area, an investor is promoting the creation of the Lake Erie 

Heritage Museum, focusing on the shipwrecks that have occurred on the lake 

throughout its history. In addition, the tourism industry is currently developing an 

agricultural trail that will highlight area farmers’ markets, seasonal farm stands, and 

the County’s maple syrup producers. After many years of ignoring the industry, the 

County government now provides funds to the Chautauqua County Visitors’ Bureau 

for marketing and development. 

Two recent changes are expected to heighten the appeal of Chautauqua County as a 

tourist destination. First, the upgrade of the Appalachian Development Highway 

corridor T (also known as US 17 and designated as I-86 in the upgraded sections) to 

Interstate standards has dramatically improved access to the region. Although some 

businesses expressed concern that these improvements would simply facilitate the 

exodus of people from Chautauqua to shopping destinations in tax free Pennsylvania, 

there are indications that the highway is helping to bring more people to the County 

for recreation. There are two new hotels in Jamestown near the highway interchange, 

and representatives of the tourist industry report that visitation to the County’s 

attractions is increasing. The tourism community would also like to utilize the two rest 

areas along the Interstate to better promote the region’s attractions. 

Second, swayed by considerable lobbying by the Chautauqua County Chamber of 

Commerce, the County legislature voted this year to allow the County to abolish its 

sales tax, reducing the overall sales tax from 8.5 percent to 4.5 percent. Retailers and 

economic development professionals anticipate that this reduction will help dissuade 

residents from leaving the County to shop in Pennsylvania. 

Industry-setting Labor Markets. Because manufacturing dominated the economy of 

Chautauqua County for so many years, the region developed a highly skilled blue 

collar workforce with a strong work ethic. Despite the decline in manufacturing jobs, a 

blue collar “culture” continues to dominate the workforce. Many families have passed 

down to their children the expectation that they will work in manufacturing, a goal 

which has become harder to achieve as the sector has declined. Additionally, the 

earning potential of the blue collar workforce has decreased as manufacturing firms 

have been forced to cut wages to remain competitive in the global marketplace, and 

jobs in the tourism industry typically pay lower wages than manufacturing jobs. 

Prior to the decline of the metals industries, Chautauqua County was known as a 

strong union area. Today, the strength of the unions has declined, as is evidenced by 

the $10/hour wages offered at the newly opened Dunkirk Specialty Steel. Some 

economic development professionals believe that the history of union activity in 

Chautauqua and the proximity of the region to Buffalo, a once-strong union town, 

continue to hinder the County’s ability to attract new firms. The County’s economic 

development community often tries to downplay its proximity to Buffalo when 

courting new manufacturers to the region. 
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In 2000, 91,261 Chautauqua County residents were over the age of 25, and the labor 

force participation rate was 61.4 percent. Just over eighty-one percent of the labor 

force had at least a high school education, compared to 45.6 percent in 1970. The 

percentage of the labor force with a four-year college education increased from 7.5 

percent in 1970 to 16.9 percent in 2000. Comparatively, 27.4 percent of the New York 

State workforce has at least a four-year college education. 

Chautauqua County suffers significant out-migration of its young, working age 

population. According to researchers at the Center for Rural Regional Development 

and Governance at the State University of New York at Fredonia (SUNY-Fredonia), 

young people between the ages of 15 and 29 account for more than 50 percent of the 

out-migration from the three counties that make up the southern tier west area of New 

York State. Further, the 2000 US Census reports that the Jamestown-DunkirkFredonia Micropolitan Statistical Area suffered the highest rate of out-migration of 

young, single, educated people (-344.8) in New York State. This compares to a state 

out-migration rate for this population group of -11.3. Economic development 

professionals report that the out-migration is particularly pronounced for the college 

educated population, and while many businesses can find skilled workers for factory 

jobs, they report difficulty filling vacancies for professional-level jobs. 

The Chautauqua County workforce primarily works within the County. The average 

travel time to work is 18.4 minutes, compared to a nation average of 25.5 minutes. 

Entrepreneurship. Economic development professionals in Chautauqua County 

indicated that there is some entrepreneurial activity in the County, but that success is 

limited. Those who are successful generally are professionals who have identified a 

specialty niche market, and spin off from a larger manufacturing establishment. There 

is also a growing number of people who grew up in the region returning to 

Chautauqua County to raise families. A small number of these people have developed 

internet-based businesses that allow them to effectively serve their clients from a 

location in Chautauqua County. However, entrepreneurship is not a major contributor 

to economic growth in the region. 

The success of entrepreneurial activity in the County can be measured by the ratios of 

the number of proprietors in the County to the number of wage and salaried worker, 

and the income of proprietors to wage and salaried workers, as well as changes in 

these ratios over time. Exhibit 3-6 shows that the ratio of proprietors to wage and 

salaried workers in Chautauqua County in 2003 was 0.238, a 6.9 percent increase 

since 1998. However, the ratio of non-farm proprietor income to wage and salaried 

employee income was only 0.083, and this ratio declined by 31.9 percent between 

1998 and 2003. These data indicate that while the number of people employed as 

proprietors is increasing, the income of these proprietors relative to the rest of the 

work force is declining. Further, the wages of proprietors in 2003 was considerably 

lower than that of wage and salaried employees. These trends suggest that, overall, 

entrepreneurial activity in Chautauqua County is not occurring in response to 
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perceived opportunities for economic growth, but instead people are becoming selfemployed due to necessity (such as job loss.) 

Exhibit 3-6. Entrepreneurial Activity - Chautauqua County, New York 

Educational Institutions. The County’s three institutions of higher education also 

play a role in promoting and supporting economic growth. The State University of 

New York at Fredonia has a reputation for high academic standards. The university 

regularly partners with the business community and public sector to provide technical 

assistance and improve the business climate in the County. The Center for Regional 

Development and Governance was created eight years ago to provide assistance to the 

26 cities and towns in the County on ways to reduce the cost of government as well as 

the cost to do business in the County. The Center has been involved with the strategic 

planning study in Jamestown, and an evaluation of the impacts on Dunkirk’s tax base 

of allowing the NRG power station to provide a payment in lieu of taxes (PILOT) to 

the city of Dunkirk. The Center recently developed a masters degree program in 

accounting program to help serve the needs of Chautauqua County businesses. A 

computer science program is under development to help attract high technology firms 

to the area, and the Center is exploring the creation of an MBA program. 

The University has been working with the Dunkirk-Sheridan Empire Zone for the past 

five years on an effort to create a high technology incubator in Dunkirk. The 

university recently earmarked $5 million to be used for the construction of the 

incubator, although operating funding is still being sought. Supporters are currently 

defining the target market for the facility. One niche under consideration is computerbased food technology applications that can support the food processing industry. The 

University is already involved with a project to evaluate opportunities for technology 

transfer between the University and the food processing industry. 

Jamestown Community College offers two-year liberal arts associates degrees. Many 

of the College’s students transfer to four-year colleges after graduation. The College 

also offers technical and career programs, and part-time study programs for job skill 

and cultural enrichment. The college has grown from an enrollment of 169 students at 
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the local high school to over 4,000 students at multiple campuses in the Southern Tier 

region. Local businesses work with the college to design job training programs 

specific to their individual business needs. The Manufacturing Technical Center at the 

College provides training for high technology and machinery industries in the County, 

and works with businesses to develop courses specific to their needs. 

Jamestown Business College offers associate degrees and certificate programs in a 

variety of business fields. The College offers coursework in marketing and 

management, information technology, entrepreneurship, accounting, and medical and 

legal fields. 

3.4 Catalysts of Change 

Prior to the 1980s, the people of Chautauqua County proudly identified their region as 

a manufacturing stronghold. For the most part, the region’s economic development 

efforts were aimed at supporting the manufacturing sector. The region boasted a 

workforce well-trained to serve the region’s metals, food processing, furniture, and 

related industries. Businesses prospered, employment and wages increased, and the 

economy grew. 

In the 1980s, regions within the United States and abroad began courting 

manufacturers with a lower wage work force, lower energy costs, and favorable tax 

structures. Chautauqua County found itself significantly handicapped by the high 

union wages that predominated, energy costs more than double other areas of the 

Country, and the notoriously high corporate, employee, and income tax rates in New 

York State. Further, innovations in shipping allowed for perishable items to be 

transported internationally, and reduced the costs of shipping bulky items overseas, 

thus improving the ability of foreign firms to serve American markets. Next day air 

services produced options for quickly delivering parts, equipment and products to 

markets around the world. Some analysts have suggested that environmental 

regulations within the United States increased manufacturing costs in the states 

relative to off-shore locations, although the evidence is mixed. In the 1980s, many 

analysts argued that while overseas companies eagerly adopted new production 

methods and innovations that produced high quality products more cheaply, American 

manufacturers failed to do so. 

Many if no all of these factors may have contributed to the decline of the 

manufacturing sector in Chautauqua County. Because the County’s identity was so 

closely tied to manufacturing, the workforce, business community, and government 

were all slow to respond to the structural shifts occurring in the economy. Instead, the 

region expanded efforts to retain the existing economic base rather than exploring 

opportunities to diversify and change. Further, significant competition existed between 

the northern and southern parts of the county, as well as between individual 

jurisdictions. The various organizations working to grow the economy operated 
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individually, and often in competition with each other to attract businesses. According 

to several economic development professionals in the region, the region needed to 

suffer through the considerable economic upheaval of the 1980s and 1990s before it 

was ready to embrace economic change and work together as a region to diversify and 

grow the economy. 

Today, the County has recast its image from being predominantly a manufacturing 

center. After years of refusing to acknowledge that tourism and service-based 

businesses could provide an important component of an economic development 

strategy, the economic development community has now embraced tourism as central 

the region’s future growth. The community also is reevaluating its assets to identify 

additional areas for growth and development, such as the development of a 

distribution center in Ripley, where Interstates 79, 90, and 86 converge. Furthermore, 

the business and economic development communities have moved from a territorial 

approach to economic development to embrace a team approach to economic growth. 

Strong leaders have emerged from both the business and public sectors to provide a 

strong voice for change both at home and in Albany. These changes in attitude and 

strategy form the basis of the County’s successful transformation to a more 

diversified, stable economy. 

A Strong Framework for Economic Development 

As Chautauqua County’s economy contracted over the past twenty years, the number 

and strength of economic development organizations in the County has increased. 

Furthermore, these organizations have dropped parochial attitudes and come together 

to attract businesses to the region instead of competing among themselves to attract 

firms. Collaborations include: 

• A partnership between the County Industrial Development Agency, the City of 

Jamestown, the two Empire Zones, the for-profit Buffalo Niagara Enterprise 

(responsible for business attraction and marketing), the Westfield Development 

Corporation, and the Workforce Investment Board. This group meets regularly 

to discuss strategies for business retention and growth. 

• The consolidation of the North county and South County Chambers of 

Commerce into a single Chamber representing the entire County. 

• The participation of the Chamber in the Committee for the Future, a superregional group of business and public sector leaders representing Chautauqua, 

Cattaraugus, Allegany, and Munroe Counties. 

• A partnership between the four Empire Zones in the Southern Tier Region, 

facilitating the sharing of leads and information. 

The strength of these local efforts at collaboration have been rewarded with state and 

federal programs and grants aimed at stabilizing the economy in light of losses in the 

manufacturing sector. Organizations involved in economic development range from 

super regional organizations to local community development agencies to business 
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organizations and private foundations. Some of the most active organizations involved 

in economic development in the County and their roles are described below. 

• The Southern Tier West Regional Planning and Development Board 

(STWRPDB) provides economic development assistance to Chautauqua, 

Cattaraugus and Allegany Counties. This regional agency produces the annual 

Regional Economic Development Strategy, which is funded by the US Department 

of Commerce Economic Development Agency (EDA), and the Appalachian 

Regional Commission (ARC). The document includes an analysis of economic 

trends, an evaluation of the region’s successes in implementing programs and 

projects aimed at improving economic conditions in the region, and a strategy 

achieving economic growth in future years. The strategy is participatory, and 

includes input from other economic development agencies, the communities 

within the region, educational institutions, businesses, and other interested parties. 

As the conduit for EDA and ARC funding in the region, STWRPDB oversees 

major infrastructure and development projects funded by these federal agencies. 

STWRPDB also works on major regional initiatives, such as the purchase of 180 

miles of rail lines serving the Southern Tier, and the reinstitution of rail service in 

the region. The agency also works with other economic development groups 

throughout the county to attract and retain businesses. 

• Chautauqua County Industrial Development Agency (CCIDA) provides 

financial assistance to area businesses through two revolving loan funds, industrial 

development bonds, and tax leases. The CCIDA also provides training for area 

businesses and works with area businesses to retain jobs. The Agency manages 

four industrial parks in the County, and was instrumental in the development of 

two speculative industrial buildings at two of the parks, an aggressive initiative 

that has proven successful in attracting new firms to the County. 

• The Greater Jamestown and Dunkirk-Sheridan Empire Zones provide tax 

incentives to certified businesses located with the boundaries of the zones. 

Incentives include wage tax credits for newly created jobs, real property tax 

credits, sales tax exemption, employment incentive credits, business tax 

reductions, and infrastructure loans. Over 150 businesses have been certified to 

participate in the Empire Zone programs. 

• The City of Jamestown Development Office works within the City to attract, 

retain and grow businesses. The department provides planning, zoning, and 

building inspection services, and administers entitlement programs such as 

Community Development Block Grants. The Development Office completed a 

consolidated plan for the City, as well as a Downtown Commercial 

Redevelopment Plan. An urban design plan and a traffic plan are currently 

underway. A key to the successful development of these plans has been 

participation by community leaders such as the president of the community 

college, the executive director of the hospital, and business and civic leaders. The 
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agency has been successful in obtaining grants for these planning studies, as well 

as a Brownfields Redevelopment Grant to inventory the existing industrial 

buildings in the City and assess what is needed to clean up the sites. The 

Jamestown Local Development Corporation is housed within the Development 

Office, and provides $10,000 to $350,000 loans to local businesses through its $6 

million revolving loan fund. 

• Chautauqua Opportunities for Development, Inc. (CODI) provides assistance 

to micro-enterprises with five or fewer employees. CODI provides loans of up to 

$15,000 to businesses that are unable to secure traditional financing. CODI’s 

clients are generally retail and service businesses. The program started in 2000, 

capitalized through HUD’s Small Cities Program. The Small Business 

Development Center at Jamestown Community College provides management and 

technical assistance to small businesses throughout the County. 

• The Chautauqua County Workforce Investment Board (CCWIB) helps match 

businesses with workers. CCWIB works with businesses to train existing and new 

employees to keep area businesses competitive. CCWIB recently worked with the 

developer of a proposed distribution center in Ripley to ensure that the region 

could supply the 1,000 plus workers expected to be needed at the facility. The 

Manufacturing Training Institution at Jamestown Community College also 

provides workforce training tailored to the needs of specific businesses. 

• The Chautauqua County Chamber of Commerce works to promote business in 

the County in several ways. It provides a networking opportunity for businesses 

throughout the County to come together to discuss economic development issues 

and develop strategies to address these issues. Further, the Chamber has evolved 

into a strong regional voice on state-level policy issues that effect business 

operations in the state, and has been affective in influencing policy changes 

favorable to the County. The Chamber also works to attract new businesses to the 

County by compiling information about business resources and workforce 

statistics and making this information available when businesses inquire about 

Chautauqua County locations. 

• The for-profit Buffalo Niagara Enterprise (BNE), located in Buffalo, acts a 

clearing house for information about business locations in western New York. 

BNE collects and compiles economic and employment data for Chautauqua 

County, and keeps and up-to-date inventory of available industrial and commercial 

sites. BNE responds to business inquires and develops and distributes marketing 

materials promoting the area to businesses. 

• The role of local foundations in economic development is unique in Chautauqua 

County. Four Jamestown area foundations have embraced efforts to improve the 

regions economy, at least one going so far as to adopt economic development as 

part of its mission. These foundations have helped fund Chautuaqua County staff 

at the NBE, paid to hire a community grant writer as well as an advocate for 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 37

Jamestown and the region in Albany, partially funded Jamestown’s urban design 

plan and activities of the Jamestown Center City Development Corporation, and 

will fund the initial recommendations of the urban design plan. One foundation 

fully financed the development of the ice arena in Jamestown to provide an anchor 

for activity in the west end and create a destination for tourists. The active 

involvement of foundations has proven critical to the region’s ability to succeed in 

stabilizing and diversifying it economy, and provides a model for other regions to 

emulate. 

In addition to the above, there are several local agencies and organizations that work 

to promote economic growth in the County. 

Key Elements of Economic Development Successes. Six elements of the County’s 

economic development program emerge as central to its successful efforts to stabilize 

the region’s economy during the past several years. 

1. Broadening of focus beyond traditional manufacturing base. For many 

years, Chautauqua County identified itself as a manufacturing area. The 

economic development community focused on attracting and retaining 

manufacturing facilities, and the workforce expected to obtain manufacturing 

employment. This strong cultural mindset prevented the region from moving 

forward with efforts to branch out and exploit other opportunities for economic 

development, including the service industry and tourism. More recently, the 

economic development community has recognized the potential of the tourism 

industry in particular to help stem the loss of jobs in the County and to provide 

options for new directions. At the same time, the County continues to provide 

assistance to manufacturers interested in locating starting up, or expanding in 

the County. 

2. Within manufacturing, a focus on supporting existing businesses. While 

the County and its municipalities continue to work with organizations such as 

Buffalo Niagara Enterprises to attract businesses, the economic development 

community is focused on working with existing businesses in the sectors 

which have proven important to the region’s economy to ensure that they 

remain viable. Examples of efforts to support these businesses at both the local 

and state level include the purchase of the old ConRail lines and reinstitution 

of rail service to the area, the development of industrial parks with spec 

buildings aimed at attracting businesses that support the major manufacturing 

sectors, assistance with reducing energy costs through subsidies, the 

configuration of Empire Zones to incorporate major manufacturers, and 

business assistance and job training programs created through the region’s 

institutions of higher education. 

3. Regional partnerships for economic development. During the past five 

years, the County has witnessed a clear shift away from an attitude of 

competition among the many jurisdictions in the County to a regional 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

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partnership for economic development. This partnership is evident in the 

creation of the Partnership for Economic Development, the participants of 

which include the Southern Tier West Regional Planning and Development 

Board, the County Industrial Development Agency, the Greater Jamestown 

Empire Zone, the Dunkirk-Sheridan Empire Zone, the Cities of Jamestown and 

Dunkirk, the Chautauqua County Workforce Investment Board, and Westfield 

Development Corporation. The Partnership provides a forum for discussing 

and solving barriers to economic development within the County, as well as a 

one-stop shop for businesses interested in learning more about business 

development opportunities within the County. The partnership has reduced 

barriers to entry by opening up channels of communication and providing 

potential businesses with information about multiple municipalities and 

programs without needing to make multiple phone calls. Further, the four 

Empire Zones in the Southern Tier (Jamestown, Dunkirk-Sheridan, 

Cattagaurus, and Allegany all work together to share leads and to present a 

united voice in Albany. 

Another regional partnership developed five years ago when the north county 

and south county chambers of commerce merged into a single chamber serving 

the entire county. The County-wide chamber has a membership of 

approximately 1,600 firms, representing more than half the businesses in the 

County. This resulted in improved efficiencies and effectiveness in running the 

chamber, and better communications between all businesses in the County. 

Two additional examples of regional partnerships for economic development 

extend beyond Chautauqua County. One is the County’s involvement with 

Buffalo Niagara Enterprises (BNE), a for-profit entity that develops marketing 

information and provides marketing leads for participating organizations. BNE 

maintains an inventory of available commercial and industrial land within its 

service area, and works with businesses and localities to solve barriers to entry. 

The services provided by BNE allow local economic development 

professionals to focus on site specific issues rather than the maintenance of upto-date site inventories and marketing materials. In addition to BNE, the 

Committee for the Future is a consortium of four counties – Chautauqua, 

Cattaragus, Allegany, and Monroe – whose business and political leadership 

have come together to identify strategies for moving the region forward. 

On a smaller scale, the City of Dunkirk and Town of Dunkirk were able to 

come to overcome boundary issues by developing a revenue sharing agreement 

for development in the Town adjacent to the Interstate 90 interchange. In this 

instance, the City was asked to extend services to the land around the 

interchange so that the Town could attract development. At first the City 

balked at extending services without benefit of any of the resulting tax 

revenue. Working with SUNY Fredonia, the City and Town worked out a 

revenue sharing agreement, and the City extended the necessary services. 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 39

These regional approaches to economic development represent a significant 

shift away from parochial competition for single businesses. The organizations 

that have joined forces recognize that economic growth anywhere in the 

County or the broader region can benefit all of the partners by creating 

economic activity that can attract additional businesses, and by providing jobs 

for area residents. These regional efforts have been instrumental in creating a 

climate of cooperation that is evident to businesses and in helping to stabilize 

the economy of the County. 

4. Leadership. Another key element of the economic development efforts in the 

County is leadership by both the public and private sectors. Community 

leaders including private sector business executives, the president of SUNY 

Fredonia, and the County Executive have come together in efforts to stimulate 

economic growth. They have participated in strategic planning initiatives and 

in business attraction efforts. Business leaders participating in planning efforts 

bring a results- oriented attitude to the table, and insist on developing 

achievable goals with clearly defined steps and assigned responsibilities for 

making things happen. The County Executive and the Director of the County 

Industrial Development Agency have shown leadership in developing 

speculative buildings at the County’s businesses parks, which have succeeded 

in attracting new firms to the area. Both the County’s strong leadership and the 

united voice provided by its partnerships have been instrumental in garnering 

financial support and grants from both the state and federal governments. The 

can-do, won’t-take-no-for-an-answer attitudes of the County’s leadership and 

its elected representatives in Albany and Washington have been instrumental 

in helping the County move forward. 

5. The participation of non-profit foundations. Traditionally, non-profit 

family and community foundations do not target economic development 

activities for the focus of their giving. However, in Chautauqua County, four 

such foundations have proven instrumental in providing funding for economic 

development programs, and the Gebbie Foundation actually incorporated 

economic development into its mission. The Gebbie Foundation, as well as the 

Chautauqua Region Community Foundation, the Lenna Foundation and the 

Sheldon Foundation have all contributed to strategic planning efforts and local 

development corporations. The Gebbie Foundation funded the construction of 

the Jamestown Savings Bank Ice Arena in an effort to provide an anchor for 

the redevelopment of the west end of downtown Jamestown. By providing 

funding for key planning and economic development programs, these 

foundations have leveraged scarce public dollars to better achieve economic 

development goals in the County. 

6. Transportation investments. Two significant transportation investments have 

occurred in Chautauqua County over the past decade, both of which have 

supported business attraction, retention and expansion efforts. In 1999, New 

York State Department of Transportation completed work on the upgrade of 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 40

the two-lane Route 17 through Chautauqua County to the new four-lane 

Interstate 86. The project included upgrading 185 miles of two lane roadway to 

four lanes, as well as a bridge across Lake Chautauqua, saving significant time 

for travelers. The impacts of the highway improvements are still being realized 

as more businesses take advantage of the improved access provided by the 

upgraded facility. Early business investments associated with the highway 

included new hotels built at or near the highway interchange at Jamestown, and 

expansion of retail and restaurant uses. The new highway also improves the 

attractiveness of Ripley as the location of a distribution center. 

Another important transportation investment in the region was the purchase, 

rehabilitation and reopening of the old Southern Tier Extension railroad line, 

which serves southwestern New York Counties. The project resulted from the 

efforts of a large consortium that included Southern Tier West Regional 

Planning and Development Board, Norfolk Southern, Allegany County, 

Cattaraugus County, Chautauqua County, Steuben County, the Southern Tier 

Extension Railroad Authority (STERA - which was created to own the 

railroad), the New York Department of Transportation, and the Western New 

York and Pennsylvania Railroad. The railroad is owned by STERA and leased 

back to Norfolk Southern “to facilitate a tax abatement incentive program to 

redevelop the line.” (Southern Tier West Regional Planning and Development 

Board, p. 16). The line reopened in 2003. Prior to the redevelopment of the 

line, only 70 carloads per year were shipped on the line. Today, 35,000 

carloads per year pass over the line. Although not all of these shipments 

originate or terminate in Chautauqua County, several businesses including 

metal fabricators and farm suppliers are using rail sidings and shipping goods 

via rail. The cheaper cost of shipping by rail has also created competition for 

trucks, and led to a reduction in truck shipping costs for some businesses. 

3.5 Lessons Learned 

Chautauqua County’s success in stabilizing and diversifying its economy in response 

to significant structural changes provides several lessons for others facing similar 

circumstances. 

Work to identify and embrace non-traditional opportunities for economic growth. 

Many regions that have grown and prospered as a result of strong manufacturing 

sectors have difficulty embracing non-manufacturing sectors as opportunities for 

growth and expansion. This was true of Chautauqua County for many years. However, 

in recent years, fueled by the undeniable robust growth in the tourism industry, the 

Chautauqua County economic development community has embraced tourism as an 

important component to successful economic diversification and growth. The 

County’s success in diversifying by being willing to look beyond its historical 

strengths to new opportunities for growth can provide an important role model for 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 41

other Appalachian regions. 

Continued support for existing business sectors. A typical mistake that distressed 

regions make is to pursue new national growth sectors at the expense of their existing 

economic base. While the Chautauqua County economic development community 

embraced tourism, a new and growing sector, they did not abandon the existing 

manufacturing businesses within the County. Through selective retention the county 

recognized that, although declining, the manufacturing base still accounts for the 

largest share of jobs, and that many in the workforce still depend on these jobs. 

Therefore, the County continues to offer and develop programs to help the 

manufacturing businesses in the community. This strategy has proved effective in 

ensuring a diversified economy by adopting a multifaceted approach to economic 

development that embraces change while not abandoning its history. 

A united voice can provide results. Much of Chautauqua County’s success in 

stabilizing its economy is attributable to the many partnerships that developed to 

promote economic growth in the throughout the County. The recognition that growth 

anywhere in the County benefited the whole County, the region was able to come 

together to provide a united, cohesive image to businesses. This united approached 

allows for efficiencies in program delivery, facilitates problem solving, and allows for 

effective, streamlined communication with the business community. In rural areas 

where economic development resources are in short supply, this approach can be 

particularly effective. A united voice can also prove effective in garnering state and 

federal attention and support. 

Engage community leaders in economic development efforts. Chautauqua County’s 

economic development program has greatly benefited from strong leadership. This has 

included the involvement of community leaders in planning initiatives, financial 

leadership provided by local foundations, and political leadership on policy issues at 

all levels of government. A successful economic development program, particularly in 

distressed areas, requires participation by people who do not accept the status quo and 

who are committed to making change happen. 

3.6 Interviewees 

Diane G. Hewitt, Director of Economic Development, Chautauqua Opportunities 

for Development 

Michael P. Sullivan, Director of Institutional Relations and Public Affairs, 

Chautauqua Institution 

Richard L. Alexander, Director, County of Chautauqua Industrial Development 

Agency 

Pamela S. Lydic, President, Chautauqua County Chamber of Commerce 
Vol.2 Case Studies of Economic Growth Processes Chautauqua County 

Sources of Growth in Non-Metro Appalachia page 42

Donald Rychnowski, Executive Director, Southern Tier West Regional Planning 

and Development Board 

Terry Norman, Comptroller, Cummins Engines 

Rebecca Congdon, Executive Director, Dunkirk-Sheridan Empire Zone 

Cory Zahm, Planner, Greater Jamestown Empire Zone 

Steven Centi, Director of Development, City of Jamestown 

Greg Lindquist, Economic Development Director, City of Jamestown 

Andrew Nixon, Executive Director, Chautauqua County Visitors’ Bureau 

Dr. Leonard Faulk, Director, Rural Regional Development Center, SUNY 

Fredonia 

Greg Serto, Plant Manager, Truck-Lite 

Pam Frank, Executive Director, Westfield Development Corporation 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 43

4 PIKE COUNTY, KY: EVOLUTION 

AS A TRADE CENTER

4.1 Introduction 

Pike County, KY is the eastern-most county of a five county Local Development 

District that sits adjacent to the WV border. Pike County has managed to move from 

distressed to transitional status since 2003. However the four remaining counties in 

this mining-dependent LDD area have not faired the same. The case study explores 

reasons for Pike’s gradual success, the lack of beneficial spillover to its neighboring 

counties and transferable lessons to other mining-dependent areas of Appalachia. 

To many outsiders, Eastern Kentucky’s image has remained for decades as a region of 

persistent poverty. However, the reality is far different. The region is populated with 

hard working people who have worked diligently to change their image and fortune. 

Nowhere is this truer than in Pike County. 

This case study seeks to determine the root causes of Pike’s improvement through a 

survey of recent studies, statistics and interviews. It explains how Pike County’s 

transformation took place over decades, through a combination of vision, leadership 

and good fortune that allowed Pike to be transformed into a regional hub with a 

diversified economy. The “Cut-Thru Project” – a massive infrastructure initiative – 

was a first step in the County’s progress and has brought additional development, 

access and a spark of belief in itself. Pike’s past reliance on coal is being replaced by 

new economic growth as a regional health, service and retail destination. 

4.2 Regional Profile 

Pike County lies at the crossroads of eastern Kentucky, although it is located quite a 

distance from the nearest metropolitan areas in three states (Exhibits 4-1,2). It is also 

the first in the Big Sandy Area (BSA) Local Development District to graduate from 

“distressed” to “transitional” status (as designated by the Appalachian Regional

Commission in 2003), while its neighboring counties ― Floyd, Johnson, Martin and 

Magoffin –have seen fewer economic opportunities. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

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Exhibit 4-1. Pike County's Location Within Kentucky 

 

Exhibit 4-2. Distance from Pike County, KY to Selected Major Cities 

Pike County, with a population of 70,000 and square mileage of 788 is by far the 

largest county in the region. Although the local quality of life is improving, the area 

has been losing population since 1980 (see Exhibit 4-3). The 1970s brought the coal 

boom and its subsequent influx of people and capital as the industry expanded because 

of a sharp increase in the price of coal due to regulatory changes and the OPEC oil 

embargo. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 45

Exhibit 4-3. Big Sandy Region Changes in Population, 1970-2000 

Population by County 1970 1980 1990 2000 1970-80 1980-90 1990-2000 1970-2000

Floyd 35,889 48,764 43,586 42,441 36% -11% -3% 18%

Johnson 17,539 24,432 23,248 23,445 39% -5% 1% 34%

Magoffin 10,443 13,515 13,077 13,332 29% -3% 2% 28%

Martin 9,377 13,925 12,526 12,578 49% -10% 0% 34%

Pike 61,059 81,123 72,583 68,736 33% -11% -5% 13%

State of Kentucky 3,218,706 3,660,777 3,685,296 4,041,769 14% 1% 10% 26%

Source: US Census Bureau.

Population % Change

After the mid-1980s coal bust, brought about by declining oil prices, new mining in 

the Western US and industry technology changes, coal mining declined but still 

maintained a presence in the region. As mining technologies changed, workers were 

not as needed as pure labor, but rather as machine operators and technicians. This 

technology shift had a large impact on employment: once plentiful high-wage, lowskill jobs vanished. As jobs and amenities vanished, so did area residents. 

While this decline is leveling off, it has left the region without much of an increase in 

population since the 1970s. Partially because of this stagnation, and partially because 

of lingering stereotypes about employee skill levels and poverty levels, businesses 

have hesitated to locate in the area. Travel to the metro Lexington or Ashland / 

Huntington / Charleston is necessary to go to a large shopping mall or national chain 

restaurant or for recreational activities like ice skating. As a result, area college 

graduates who have spent the last four-plus years in a metro area hesitate to return 

home. 

Changes in population are mirroring a dramatic change in the county’s industry mix. 

Once ruled by coal, Pike is now showing strength in services (especially healthcare) 

and retail, but this has not meant that coal has completely diminished as a vital sector 

the county’s economy. 

Mining companies have been expanding recently, thanks to new demand for coal and 

new processing technology. However, employers report difficulty in finding qualified 

applicants. Many companies are willing to pay for training “for the right person,” and 

are offering high starting salaries ($40,000+). The importance of mining to the 

county’s economy is illustrated by the fact that despite dramatic reductions in 

employment levels from 1970, 14% of the workforce still is employed by the industry. 

Exhibit 4-4 shows just how concentrated the coal industry is in Pike County, with the 

county showing a location quotient of 16.8 compared with Kentucky as a whole and 

58.1 when compared with the nation. Considering that anything over a 1.0 is 

considered a higher than expected concentration, it is clear that Pike County remains a 

coal-based economy. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 46

But as Exhibits 4-4 and 4-5 show, there are other sectors that are emerging. Of special 

note is the location quotient of 1.2 for health care and social services. The increased 

importance of the health care industry suggests a new direction for Pike County, one 

that is having dramatic impact on the ways in which the county serves as a hub for 

neighboring communities. 

Exhibit 4-4. Pike County Employment by Industry, 1970-2000 

1980 1990

EMPLOYMENT % of Tot. % of Tot.

Farm employment 40 0.3% 50 60 50 0.2%

Agricultural Services 20 0.1% 40 140 200 0.6%

Mining 5,490 35.1% 9,950 6,420 5,200 16.8%

Construction 550 3.5% 1,360 1,150 1,200 3.9%

Manufacturing 260 1.7% 320 420 910 2.9%

Transportation, Comm., PU 1,110 7.1% 2,020 2,010 2,040 6.6%

Wholesale Trade 390 2.5% 810 840 930 3.0%

Retail Trade 2,500 16.0% 4,110 5,520 6,830 22.0%

FIRE 420 2.7% 750 1,160 1,380 4.4%

Services 2,550 16.3% 4,330 6,190 8,380 27.0%

Government 2,300 14.7% 3,350 3,750 3,910 12.6%

Total 15,630 100.0% 27,090 27,660 31,030 100.0%

% CHANGE 1970-80 1980-90 1990-2000 1970-2000 1980-2000

Farm employment 25.0% 20.0% -16.7% 25.0% 0.0%

Agricultural Services 100.0% 250.0% 42.9% 900.0% 400.0%

Mining 81.2% -35.5% -19.0% -5.3% -47.7%

Construction 147.3% -15.4% 4.3% 118.2% -11.8%

Manufacturing 23.1% 31.3% 116.7% 250.0% 184.4%

Transportation, Comm., PU 82.0% -0.5% 1.5% 83.8% 1.0%

Wholesale Trade 107.7% 3.7% 10.7% 138.5% 14.8%

Retail Trade 64.4% 34.3% 23.7% 173.2% 66.2%

FIRE 78.6% 54.7% 19.0% 228.6% 84.0%

Services 69.8% 43.0% 35.4% 228.6% 93.5%

Government 45.7% 11.9% 4.3% 70.0% 16.7%

Total 73.3% 2.1% 12.2% 98.5% 14.5%

Source: CEDDS Volume II, 2002 Woods and Poole Economics, Inc.; EDR Group

1970 2000
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 47

Exhibit 4-5. Selected Pike County Industry Location Quotients, 2002 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 48

Pike County has acknowledged the need to diversify industry, and has strongly 

supported the development of two industrial parks: the 200-acre Honey Branch 

Regional Industrial Park (located in Martin County) and the 11-acre Mossy Bottom 

Industrial Park (located in Pike County). The Honey Branch Park was a cooperative 

effort between all of the Big Sandy Area counties and is adjacent to a federal prison 

site. This park has been successful in attracting tenants, and the coalition of counties 

shares in the generated tax revenues. Mossy Bottom has had more difficulty in 

retaining tenants but has several successful firms. 

Another industrial force is the Kellogg Company’s Pikeville Plant. Originally 

recruited by former Kentucky Governor Paul Patton when he was Pike County Judge 

Executive, Kellogg has developed the facility into one of the country’s largest 

suppliers of Nutri-Grain bars and Pop-Tarts. Nearly 400 people are employed there. 

Pike also benefits from an innovative telecommunications company locating its 

headquarters in downtown Pikeville, and from focusing on additional training for both 

the police and fire departments. These two departments have state of the art equipment 

and have begun training other departments in the region. 

Perhaps the greatest influence in Pike’s changing industry mix and development into a 

regional center is the partnership between Pikeville College and Pikeville Medical 

Center (PMC). PMC is the largest private employer in Eastern Kentucky with 1,300 

employees; with 261 beds and over 535,000 square feet, it operates as the region’s 

medical center. Pikeville College is a private four-year college that offers four 

associate degree programs, 23 baccalaureate degree programs and one doctoral 

program – the School of Osteopathic Medicine, which ARC helped to fund. 

4.3 Evolution of Progress 

As the largest county in the Big Sandy Region, Pike County serves as the area’s hub of 

activity. Both people and materials flow into the county in a way that has helped spur 

the county’s relative growth. 

Transportation Access. Part of the reason for its ability to act as a hub among other 

counties in the region is the county’s location at the intersection at some of the best 

roads in the region. Pike County stands at the crossroads between Kentucky, West 

Virginia and Virginia and is the center hub between Lexington (KY), Knoxville (TN) 

and Huntington and Charleston (WV). It is home to four major US highways: US 

460, 23, 119 (ADHS Corridor G) and 80. Highway 23 (segment south of Pikeville, 

Corridor B), which was tuned into a four-lane highway nearly a decade ago, has 

become a major north-south connector. Another highway, 119, should be completed 

within the next year, and will connect Pikeville with South Williamson, WV (see 

Exhibit 4-6). Pike is also home to a regional private airport with a 5,000-foot runway 

and also has good rail line access. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 49

Exhibit 4-6. Highway Access for Pike County 

The newly built Eastern Kentucky Exposition Center in Pikeville, which also has a 

7,000-seat arena, is expected to be a strong area attraction because of its conventions, 

cultural events and entertainment. Built on land reclaimed as part of the Cut-Thru 

Project, it is estimated that the Center will have a $10 million impact on the local 

economy with the bulk of that figure coming from visitors outside the state. The 

Center’s projected impact will also bolster two currently booming industries in Pike 

County: retail trade and services—and a new downtown hotel hopes to capture some 

of the traffic generated by the facility. 

The improvements in infrastructure make Pike more “commuter friendly,” and are 

expected to bring more people into the region, which will further develop Pike’s 

reputation as a hub, especially its ability to attract shoppers from neighboring counties. 

While it does not have a large mall with its typical mix of national retail 

establishments, it does offer a number of department-type retail stores in addition to 

big-box retailers. Pike offers nearly double the number of both food service/drinking 

places and retail trade establishments than the next largest concentration, as shown in 

Exhibit 4-7. 

The City of Pikeville is also burnishing its image through two major renovation 

projects: relocating all overhead wires (power, cable and phone) to underground 

conduits and the Main Street Renaissance Program, which is a large-scale 

beautification project. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 50

Exhibit 4-7. Number of Establishments and Revenue, 2002 

Type of 

Establishment County 

Number in 

Category 

Business Revenue 

(in ‘000s) 

Pike 74 $50,943

Floyd 39 $19,831

Johnson 31 $19,393

Martin 13 D

Food services & 

drinking places 

Magoffin 11 $4,994

Pike 324 $783,587

Floyd 191 $341,056

Johnson 121 $267,789

Martin 46 $68,629

Retail trade 

Magoffin 43 $51,515

Source: US Census Bureau D = not disclosable; data is suppressed 

Industry. In addition to being the region’s top coal producer (35 million tons, almost 

20 percent of Kentucky's total coal production), Pike County is headquarters for 

financial institutions and holding companies worth more than $1 billion in assets – the 

third largest concentration in Kentucky. It also ranks fifth in Kentucky in both total 

retail sales and effective buying income, and is the seventh biggest contributor to the 

state’s budget in individual income tax paid and eighth in sales tax paid. 

In terms of employment, Pike County’s commuting patterns (Exhibit 4-8) show that 

most of Pike County’s residents work in Pike County – only 14.6 percent of Pike 

County’s labor force participants working in another county. Pike County also attracts 

workers from throughout the region, with Floyd County providing the most workers 

from the Big Sandy region. However, as this data is nearly five years old, new patterns 

may have emerged, especially with new highways opening. The expansion of US 23, 

460 and 119 has – and will – enhance mobility throughout the region, making it easier 

for workers to travel for their jobs and for companies to expand and relocate. 

Exhibit 4-8. Workers Commuting to Pike County, 2000 

Lives in 

Works in 

Pike 

As % of home county 

employed labor force 

Percent of home county workers 

working elsewhere (not including Pike)

Pike 18,455 85% 15% 

Floyd 2,221 18% 13% 

Mingo, WV 1,244 16% 21% 

Martin 186 6% 27% 

Johnson 395 5% 28% 

Magoffin 206 5% 36% 

Buchanan, VA 362 4% 24% 

Source: Kentucky State Data Center; Virginia Employment Commission; Bureau of Business and 

Economic Research, West Virginia University 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 51

As noted before, the Pikeville Medical Center (PMC) has boosted Pike County’s 

economic and healthcare profile. It offers services unavailable elsewhere in the greater 

Big Sandy region, such as: 

• Neurosurgery 

• The Heart Institute with Philips Allura Interventional Catheterization Labs 

• The Leonard Lawson Cancer Center 

• The Family Practice Clinic 

• 40-bed Inpatient Rehabilitation Hospital 

• Home Health 

• Medical Detoxification Unit 

• MedFlight of East Kentucky 

• Level II +/- III NICU 

• Sleep Disorders Clinic 

• The Birth Place (largest obstetrical service in the region) 

• Pediatric Transitional Care Unit 

• Siemens ONCOR Linear Accelerator (second in the nation) 

• PET Scanning and other state of the art diagnostic department 

These divisions offer top-notch care to the residents of Eastern Kentucky and beyond, 

as it attracts patients who would rather be closer to home than travel to Lexington, 

Louisville, or Huntington/Charleston for top-tier services. With its almost holistic 

provision of services, the hospital is able to provide for almost any need the local 

population has, from heart surgery to addiction. Although there are several other 

hospitals in the region (Paul B. Hall in Johnson County, Highlands Regional Medical 

Center in Floyd and Our Lady of the Way in Martin), they do not offer the level of 

care PMC does. 

The hospital is also spurring some concurrent cluster growth, with physicians setting 

up private practice and medical service centers. Community leaders also hope for 

medical suppliers and research to locate in the area, especially because of the chronic 

health needs of the population. 

Part of the reason for this boom is the unique partnership between the PMC and 

Pikeville College’s School of Osteopathic Medicine, which opened in 1997 with a 

class of 60 students. Its genesis was the result of a Johnson County resident who 

believed the area needed a medical school to alleviate chronic shortages of doctors. 

Because Pikeville College had the existing infrastructure necessary to support such an 

initiative, the school was opened at the College. To date, 282 Doctors of Osteopathic 

Medicine have graduated from the school, and many have stayed in the region as 

primary care providers. The local medical community is very supportive of the 

medical college’s mission, and many medical students complete residencies at PMC. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 52

PMC also recently established the Pikeville Medical Development Corporation with 

the mission to: 

 • Develop collaborative research initiatives between PMC and major medical 

research entities in Louisville as well as Pikeville College School of 

Osteopathic Medicine and other similar institutions. 

 • Create an e-Health system at PMC which will serve as a model for other 

medical entities throughout the Commonwealth. 

 • Obtain public and private funding for the development of the local economy 

and for the expansion of the programs and facilities at PMC. 

 • Create a high-tech, regional research and development Institute in eastern 

Kentucky to work with other medical partners in fostering advanced medicalrelated concepts and other new economy-type startup companies. 

 • Encourage private companies to locate in eastern Kentucky so as to enhance 

economic stability. 

• Link the economic engine of PMC with the economic development activities 

of governmental, non-profit and for-profit entities in eastern Kentucky. 

 

While lofty, these goals provide an ideal – much like the Cut-Thru Project – for 

leaders to aspire. A 2003 report by the KY Rural Health Works estimates that the 

economic impact of Pike’s health care system was significant to the county’s growth. 

This report estimates that for every health care job, an additional .40 jobs are created 

in the local economy; for every dollar of health care labor income, an additional $0.25 

of income was generated in the local economy. Assuming a similar effect in Pike 

County, an average wage of $30,000 would provide approximately $7,500 for the 

local economy. 

 

In addition to keeping local health care dollars local and attracting outside dollars, the 

industry also has the potential to act as a magnet for both similar and other industries. 

4.4 Catalysts of Change 

Pike County has benefited from three opportunities that are not as pronounced in the 

rest of the Big Sandy Region: vision and leadership, political fortune, and funding. 

Vision and Leadership. Shortly after his election in 1960, Pikeville Mayor William 

Hambley first began talking about the Cut-Thru Project. Mayor Hambley’s vision was 

to move mountains – a mile and a half long channel through them – to provide a 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 53

corridor that would contain railroad tracks, US highways 23, 80, 119 and 460, and the 

Levisa Fork of the Big Sandy River. This project, completed in 1987, alleviated 

annual flooding and provided nearly 400 acres of additional developable land, almost 

all of which has been developed. Although this project took nearly three decades, 

Mayor Hambley never wavered in his belief in the project, and was able to promote it 

to many other local leaders. 

Former Governor Patton, a protégé of Mayor Hambley, also developed programs that 

gave Pike County a good jumping-off point. As County Judge Executive, he instituted 

mandatory solid waste pickup and began the process of installing water and sewer 

lines throughout the county. While these projects may seem minor, it is important to 

remember Pike’s difficult terrain, as well as the difference made by water, sewer and 

solid waste pickup access. Governor Patton also provided momentum during his time 

as governor and lieutenant governor by supporting progressive policies, infrastructure 

and investing state dollars in local initiatives; by some estimates, up to $500 million 

was brought to the area. 

The current Pikeville City Manager, Donovan Blackburn, has also initiated several 

recent projects to boost the area’s profile. A 2003 comprehensive strategic plan was 

the first in more than a decade and part of an initiative to modernize and enhance local 

development, which also included the hiring of a dedicated maintenance manager and 

an economic development officer, and the expansion of access to technology 

throughout city government. 

Other local leaders are promoting development strategies around tourism. Pike County 

Magistrate Chris Harris believes strongly in the potential of an ATV trail system. West 

Virginia’s trail system is heavily used and draws tourists from a wide geographic area. 

Harris believes that connecting West Virginia and Kentucky through a trail system 

will bring additional visitors to the area, and keep visitors in the area longer. 

Another potential attraction is a series of reclaimed mining areas. These areas have 

been used to demonstrate varying methods of reclamation, and some are fully forested 

and support elk populations. It is hoped that these areas will become nature preserves, 

and they may also offer the possibility of an annual elk hunt. 

Political Fortune and Funding. While much of the credit for the area’s turnaround 

must be given to the local community, it is also very important to note the impact of 

state funding targeted to Pike during both Mayor Hambley’s tenure and Governor 

Patton’s time in Frankfurt. Estimates of state program funding during Patton’s tenure 

run as high as $500 million – a significant amount for a county of roughly 70,000 

people. Granted, the area’s needs were great, both in terms of infrastructure and social 

and development programs, but this amount gave Pike an extra boost as compared to 

its BSA neighbors. 

Pike also benefits from the Coal Severance Tax. This tax is designed to provide a 

resource for economic and social development as a means to replace the income lost 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 54

when area coal resources are depleted. State statute requires that 50 percent of this tax 

is returned to coal-producing counties, with the remainder going to the state’s general 

fund; however, it is difficult to determine whether this mandate has been filled. 

According to the Kentucky Department of Revenue, tax receipts since the 1970s have 

totaled more than $4.6 billion dollars, with nearly $1 billion collected from coal mined 

and/or processed from Pike County. These dollars have been used to fund local 

projects, such as water and wastewater projects, osteopathic medical school loan 

forgiveness, and industrial park and economic development. 

Exhibit 4-9 shows the value of severed and processed coal, along with the tax receipts 

incurred by the Coal Severance Tax. The tax assesses 4.5 percent of the value of 

mined (“severed”) coal, for at least $0.50 per ton. Pike County provides the largest 

share of the tax receipts of all mining counties throughout the state, nearly $40 

million. Martin County accounts for the next highest share of revenue, with slightly 

over $8 million. Pike’s revenue from the tax, according to formula, should be $20 

million. 

Because of their larger tax base, Pike County has been able to take the “extra steps” 

needed to wean itself from traditional industries as well as provide development 

funding for the County. Nevertheless, Pike still faces challenges in industry 

diversification and workforce training. 

Exhibit 4-9. Coal Severance Tax Revenue, FY 2002-2003 

Gross Value of 

Severed Coal 

Tax on Severed 

Coal 

Gross Value of 

Processing 

Total Tax 

Receipts 

Kentucky Total $2,838,514,744 $125,530,144 $356,948,150 $141,488,813 

E. Kentucky $2,390,638,851 $105,361,781 $300,451,561 $118,783,697 

Pike $766,110,260 $34,112,484 $120,971,083 $39,568,774 

Perry $250,870,326 $11,509,372 $39,392,890 $13,287,261 

Knott $278,760,063 $12,295,994 $17,513,323 $13,156,757 

Harlan $255,715,512 $11,497,770 $11,256,571 $12,024,444 

Martin $155,881,929 $6,956,284 $27,915,765 $8,223,374 

Leslie $129,087,420 $5,810,747 $19,042,562 $6,669,475 

Bell $85,172,600 $3,793,572 $13,004,196 $4,414,843 

Floyd $87,133,866 $3,789,958 $4,685,984 $3,994,261 

Johnson $16,926,563 $761,696 $2,346,803 $867,303 

Magoffin $17,155,789 $772,012 $246,405 $783,095 

Source: Kentucky Revenue Cabinet 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 55

4.5 Lessons Learned 

While it is difficult to replicate the specific factors that guided the Pike County’s 

growth, other mining dependent areas of Appalachia can take away some guidance 

from its experience. 

Leadership and Regionalism. Perhaps the most important asset of Pike County is the 

strong leadership by several public servants, including former Mayor Hambley and 

former Governor Patton. They both developed visions for Pike County, remained 

focused on the outcomes, and fought for resources to fulfill their goals. While this 

tactic was arguably simpler because of Governor Patton’s position and political skill, 

their initial leadership was the starting point. While many in the area thought the CutThru Project would never come to fruition, it did – bringing respite from floods, 

additional developable land, and more importantly, a sense of pride and 

accomplishment. 

The five county region now works together more frequently, continuing a 

collaborative approach begun years ago. The BSA has been particularly successful on 

major projects such as the Honey Branch Regional Industrial Park, in which 200-acres 

was developed for industrial use and is adjacent to a federal prison. This initiative was 

perhaps one of the first collaborative projects in the BSA, and resulted in new jobs and 

a landmark profit-sharing agreement. Another example is the City of Pikeville’s 

willingness to install water and sewer lines outside of city limits, particularly along the 

US 23 corridor between Pikeville and Floyd County. This infrastructure will induce 

additional development and will benefit both counties. The counties are also 

benefiting from leaders who are willing to try new approaches, such as those in Pike 

and Johnson Counties. 

While relationships between the counties are not always ideal, and such bonds are 

difficult to form, it is important to note the possibilities and results from such 

communication. By having strong leaders willing to work as a region, municipal subunits of that area are more likely to prosper. This will be particularly important to 

Johnson, Martin, and Maggofin counties which while adjacent to the economic 

progress of Pike County are not sharing in the opportunity. Floyd County has decent 

highway access to Pike County which has clearly enabled 18% of Floyd’s labor force 

participants to import their wages from jobs held in Pike County. 

Determination, or, Slow and Steady Wins the Race. Pike County leaders developed 

some very ambitious projects – some would say overambitious or impossible, as in the 

case with the Cut-Thru Project. However, these leaders persevered and continued to 

work on their goal: additional developable land and jobs. Although it has taken 

decades, Pike County has gained developable land, jobs, infrastructure and, as noted 

before, a spark of encouragement. This spark led to a number of projects, including a 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 56

new multi-use park and a performing arts center in downtown Pikeville. Area leaders 

believe this project will begin a chain reaction of development. Even before opening, 

Pikeville was able to attract a new chain hotel for its downtown, which will begin 

construction shortly. Pikeville approached a national chain about building a 

downtown hotel a few years ago and was told it wasn’t feasible. However, they went 

back after some time, made another presentation, and expect the new hotel to be ready 

soon. 

Industrial diversification is key but not easy

Pike County is growing in part because community leaders have recognized that 

depending on coal for their county’s livelihood no longer made sense. In 35 years, the 

percentage of the county’s employees in the mining industry has dropped from 35 

percent to 14 percent, a huge shift. Accordingly the county has begun to look to other 

industries to fill the void left by the shrinking mining industry. 

The best example occurs with the growing health care industry in the county. The 

partnership between Pikeville College and the Pike Medical Center show how a 

community asset, in this case the college, can build a new industry, in this case health 

care. It also shows how Pike is recognizing its role as a regional hub. The Medical 

Center now is the preeminent health care facility in the Big Sandy Region. 

Another key has been recognizing that the environmental impacts of the mining 

industry can be addressed in a way that is positive to the economy. The University of 

Kentucky’s College of Agriculture has been successful in determining the most 

effective ways of reclaiming former mine sites, and County leaders are looking at 

ways to reclaim this land into such uses as wildlife preserves and ATV trails that could 

attract tourists to the naturally beautiful region. 

Of course, the large-scale economic shift away from mining is not without its 

challenges. The biggest one being the presence of so many displaced, and in this case 

often disabled, workers. Helping these workers find new opportunities, especially in 

jobs that can pay beyond a living wage, is a consistent challenge facing both 

workforce and economic development teams. This is especially difficult given that 

manufacturing, which at least would seem to offer similar wages and use similar skills 

to mining, has been so slow to develop in the county. If the county wants to continue 

to grow, figuring out ways to enhance the manufacturing sector will be critical. 

Similarly, because the mining industry seems to be experiencing a resurgence, and 

because these firms provide crucial tax revenues, adequate workforce training should 

be made available to those seeking work as a miner. Because technology plays such an 

important role now, possible cross-training opportunities (i.e. safety or heavy 

equipment) could exist with other industries. 
Vol.2 Case Studies of Economic Growth Processes Pike County 

Sources of Growth in Non-Metro Appalachia page 57

4.6 Interviewees 

Donovan Blackburn, Pikeville City Manager 

R. Tucker Daniel, Johnson County Judge/Executive 

William Deskins, Pike County Judge/Executive 

Dennis Dorton, President, Citizens National Bank 

Robert M. “Mike” Duncan, Chairman and CEO, Inez Deposit Bank and 

Republican National Committee General Counsel 

Don Graves, professor and former chair, University of Kentucky College of 

Agriculture 

Chris Harris, Pike County Magistrate (District 6) 

Paul Patton, former Kentucky Governor and Lieutenant Governor and Pike 

County Judge/Executive 

David Pelphrey, Dean of Community, Workforce, Economic Development, Big 

Sandy Community and Technical College 

Karen Sue Ratliff, Deputy Pike County Judge/Executive 

Sandy Runyon, Executive Director, Big Sandy Area Development District 

Terry Spears, Vice President for Development, Pikeville College 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 58

5 MARION & MONONGALIA CO, WV:

HIGH-TECH INITIATIVE

5.1 Introduction 

Monongalia County, WV is a metro county (home to Morgantown), while adjacent 

Marion County is a “micropolitan” area. Marion County had prior mining roots. This 

case study examines the development of a hi-tech initiative in these two counties with 

emphasis on the role of educational-training assets and the extent to which Marion 

County is achieving diversification in its economy. 

The two-county region of Monongalia County and Marion County, in West Virginia, 

show clear signs of economic growth and improvement over the past five years. The 

average income and average wage of the two counties have increased, and the rate of 

job growth and entrepreneurship is also increasing. These changes are the product of a 

confluence of several factors: the presence of higher education assets; the increasing 

role of local capital; and, importantly, federal investments which have combined to 

create local and regional economies whose upward dynamics contrast with the trends 

governing the economy of the rest of West Virginia. The hypothesis going into this 

study is that the role of technology development may have been pivotal in bringing 

about these changes. As the study shows, technology has been a key factor but without 

the region’s entrepreneurial energy and collaborative capacities, technology could not 

have brought about the changes that the region has seen. 

5.2 Regional Profile 

Tucked into the north central nook of uppermost West Virginia, the MonongaliaMarion region is at the crossroads of two regions – often called the northernmost point 

in the South and the southernmost point in the North (Exhibits 5-1 and 5-2). 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 59

Exhibit 5-1. Location of Marion and Monongolia Counties 

Exhibit 5-2. Marion and Monongalia County Detail 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 60

The region’s economic, industrial, and cultural identity is distinct from that of West 

Virginia as a whole. While the rest of the state has experienced the loss of much of 

the mining and manufacturing jobs that have dominated its economy for years, 

Monongalia and Marion are experiencing increases in both jobs and income, as well as 

expansion in high-value growth industries. The growth in this region stands in strong 

contrast to the economic trends governing the rest of the state. In fact, the region 

added jobs at an average annual rate of 1.3 percent between 2000 and 2004, while the 

state as a whole (and the nation as a whole) lost jobs (source: West Virginia Economic 

Outlook: North Central Region and Morgantown MSA Outlook, Forecast 2005 – 

2009. Bureau of Business and Economic Research, West Virginia University).

Population Growth. As shown in Exhibit 5-3, population in Marion County has 

declined over the past 30 years while Monongalia County has continually gained 

population. In fact, between 1990 and 2000 while the State as a whole grew by less 

than 1 percent, Monongalia County grew by more than 8 percent. 

Exhibit 5-3. Population Growth, 1970-2000 

Population by County 1970 1980 1990 2000 1970-80 1980-90 1990-2000 1970-2000

Marion County 61,356 65,789 57,249 56,598 7.2% -13.0% -1.1% -7.8%

Monongalia County 63,714 75,024 75,509 81,866 17.8% 0.6% 8.4% 28.5%

State of West Virginia 1,744,237 1,949,644 1,793,477 1,808,344 11.8% -8.0% 0.8% 3.7%

Source: US Census Bureau.

Population % Change

In Monongalia County, the major city is Morgantown, which has recently grown 

enough to be designated a metropolitan statistical area (comprising Monongalia and 

Preston counties). Morgantown is the largest, fastest-growing, and most prosperous 

municipality in the region, with a population increase of 3.6% (25,879 to 26,809) 

between the 1990 and 2000 Census, and with an average income far exceeding other 

communities. Marion County’s corridor city is Fairmont, also growing, but less 

rapidly than Morgantown. Morgantown is the home of West Virginia University, 

which includes a large undergraduate and graduate student body as well as a 

comprehensive health system; the university employs over 5,000 faculty and is largely 

responsible for the region’s high concentration of employment in government. 

Composition of the Regional Economy. Exhibits 5-4 and 5-5 show employment by 

industry trends in each county between 1997 and 2002. However, the economy of this 

two-county region is heterogeneous, with quite distinct trends governing the economic 

changes in the region’s two major towns as compared to the region’s more rural areas. 

In fact, the regional economy is best understood by expanding the view beyond these 

two counties to include Preston County, directly east of Monongalia County, and 

Harrison County, directly south of Marion County. 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 61

Exhibit 5-4. Marion County Employment by Industry, 1997-2002
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 62

Exhibit 5-5. Monongalia Co. Employment by Industry, 1997-2002 

2002 LQ

NAICS Sector 1997 2002

111 Crop Production 325 170 -12.1% -8.8% -2.9% 0.3

112 Animal Production 179 294 10.4% 11.7% 8.1% 0.4

113 Forestry & Logging 46 40 -3.1% -4.0% -4.9% 0.0

114 Fishing, Hunting & Trapping 0 0 0.0% 142.2% 12.3% 0.0

115 Support for Agriculture & Forestry 13 6 -15.1% -2.3% 1.1% 0.0

211 Oil & Gas Extraction 3 128 106.6% -1.7% -5.7% 0.3

212-213 Mining & Support Activities 748 503 -7.6% -1.6% 2.9% 0.3

221 Utilities 367 305 -3.6% -5.4% -4.9% 0.7

230 Construction 2,717 2,891 1.2% -3.9% 0.2% 0.8

311 Food Products 14 62 34.5% -0.2% 0.4% 0.2

312 Beverage & Tobacco Products 0 3 0.0% 2.9% -0.6% 0.0

313 Textile Mills 0 0 -100.0% -0.2% -6.8% 0.0

314 Textile Product Mills 1 2 10.8% -6.6% -4.4% 0.0

315 Apparel Manufacturing 83 3 -47.4% -20.3% -13.6% 0.0

316 Leather & Allied Products 0 0 -100.0% -45.2% -11.5% 0.0

321 Wood Products 82 56 -7.1% -1.2% -3.8% 0.1

322 Paper Manufacturing 0 0 -100.0% -2.0% -3.6% 0.0

323 Printing & Related Support Activities 54 64 3.5% -0.8% -3.6% 0.5

324 Petroleum & Coal Products 0 14 0.0% 16.0% -2.9% 0.0

325 Chemical Manufacturing 818 1,316 10.0% -5.7% -4.5% 1.7

326 Plastics & Rubber Products 23 6 -23.1% 9.1% -3.0% 0.0

327 Nonmetallic Mineral Products 146 151 0.6% -4.9% -2.6% 0.4

331 Primary Metal Manufacturing 1 0 -100.0% -6.0% -4.8% 0.0

332 Fabricated Metal Products 535 289 -11.6% -3.9% -2.5% 0.6

333 Machinery Manufacturing 98 140 7.3% -5.5% -4.7% 1.0

334 Computer & Electronic Products 52 79 8.6% -5.7% -5.3% 1.0

335 Electric Equipment, Appliances, etc. 2 4 16.6% -12.8% -5.3% 0.0

336 Transportation Equipment 7 5 -5.8% 3.2% -3.9% 0.0

337 Furniture & Related Products 40 38 -1.2% 0.9% 0.0% 0.0

339 Miscellaneous Manufacturing 76 32 -16.2% -2.2% -2.0% 0.0

420 Wholesale Trade 1,137 989 -2.7% -5.2% -3.2% 0.6

441-454 Retail Trade 5,912 5,657 -0.9% -0.8% -0.3% 0.8

481-487 Transportation 396 419 1.1% 0.7% 1.7% 0.3

491-493 Mail, package delivery & warehousing 230 207 -2.1% 1.3% 6.4% 0.3

511 Publishing Industries (except Internet) 239 271 2.6% -4.4% -6.0% 1.0

512 Motion Picture & Sound Recording 62 34 -11.4% -15.7% -4.4% 0.0

513 Broadcasting 284 329 3.0% 0.2% 2.9% 0.6

514 Internet & data process svcs 50 189 30.4% -5.7% 6.1% 4.6

521-523 Monetary, Financial, & Credit Activity 728 724 -0.1% 0.8% 1.8% 0.7

524 Insurance Carriers & Related Activities 343 237 -7.1% 0.2% -1.2% 0.3

525 Funds, Trusts, & Other Financial Vehicles 19 153 51.3% 3.4% 12.3% 7.5

531 Real Estate 656 1,617 19.8% 8.6% 9.7% 1.6

532 Rental & Leasing Services 164 295 12.5% 7.4% 6.9% 0.8

533 Lessors of Nonfinancial Intangible Assets 7 0 -100.0% -15.3% -8.5% 0.0

541-551 Professional Scientific, Technical, Services 1,790 3,310 13.1% 0.9% 7.0% 1.3

561 Administrative & Support Services 1,337 2,444 12.8% 1.7% 0.0% 1.0

562 Waste Management & Remediation 39 50 5.5% 3.5% 8.9% 0.0

611 Educational Services 250 247 -0.2% -0.7% 0.1% 0.3

621-624 Health Care & Social Services 6,306 9,571 8.7% 2.8% 2.6% 1.4

711-713 Amusement & Recreation 457 413 -2.0% 7.7% 3.7% 0.4

721-722 Accommodations, Eating & Drinking 3,672 4,434 3.8% 1.3% 2.3% 1.1

811-812 Repair, Maintenance, & Personal Services 1,373 2,229 10.2% 3.1% 3.8% 1.1

813 Religious, Civic, Professional, Organizations 578 355 -9.3% -4.3% 1.4% 0.4

814 Private Households 222 336 8.7% 9.0% 10.1% 0.5

920 Government & non NAICs 12,568 16,700 5.8% -0.5% 1.0% 1.9

TOTAL 45,249 57,811

Source: EDR-LEAP (with IMPLAN data).

Monongalia Co./ 

West Virginia

Monongalia County Annual Growth

West Virginia

United 

States

Annual 

Growth
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 63

In this four-county region, there are two distinct economic trends – one governing the 

towns and small cities close to I-79, the other governing the rest of the four-county 

area. The towns and cities form a swath locally referred to as the I-79 Corridor. Along 

this corridor, incomes are higher, wages are greater, jobs are growing faster, and the 

growth industries are primarily those that require high levels of skill and technology to 

create competitive advantage. The I-79 corridor, in short, is booming. Along this 

corridor, communities are collaborating with each other to produce growth in 

technology-based industries, and the primary sites of this growth are Morgantown, in 

Monongalia County, and Fairmont, in Marion County. The corridor is not, however, 

taking the rest of the region with it. I-79 is a clear dividing line between the areas of 

the region that are prospering and those that are not. 

In both Monongalia and Marion, the strong concentration in government jobs is due 

primarily to the presence in Morgantown of West Virginia University and in Fairmont 

of Fairmont State University and Fairmont State Community and Technical College. 

And in both counties, the strong service sector is concentrated primarily in business 

services, engineering services, and computer services- skill-intensive, high-wage 

industries that are a strong indicator of the region’s growth in high-value industries. 

It has been years since Monongalia and Marion county depended to any real extent on 

the mining industries for their economic welfare – in fact, neither county has ever had 

the level of mining dependence seen in the rest of West Virginia. As the above tables 

show employment in Mining & Support Activities for both these counties has been 

shrinking at a faster rate than mining jobs in West Virginia. For at least the past ten 

years, the region’s economy has been much more strongly centered on services, retail, 

and government. 

Place-Based Amenities. Like much of West Virginia, Monongalia and Marion 

counties are abundant with considerable natural beauty. The region’s mountains, 

forests, and rivers combine to create a topography that is unpredictable and striking. 

Morgantown in particular is starting to build on its riverfront, with walking and biking 

trails that stretch for miles along the river’s edge and culminate in community 

gathering places close to the city’s downtown. Fairmont has not done as much with its 

riverfront, whose banks are beautiful but still obscured by various retail operations, a 

few private homes, and other structures; however, plans are being developed to 

develop the riverfront into a community amenity. 

As beautiful as the region is, some of the same factors that create this beauty also 

contribute to the economic disparities between its rural and metro areas. The 

mountainous terrain makes it very difficult to move around within the counties – not 

only making it harder for residents of the counties to reach jobs in Fairmont and 

Morgantown, but also creating significant infrastructure challenges. Transportation, 

water and sewer, landfills, construction – all are made very difficult by the rugged 

topography, with the result that all are in poor condition. Transportation in particular is 

frequently cited as one of the major challenges of the region outside Morgantown and 

Fairmont; as Figure 3 below shows, there are virtually no good east-west connectors in 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 64

the entire northern half of the state, and the roads that do exist are, to a great extent, 

old and in disrepair. 

The effects of the mountainous terrain are seen not only in the region’s infrastructure, 

but also in its culture. West Virginians are famous for their local orientation; many 

residents agree that a typical West Virginian identifies with the smallest community 

possible, thinking of himself not as a resident of a particular county, but of the west 

side of that county, of a small town within the county, or even of one side of the river 

in that small town. The topography plays a large role (and reinforces) insularity; 

because it is so difficult to get from one place to another, residents of towns and 

communities become used to thinking of themselves as isolated and independent, and 

resist attempts to connect them with outside communities. While insularity is not 

necessarily a negative quality –in fact often helping to promote a sense of 

independence and self-reliance that can be necessary when isolated from external 

assistance and support – it can make it more difficult to create the kind of regionally 

coordinated approach that is often necessary for building on place-specific assets to 

create economic growth that reaches all of the region’s citizens. 

Labor Markets. Like other aspects of the region’s economy, the labor market in the 

Monongalia – Marion region is an uneven picture. Though unemployment is fairly 

low throughout the region (4.2 percent, compared to 5.2 for the state and 5.5 for the 

nation), the region’s urban areas have distinctly different labor markets from its rural 

areas. As discussed earlier, poor transportation infrastructure and the mountainous 

terrain make it difficult for those outside the urban areas to have access to these job 

markets. Even in areas where the commute would not be physically difficult, 

however, there appears to be a strong cultural bias regionwide against commuting. 

County planners and workforce development directors report that most people in the 

area expect to drive no more than 10-15 minutes to work, and simply do not tend to 

consider employment options outside that radius. One exception to this rule, however, 

is that many of the people filling high-level and technology-intensive jobs in Fairmont 

live in Morgantown instead and commute to Fairmont, which can’t offer the same 

level of amenities and housing options that Morgantown can. Approximately 9.5 

percent of the workforce in Marion County commutes to Monongalia County for work 

and 7.5 percent of the workforce in Monongalia County commutes to Marion County 

for work. In fact, one of the key factors hindering the technology-intensive job growth 

in Fairmont is that it’s difficult for employers in these industries to find enough 

workers. 

Educational Institutions. Both Monongalia and Marion have high-performing K-12 

school systems; Monongalia’s, in fact, was rated by Offspring Magazine and 

Expansion Management Magazine as one of the best public school systems in the 

country. Exhibit 5-6 displays some recent education statistics for Monongalia County. 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 65

Exhibit 5-6. Monongalia County Public School Statistics, 2003-2004 

Total Public School Enrollment 10,206 

Total Graduates (from three high schools) 691 

Average SAT Test Scores (Math) 538 

Average SAT Test Scores (Verbal) 538 

Pupil/Teacher Ratio 15:1 

Source: National Center for Education Statistics, <http:www//nces.ed.gov>; accessed 12/5/2005 

As discussed earlier, the region is also home to several excellent post-secondary 

institutions. West Virginia University in Morgantown is the flagship institution of the 

state’s 14-college state university system. Fairmont State University is a regional 

public university that merged, a few years ago, with Fairmont State Community and 

Technical College. The two institutions now operate somewhat independently, but are 

administrated jointly and coordinate their offerings and outreach activities to ensure 

that they do not overlap and that where possible they leverage and build on each other. 

Fairmont State University has focused primarily on its education mission; until now, it 

has thought of its role in economic development to be primarily educating the 

workforce that will drive economic growth. The leadership has begun, however, to 

consider the possibility of becoming involved in research and development and other 

more industry-focused economic development activities. 

Fairmont State Community and Technical College, in Fairmont, is the two-county 

region’s only post-secondary institution of career and technical education. It serves 

7,200 students from a wide-ranging service area that covers thirteen of West 

Virginia’s counties (some of which are served by satellite campuses and/or mobile 

programs). Nevertheless, much of the population of the two-county region lives 

outside practical commuting range from most of what FSCTC offers, and has little 

access to postsecondary technical education. 

Entrepreneurship. Most of the region’s entrepreneurial activity is centered in 

Morgantown. The university has begun playing a major role, as will be discussed 

below, in technology-based spin-offs, and the city attracts a considerable number of 

entrepreneurs who locate their businesses there primarily because it’s where they want 

to live. Many of the entrepreneurs currently doing business in Morgantown say that 

they located there because the city has a “buzz,” an “entrepreneurial energy,” that 

makes it feel like a place where things are happening. The city has a wide range of 

public and private organizations that support entrepreneurs at all stages; however, 

these appear to be more an outgrowth of the entrepreneurial activity rather than its 

cause. The services and support they provide are important to entrepreneurs currently 

doing business in Morgantown, but they appear to value even more the connections 

and networks that these organizations, and the people in them, help create. Exhibit 5-7 

shows how each of these counties compare to state-level averages for several 

measures gauging entrepreneurial activity. 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 66

Exhibit 5-7 Growth for Self-Employed and Wage Earning Segments of Economy 

 Growth # Selfemployed 

1998-2003 

# Self-Emp: 

Wage 

Earners 

Growth in Income 

of Self-employed 

1998-2003 

Growth in Wage 

Earnings 1998-

2003 

Marion County 3.0% 1:4 53% 21% 

Monongalia County 9.3% 1:6 138% 37% 

West Virginia 4.3% 1:5 33% 16% 

Source: REIS data and EDR Group calculations 

5.3 Evolution of Progress 

The two-county region of Marion and Monongalia has seen significant economic 

improvements over the past five to ten years. Income, employment, educational 

attainment, and other indicators all tell the story of a region on the rise. Exhibit 5-8 

shows the economic indicators for the two counties over 1990 to 2003. 

Exhibit 5-8. Economic Trends in Monongalia and Marion Counties, 1990-2003 

Monongalia County Marion County West Virginia U.S. 

 1990 2000 2003 1990 2000 2003 1990 2003 1990 2003 

Median 

household 

income 

$22,183 $28,625 $32,971 $20,386 $28,626 $31,468 $23,147 $32,967 $29,943 $43,318 

Median 

housing 

value 

$64,600 $95,500 n/a $42,300 $63,600 n/a $47,900 $85,709 $79,100 $147, 

275 

%>25 w/ 

H.S. 

dipoma 

75% 83.6% n/a 71.4% 79.5% n/a 66% 78.3% 75.2% 83.6% 

%>25 w/ 

college 

degress 

45% 32.4% n/a 12.5% 16.0% n/a 12.3% 17.0% 20.3% 26.5% 

Unemployment 

rate 

6.6% 4% 4% 11.2% 6% 6% 8.6% 8.4% 5.6% 7.6% 

Poverty 

rate 32% 15.3% 15.3% 16.3% 16.2% 16.2% 16.3% 13.5% 13.1% 12.5% 

Source: U.S. Census Bureau, “State and County Quick Facts” >

Differences between these two centers of growth. In this analysis, Morgantown and 

Fairmont have been singled out from the two-county region as the actual locations of 

the growth that has been detected at the county level, because the economic trends and 

dynamics that are producing the growth appear to be at work in these two towns much 

more than in any other place in either county. Morgantown and Fairmont are much 

more like each other than either of them is like the rest of their respective counties. 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 67

Yet the resemblance between the two places, seemingly so strong when they are 

viewed in the context of their counties, the larger region, and the rest of West Virginia, 

fades when the two cities are compared just to each other. 

The large student population in Morgantown makes it difficult to find indicators such 

as income, wage, and employment to demonstrate Morgantown’s better position, but 

reports from stakeholders in both towns confirm that Morgantown has made a leap to 

a new plane of growth and development that has not occurred (yet) in Fairmont. In 

fact, more than one stakeholder (including those that are working in and invested in 

Fairmont’s economy) referred to the path of recent prosperity growth as flowing 

between Morgantown and Harrison County but “hopping over” Fairmont. 

One area in which this can be clearly seen is in the level of entrepreneurship and of 

reinvestment in the community by local entrepreneurs, which is much greater in 

Morgantown than in Fairmont. The entrepreneurial buzz that is so frequently 

referenced in descriptions of Morgantown is not mentioned in descriptions of 

Fairmont – even by those whose job it is to sell the town and its economic assets. 

Clearly, one major reason for this is that Morgantown has WVU and Fairmont does 

not – though that is not as clear a reason as it might appear. 

The two towns are about a twenty to thirty minutes’ drive apart – even allowing for 

increased travel difficulties at different times of the day and due to inadequate 

transportation infrastructure, the distance is certainly far less than the informal (and 

admittedly unscientific) “one day’s drive” rule of thumb often observed in the flow of 

venture capital – i.e., that the majority of funds will flow less than one day’s drive 

away from their source, so that funders can easily keep an eye on their investments. In 

other words, the distance between the two towns should not be a deterrent to the 

university’s evolving technology development and commercialization relationships 

with firms in Fairmont. Morgantown must clearly be the primary beneficiary of 

WVU’s economic energy – but shouldn’t lower development costs, if nothing else, be 

encouraging at least some spillover into Fairmont? Why shouldn’t university spin-offs 

be locating in Fairmont, perhaps not just as much as in Morgantown, but at least to a 

noticeable extent? 

Several reasons are responsible. For one thing, Fairmont is working with certain 

disadvantages that Morgantown is not. One is its degraded physical assets and 

housing stock. Simply on the basis of its residential real estate offerings, Fairmont 

cannot compete with Morgantown in the attraction and development of the type of 

entrepreneur who makes business location decisions based on where he or she wants 

to live. Less tangibly, Fairmont also is said to be lacking in the entrepreneurial 

energy, as well as the dense network of connections and resources, that are so 

important, if hard to quantify, in attracting and growing entrepreneurs. 

Fairmont also is described as having relatively undeveloped retail offerings (though, 

as shown in the entrepreneurship analysis, this may be changing), which both detracts 

from its attractiveness as a place to live and works against its potential role as a retail 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 68

hub for the adjacent counties. According to econometric and spatial analyses, 

Fairmont should be serving as a center of retail activity for the more rural areas 

surrounding it that lack a micropolitan areas of their own, and so look to Fairmont for 

goods and services. Instead, as described above, nearly the opposite is happening. 

Economic activity, instead of flowing naturally into Fairmont, is “hopping over” 

Fairmont to get between Morgantown and Harrison County. At one point in its 

history, about forty years ago, Fairmont was serving as a retail center for the region, 

with a thriving downtown that provided retail, restaurants, services, and other 

attractions that pulled in consumers from the surrounding rural areas, as well as from 

Monongalia (including Morgantown, whose retail offerings were limited at that time) 

and from Harrison County. Then the fateful decision was made to build a mall on the 

outskirts of town. The mall, quite modest by today’s standards, was the first in the 

region, and effectively killed Fairmont’s downtown in a matter of two or three years. 

By that time, a larger and fancier mall was under construction in Harrison County, 

drawing business away from Fairmont, and the Mall War, much like those that have 

been waged in similar communities all over the U.S., was on. 

At the same time, the purchasing power of the rural areas that, theoretically, would be 

looking to Fairmont as a retail center, has been steadily eroding, along with the 

transportation assets that would facilitate the flow of consumers from the rural areas 

into Fairmont. (The only truly well-developed transportation route in the region is 

Interstate 79, which connects throughout the region all the way up to Pittsburgh and 

beyond – with the result that those in the region willing to travel to find consumer 

opportunities often simply drive into Pittsburgh, which of course has retail options 

well beyond anything northern West Virginia has to offer.) Fairmont’s retail offerings 

have been relatively under-invested ever since, to the point where they not only do not 

attract much business from outside Fairmont but are a detriment to its attractiveness as 

a place to settle and live. Consequently potential entrepreneurs may be discouraged 

from locating there not only on their own account, but also because of the increased 

difficulty of finding, attracting, and keeping talented employees. 

Another major difference between the two towns is in the role that federal investment 

has played in the local economy. Both have been the beneficiary of federal dollars. If 

anything, Fairmont has received more direct investment than Morgantown, since 

Congressman Mollohan is focusing primarily on Marion County while Senator Byrd 

looks at the entire state. In fact, one of the major investments that Senator Byrd 

secured for West Virginia, the FBI laboratory, was located in Harrison County rather 

than in Morgantown in order to keep it from being too close to the state border and 

providing jobs for Pennsylvania residents rather than West Virginians. 

It appears, however, that the investments made in the Morgantown area have been 

parlayed into increased economic capacity, while those made in Fairmont have not. 

The firms in the Morgantown area that have been the beneficiaries of federal contracts 

have, for the most part, used the federal work to create or expand their ability to 

compete in the private sector, while the Fairmont firms are still quite dependent on 

federal contracts. If the federal investments dried up tomorrow, most of the affected 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 69

firms in Morgantown would be able to survive through their private sector work; most 

or all of the Fairmont firms would close immediately. 

This continued dependence on the federal dollars is partly due to the fact that at the 

time the federal investments started coming in, Morgantown was simply farther along 

in its economic development path than Fairmont was. There were more assets already 

in place for the federal dollars to leverage, and the firms already had some experience 

in competing in the private sector technology-intensive marketplace. The most 

significant investments in Fairmont were not building on existing technology capacity; 

many of the firms now working on federal contracts did not exist prior to the federal 

investment. Being at an earlier stage than Morgantown in its technology development, 

added to the disadvantages described above, has meant that Fairmont has not yet used 

its federal dollars to create lasting technology and economic capacity. 

A brighter future for Fairmont. There is, however, good reason for optimism 

regarding future progress in Fairmont. As mentioned above, Fairmont is in an earlier 

stage of development than Morgantown, and has many strong public and private sector 

players working to move the town along its development path to a point similar to 

where Morgantown is now – at which technology, entrepreneurship, and outside 

investment all converge to create a surge of economic activity. The West Virginia 

High Technology Center, for example, located in Fairmont and created through federal 

funds obtained by Congressman Mollohan, works to help technology-based businesses 

that focus primarily on federal contracts diversify and develop the capacity to compete 

in the private sector. At the same time, Fairmont’s economic development authorities 

and Chamber of Commerce are thinking about how to parlay the technology-based 

businesses that have developed as a result of federal investment into more economic 

growth in other sectors. 

One way in which they are already learning from Morgantown’s example is in the 

reinvestment of entrepreneurial wealth into the community. Technology 

commercialization combined with entrepreneurial energy has produced a great deal of 

wealth in Morgantown, and the Chamber of Commerce and city planners have been 

successful in securing some of this wealth as investment in new development in the 

city. Several new retail, service, and other amenity developments are underway in 

Morgantown, financed by local entrepreneurs. These developments continue to add to 

Morgantown’s attractiveness as a place to live, work, and invest. 

Fairmont stakeholders see the wealth that is being created in its technology-based 

businesses as a result of federal investment, and know that one of the primary ways in 

which that wealth can be parlayed into greater overall prosperity for the town is for the 

entrepreneurs creating the wealth to reinvest in the community. They also know, 

however, that it is difficult to attract investors to a place that does not appear to be 

receiving other kinds of investment – not to mention that if they want to create the sort 

of attachment to place that helps inspire Morgantown’s entrepreneurs to invest there, 

the town itself must work to make Fairmont a place in which entrepreneurs want to 

live and build. The town is therefore about to start a comprehensive downtown 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 70

revitalization project that will create new access to downtown from I-79 (also partly 

financed through federal funds) and will include riverfront development and retail and 

street-level renovation as well. The project will address some of Fairmont’s primary 

obstacles to growth: difficulty of access, minimal retail offerings, and undeveloped 

amenities, and may prove the catalyst that, by improving the town’s aesthetic and 

place-based appeal to its entrepreneurs and other potential investors, will help move 

Fairmont onto a different trajectory of growth – the one that has brought prosperity to 

its neighbor and collaborator Morgantown. 

5.4 Catalysts of Change 

Several notable individuals were identified as connected to growth initiatives in 

Morgantown and Fairmont, particularly Senator Byrd and Congressman Mollohan for 

the federal investment that they have brought to the region, and President Hardisty for 

the focus on technology commercialization that he has brought to West Virginia 

University. 

Even more significant than these key individuals, however, and what makes the region 

truly distinctive, is the strong network of connections that brings together stakeholders 

from different realms – the university, the economic development community, the 

workforce development fields, community planners, the private sector, and others. 

While it may seem that networks like these exist in all economically dynamic 

communities, the capacity to connect and collaborate outside the borders of a given 

identified community is not one that is native to most of West Virginia (though West 

Virginians are known for fierce loyalty and strong community feeling within the 

communities they claim as their own). 

Earlier, it was described how the state’s counties, towns, and communities tend to 

define themselves inwardly, rather than as part of a larger unit, and to resist 

connecting with those they consider outsiders. One rather extreme illustration of this 

point is found in the experience of the Monongalia County officials have made more 

than one attempt to spread some of the wealth from Morgantown into the rest of the 

county. The officials met with intense resistance from the county residents, who 

resented intruders from outside their own communities attempting to influence the 

path of their development. When the officials persisted, the county residents 

responded by calling a special referendum and voting to disband the county planning 

commission. Just as, in resisting commuting, the county’s residents resist a 

geographical connection with Morgantown and other “outsider” communities, they 

also resist any sort of external planning or development process that might take some 

control out of their own hands – even if, on their own, they cannot hope to develop 

very far. 

In contrast, Morgantown and Fairmont are part of a vibrant network of collaborators 

and connectors, entrepreneurs and entrepreneurially minded public sector officials 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 71

who include other towns, communities, and counties in nearly every initiative they 

undertake. In fact, they are much more identified with the I-79 Corridor, and the 

people and organizations behind it, than they are with the rest of their own counties. 

This spirit of collaboration was noted by more than one community stakeholder as 

what truly makes Morgantown, in particular, and Fairmont as well, different from the 

rest of their respective counties. 

5.5 Lessons Learned 

One of the most difficult aspects of discerning the lessons learned in a place like 

Morgantown/Fairmont is separating out the effects of non-replicable advantages, such 

as large flagship universities and federal investment, and the effects of what has been 

created here and could be created elsewhere. The most fruitful way to think about it is 

not to say that every community should have a university and powerful Congressional 

representation if it possibly can, but rather to hold Morgantown and Fairmont up 

against comparable communities. There are other communities that have universities, 

and even some level of federal investment, that do not embark on a tech boom. Given 

the presence of these advantages, what should a community do with the resources it 

can control to try to parlay them into lasting economic growth? 

First, follow the example of WVU’s President Hardisty: give the university a specific 

and focused role in local technology commercialization. Many state and regional 

universities consider themselves economic assets merely because of the jobs they 

create or even the technology they deploy and sell; it is the explicit focus and followthrough on the creation of new businesses, or new product lines in existing businesses, 

that makes WVU not just an economic asset, but an economic driver. 

Second, think and act regionally and in complementary fashion. Morgantown and 

Fairmont, alone out of their counties, are thriving; and they alone are connected to 

communities beyond their own, and show an awareness in their planning of being 

situated in a regional economic context that has little to do with political borders. 

While it is the work of years to overturn a community’s insular tendencies, a few 

leaders who are willing to do the uphill work of making the initial connections, and 

demonstrating the economic power of these connections, especially when each city’s 

economic activity has been focused to complement rather than compete with the 

other’s. 

Third, turn investment into capacity. By connecting its federal investments with 

other industries, by using its developing technology expertise and workforce to attract 

private sector firms and private capital, by assisting federally-invested firms to expand 

into the private sector market, Morgantown has avoided the dependency trap into 

which so many recipients of federal investment fall. Fairmont, on the other hand, has 

yet to make these connections, and is still mostly dependent on its government 

contracts. 
Vol.2 Case Studies of Economic Growth Processes Marion & Monongalia 

Sources of Growth in Non-Metro Appalachia page 72

Finally, what do you do if you are the “other” city – the Fairmont, the town that is 

doing pretty well, considering its context – but receiving less attention than 

Morgantown, the university city that attracts all the attention, entrepreneurs, new 

firms, and venture capital? The primary thing is to assess where your success can fit 

into the other city’s and the region’s success. In Fairmont’s case, it is beginning to 

realize that there are considerable opportunities to be had in supporting and facilitating 

the economic activity taking place in Morgantown—more than there are in attempting 

to compete with Morgantown for that activity. For instance, Fairmont is not likely to 

create another center of commercially-oriented, high-technology research and 

development. It can, however, create a center of R&D that focuses on technology 

application and transfer, to complement the R&D being performed in Morgantown, 

and to foster the growth of businesses that can serve as complements to the high-level 

technology firms spinning off WVU. Fairmont State University is beginning to 

examine how it can develop its own R&D capacities to complement those of WVU. 

5.6 Interviewees 

Charlie Reese, Director, Marion Regional Development Corporation 

Bruce McDaniel, City Manager, City of Fairmont 

Dr. Dan Bradley, President, Fairmont State University 

Blair Montgomery, President, Fairmont State Community and Technical College 

Dale Bradley, Assistant Provost of Workforce Development at Fairmont State 

Community and Technical College 

Paul Schreffler, Director of Economic Development and Workforce Education at 

Fairmont State Community and Technical College 

Jim Hall, LDD Director 

Don Reinke, Director, Morgantown Area Economic Partnership and Monongalia 

County Economic Development Authority 

Bob McLaughlin, President, I-79 Economic Development Council 

Russ Lorince, Director of Economic Development at West Virginia University 

Christopher Fletcher, Monongalia county planner 

Scott Rostruck, Morgantown Area Chamber of Commerce 

Jim Estep, President, West Virginia High Technology Consortium. 

Barbara DeMary, Executive Director, Region VI Workforce Investment Board 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 73

6 SOUTHEAST TN & SOUTHWEST NC:

RE-TOOLING ASSET-BASED GROWTH 

6.1 Introduction 

SE Tennessee/SW North Carolina are covered by two adjacent Local Development 

Districts that are connected by Appalachian Highway Corridor K. For McMinn 

County, TN the case study examines the role of entrepreneurship and the arts in 

maintaining the economic viability of a rural area that has had substantial loss of 

manufacturing employment. In addition, the case study traces economic development 

efforts to develop cultural and recreation tourism along Corridor K between 

Chattanooga, TN and Asheville, NC. 

This case study profiles economic development efforts focused on tourism potential in 

recreation-based and cultural heritage, principally along Corridor K in the 

Southeastern Tennessee and Southwestern North Carolina located between the metro 

areas of Chattanooga, TN and Asheville, NC. 

This region presents an interesting case study as it spans multiple counties, state, and 

the Local Development District boundaries. These jurisdictions are competing with 

each other for employers, workforce, tourists and resources, yet they have organized 

themselves for economic development as a cohesive unit, pooling resources and 

efforts to create the critical mass necessary to attain their economic development 

goals. One reason is that the case study counties are linked by Corridor K – a planned 

link in the Appalachian Development Highway System (ADHS) 

The region is also of interest as a case study because the geographic factors that 

isolated it also led to the emergence of a regional trade center in Murphy, NC. Finally, 

the region’s natural resources—forests, rivers, railroads, and mines—that were once a 

source of industrial activity are now being used diversify their economy by building a 

tourism industry based on recreation and cultural heritage. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 74

6.2 Regional Profile 

The study area is comprised of counties that are linked by the planned highway of 

Appalachian Corridor K, including the North Carolina counties of Cherokee, Graham, 

Jackson, Swain and the Tennessee counties of Bradley, Hamilton, and Polk (Exhibit 6-

1). The study area also includes the adjacent counties of Clay and Macon (NC) and 

McMinn and Meigs (TN) because their economies are closely linked to those located 

along the corridor (Exhibit 6-2). Though not part of this study, the area also has 

economic ties to northern Georgia, particularly with Fannin, Union and Towns 

Counties. 

While the counties of interest have a combined population of nearly 589,000, more 

than half of this is attributable to the Chattanooga metro area. This case study focuses 

on economic development potential in the non-metropolitan areas east of Chattanooga, 

TN and southwest of Asheville, NC. This area is isolated from the State capitals, and 

at one time even harbored an independence movement to succeed from Tennessee, 

North Carolina and Georgia to form their own state to be called Franklin. 

Large portions of the study area are public lands including Cherokee National Forest, 

Nantahala National Forest, and Hiwassee Lake State Park. For example, Swain 

County is approximately 87 percent public lands. The region is relatively isolated, 

ringed in high mountains and bisected by dramatic river gorges. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 75

Exhibit 6-1. Corridor K of the Appalachian Development Highway System 

Exhibit 5-2. Map of the Full Case Study Area 

Source: Microsoft Streets & Trips and EDR Group 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 76

Originally home to the Cherokee and vibrant early-American trade paths, the region 

has been populated since before the 1700s. In the 1820s, gold was discovered and in 

the 1840s copper was discovered. The copper mining industry in particular drove 

boom-town style growth. Railroads were constructed to serve the mining industry, 

which allowed the logging and textile industries to flourish. The textile industry 

remained strong until the late 1980s when the national textile industry began to 

experience significant losses as the industry shifted to lower-cost manufacturing in 

China. The furniture industry, which had grown out of the region’s logging activities, 

remained strong until the last decade when this industry also went abroad. 

Population Growth. Exhibit 6-3 presents population in each of the study area counties 

for the past four decennial Censuses. The largest county by far is Hamilton, TN (pop. 

307,896), which includes Chattanooga. Bradley and McMinn Counties also have 

relatively large populations, while Graham and Clay have populations under 9,000. 

During the 1990s, population growth in three of the six North Carolina counties 

outstripped that of the state as a whole, while three of the five Tennessee counties 

surpassed the rate of growth statewide. 

Exhibit 6-3. Population and Population Growth 

Population by County 1970 1980 1990 2000 1970-80 1980-90 1990-2000 1970-2000

Hamilton County, TN 256,190 288,370 285,536 307,896 13% -1% 8% 20%

Bradley County, TN 51,230 67,770 73,712 87,965 32% 9% 19% 72%

McMinn County, TN 35,600 42,030 42,383 49,015 18% 1% 16% 38%

Jackson County, NC 21,593 25,811 26,846 33,121 20% 4% 23% 53%

Macon County, NC 15,910 20,380 23,499 29,811 28% 15% 27% 87%

Cherokee County, NC 16,410 18,990 20,170 24,298 16% 6% 20% 48%

Polk County, TN 11,720 13,650 13,643 16,050 16% 0% 18% 37%

Swain County, NC 8,835 10,283 11,268 12,968 16% 10% 15% 47%

Meigs County, TN 5,250 7,450 8,033 11,086 42% 8% 38% 111%

Clay County, NC 5,200 6,660 7,155 8,775 28% 7% 23% 69%

Graham County, NC 6,560 7,210 7,196 7,993 10% 0% 11% 22%

State of Tennessee 3,926,018 4,591,120 4,877,185 5,689,283 17% 6% 17% 45%

State of North Carolina 5,082,059 5,881,813 6,628,637 8,049,313 16% 13% 21% 58%

Source: US Census Bureau.

Population % Change

Economic Profile. Exhibit 6-4 presents employment trends by NAICS sector in the 

study area for 1997 and 2002. The top three sectors across both years are government, 

retail trade, and health care/social services which is typical for many areas in the 

nation, particularly under-performing areas such as those found in Appalachia. 

Accommodations, eating & drinking is the fourth largest source of employment, and is 

important to regional tourism. Construction is fifth, which is supported by the pace of 

second-home construction discussed later in this case study. Transportation is sixth, 

which reflects the presence of Interstate 75 at the western edge of the study area, a 

major east coast north-south trade corridor. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 77

Exhibit 6-4. Employment by Industry, 1997-2002 

2002 LQ

Annual

NAICS Sector 1997 2002 Growth

524 Insurance Carriers & Related Activities 10,147 14,241 7.0% -0.3% -1.2% 2.7

335 Electric Equipment, Appliances, etc. 5,703 6,612 3.0% -4.2% -5.3% 2.5

325 Chemical Manufacturing 4,349 8,355 13.9% -3.0% -4.5% 2.4

311 Food Products 8,413 9,373 2.2% 1.4% 0.4% 2.2

481-487 Transportation 10,344 20,878 15.1% 1.1% 1.7% 2.2

322 Paper Manufacturing 3,268 3,765 2.9% -1.3% -3.6% 2.0

114 Fishing, Hunting & Trapping 23 552 88.4% 44.1% 12.3% 1.9

331 Primary Metal Manufacturing 3,887 1,570 -16.6% -7.3% -4.8% 1.8

813 Religious, Civic, Professional, Organizations 6,751 6,781 0.1% -1.6% 1.4% 1.3

212-213 Mining & Support Activities 328 455 6.8% -3.6% 2.9% 1.2

315 Apparel Manufacturing 6,328 2,707 -15.6% -17.2% -13.6% 1.2

441-454 Retail Trade 45,460 45,340 -0.1% -0.4% -0.3% 1.1

333 Machinery Manufacturing 4,095 3,218 -4.7% -3.3% -4.7% 1.0

339 Miscellaneous Manufacturing 1,913 1,589 -3.6% -3.7% -2.0% 1.0

491-493 Mail, package delivery & warehousing 3,675 5,944 10.1% 8.3% 6.4% 1.0

531 Real Estate 7,100 9,913 6.9% 8.2% 9.7% 1.0

541-551 Professional Scientific, Technical, Services 17,006 21,846 5.1% 7.3% 7.0% 1.0

721-722 Accommodations, Eating & Drinking 23,510 26,377 2.3% 2.6% 2.3% 1.0

811-812 Repair, Maintenance, & Personal Services 9,912 12,883 5.4% 4.9% 3.8% 1.0

113 Forestry & Logging 811 423 -12.2% -7.3% -4.9% 0.9

221 Utilities 1,005 747 -5.8% -9.0% -4.9% 0.9

230 Construction 26,505 22,965 -2.8% -0.7% 0.2% 0.9

332 Fabricated Metal Products 4,003 3,486 -2.7% -1.5% -2.5% 0.9

532 Rental & Leasing Services 1,148 1,908 10.7% 7.6% 6.9% 0.9

621-624 Health Care & Social Services 27,790 29,153 1.0% 2.7% 2.6% 0.9

814 Private Households 1,886 3,646 14.1% 11.0% 10.1% 0.9

920 Government & non NAICs 44,422 47,674 1.4% 1.4% 1.0% 0.9

112 Animal Production 2,269 2,659 3.2% 4.9% 8.1% 0.8

323 Printing & Related Support Activities 2,130 1,420 -7.8% -4.0% -3.6% 0.8

337 Furniture & Related Products 4,676 3,160 -7.5% -4.3% 0.0% 0.8

420 Wholesale Trade 14,036 11,800 -3.4% -2.6% -3.2% 0.8

521-523 Monetary, Financial, & Credit Activity 6,053 6,797 2.3% 2.3% 1.8% 0.8

561 Administrative & Support Services 20,388 18,300 -2.1% 1.1% 0.0% 0.8

562 Waste Management & Remediation 895 533 -9.8% 1.0% 8.9% 0.8

611 Educational Services 3,212 4,098 5.0% 1.9% 0.1% 0.8

711-713 Amusement & Recreation 4,709 5,320 2.5% 4.9% 3.7% 0.8

312 Beverage & Tobacco Products 1,135 842 -5.8% -1.3% -0.6% 0.7

313 Textile Mills 3,199 2,908 -1.9% -8.6% -6.8% 0.7

336 Transportation Equipment 1,683 3,123 13.2% -2.5% -3.9% 0.7

512 Motion Picture & Sound Recording 647 414 -8.6% -7.1% -4.4% 0.6

513 Broadcasting 1,717 1,762 0.5% 2.8% 2.9% 0.6

321 Wood Products 1,398 1,110 -4.5% -5.3% -3.8% 0.5

326 Plastics & Rubber Products 2,078 1,583 -5.3% -2.4% -3.0% 0.5

327 Nonmetallic Mineral Products 1,602 751 -14.1% -3.6% -2.6% 0.5

511 Publishing Industries (except Internet) 1,463 927 -8.7% -4.6% -6.0% 0.5

111 Crop Production 2,377 1,598 -7.6% -4.3% -2.9% 0.4

314 Textile Product Mills 1,752 384 -26.2% -5.5% -4.4% 0.4

334 Computer & Electronic Products 189 932 37.6% -4.6% -5.3% 0.4

115 Support for Agriculture & Forestry 272 219 -4.2% 10.4% 1.1% 0.0

211 Oil & Gas Extraction 120 0 -100.0% 40.5% -5.7% 0.0

316 Leather & Allied Products 283 177 -9.0% -15.5% -11.5% 0.0

324 Petroleum & Coal Products 48 208 34.2% 4.4% -2.9% 0.0

514 Internet & data process svcs 196 242 4.3% 15.7% 6.1% 0.0

525 Funds, Trusts, & Other Financial Vehicles 172 27 -30.9% 4.9% 12.3% 0.0

533 Lessors of Nonfinancial Intangible Assets 58 41 -7.0% -2.9% -8.5% 0.0

TOTAL 358,539 383,736 7.0%

Source: EDR-LEAP (with IMPLAN data) and EDR Group.

Study Area/ TN 

& NC

Study Area Annual Growth

TN & NC 

Combined

United 

States
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 78

As shown in Exhibit 6-4, the region’s major manufacturing sectors include food 

products, chemical manufacturing, electrical equipment and appliances, furniture, 

paper, textiles, apparel, fabricated metal products, machinery, and transportation 

equipment. 

Despite losses between 1997 and 2002 in historically important sectors such as textile 

mills, apparel manufacturing, forestry and logging, furniture and wood products, and 

mining, the region experienced a net gain of more than 25,000 jobs. The computer and 

electronic products industry grew from under 200 employees to nearly 1,000 and other 

large gains occurred in the transportation services, chemical manufacturing, 

transportation equipment manufacturing, and package delivery & warehousing 

industries. 

The last column of Exhibit 6-4 also shows the 2002 location quotients (LQ) for the 

study area. In comparison to the employment mix of Tennessee and North Carolina 

combined, the study area has particularly high shares of employment in insurance, 

electric equipment and appliance manufacturing, chemical manufacturing, food 

products and transportation. The region also has relatively higher shares of 

employment in paper manufacturing, fishing/hunting/trapping, primary metal 

manufacturing, mining, apparel manufacturing, apparel manufacturing, furniture 

manufacturing, and mining. These industries reflect historical strongholds of the 

region. 

Regional Connections. One of the most notable features of the region is the lack of 

connection between Chattanooga and Asheville/Charlotte (Exhibit 6-5). Currently, the 

trip from Chattanooga to Asheville involves a five-hour, 230 mile journey around 

Great Smoky Mountains National Park through Knoxville on I-75 and I-40. An 

alternative route runs through Cherokee and Nantahala National Forests, a 200-mile 

journey taking nearly six hours. This second route along Corridor K involves several 

stretches of modern 4-lane divided highway, however in two key spots along the 

Ocoee and Nantahala Gorges, the road narrows to just two lanes, so that trucks must 

sometimes maneuver into oncoming traffic to negotiate tight curves. During the 

summer months, the route becomes congested with tourists flocking to rafting, 

mountain biking, hiking and other recreational activities in the two gorges. 

Traditionally, Chattanooga was the industrial anchor of the region, connected to 

Cincinnati and Atlanta via I-75 and to Nashville via I-24. Murphy, NC also developed 

an industrial concentration, and has a highway connection south to Atlanta. However, 

the region has weak east-west linkages, which economic development leaders see as a 

barrier to business attraction and job growth. In particular, it is seen as limiting access 

to East Coast international trade and European market connections. 

Economic development leaders in the region have identified the potential for job 

growth and economic benefits for Chattanooga; increased commerce between 

Chattanooga and points east; and the opportunity for Murphy and Sylva, NC to tie into 

the I-65 “Auto Parts Alley” as likely outcomes of improved east-west connection and 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 79

improved linkages to the Interstate Highway system. Though regional leaders see 

Chattanooga experiencing the greatest benefits from such improved connections, they 

expect positive spillover effects to occur throughout the region. 

Exhibit 6-5. Study Area Interstate and Highway Connections 

6.3 Evolution of Progress 

Despite the region’s relative isolation and manufacturing losses which parallel those 

that have occurred in the national economy, the region has experienced some 

economic development success by fostering its tourism industry, and building on 

manufacturing assets to recoup a portion of jobs lost. 

Tourism. The study area is outside of higher profile tourist areas near Asheville such 

as the Great Smoky Mountains and Blue Ridge Parkway. However, as the tourism 

industry has shifted away from more traditional types of outdoor recreation based on 

relaxation and visual beauty toward more active forms of recreation such as hiking, 

rafting, mountain biking and so-called “ultimate sports”, the region has been able to 

capitalize on its outdoor and cultural heritage assets. For example, Cherokee County 

Chamber of Commerce has changed its strategy from promoting “Peace in the 

Mountains” to promoting “Fun in the Mountains”. Regional outdoor recreation, 

cultural heritage, and second-home market assets are described below. Later ections 

describe how regional leaders have cooperated to develop and promote these assets. 

Corridor K

Corridor A

Study Area
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 80

Outdoor Recreation. Outdoor recreation assets include the challenging class IV rapids 

of the Ocoee River, which was the site of the 1996 Atlanta Olympic Games. The 

Nantahala River has class II and III rapids which are less challenging and more 

oriented towards beginner and intermediate rafters (though Ocoee outfitters offer trips 

that do not require previous rafting experience). Local companies also facilitate 

outdoor activities including hiking, mountain biking, horseback riding, fishing, 

hunting, rock climbing and repelling, water skiing, llama trekking, hang gliding, and 

bird-watching, though many of these activities can also be done by individuals on their 

own. Outdoor attractions include the Unicoi Turnpike Trail, Benton-MacKaye Trail, 

Appalachian Trail, Lost Sea cavern and underground lake, and Cherohala Skyway. 

Cultural Heritage Tourism. In addition to outdoor recreation amenities, the region is 

rich in cultural heritage sites and activities. The region is crisscrossed by 1,000 year 

old trade passages, steeped in Native American history, and has many remnants of the 

Industrial Revolution. Organizations such as the Tennessee Overhill Heritage 

Association (discussed below) are working to restore, revitalize, and interpret these 

cultural heritage amenities to attract visitors, increase visitor spending, and contribute 

to the region’s economic base. Cultural heritage attractions include the John C. 

Campbell Folk School, Sequoyah Birthplace Museum, Ducktown Basin Museum, 

L&N Depot and Rail Museum, McMinn County Living Heritage Museum, Swift 

Museum (Globe and Temco Swift airplanes), Englewood Textile Museum, Mason’s 

Corn Maze, and Fields of the Wood Bible Park. 

Second Home Market. In recent years, the region has experienced a surge in second 

home construction. In fact, Murphy, NC recently appeared on Forbes list of emerging 

second home markets. This is seen as a positive development as seasonal residents 

demand very little of municipal services while contributing to the local economy 

through dining, entertainment, health care, property taxes and other expenditures. 

Recently, the completion of Corridor A to the east of the study area has improved the 

region’s connection with points east, which has fanned the second home market in 

counties toward the eastern end of the study area. In fact, several area towns double in 

population during the summer as city-dwellers primarily from Florida and Georgia 

escape to their vacation homes. In some areas, the pace of growth is beginning to 

generate concerns of gentrification, though there has not yet been a movement for 

zoning or land use restrictions most areas. A zoning referendum recently put to voters 

in McMinn County was defeated. The more isolated counties such as Polk and 

Cherokee have not yet experienced the same pace of growth, thus there has been little 

interest in regulation. 

Building Critical Mass. Many of the region’s tourism assets have traditionally drawn 

either from a day-trip market or catered to very specialized interests such as bird 

watchers or crafts enthusiasts. In a later section, this case study shows how regional 

organizations are working together to expand their offerings and bundle them in a way 

that transforms the region from a local attraction drawing from adjacent areas into a 

major tourist area drawing overnight visitors from throughout the southeastern US. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 81

Industry and Trade Center. Throughout the region’s history, its topography 

reinforced regional cohesiveness. The high mountains that ring the region create a 

“bowl” around the Tennessee county of Polk, North Carolina counties of Cherokee 

and Clay, and Georgia counties of Fannin, Union (GA) and Towns (GA), with 

Cherokee County at the center. This bowl, together with highway connections 

centered on Cherokee County, made the county a natural regional trade center (Exhibit 

6-6A-B.) Though the county itself has a population of only about 24,300, the market 

area population is approximately four times that. As evidence of Cherokee County’s 

role as a trade center, in 2005 the county ranked 71st in population but 20th in per 

capita retail sales among the state’s 100 counties. Most of this activity takes place in 

Murphy, the county’s largest city. 

Exhibit 6-6A Topography Defining the Murphy, NC Trade Center 

Exhibit 6-6B Highway Links to the Murphy, NC Trade Center 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 82

In addition to creating a retail trade center, topography and highways supported a 

natural labor market. This labor market combined with metals from the mining 

industries, and extensive railroad connections, fostered significant manufacturing 

activity that would have been unlikely without this labor market. Despite 

manufacturing losses that mirror those experienced in the nation as a whole, the region 

has a strong presence of metal fabrication and machine and equipment manufacturing. 

Major companies in Murphy include MGM Brakes, Moog Components and Sioux 

Tools. 

Though much of the economy is in transition toward tourism, regional leaders are still 

pursuing industrial development to ensure that the regional economy stays balanced 

and includes jobs with health care and retirement benefits. Through industrial 

recruitment efforts, the region has been able to regain about half the manufacturing 

jobs lost due to the relocation of the textile and furniture industries. 

Industrial recruitment is primarily achieved through networking with companies 

familiar with the area. Though Murphy has connection south to Atlanta in which the 

journey is all interstate after the first seven miles, industrial recruitment efforts are still 

challenged by the perception of greater transportation access difficulties going east or 

west. This is compounded when business prospects arrive via alternative routes 

through narrow, winding, mountain roads. Furthermore, though there is an abundance 

of flat land suitable for industrial sites, outsiders often harbor the perception that the 

entire region is mountainous. 

While Corridor K will help to address the access problem, for now much of this area is 

poorly connected to both Asheville and Chattanooga. The lack of access is felt to 

substantially reduce the representation of auto parts related industry in the region, 

though that could change if future highway improvements better link it to the “Auto 

Alley” of parts and assembly plants located along I-75. At the other end of the study 

area, an improved connection with Ashville would also connect the area to Charlotte, 

which is a key location for export-oriented industries. 

6.4 Catalysts of Change 

The study area spans several County, State and Local Development District 

boundaries. Though these jurisdictions are competing with each other for jobs, 

workforce, tourists and resources, they have organized themselves for economic 

development as a cohesive unit, pooling resources and efforts to create the critical 

mass necessary to attain their economic development goals. 

Local Development Districts. The study area spans the jurisdiction of two 

Appalachian Regional Commission (ARC) Local Development Districts (LDDs), the 

Southeast Tennessee Development District and Southwestern North Carolina Planning 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 83

and Economic Development Commission. In addition to addressing their local 

responsibilities, they have also created a partnership organization, the Southeast 

Industrial Development Association (SEIDA), which has been a catalyst for the 

region’s major economic development efforts. 

Southwestern North Carolina Planning and Economic Development Commission. 

This LDD encompasses North Carolina’s seven western-most counties and addresses 

housing and community development, land and water conservation and preservation, 

transportation planning, infrastructure, workforce development, and education. The 

organization also operates a revolving loan fund to promote access to capital for local 

businesses. 

Southeast Tennessee Development District. The Southeast Tennessee Development 

District covers a 12-county service area which includes three Georgia Counties (Dade, 

Walker, and Catoosa). The organization encompasses 11 functional areas: Aging and 

disability planning, regional planning, utility development, tourism development, 

housing development, industrial recruitment and marketing, transportation planning, 

economic development, recreation and cultural resources, solid waste and natural 

resources and workforce development. 

The LLD operates as part of a consortium called the Southeast Development Resource 

Group, which includes the Chattanooga Area Regional Council of Governments, 

Southeast Tennessee Local Development Corporation, and several other business, 

industrial and community development agencies all of which are staffed and 

administered by the LDD. The LDD also staffs and operates SEIDA, which has been a 

powerful force for regional economic development. 

Southeast Industrial Development Association. The Southeast Industrial 

Development Association (SEIDA) is a unique example of cross-jurisdictional 

cooperation. The organization is comprised of the economic development arms of the 

Tennessee Valley Authority (TVA) power districts in Tennessee, North Carolina and 

Georgia as well as three regional LDDs— Southeast Tennessee Development District, 

Southwestern North Carolina Planning and Economic Development Commission, and 

the Coosa Valley Regional Development Center (Georgia). These three organizations 

contract with the Southeast Tennessee Development District to staff and administer 

SEIDA programs and initiatives. In turn, SEIDA promotes and coordinates industrial 

development and tourism initiatives in the region. 

Three decades ago, the organization laid out a comprehensive plan for regional 

economic development with 10 functional areas and capital budgeting. Preservation of 

the region’s natural resources was a key element of the strategy, and the organization 

has been involved in land swaps that have allowed large tracts of land to remain 

pristine and undisturbed while preserving the natural beauty of recreation lands and 

facilitating industrial development on less sensitive areas. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 84

According to local economic developers, the willingness of local leaders to work 

together emerged several decades ago through participation in ARC project 

prioritization exercises. Rather than seeing themselves as leaders of individual 

counties, they see themselves as regional leaders. They are comfortable promoting 

projects outside of their own jurisdiction because they recognize that they will benefit 

either as “the rising tide lifts all boats” or simply because working together provides 

the critical mass necessary to fund and execute projects that each county alone could 

not. 

In terms of tourism promotion, regional leaders have realized that outdoor recreation is 

a given, thus their focus is on extending the season beyond the Memorial Day-Labor 

Day period (“add shoulders to the season”), extending stays (adding room nights and 

meals), and promoting activities that generate revenue (“make the cash registers 

ring”). 

One strategy to achieve these goals is water releases on the upper Ocoee River. 

Currently, the river has approximately 120 days of water on the lower portion which is 

long enough for half-day rafting excursions. Releases into the upper portion of the 

Ocoee River would allow for full-day rafting trips. Half-day rafting trips draw visitors 

from about a 150-miles radius, whereas full-day excursions draw from beyond that 

and encourage overnight stays even from visitors from within the current market area, 

as a full day of rafting is physically taxing. After a long, complicated planning 

process, SEIDA was able to borrow $1.1 million to buy water from TVA for release 

into the upper Ocoee River for 34 of the existing 120 days. SEIDA has signed longterm contracts with rafting outfitters wishing to operate on the upper Ocoee, and will 

repay the loan using fees collected from these outfitters. 

Tennessee Overhill Heritage Association. One successful example of crossjurisdictional cooperation for tourism development is the Tennessee Overhill Heritage 

Association, which focuses on McMinn, Monroe, and Polk Counties in Tennessee. 

SEIDA formed this organization in 1990 as a pilot through the National Trust for 

Historic Preservation’s Heritage Tourism Initiative. During that period, the 

development of I-75 just west of the study area had shifted manufacturing away from 

the McMinn, Monroe, and Polk Counties and towards the I-75 corridor. Around the 

same time, there was a reduction in the amount of timber allowed to be cut, drastically 

limiting a major source of local government revenues. 

Regional leaders realized the need to diversify their economic development strategy to 

include more emphasis on cultural heritage tourism. Tennessee Overhill was created to 

jointly market the region’s small museums with the idea that though each individual 

museum would not attract many overnight visitors, together they would form enough 

of an attraction to draw from a larger overnight market. Within the first year, the need 

to include the region’s recreation and outdoor assets became apparent. At the end of 

the three-year pilot, the organization became permanent, receiving operational support 

from the three county governments, City of Etowah, and special projects support 

through grants, donations and earned income. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 85

Tennessee Overhill is unique in several ways. First, it does not operate on a “pay to 

play” basis which requires attractions to pay to be included in marketing efforts. 

Instead, Overhill considers each attraction’s contribution to area hotels, restaurants 

and shops and its role as a piece of the whole tourism experience. Second, rather than 

a blanket marketing campaign, Overhill advertises in less expensive and more targeted 

venues, such as birding and whitewater rafting publications. Finally, cultural heritage 

travelers are not motivated by a list of an area’s attractions. Instead, they are looking 

for content and interpretation, which Overhill brochures, publications and website 

provide. 

The organization’s major outdoor initiative is the rehabilitation of the 40-mile Old 

Line Haiwasee River Gorge Rail Road and operation of passenger excursions. This 

railroad used to serve a major copper mine operated by Tennessee Minerals, LLC that 

has since left the region. As the line was no longer in use, CSX planned to dismantle 

it. Seeing an opportunity to preserve a piece of the region’s history and offer passenger 

excursions through otherwise inaccessible wilderness, SEIDA negotiated acquisition 

of line from CSX and entrusted it to Tennessee Overhill. Before acquiring the railroad, 

SEIDA and Overhill invited a team from the National Forest Rails to Trails program 

to inspect its condition, and the team found that if necessary the materials could be 

salvaged with minimal invasiveness, leaving the bridges and surrounding landscape in 

tact. This provided SEIDA and Overhill with the confidence of a “plan B” in the event 

that it was not profitable. 

SEIDA also oversaw an ARC-sponsored feasibility study which determined that the 

line would need to run approximately 500 cars of freight in addition to passenger 

excursions to remain profitable. Some 10 million tons of iron oxide, commonly called 

calcine and recognizable as rust remain on the former mine site, and Tennessee 

Minerals has decided to sell it to “pig iron” manufacturing operations in China. Thus, 

Glenn Springs Holdings, a wholly owned subsidiary of Occidental Petroleum charged 

with the environmental remediation of the former mine, restored the tracks to the point 

that they could carry the freight. The company then entered a contract with Overhill 

for use of the tracks for cargo and in 2005, the first eight trainloads were shipped out. 

The contract also preserved the right for Overhill to operate passenger excursions, 

which will consist of shorter trips to the Appalachia Powerhouse, the Bald Mountain 

Loop, and a few full-day excursions to the Copperhill mine. 

The organization sees potential for future tourism development associated with the 

Benton-MacKaye trail, a 300 mile footpath between Springer Mountain in Georgia to 

Davenport Gap on the northern edge of the Great Smoky Mountains National Park 

through isolated backcountry and numerous federally designated Wilderness and 

Wilderness Study Areas. Overhill has observed that there are numerous cultural and 

historic points of interest along the trail that could be further developed and promoted. 

Southeast Tennessee Tourism Association. Another entity staffed and administered 

by the Southeast Tennessee Development District is the Southeast Tennessee Tourism 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 86

Association, which markets the region as a visitor destination. Recently, the 

organization embarked upon an interesting project to promote the region’s diverse 

religious heritage. In 2005, the organization secured a $60,000 relief grant from the 

Economic Development Administration to help the region overcome damage done by 

a severe infestation of Southern Pine Beetles that hit two key regional industries, 

logging and agriculture. 

The organization used the funds to hire a consultant from the National Trust for 

Historic Preservation to identify regional assets. The consultant identified the 

opportunity for the development of a religious heritage trail, which would capitalize 

on the region’s diverse religious history. The concept was well received by the public, 

and the concept has been developed into the “Glory Land Road”. The self-guided trail 

features the courthouse where the “Scopes Monkey Trial” took place; a Holocaust 

Museum in Whitwell; Beth Salem, a historic black church in McMinn County; the 

Brainerd Mission in Chattanooga where Cherokee Indians were converted to 

Christianity; and Our Lady of the Poor Shrine in Marion County which is a replica of 

a famous church in Belgium where the Virgin Mary is said to have appeared. The 

Tourism Association estimates that the region draws approximately 60,000 to 80,000 

visitors each year for religious events and conferences, and the Religious Heritage 

Trail is designed to lengthen their stay, generate return visits, and increase 

expenditures. 

Among other efforts, the organization also publishes the Outdoor Recreation Guide, 

Shopping Guide to the Art Trails, and Out to The Farm Agritourism Guide, all of 

which string smaller attractions together into a more comprehensive experience. 

Coker Creek Economic Development Group. CCEDC provides an example of a local 

economic development organization’s tourism promotion efforts. Coker Creek, 

Tennessee is surrounded by National Forest and is cut off from industrial development 

alternatives by Tellico Mountain on the North, Farner Mountain on the south, Unicoi 

Mountain on the east, and Cataska mountain on the west. Despite a lack of 

manufacturing, the area is rich in craft and folk art tradition, and the Coker Creek 

Economic Development Group (CCEDG) was initially formed to preserve and pass on 

these traditions to local residents. The organization found that outsiders were also 

interested in learning folk art traditions, and now craft workshops include visitors from 

Georgia, Florida and other areas. CCEDG operates the local post office which is 

adjacent to the organization’s Welcome Center and arts and crafts retail space. 

Barriers to Cooperation. Though the organizations discussed above have coordinated 

to overcome jurisdictional barriers to economic development, some difficulties 

remain. The State tourism bureaus of Tennessee, North Carolina, and Georgia have 

resisted funding cross-jurisdictional marketing efforts. A result is that though tourists 

consider the region a contiguous recreation area, there are separate guide materials for 

each of the three states. 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 87

The State transportation departments also lack mechanisms for working together, 

which has challenged the region’s efforts to improve transportation connections. 

Priorities for the Future. Regional economic development leaders are virtually 

unanimous regarding the region’s leading economic development priority, and that is 

the completion of Corridor K. Corridor K is part of the Appalachian Development 

Highway System (ADHS) authorized by the Appalachian Development Act of 1965. 

As a whole, the ADHS encompassed more than 3,000 miles of highway necessary to 

connect Appalachia with economic development opportunities, and to date 85 percent 

of the system has been completed. Corridor K is one of the last remaining pieces of the 

system. Though the final alignment has not yet been determined, Corridor K would 

run from I-75 near Cleveland, Tennessee to Corridor A near Sylva, North Carolina, 

resulting in a direct connection from Chattanooga to Ashville (and consequently, 

Charlotte). 

The North Carolina portion of Corridor K has already gone through the Environmental 

Impact Study (EIS) process and been approved. The Tennessee portion has been more 

contentious, however. The initial alternatives considered by the EIS received heavy 

criticism because among other concerns, the alignments included several bridges over 

the Ocoee River which stakeholders felt would detract from the natural beauty of the 

Gorge as well as the river rafting experience. A second alternative that cut expensive 

passes and tunnels through the mountains was rejected due to its billion dollar price 

tag. 

A second regional priority, which is primarily relevant to the western end of the study 

area is the potential for a high speed rail connection from Chattanooga to Atlanta. 

Atlanta is a major airline hub and hosts the headquarters of numerous major 

companies. The two metropolitan areas already have strong economic ties, but 

increasing traffic congestion around Atlanta is limiting further integration of the two 

economies. A high speed rail connection would mitigate the barrier of traffic 

congestion by allowing tourists, businesspeople and residents to move more freely 

between the two regions. 

6.5 Lessons Learned

1. Cross-Jurisdictional Planning, Coordination and Implementation - Potential 

employers and tourists alike view the study area as a region, rather than a collection of 

counties spanning multiple states. If regional leaders insisted upon marketing only 

their own individual tourist attractions and improving business conditions and 

transportation linkages only within their own jurisdictions, they would lack the critical 

mass needed to draw overnight tourists, and create and retain jobs in key industries. 

By creating an organization that rewards county leaders for thinking and acting like 

regional leaders (SEIDA), they have been able to better leverage resources from their 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 88

respective states and raise the profile of their tourism assets to create a destination for 

overnight visitors who come to the are from a greater distance than the day trippers. 

2. Understanding that Tourism Preferences are Changing - Just as the 

manufacturing sector has changed over the past several decades, the tourism sector is 

changing as well. Tourism once centered on relaxation and natural beauty. Two key 

emerging trends are eco-tourism and cultural heritage tourism, which reflect a 

preference for activity-based vacations. Thus the industry is shifting away from “one 

size fits all” attractions and toward those that allow visitors to create a more tailored, 

specialized vacation experience. 

Regional efforts are helping ensure that local tourism attractions cater to these new 

preferences. Tennessee Overhill efforts such as “Furs to Factories Heritage Area: 

Exploring the Industrial Revolution in the Tennessee Overhill” and “From Native 

Gardens to Cheese Farms: AgriCulture in the Tennessee Overhill” string together 

individual attractions into a more unified, complete experience. 

3. Sheer Creativity - This case study illustrates the importance of creatively using one 

problem to leverage a solution for another. For example, the concept for the “On the 

Glory Land Road” religious heritage trail emerged from a National Trust for Historic 

Preservation study funded by monies apportioned by TVA as relief from a devastating 

infestation of Southern Pine Beetles. 

Another example was the acquisition of the Old Line Haiwasee River Gorge Rail 

Road by the Tennessee Overhill Heritage Association through SEIDA. The closure of 

the mine could have been a net loss for the region, but instead it is being used as a 

recreation asset and generating earned income through cargo shipments. 

4. An Asset-Based Economy Depends on Market Access. Though the region has 

managed to assemble a unique and diverse selection of tourism offerings, regional 

leaders recognize that their ability to fully capitalize on them depends upon 

transportation access. Expansion beyond the day trip market and attracting visitors 

from throughout the southeast depends on safe and efficient highway linkages to the 

southeast’s major metropolitan areas and tie-ins with the Interstate Highway system. 

6.6 Interviewees 

Hale Booth, Executive Director, Southeast Tennessee Development District 

Linda Caldwell, Executive Director, Tennessee Overhill Heritage Association 

John Carringer, Manager, Murphy Electric Power Board 

Vicki Greene, Director of Planning and Development, Southwestern North 

Carolina Planning and Economic Development Commission 
Vol.2 Case Studies of Economic Growth Processes SE Tenn & SW N. Carolina 

Sources of Growth in Non-Metro Appalachia page 89

Bill Gribson, Executive Director, Southwestern North Carolina Planning and 

Economic Development Commission 

Joe Guthrie, Manager, Southeast Local Development Corporation 

Larry Kernea, Assistant Manager, Murphy Electric Power Board 

Sandra H. Kimball, Executive Director, Cherokee County Chamber of 

Commerce 

Hoyt T. Firestone, County Executive, Polk County 

Bill Forsyth, Executive Director, Cherokee County Economic Development 

Commission 

Beth Jones, Deputy Director, Southeast Tennessee Development District 

Larry Mashburn, President, Ocoee Adventure Company 

Delos Monteith, Institutional Research and Planning Officer, Southwestern 

Community College 

Barbara Palmer Vicknair, Vice-Chairman (District 2), Cherokee County Board 

of Commissioners
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 90

7 ALABAMA: AUTO ALLEY

7.1 Introduction 

Alabama provides a state-level case study that traces how Alabama’s automotiverelated manufacturing activity (initiated by attracting Mercedes-Benz to Tuscaloosa, 

followed by auto parts suppliers) is raising the economic prospects in the Appalachian 

portion of the state. 

The Appalachian counties of Alabama have been at the center of a major industrial 

revival powered by foreign investments in the auto industry. The efforts Alabama 

made to attract Daimler Chrysler, Honda, Hyundai, and key suppliers resulted in the 

growth of an auto cluster that is now a well-known story. This case study examines 

the conditions that led to that success but also the extent to which the benefits reached 

the Appalachian non-metro parts of the state. 

The strategy to attract the auto industry was an economic development, not a rural 

development strategy. Conditions in rural Appalachian Alabama, though not as dire 

as more southern areas or as other parts of Appalachia, nonetheless were poor 

compared to national standards. How did the auto cluster affect rural Alabama? Was 

there a spillover of investment and jobs into rural areas? Did it create new jobs and 

wealth or did it deplete non-metro counties by attracting the best and brightest to the 

higher paying jobs in the auto companies locating in metro Alabama? These issues 

motivated this case study. 

7.2 Regional Profile 

The Appalachian region covers the northern half of the state, as shown in Exhibit 7-1. 

Since this case study is vastly different from the other case studies in terms of its 

geographic size and level of diversity, the economic development context of the region 

is best understood by reviewing its historical evolution, and the predicament that it 

presented. 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 91

Exhibit 7-1. Northern Half of Alabama is within the Appalachian Region 

This northern part of Alabama has a long industrial history that was built on the steel 

and related industries in and around Birmingham and, in the more rural counties, the 

traditional non-durable goods industries that migrated South during the middle third of 

the 20th century to take advantage of surplus labor, low costs, and right-to-work laws. 

The Appalachian counties of Alabama, in particular, benefited from the state’s 

incentives, physical infrastructure, low wages, and federal investments. 

During this period, manufacturing industry thrived in the rural areas. In 1984, the 

percent of employment in non-metro Alabama was 41.1 percent—and growing. The 

percent in metro counties was 18.5 and dropping. (source: Stuart Rosenfeld and Edward 

Bergman, Making Connections: After the Factories Revisited. Research Triangle Park: 

Southern Growth Policies Board, 1989). The share of metro workers employed in 

technical occupations, however was more than double the technical work force in nonmetro counties. The biggest non-metro employers were furniture/wood (mainly in the 

northwest part of the state), metals, and apparel/textiles, rather than technology 

industries. 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 92

At the northernmost fringe of the state, in Madison County, Huntsville emerged as a 

high tech oasis. In the late 1940s, when Huntsville was still a small city in a nonmetro county, the military brought German rocket scientist Werner Von Braun to the 

Redstone Arsenal. In1960 President Eisenhower dedicated the NASA-funded 

Marshall Space Fight Center there, and Huntsville became “Rocket City,” one of the 

leading high tech centers in the nation. 

The benefits of the space programs, however, did not reach out very far across the 

state, and in the early 1980s, Alabama began to realize that its still heavy reliance on 

low costs to recruit labor-intensive branch plants was increasingly precarious. Less 

developed countries could promise much lower costs with adequate skill levels and 

technological capacities. 

7.3 Evolution of progress 

In the1980s the University of Alabama, influenced by the work of the Southern 

Growth Policies Board and Southern Technology Center to connect universities and 

economic development, began looking for new investment. The northern area was 

home to the Southern Research Institute, University of Alabama, and a recognized 

high tech industry capability. 

As part of the search for new and more stable industry, a university team and state 

officials together saw a window of opportunity in autos, a product for which growing 

domestic demand was likely to provide continued advantage and stability. The 

University of Alabama had prepared a strong proposal in the bidding to locate the 

Saturn plant in Tuscaloosa. Although GM ultimately chose the Nashville area, 

Huntsville came close enough that the die was cast to pursue the next big opportunity. 

That came in 1993, when Mercedes Benz announced it was looking for a U.S. location 

for a large new production facility, in large part to gain entry to the growing American 

SUV market, avoid currency fluctuations, and access new sources of knowledge. 

Mercedes’ needed a place in the United States that it could manufacture for both U.S. 

and export markets. To narrow down the options, the company established the 

following criteria: 

1. labor force (underemployed but capable) and training system 

2. physical infrastructure to make it easy to quickly reach suppliers and markets 

3. supportive business climate (right-to-work, taxes) 

4. existing and potential supplier base 

5. strong university presence for access to business and engineering graduates 

plus adding to the area’s “melting pot” environment 

6. good quality of life, melting pot of auto cultures 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 93

Later, Honda’s criteria would be quite similar, focusing first and foremost on the 

availability of a trained and trainable work force, logistics to accommodate movement 

of supplies and products, physical infrastructure, and opportunities for growth, and an 

accepting and cooperative community. 

7.4 Catalysts of change 

On all counts, Alabama was competitive. Its extensive road, rail, port, and air systems 

were essential parts of the infrastructure. Some suppliers were in place. The 

Appalachian region of Alabama was already home to a small part of the auto supply 

chain, including two General Motors suppliers in Tuscaloosa. Mercedes visited JVC 

during its decision-making process to see how the company operated in Alabama and 

observe any differences from what they were accustomed to in their German facilities. 

Education and training, in particular, greatly enhanced the state’s competitive position, 

but for different reasons. The most important, according to those involved in the 

process, was its customized training. The Alabama Industrial Development Training, 

which is funded through a direct state budget line item, was set up to deliver various 

levels of training plus a wide range of recruitment, filtering, and selection activities. 

The state’s community and technical college system was not only already organized to 

prepare skilled technicians for many of the occupations critical to the auto industry 

through its Bevill Centers but also was a principal part of the state’s manufacturing 

extension partnership (Alabama Technology Network). The primary impacts of the 

universities were the cultural and multi-cultural amenities and hospitable and open 

social environment they created in their respective regions. 

The University of Alabama, with one of the first U.S. Department of Commercefunded International Trade Centers, played a key role in convincing the German 

company to choose Alabama for its U.S. home. The universities also were sources of 

local expertise and problem solutions via their faculty and students in their science and 

engineering programs. The research capabilities of the universities have been more 

important to suppliers than the OEMs which continue to conduct most of their 

research near their home offices. The most valued contribution of the university, 

however, was the open and tolerant environment they instilled in their communities, 

particularly important to welcoming different cultures. 

The elementary school system was undoubtedly the weakest link in the educational 

system. Although on average, Alabama is very close to the South’s average, it has 

some of the most pressing rural education problems in the nation—ranked fifth most 

serious among all states(source: Jerry Johnson and Marty Strange, Why Rural Matters 

2005, Rural School and Community Trust, 2005). But so long as a selection of good 

schools, public and private, were available to the employees and most employees were 

drawn from metro counties, overall statistics were not as barrier. It may, however, be 

one of the reasons that employment was so concentrated in urban areas. 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 94

The state further sweetened the pot—as did all the final candidates—with a sizable 

incentive package of $372 million, one of the largest given for new investment at that 

point in time. 

Giving Birth to the Auto Supply Chain Cluster. The site selection team from 

Mercedes began with proposals from 30 states, reduced it to 12, then six, then three 

(North Carolina, South Carolina, and Alabama). In 1997 Mercedes announced its 

decision to build its plant in Tuscaloosa, Alabama. That decision put Alabama on the 

map of the U.S. auto industry, extending the automotive manufacturing corridor along 

Interstates 65 and 75 dubbed “Auto Alley” that ran from Detroit through Indiana, 

Ohio, Kentucky, and Tennessee into Appalachian Alabama. 

With the rapid growth of auto industry in Alabama (and Nissan in Mississippi), the 

center of gravity of that dispersed supply chain has drifted farther and farther south. 

The choice of Tuscaloosa by Mercedes proved to be the birth of a full-blown regional 

supply chain cluster—that is, the state eventually developed the exporter-OEMs, a 

base of suppliers some of which are shared, specialized support, expertise, work force, 

education and training system, and social infrastructure that together comprise a 

cluster. The key was landing Mercedes. According to University Chancellor 

Malcolm Portera, “Had Alabama not landed Mercedes, there would be no auto 

assembly in Alabama.” In 1998, the first year of production, Mercedes produced 

68,800 units. (See map of the Alabama auto cluster in Exhibit 7-2.) 

Exhibit 7-2. Alabama Auto Assembly and Parts Manufacturers 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 95

The incentives were important to the location decision but, according to site selection 

team members and economic developers, the value of incentives to the final decision 

was overrated and they were not the deciding factor. (Of course, only DaimlerChrysler knows whether or not incentives were decisive and like all auto 

manufacturers, are unlikely to speak forthrightly about their importance.) They 

remain important, however, because virtually all U.S. regions offer them, and the 

absence of incentives could rule a location out of contention. State and business 

leaders offered different perspectives on the relative importance of the different 

reasons the two OEMs gave for their location decisions, but they all agreed that 

presence of a trainable labor force and state supported industry oriented education and 

training programs were the most important assets. 

7.5 Lessons learned 

The Human Capital Factor - Alabama is a good example of how an educational 

system can be focused to prepare for new job opportunities. The key to acquiring and 

growing the auto cluster was the presence of an underemployed but trainable labor 

force with the work ethic and learning potential the plants needed. The assembly 

plants had to hire large numbers of pre-qualified workers in a relatively short time 

since they did not intend to bring their work force with them. Mercedes officials 

initially were somewhat skeptical of the ability of Alabama to match the skills of 

Mercedes’ German work force that was trained in its renowned “dual” vocational 

system (classroom theory combined with extensive work-based experience). By the 

time Honda arrived, however, it was clear that export-quality vehicles could be 

assembled in Alabama. A by-product of this workforce redirection towards in-state 

automotive assembly functions also meant that Alabama could be home to other 

vertically-linked businesses claiming a node position in several automotive supplychains. These firms are then tied to auto-related business in Tennessee, Kentucky, 

Ohio, Michigan, Georgia, South Carolina and North Carolina. 

Although Alabama as a state had lower levels of education than most states (in 1980 

almost of third of adults living in non-metro counties had no more than eight years of 

education), attainment levels were higher in the Huntsville area and the state had a 

good record in vocational-technical education. Mercedes was concerned but believed 

the work ethic and training capabilities were on balance an advantage. They also 

realized that because their pay and benefits were going to be far above the state 

average, they would be able to attract the best from the incumbent work force. The 

companies that lost employees, then, would have to replenish their work force from 

the school systems. But this also meant that new job opportunities would eventually 

be dispersed across the region. 

Alabama Industrial Development and Training (AIDT), according to multiple sources, 

was particularly important to both assemblers. Although customized training is now 

ubiquitous across the U.S., AIDT offers a fuller set of services than most competing 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 96

states, including advertising positions, taking applications, screening and then 

interviewing applicants, and conducting pre-employment training at plant sites, one of 

its permanent training centers, or in one of its 34 mobile units outfitted to meet a 

variety of technical training needs. The state’s community and technical college 

system, which extended across the state, was an additional plus for Mercedes. 

Mercedes initially brought over 150 Germans to conduct in-plant training and took 

workers to Germany for experience in German industry. But with the need to gear up 

and train a large workforce in a short time, the services and capabilities of Alabama’s 

Industrial Development Training (AIDT) program quickly became a key to workforce 

recruitment, selection, and development. 

The role of the university system is less clear cut. According to one state leader, it 

was “the spark that caused the automobile manufacturing fire” in the state. In this 

view, Alabama’s engineering programs would provide the stream of engineers, student 

interns, and faculty consultants required to produce high-quality automobiles in a state 

with little experience in automotive manufacturing. Some graduating engineering did 

enter the industry but in general in the upper tiers of the auto industry, searches for 

engineers and top level managers tend to be national to attract the most experienced 

people possible. At the same time, engineering graduates from Alabama universities 

look nationally for employment—the majority of the state’s engineering graduates 

leave Alabama to find jobs. The role of engineering students and programs, however, 

is clearer. Many students enter into coops with the industry and/or design engineering 

projects in cooperation with the industry. The presence of the schools offers 

opportunities for employees to get more advanced degrees or just upgrade their skills. 

In the supply sector, there is little demand for engineering skills, as design and 

engineering are generally performed at existing sites in places like Michigan, Ohio, 

Germany, and Japan, rather than at new branch plants. There has been some 

movement into engineering functions at the 4th tier in Alabama and some prospects for 

further involvement. Overall, though, it might be the case, as one local official 

suggested, that the auto industry might rely more on University of Alabama’s business 

school than on its engineering school. 

Building blocks of human resource development - The state’s formal education 

system starts with pre-school and continues through post-graduate education. But a 

comprehensive human resource development system also includes a variety of vendor-

, industry- and company-based education and training, continuing and management 

education and informal learning. In Alabama, the public sector plays a major role at 

all levels. 

1. Alabama Industrial Development Training, created in 1971 by the state 

legislature to support economic development, was a major resource for the auto 

cluster. Administered by the Department of Postsecondary Education, AIDT’s 

highly placed advisory council from business, industry, and education guide and 

advise its work. In 2004, the governor and Mercedes jointly announced a new 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 97

“backfill” program called Focused Industry Training at 34 sites to help fill the 

positions left open by those hired by the auto industry. Sites are located in and 

around the three assembly sites. A 400-hour curriculum produced an Alabama 

Certified Worker credential recognized by many of the state employers. To 

underline its importance to Alabama, AIDT has a line item in the state’s budget. 

2. The University of Alabama was instrumental in focusing the state’s resources on 

the auto industry and in developing the proposals for both Mercedes and Honda. 

Its engineering and business schools and its International Trade Center were key 

factors, but it also created a welcoming environment for foreign investment. It 

runs, for example, a German Saturday school on campus that teaches math, 

science, and German language. It helped orchestrate the “Commission on 

Community Change” to help improve the quality of life in the area. The Alabama 

Productivity Center at UA was available to work on production problems with 

companies and the Center for Advanced Vehicle Technologies was established in 

1998, the year Mercedes began production, with funding from the U.S. 

Department of Transportation. 

3. Alabama’s community college system was also an important factor. In the final 

competition, North Carolina’s governor even offered as part of its package of 

incentives to build a “Mercedes University” within its already strong community 

college system. Alabama’s approach, however, was to offer to target the resources 

of its existing system. Bessemer Technical College (now merged with Lawson 

State Community College) and Shelton State Community Colleges both offered 

relevant education and training. The well-equipped Bevill Center for Advanced 

Manufacturing at Gadsden State Community College offered a nationally known 

and respected education and training programs in precision manufacturing and 

they housed a key part of Alabama’s manufacturing extension service. That 

Center was the result of a historic partnership between the college, the University 

of Alabama, and the city of Gadsden growing out of successful efforts there to 

save a GM plant in the early 1980s. (source: Stuart Rosenfeld, New Technologies 

and New Skills: Two- Year Colleges at the Vanguard of Modernization, American 

Association of Community Colleges, 1995). 

The auto industry relies on the community college system for its more advanced 

training and to improve employees’ opportunities for advancement. Honda 

encourages (and reimburses) its associates to work towards Associate Degrees at 

the community colleges. To accommodate Honda’s policy of rotating its work 

force among shifts, the college system has arranged programs in which faculty 

alternate their class hours between day and evening consistent with the shift 

changes. 

4. The state’s public school system was probably the weakest link in Alabama’s 

human resource development chain. While improving, it remains low compared to 

most other states, and was given a “D” by the Corporation for Enterprise 

development in its 2005 scorecard. Alabama ranks in the bottom ten states in 
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 98

reading and math proficiency of 4th and 8th graders. One place the state has 

improved is in reducing the disparities among races and between genders, although 

disparities according to income were second highest in the nation. Graduation 

rates, while climbing, are still among the lowest in the nation. This is a particular 

issue for non-metro counties that, according to the Rural School and Community 

Trust. Alabama has the fifth most critical rural educational needs in the nation 

based on availability and distribution of educational funds, special needs, and 

poverty rates. (source: Jerry Johnson, Marty Strange. 2005. Why Rural Matters: The 

Facts about Rural Education in the 50 States. Rural School and Community Trust .) 

The Topography Factor – also conducive to the entrenchment of automotive 

assembly activities in Alabama has been the availability of readily developable, 

affordable land amidst situated within an ample highway network.
Vol.1 Project Background and Summary Alabama Auto Alley 

Sources of Growth in Non-Metro Appalachia page 99

7.6 Interviewees 

Gregg Bennett, Alabama Technology Network and The Bevill Center for 

Advanced Manufacturing 

Ernie Cowart, Senior Economic Development Specialist, Economic Development 

Partnership of Alabama 

Robert Culver, Top Alabama Regional Council of Governments 

Austin Dare, Mercedes-Benz 

Brad Davis, Director, Alabama Technology Network 

Steven Dean, Randolph County Chamber of Commerce 

Dale Greer, Cullman Economic Development 

Lee Hammett, Automotive Group Manager, AIDT 

James Hayes, President, Economic Development Partnership of Alabama 

Greg Knighton, Director of Business Information, Economic Development 

Partnership of Alabama 

Jeff Newman, University of Alabama 

Linda Paulmeno, Mercedes-Benz 

Ray Perez, Honda Corporation 

Malcolm Portera, Chancellor, University of Alabama 

Bernard Schroer, Alabama Automotive Manufacturers Association 

Steve Sewell, Executive Vice President, Economic Development Partnership of 

Alabama 

Dana Stone, Program Manager, Alabama Technology Network 

Perry Ward, President, Lawson State Community College 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 100

8.0 Conclusions – Strength of Growth Processes 

The preceding six locations examined through case study exhibit distinct bases upon 

which their regional economies are organized, and varying degrees of success in 

achieving growth. Why do some of these studies show more pronounced evidence of 

a growth path than others? For locations with improving economic results can we 

attribute this to a growth orientation that’s just right for the local conditions regardless 

of whether the evolution was organic or achieved by planning intervention? Or even if 

regional planners and local stakeholders diligently frame a growth path and strategy 

are they guaranteed success? What role does geography play in determining the 

strength of growing trade center, or a tourism market for example? 

We summarize the highlights as follows: 

Growth Affirming Case Studies 

Case Study Chautauqua, NY 

Growth 

Process 

Tourism Development 

 

 

What worked 

*Working to organize, market and leverage tourism assets into a yearround offering 

 

*Diversify the county’s economy to reduce the exposure to shrinking 

manufacturing presence 

 

What Else worked 

*Reinforced a vital segment of its Trans Equip MFG base & facilitated its 

expansion creating higher value-added/paying jobs and advancing the 

skills of the workforce. The latter with the help of the county’s Workforce 

Investment Board and the community college’s Manufacturing Training 

Institute 

 

*Selective retention of this firm and other manufacturing likely not possible 

without ADHS corridor “T “ which offers a viable connection to I-79 and I90. 

 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 101

Growth Affirming Case Studies 

Case Study Alabama's Manufacturing Resurgence 

Growth 

Process 

Automotive Assembly 

What worked

*Incentives alone were not expected to be the silver bullet 

*State's Educational system (k-12, Community Colleges and advanced 

degree conferring institutions) has been a proactive and responsive element 

to assuring the workforce needs of all aspects of the vertical-chain of firms 

*Leadership from both state and regional agencies persevered to succeed in 

the germinal event - attracting Mercedes-Benz to Tuscaloosa 

What Else worked

*Workforce programs assisted in retraining textile workers from jobs going 

overseas to automotive applications growing in state. 

* Ample land resources (typically flat green fields) with relatively 

unencumbered permitting process 

*Good highway accessibility to parcels developed 

Growth Affirming Case Studies 

Case Study Corridor K Region 

Growth 

Process 

Tourism Development 

What worked

*Regional collaboration (e.g. SEIDA) between SE TN and SW NC to further 

develop eco-recreational-cultural tourism assets 

*Addressing the road access capacity to carry regional visitors 

*Water resource planning to support rafting tourism 

What Else Needs to Happen

*Establish a direct connection between the terminating corridor cities of 

Chattanooga and Asheville that will separate through traffic from tourism trips 

to corridor communities 

*Ensure that cities such as Murphy (Cherokee Co., NC) that desire to retain a 

healthy industrial component obtain efficient highway links to I-85, and I-65. 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 102

Growth Affirming Case Studies 

Case Study Morgantown-Fairmont 

Growth 

Process 

Hi-Tech Spin-offs from Educational Assets 

What worked 

*I-79 backbone through the Morgantown MSA has facilitated regional collaboration, 

most predominantly between Morgantown and Fairmont. 

*Infusion of federal research grants secured by U.S. Congressional representative & 

research readiness 

*City of Morgantown provides good entrepreneurial support 

*Ample higher education assets: WVU (Morgantown), Fairmont State University & 

Fairmont State Community Technical College 

*Technology-transfer office of WVU and WV High Tech Consortium in Fairmont 

Fairmont’s smaller hi-tech economy appears to serve as a complement to Morgantown's 

economy 

What Else Works 

*I-68 connection (ADHS corridor E) links Morgantown to Baltimore-Washington area 

What Else needs to Happen 

*Fairmont needs to find opportunities to commercialize federal support research and 

incubate local start-up firms 

*Region needs additional east-west access without which the current limited extent of 

beneficial urban spillovers intraregionally goes unchanged 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 103

Growth Ambiguous Case Studies 

Case Study Scioto County Ohio 

Growth 

Process 

Alleged Trade Center 

What Happened 

Extra-regional N-S and E-W highways bypassed the county 

Difficult transition since 1980 as key manufacturing sectors (e.g. Steel, 

Shoes) moved jobs overseas 

Lack of critical mass in services to effectively draw surrounding rural 

consumers (household or business) 

Adverse urban backwash effect on Scioto as surrounding rural counties 

gained access to extra-regional metro area markets in Columbus, Cincinnati 

and Huntington WV. 

Geographic constraints of hilly terrain and flood plains of the Scioto and Ohio 

rivers limit developable land 

What Else needs to Happen 

Develop cultural and shopping amenities 

Remedy recruitment problem for doctors to the So. OH medical Center 

Launch high-tech health care services for hospital to serve a broad regional 

market in a realistic niche 

Build on the regional collaboration success of SODI in securing a new 

manufacturing resurgence in Scioto using joint infrastructure financing, 

brownfield redevelopment 

Build from/retrain the remnants of Scioto's strong manufacturing workforce 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 104

Growth Ambiguous Case Studies

Case Study Pike Co., KY and Big Sandy Area 

Growth Process Diversifying from Extraction Industry reliance 

What Happened 

*Unique cut-through project completed in 1987 provided Pike Co. new developable land and better 

access 

*County as recipient of Coal Severance Tax to fund community development projects 

*Though still an economy based on coal mining, Pike Co. has taken on a function as a regional 

economic hub for other counties in the BSA 

*Emergence in healthcare services the result of cooperative efforts between Pikeville Medical College 

& Pikeville Medical Development Corporation (PMDC) 

*PMDC also champions non-healthcare economic development opportunities for the county 

*Pike Co. has good highway access, offers rail freight service and a regional airport 

What Else needs to Happen 

*Furthering industry diversification will require retraining of former mining workforce given population 

losses 

*More multi-county planning initiatives to remedy the paucity of positive economic spillover from Pike 

Co. to the four other BSA counties which remained distressed 

*Investment in regional amenities to retain/attract working age population 

These examples each show that numerous interventions or aspects contribute to the 

economic situation counties find themselves with. Whether economic progress or 

stagnation is the situation, these processes take time. This is both double-edged. First, 

there is no quick turn-around strategy. Well conceived program investments and 

targeted policy must be committed to with patience and a willingness to reassess over 

time and readjust as the background regional or macroeconomic conditions change. 

Second, there is always a time to take action towards improving the economic 

prospects of communities that have been in persistent distress. Some of these 

communities have high hurdles to ever developing a modest sized employment centers 

for their working age residents. This does not however preclude ensuring that 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 105

populations gain access to regional employment opportunities – so that expanding 

healthcare services growth in Pike County may spell more of an opportunity for 

workers from Floyd, Martin, Magofinn and Johnson counties as well. 

Likewise Scioto County, a distressed micropolitan area, along with six other distressed 

Appalachian counties in the local development district need to build more regional 

solutions that can leverage the economic success at the periphery of the district and 

Appalachian boundary (in Brown, Clermont, Highland & Ross counties) to help 

revitalize the core of the region. 

Many of the barriers or challenges to growth can be named: topographical (land 

constraints or access barriers), underserved by necessary transportation infrastructure, 

limited local market demand, limited local services, limited labor supply and quality 

from persistent out migration and limited educational-training resources, neighboring 

economies that compete or fail to synergize regional opportunity, adverse urban/core 

backwash effects. 

Last, the case studies demonstrate that several key aspects must improve in sync to 

welcome economic development. All growth paths require good transport access and 

a suitably trained workforce for the employment center they will access. Other 

important components depending on the growth strategy are attracting federal research 

dollars to advance the role of higher-education institutions that are present, and 

learning to commercialize research and spur small business starts; evolving recreation 

and cultural assets into a tourism product with critical mass and learning to market the 

product; and improving both housing stock and local amenities as a mean to retain and 

attract working age population. 

In the next volume of findings for the study of Sources of Growth in non-metro 

Appalachia, we interpret several statistical tests of the possible spatial influences at 

work in the economic outcomes of Appalachian counties. 
Vol.1 Project Background and Summary Conclusions 

Sources of Growth in Non-Metro Appalachia page 106
 
 

Sources of Regional Growth in Non-Metro Appalachia 

Vol. 3 Statistical Studies of 

Spatial Economic Relationships 

Prepared for the Appalachian Regional Commission 

Prepared by: 

Economic Development Research Group, Inc. 

MIT Dept. of Urban Studies & Planning

Revised 2007 
Vol.3 Statistical Studies Project Overview

Sources of Growth in Non-Metro Appalachia 

SOURCES OF GROWTH PROJECT

The Sources of Growth project is part of a series of research efforts funded by the Appalachian 

Regional Commission to improve our understanding of factors affecting economic growth in 

rural and distressed areas. As stated in the Volume 1 Introduction, “the starting premise of 

this project is that there can multiple paths that an area can pursue in successfully enhancing 

job and income creation. They may build on natural resources, cultural resources, human 

resources, local amenities, institutional facilities or location advantages. The resulting 

direction of economic growth may involve manufacturing or supply chain development, 

resource extraction or tourism development, educational development or trade center 

development.” This research is intended to provide a basis of information that can ultimately 

be useful for enhancing the effectiveness of policies and tools aimed at improving the region’s 

economic development. 

This is Volume 3 in a series of reports prepared as part of this project: 

• Executive Summary –synthesis of findings from all work products related to the 

study’s four main research components. 

• Volume 1, Project Background and Prior Research on Economic Growth Paths – 

study objectives, characteristics of non-metro Appalachian counties, classification of 

economic development growth paths, and a synopsis of white paper findings on theory 

relating to economic development growth paths. 

• Volume 2, Case Studies of Local Economic Development Growth Processes –

findings related to growth paths as observed for selected case studies covering 

manufacturing industry specialization clusters, supply chain-based development, 

tourism-based development, advanced technology development, and diversification 

from resource-based economies. 

• Volume 3, Statistical Studies of Spatial Economic Relationships – findings from a 

series of econometric modeling and GIS-based analyses, focusing on roles of spatial 

adjacency, market access and transportation in determining economic growth and 

development of trade centers. 

• Volume 4, Tools for Economic Development & Study Conclusions– description of 

new and updated tools available to ARC and its Local Development Districts to assess 

economic development opportunities and potential directions for economic growth. 

• Appendices – (A) Spatial Analysis of Economic Health, (B) Economic Analysis of 

Hub-Spoke Relationships, (C) White Papers on Economic Growth Theories, (D) 

Literature Review of Empirical Studies on Spatial Influences in Economic 

Development. 
Vol.3 Statistical Studies 

Sources of Growth in Non-Metro Appalachia page i

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i 

1 INTRODUCTION..........................................................................................................................1

1.1 BACKGROUND .........................................................................................................................1

1.2 STUDY SUMMARIES AND THEIR FOUNDATIONS .......................................................................2

2 ECONOMIC BASE MODELING OF HUB AND SPOKE GROWTH PATTERNS..............5

2.1 INTRODUCTION........................................................................................................................5

2.2 EXPORT-BASE ANALYSIS METHODOLOGY ..............................................................................9

2.3 OVERVIEW OF THE RESULTS .................................................................................................17

2.4 USES AND LIMITATIONS OF THE FINDINGS ............................................................................23

2.5 REFERENCES .........................................................................................................................26

3 TWIN COUNTIES STUDY UPDATE.......................................................................................28

3.1 INTRODUCTION......................................................................................................................28

3.2 APPALACHIAN GROWTH, 1969-2000.....................................................................................29

3.3 THE ROLE OF HIGHWAY INVESTMENTS.................................................................................34

3.4 USES AND LIMITATIONS OF THE FINDINGS ............................................................................38

3.5 SURVEY INSTRUMENT ...........................................................................................................39

4 POPULATION BASE & ACCESS TO AIRPORTS ...............................................................43

4.1 INTRODUCTION......................................................................................................................43

4.2 (A) POPULATION BASE: METHODOLOGY ..............................................................................43

4.3 (B) POPULATION BASE: RESULTS..........................................................................................46

4.4 (B) AIRPORT ACCESS: METHODOLOGY:................................................................................52

4.5 (B) AIRPORT ACCESS: RESULTS ............................................................................................53

4.6 USES AND LIMITATIONS OF THE FINDINGS ............................................................................58

5 SPATIAL INFLUENCES IN COUNTY ECONOMIC PERFORMANCE ............................59

5.1 INTRODUCTION......................................................................................................................59

5.2 EXPLORATORY STATISTICAL ANALYSIS................................................................................59

5.3 MODELS TO PREDICT COUNTY ECONOMIC LEVEL.................................................................62

5.4 MODELING CHANGES IN ECONOMIC HEALTH........................................................................72

5.5 USES AND LIMITATIONS OF THE FINDINGS ............................................................................81

5.6 REFERENCES .........................................................................................................................84
Vol.3 Statistical Studies 

Sources of Growth in Non-Metro Appalachia page ii

ACKNOWLEDGEMENTS

This volume was authored by staff of Economic Development Research Group, Inc. and by 

faculty and graduate students of the Dept. of Urban Studies & Planning at the Massachusetts 

Institute of Technology. Other volumes in this series were also co-authored by staff of 

Regional Technology Strategies, Inc. 

The Sources of Growth project involved a team of researchers including: 

• Economic Development Research Group, Inc. (EDRG) – Lisa Petraglia (Project 

Director), Glen Weisbrod and Teresa Lynch, with research support from Tyler 

Comings, Brett Piercy and Susan Moses; 

• Regional Technology Strategies, Inc. (RTS) –Stuart Rosenfeld, Phil Psilos and Dan 

Broun; 

• Massachusetts Institute of Technology, Department of Urban Studies & Planning 

(MIT-DUSP) – Prof. Karen R. Polenske, Prof. Joseph Ferreira, Jr., Ayman Ismail, 

and Li Xin, with research support from Tan Zhijun, and Isabelle Yi Xu. 

The project also benefited from the expertise of outside policy and research experts who 

reviewed documents, participated in project meetings and provided technical guidance: Deb 

Markley (Co-Director of the Center for Rural Entrepreneurship, a Rural Policy Research 

Institute), Joseph Cortwright (Vice-President of Impresa Consulting), Ken Poole (Executive 

Director of ACCRA: The Council for Community and Economic Research), David Freshwater 

(Professor of Agricultural Economics and Public Policy at the University of Kentucky), David 

McGranahan and Luc Anselin (Professor, Dept. of Agriculture & Consumer Affairs and 

Regional Economics Applications Laboratory at the University of Illinois, UrbanaChampaign). 

Overall project direction and oversight was provided by Dr. Greg Bischak of the Appalachian 

Regional Commission, whose wide range of research experience served to focus the project 

team on the development of policy applications. Important insight and suggestions were also 

provided by officials of the Appalachian Regional Commission who participated in a day-long 

symposium with the project team, including Thomas Hunter (executive director of ARC, Ann 

Pope (federal co-chair of ARC) and Rick Peltz (alternate federal co-chair). In addition, Ken 

Wester and Jason Wang of ARC assisted the project team in collecting and assembling 

transportation and geographic data. 

Finally, the project team acknowledges the important role of prior ARC-funded research 

studies by Andrew Isserman, Ed Feser and Oleg Smirnov that provided a foundation for this 

project to build upon. 
Vol.3 Statistical Studies Ch.1 Introduction 

Sources of Growth in Non-Metro Appalachia page 1

1 INTRODUCTION 

1.1 Background 

Role in the Sources of Growth Project. Volume #3 presents results of four empirical 

research studies conducted as part of the Sources of Growth project. These studies 

build directly on the discussion of theory and prior research which are covered in 

Volume 1, and corroborate some of the case study findings of Volume 2. 

The prior documents identified a consistent set of location and access factors that 

affect the economic viability and opportunity of various growth paths. They are 

summarized in Exhibit 1-1. Accordingly, all four of the empirical research studies 

presented here examine an aspect of the relationship between a county’s spatial 

location or access characteristics and its pattern of economic growth and development. 

All four also utilize some form of econometric modeling and/or geographic 

information system to examine these relationships. 

Exhibit 1-1. Location and Access Factors Affecting Economic Growth Paths 

Basis for County’s 

Economy Growth 

Examples of Location and Access Factors 

Trade Center • Adjacency of rural markets (spokes) to micropolitan trade 

centers (hubs); 

• Scale of markets relative to regional population 

Agglomeration 

(e.g. cluster economy)

• Labor force size 

• Delivery market reach 

Supply-Chain 

(e.g. dispersal economy) 

• Distance to highway, rail terminal, air or marine port 

• Same day delivery distance 

Natural Amenity or 

Cultural Assets 

• Access to visitor markets 

• Distance to highway 

Knowledge (Learning) 

Assets 

• Labor force or population size 

• Proximity to major education or technology institutions 

The motivation for this research comes from three directions: (1) recognition that 

while the various paths of economic growth serve different markets, they all depend in 

some way on access; (2) the fact that many of ARC’s programs aim to reduce isolation 

and improve access, and (3) the availability of relatively new analytic methods for 
Vol.3 Statistical Studies Ch.1 Introduction 

Sources of Growth in Non-Metro Appalachia page 2

examining spatial relationships among counties. This research thus aims to build upon 

prior ARC-funded research and to advance our understanding of how ARC 

investments promote economic development by reducing isolation and increasing 

local capacity for growth. 

1.2 Study Summaries and their Foundations 

Extending Prior Research. It is important to note how these research efforts build 

upon prior studies. 

• The first study focuses on enhancing our understanding of relationships 

between counties that serve as rural trade centers (economic hubs) and adjacent 

counties that are served by them (economic spokes). This work by Ayman 

Ismail of MIT utilizes new economic base techniques first explored by 

Smirnov and Smirnova (See ““An Assessment of the Economic Base of 

Distressed and Near-Distressed Counties in Appalachia,” 2000) and revisits the 

evaluation of county-level “spatial regional multipliers” based on more recent 

employment data. 

The Pike County case study of Volume 2 can be better understood from the 

perspective of how well its economy ties into those of the four other counties 

in the Big Sandy Area (BSA) – all distressed counties. Pike County’s 

transitional status has been achieved through attempts to gradually diversify its 

mining economy, and through a unique public works project that removed 

barriers to development, and opened access options. The BSA counties of 

Maggofin and Martin exhibit the weakest spatial regional multipliers of the 

five counties, and all five counties have economic compositions that tend to 

hinder each in benefiting from growth stimulated in a neighboring economy 

(low total spatial linkage multiplier values). 

The Morgantown-Fairmont case study on the other hand now can be further 

understood as each county (Monongalia and Marion) having strong internal 

economic linkages (high spatial regional multipliers), and room for their 

economies to become more reinforcing if mutually desired (low values for 

their total spatial multiplier as of 2002 and four of the top 5 employing sectors 

are in common). Monongalia County’s metro status explains in large part why 

this county has a local spatial linkage multiplier that is more than double that 

of Marion County. 

We can also understand that the Corridor K case study county of Cherokee, 

NC though transitional, exhibits as strong an internal employment multiplier 

and local spatial linkage multiplier as the corridor’s terminating metro counties 

which have competitive economic status. This result for Cherokee County can 

be attributed to the trade center role exerted by the City of Murphy on 
Vol.3 Statistical Studies Ch.1 Introduction 

Sources of Growth in Non-Metro Appalachia page 3

surrounding counties in NC, GA and TN. 

• The second study focuses on enhancing our understanding of relationships 

between highways, ARC investments and subsequent economic growth over a 

long period of time. This work by Teresa Lynch of EDR Group utilizes time 

series regression techniques. It updates and extends a direction of research 

using “twin counties” that was initially developed by Andrew Isserman (see 

“The Economic Effects of the Appalachian Regional Commission”, by 

Isserman and Rephann, 1995.) An improved specification for ADHS highway 

capacity and access was tested and found to significantly contribute to the 

differential income and earnings growth experienced from 1969 to 2000 for 

ARC counties relative to their twins’ performance. 

The Scioto County case study in Volume 2 revealed that Scioto has been 

bypassed by recent highway investments while the ring of neighboring 

counties have benefited through improved highway access to the metro areas 

of Cincinnati, Columbus. These extra regional economies exert an adverse 

urban backwash effect on Scioto County that challenges any geographic 

predilection for it to serve as a thriving trade center. 

Likewise the partial explanation of positive differential growth outcomes for 

Appalachian counties from highway access improvements is a welcome 

expectation for the counties in SE Tennessee and SW North Carolina aligned 

along Corridor K. Whether improved economic outcomes result from better 

market reach of the region’s eco-tourism and cultural heritage assets and/or 

eventual economic integration into the metro Appalachian counties that 

terminate the corridor (Hamilton Co., TN and Buncombe Co., NC) it will not 

occur without better access through the region. 

• The third study focuses on enhancing our understanding of the relationship of 

business mix to (a) the size of the local population base and to (b) accessing 

quality air services. The analysis of market scale shows how trade centers 

differ in industry composition depending on market size. The analysis of 

airport access shows how highway drive times to airports also affect industry 

mix. This work by Teresa Lynch, Glen Weisbrod and Tyler Comings of EDR 

Group uses non-linear regression techniques and geographic information 

systems. It builds upon the prior ARC report, “Handbook for Assessing 

Economic Opportunities from Appalachian Development Highways” by 

Weisbrod et al., 2001.) 

• The fourth study focuses on use of new advances in geographic and spatial 

analysis techniques to illustrate how proximity to mountains and roads affects 

economic development patterns and trends among counties. This work by 

Prof. Joseph Ferreira, Jr., Ayman Ismail, and Li Xin shows the use of GeoDa 

software for spatial analysis. It represents a pilot effort to demonstrate the 

value of spatial analysis to better understand factors affecting the economic 
Vol.3 Statistical Studies Ch.1 Introduction 

Sources of Growth in Non-Metro Appalachia page 4

development of Appalachian counties. 

The case studies from Volume 2 that in part have some aspect of economic 

performance tied to physical terrain (as constraint or not) include Pike County 

KY and its neighbors in the Big Sandy Area, Scioto County OH embraced by 

two rivers, Corridor K’s Cherokee County NC as trade center to a group of 

counties surrounded by a mountain ring, and for the case of Alabama an 

abundance of relatively flat land with broad highway coverage. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 5

2 ECONOMIC BASE MODELING OF 

HUB AND SPOKE GROWTH 

PATTERNS

“Economic Base Modeling to Test Growth Patterns” 

by 

Ayman Ismail, 

Massachusetts Institute of Technology 

2.1 Introduction 

This chapter presents an update of an economic base analysis of Appalachia’s 

distressed and transitional (380 counties combined) counties using economic base 

theory which has been augmented to address possible spatial influences on a county’s 

economic strength. This analysis was first conducted for the ARC (2000) by SmirnovSmirnova to test whether distinct spatial growth patterns have a role to play in the 

performance of Appalachia’s distressed, and near-distressed Transitional (153 counties 

at the time of the original analysis). The original study monitored employment growth 

performance (based on the strength of the regional employment multiplier) from 1992 

through 1996. This update focuses on the period 1997 through 2002. 

Summary of Original Research. In their “Assessment of the Economic-Bases of 

Distressed and Near-Distressed Counties in Appalachia,” presented to the Appalachian 

Regional Commission (ARC) in 2000, Smirnov and Smirnova (hereafter referred to as 

S&S) use economic-base and location-quotient techniques to provide a detailed 

assessment and typology of 111 distressed and 42 near-distressed1

 counties in the 

Appalachian region in 1992 and 1996. The authors perform three key analyses to 

understand and assess the counties under study. First, they analyze the economic-base 

of distressed counties to identify their strengths and weaknesses and their potential for 

economic growth. The economic-base is defined as the collection of establishments in 

which the county specializes, where a county’s employment in that industry is greater 

than the average for the rest of the country (i.e., it has an employment locationquotient greater than one).2

 Second, they identify the industrial-mix of economic-

 

1

 The ARC has since changed the terminology for the subset of transitional counties previously identified as neardistressed to at-risk.

2

 We will sometimes refer to the economic-base of a region as the export-base. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 6

bases and regional spatial effects as important factors in shaping the regional 

economies of distressed and near-distressed counties. Third, they establish a typology 

for key parameters that characterize the economic potential of the economic-bases of 

distressed and near-distressed counties using regional employment multipliers and 

strength of spatial linkages; the latter measured by a spatial multiplier. 

Based on their empirical analysis, S&S, identify several relationships and patterns that 

affect the economic development status in Appalachian counties: 

 Within select types of economic-bases and specific classes of economic distress a 

strong relationship exists between the key parameters, such as population, 

employment, average wages, and per capita income of distressed and transitional 

counties. The S&S comparison of economic-bases between the distressed and 

near-distressed counties against the more prosperous economies in Appalachia 

reveals significant disparities in their key parameters. 

 Regional employment multipliers show a direct (positive) relationship between the 

level of economic distress and the strength of the economic-base. In 1996, the 

average regional multiplier for distressed and near-distressed counties was 1.79, 

which is 11 percent lower than the average regional multiplier of 1.99 for all 

Appalachian counties. Distressed counties with higher values of regional 

multipliers tend to perform better and have higher economic growth potential than 

those with lower multiplier values. 

 The industrial mix of the economic-bases of distressed and near-distressed 

counties is dominated by resource-oriented, technologically disadvantaged 

industries, many of which pay relatively low wages, have a low potential for 

growth of employment, and have little positive effect on local demand. The 

traditional components of Appalachia’s industrial-mix are resourceoriented/extraction industries, such as coal-mining and agricultural production, 

where steady declines caused economic distress in affected counties. More 

dynamic and technologically advanced industries are virtually non-existent in the 

distressed areas of Appalachia. 

 Spatial effects play an important role in shaping the economic-bases of all 

economies. The magnitude, direction, and scope of spatial effects for distressed 

and near-distressed counties differ from those of other counties in Appalachia. 

Distressed and near-distressed counties have very weak local and global economic 

linkages that lead to their limited economic opportunities and slow growth rates. 

 The gap between distressed and prosperous counties in Appalachia is widening. 

On average, socio-economic parameters, such as population, employment, average 

wages, and per capita income of distressed and near-distressed counties, are 

growing at a substantially slower rate than they are in the rest of Appalachia. S&S 

identify four key characteristics as defining patterns of persistent self-reinforcing 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 7

economic distress: (1) small size of rural economies, (2) non-diversified economicbases, (3) stagnant industrial mixes, and (4) weak spatial linkages. 

Based on these patterns, S&S find that the economic growth potential differs among 

distressed and near-distressed counties. There are a total of 13 distressed and neardistressed counties (termed Type I) that have well-diversified economic-bases, strong 

spatial linkages, and their economic-growth potential is as strong as that of prosperous 

counties in Appalachia. The majority of these counties are perceived as potential 

hubs—regions that are capable of propagating economic growth in the neighboring 

regions. Also, 21 counties (termed Type II) are approaching a similar level of potential 

for economic growth. An important distinction is that these counties form ‘tight spatial 

clusters’. These counties are likely to overcome economic distress and achieve a 

pattern of self-sustainable economic growth, however, their economic development 

initiatives have to be coordinated at the multi-county level. In total these 34 counties, 

with somewhat diversified economic-bases and some economic-growth potential, form 

spatial clusters, which highlights the need for policies and initiatives that promote 

closer economic ties between neighboring counties. Exhibits 2-1 and 2-2 identify the 

counties whose economies have been portrayed as functioning as a regional hub or 

regional spokes. 

Exhibit 2-1. Potential Regional “Hubs” from among Select Appalachian 

Counties, Smirnov (2000)

Distressed and Near-Distressed Counties with Growth Potential as Regional 

Hubs (13 counties) – Type I

Spatial linkages are strong, economic base is strong and well-diversified, the type of 

economic base is either service-based or non-specialized

Distressed Counties Near-Distressed 

Scioto, OH *Talladega, AL

*Fayette, PA *Allegany, MD 

Raleigh, WV *Belmont, OH 

Randolph, WV *Guernsey, OH 

 *Jefferson, OH 

 Cumberland, TN 

 Tazwell, VA 

 Greenbrier, WV 

 Marion, WV 

* Counties with both strong exports and local inter-county spatial links; other counties 

are those with only strong, local inter-county spatial link. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 8

Exhibit 2-2. Potential Regional “Spoke” Economies from among Select 

Appalachian Counties, Smirnov (2000)

Distressed and Near-Distressed Counties with Potential Influence on Neighboring 

Counties (21) – Type II

Spatial linkages are relatively strong, economic base is relatively strong 

and relatively well-diversified

Distressed Counties Near-Distressed 

Bell, KY Jackson, KY 

Breathitt, KY Greene, TN 

Floyd, KY McMinn, TN 

Harlan, KY 

Johnson, KY 

Knox, KY 

Perry, KY 

Pike, KY 

Rowan, KY 

*Whitely, KY 

Alcorn, MS 

Monroe, MS 

Oktibbeha, MS 

*Athens, OH 

*Gallia, OH 

Wise, VA 

*Logan, WV 

Upshur, WV 

* Counties with both strong exports and local inter-county spatial links; other counties 

are those with only strong, local inter-county spatial link. 

Complementary industrial and labor market linkages among closely located counties, 

or clusters of counties, have substantial beneficial effects for all counties involved, 

enhancing competitiveness of local products and services, and creating a base for 

successful multi-county industrial clusters. Poor choice of the industrial mix to be 

promoted in one county might undermine economic opportunities in the neighboring 

counties. 

Update from the Spatially-augmented Export-base Analysis. The original analysis 

was updated for the Sources of Growth study using a more current set of data (years 

1997, 2002) sourced from IMPLAN3

 and provided specifically for this task by the 

ARC. The analysis methodology is reviewed in the next section before presenting the 

findings for the 1997-2002 period. Additional information is provided in a separate 

Appendix document. 

 

3

 IMPLAN ® is an economic-impact modeling system provided by the Minnesota IMPLAN Group, Inc. Industrylevel data are developed primarily from County Business Pattern data and select REIS data totals 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 9

2.2 Export-base Analysis Methodology 

S&S derive a spatial export-base model by applying the principle of demand-driven 

modeling to the two-level hierarchy of regional economy emanating from the county 

level (the first being the county itself, and the second being the county and its 

neighbors). This results in a three-sector economy with one non-basic sector and two 

basic sectors (serving local and global exports). They use a county’s employment in 

export-designated industries relative to the entire United States as an indicator of its 

economic-base; and location-quotients to identify a county’s export employment in an 

industry against the rest of the United States. Based on this model, they perform four 

key analyses: 

 Strength of economic-base using regional employment multipliers 

 Strength of spatial linkages 

 Degree of diversification in the economy 

 Classification of counties by growth potential 

Strength of the Economic-base. S&S use a concept of Regional Employment 

Multipliers (REM) to measure the strength of the economic-base. REM is defined as 

the number of new jobs generated in the county’s economy as a result of an additional 

job in the export-base sector. Higher REM values correspond to a stronger economicbase. 

The classical export-base model establishes that the total employment in the county 

(X) is the sum of export-base employment (E) and non-base employment (L): 

X = L + E (1) 

The critical assumption of the export-base model is that employment in the local 

sector is related only to the total employment in the county: 

L = aX (2) 

Where (a) is the requirement coefficient, which denotes the demand for local services 

by the regional economy, and 0 < a < 1. 

Combining (1) and (2), we obtain: 

E

a

X − = 1

1

 (3) 

where the 

1− a

1 coefficient is the Regional Employment Multiplier (REM), which 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 10

indicates how change in the export-base employment affects the regional economy, 

i.e., every additional job in the export-base sector creates a total of X jobs in the 

regional economy. 

The standard export-base model analysis approach does not include spatial elements in 

the parameters of the model, e.g., county location or socioeconomic environment. 

These assumptions limit the application of the model to the analysis of large 

geographical areas, such as states. 

Strength of Spatial Linkages. S&S modified the export-base model to include spatial 

linkages to the neighboring counties and the rest of the world, based on a two-region 

model (Exhibit 2-3). The first region is represented by a county (County A). The 

second region is represented by the expanded region of neighboring counties, which 

comprises the county and its direct neighborhood of adjacent counties. The export 

base model for that region is similar to the single county case: 

R R R X = L + E (4) 

R R L = cX (5) 

Total employment in the context of this expanded region is related to the employment 

in the basic sector via the multiplier effect: 

XR = 1

1− c

ER

 (6) 

The spatial export-base model implies no cross hauling within the aggregate multicounty region; i.e. a sector’s product exported to the neighboring counties is not 

subsequently exported. 

Exhibit 2-3: A two-region, three-sector, export-based model

Source: MIT Multiregional Planning Research Group. 

This spatial export-base model links two levels of the regional hierarchy: (1) the 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 11

county model, and (2) the expanded region of neighboring counties model. Exports 

from a county comprise two components: local exports Es (exports to the expanded 

region of neighboring counties) and global exports Eg (exports beyond the county and 

its neighborhood). 

X = L + Es + Eg (7) 

where (X) is the sum of the export-base employment ( E = Es + Eg ) and non-base 

employment (L); ( Es ) is the portion of the export-base employment attributed to 

exports to the neighboring counties, and ( Eg ) is the portion of the export-base 

attributed to global exports (exports beyond neighboring counties). 

This spatial export-based model leads to a three-sector economy, with a county’s 

economy consisting of three sectors, one local non-basic sector and two basic sectors. 

We maintain the assumption of the classical export-based model that the employment 

in the local sector is related only to the total employment in the county: 

L = aX (8) 

County A’s first basic sector is the sector that provides goods and services to the 

second basic sector in the expanded region. Assuming a linear relationship, we 

determine employment in this basic sector by: 

( )

l

g g

R Es = bE = b E + E . (9) 

where ( )

l Eg is the size of the global export-base in the neighboring regions (counties), 

coefficient b (where 0 < b < 1)4

 is a coefficient that indicates the requirement for 

employment tied to local export activities in the county and its spatial neighborhood. 

The second basic sector is associated with goods and services supplied outside the 

expanded region. This assumption is a logical extension of the classical export-base 

model, which aims to explain employment in all sectors of the regional economy via 

 

4

 The technical and economic bounds on the values of the coefficients: The bounds on the value of coefficient b are 

determined from practical considerations of model use. The lower bound b = 0 implies that all county exports are 

global, i.e. industrial mix of the county is identical to the industrial mix of the aggregated region. The upper bound, 

b = 1, still does not imply that all exports are local. However, high values of the coefficient b >=1 would have 

implied a “super-efficient” job-creation by global exports: one job created in the global export would have made a 

direct impact of equal or larger magnitude on the local exports. While technically this situation is possible, it 

simply suggests that the global export industry is a pass-through industry, which is instrumental, but not the reason, 

for the global exports. For example, if local export is generated by manufacturing in county A, and global exports is 

a shipping company in the neighborhood of county A, then the co-location of the two industries is driven by the 

demand on the manufacturing goods. High values of the coefficient b contradict the major economic assumption of 

the export-base model, which postulates the demand-driven economy. For this reason, any value of b close to 1, 

such as 0.8 or 0.9 should be taken as an indication of potential violation of model assumptions. (Communication 

from Smirnov, 1/30/2006) 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 12

the employment in the basic sector. In the case of the spatial export base model, it is a 

three-sector model: global export (basic-2) – local export (basic-1) – non-basic 

employment. Eventually, the employment in the second basic sector determines the 

employment in all other sectors. 

The addition of spatial interactions to regional export-base model introduces the 

concept of regional neighborhood. Regional neighborhood can be understood as the 

sphere of immediate economic influence of a county’s economy. That influence is 

exerted via common infrastructure, economic linkages, shared labor pools, etc. 

Because most of these effects quickly decay with geographical distance, it is 

reasonable to assume initially that only cross-county border interactions affect 

neighboring counties. In this study, we use the physical contiguity criterion to define 

regional neighborhood. 

This regional neighborhood is represented by the contiguity matrix. This is a matrix of 

zeros and ones, with an element Sij equal to one if counties i and j are geographic 

neighbors. This denotes that these two counties may have close economic ties with 

each other and that their economic-bases are interdependent. In contrast, the element 

Sij is equal to zero if two counties are not contiguous. The diagonal elements in the 

matrix are set to zero because our definition of global export excludes the county’s 

output5

. 

It should be noted that other neighborhood “constructs” could be used in this type of 

spatial modeling exercise. For example, a test of the hypothesis that the relationships 

among the different counties are a function of the cross-county trade flows rather than 

geographic adjacency would require generating a similar spatial weights matrix with 

elements Sij equal to one if counties i and j pass a certain threshold of cross-county 

trade flows activity. Comparing the effect of the spatial linkages based on these two 

different notions of adjacency, would illustrate the relative strength of geographic 

neighborhood vs. trade flows on the economic influence exerted among these counties. 

The principal distinction between the classical export-base model explained in 

Equations (1) and (2) and the modified spatial model explained in Equations (7), (8), 

and (9) is that for the latter, the export-base is segmented into two components and the 

“local” oriented export-base is linked directly to global export activities in the 

neighboring counties. 

By combining Equations (7), (8), and (9), we obtain Equation (10): 

[ ( ) ] 1

1

g

l b Eg Eg E

a

X + + − = (10) 

 

5

 For this updated analysis, the contiguity matrix was assembled using GeoDa5. The “Queen” concept from chess 

was chosen for calculating contiguity, which includes all the neighboring counties whether they are adjacent at a 

single point or have a common border with the county. This is in contrast to the “Rook” concept, which includes 

contiguous neighbors only if they share a border with the respective county. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 13

which can be rearranged into Equation (11): 

l

g Eg a

b E

a

b X − + −+ = 1 11

 (11) 

Equation (11) is the reduced form of the spatial version of the export-base model. Both 

coefficients (a) and (b) are county-specific; however, in the case of the non-spatial 

version of the model, (a) alone would be the only parameter. Values of these 

parameters characterize the industrial mix of the regional economy at the aggregate 

level, based on the aggregation of NAICS-level estimates. 

Two multipliers are important in this model. First, 

a

b

−

+

1

1 is the Spatial version of the

Regional Employment Multipliers (SREM), which denotes how much increase in 

employment in the county will occur from a unit increase in its global exports. The 

introduction of the spatial effects increases its value slightly from the value in the nonspatial version. The second multiplier 

a

b

1− measures the Local Spatial Linkage 

(LSL), which indicates how much the employment in the county will increase as a 

result of a unit increase in the export-base employment in the neighboring counties. 

Guided by this model, the Location Quotient (LQ) method6

 was used to calculate these 

multipliers. For each industry in a county, the LQ indicates the following: if the 

industry employs more (less) than the average in the reference area, which is the 

United States, we denote it as an export (local) industry. The LQ was also used to 

apportion the employment dedicated to export activities in an industry. 

LQ values were computed for each of 85 industries in each of the 410 Appalachian 

counties and the U.S. (as the reference region) for 1997 and 2002. 

In the spatial version of the export-base model, two regions are involved: one 

explicitly (the county in question) and one implicitly (the county’s spatial neighbor(s) 

which includes itself). Building on this, we compute the local and global exports using 

the following process: 

 

6

 The LQ was calculated as follows: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

n

j

in

ij

E

E

E

E

LQ (12) 

where, Eij is employment of industry j in county i ; Ein is total employment in county i; Ej is employment 

of industry j in the whole United States; En is total employment in the whole United States. If employment data 

are unavailable, an analyst can use output, value added, or some other data that is available for each county. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 14

First, we compute export-base employment of the county in question (county A) using 

the location-quotient method, and export-bases of all its individual neighboring 

counties. Summing these numbers, we obtain an estimate of neighborhood’s aggregate 

exports, ET. This value represents the sum of all local and global exports from the 

county and its spatial neighbor(s). 

Second, we compute the export-base of an aggregated region, i.e., the region 

composed of the county and its spatial neighbor(s) including any contiguous nonAppalachian counties, denoting the result as EG. This number represents the exportbase of the aggregated region, or from the perspective of county A, total global 

exports. 

Third, we compute the ratio (1 )

T

G

E

E − , which represents the Total Spatial Linkages 

(TSL). This ratio is always a positive number between zero and one. Its value depends 

on the industrial mix of the economy of county A and that of its spatial neighbor(s). 

A small value for the TSL ratio indicates that the economy of a county and its spatial 

neighbors have similar economic-bases (competing substitutes) and have limited 

interactions with each other. At the limit, if these economies have an identical 

industrial mix, the TSL ratio will be equal to zero. The value of the TSL ratio is higher 

when the economy of the spatial neighbor(s) complements that of county A. At the 

limit, when these economies are perfect complements and the industrial mix of the 

aggregated economy is identical to the reference area, the TSL ratio will be exactly 

one. 

Using the TSL ratio(1 )

T

G

E

E − , we compute Eg and Es for county A: 

E

E

E E

T

G

s = (1− ) (13) 

E

E

E E

T

G

g = (14) 

where E is the export-based employment and is equal to the sum of Eg and Es . 

Degree of Diversification in the Economy. S&S measured the degree of 

diversification (or concentration) of employment in a county by the percent of total 

employment accounted for by the top five industries7

. For example, in Bibb County, 

Alabama, the top five industries listed in Exhibit 2-4 employ 55% of the total labor 

force, indicating a 55% degree of diversification. A large number indicates a high 

 

7

 Other measures of industrial diversification may be used to give a different picture, for example, comparing the 

concentration by sector to the concentration in the region as a whole or to a larger reference region like the United 

States. This measure is often used in many ‘diversity indices’ used in the analysis of ethnic and racial diversity in 

urban areas. For the purpose of this paper, we followed the same diversification index used in the S&S (2000) 

paper to enable cross-comparison of the results. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 15

concentration of a few industries in the county, and a low number indicates a more 

diversified economy. 

Exhibit 2-4: Example of Degree of industry diversification 

County FIPS, Name Industry Rank Employment

01007 Bibb County, Alabama

92 Government & non NAICs 1 1,216

230 Construction 2 621

113 Forestry & Logging 3 324

321 Wood Products 4 312

814 Private households 5 307

Industry Diversification (Percent of employment in top five industries): 55%

Source: MIT Multiregional Planning Research Group. 

Classification of Counties by Growth Potential. S&S divided the counties into four 

groups based on the values of spatial regional employment multipliers (SREM) and 

local spatial linkages (LSL). In Exhibit 2-5, we define the criteria for the county 

typology, and in Exhibit 2-6, we illustrate this classification system. In the Appendix, 

we include the numerical thresholds used for the classification for 1997 and 2002 

evaluation. 

Exhibit 2-5: County Typologies 

Type Definition Criteria 

Type I Counties with a strong economic-base, i.e., spatial 

regional employment multipliers (SREM) in top 

quartile, AND strong local spatial linkages (LSL). 

SREM in top quartile 

and 

LSL in top half 

Type II Counties with strong local spatial linkages and a 

relatively strong economic-base relative to 

Appalachian counties, i.e., spatial regional 

employment multiplier in second quartile. 

SREM in second 

quartile 

and 

LSL in top half 

Type III Counties with either a weak economic-base, i.e., 

spatial regional employment multipliers being less 

than the median, OR weak local spatial linkages 

SREM in bottom half 

or 

LSL in bottom half 

(excluding Type IV) 

Type IV Counties with a weak economic-base, i.e., spatial 

regional employment multiplier in bottom quartile, 

AND weak local spatial linkages. 

SREM in bottom 

quartile 

and 

LSL in bottom half 

Source: Smirnov and Smirnova (2000). 

SREM = Spatial Regional Employment Multipliers; LSL = Local Spatial Linkages 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 16

Exhibit 2-6: Calculations of County Typology 

SREM 

LSL 

Top quartile 

75% 

Second quartile 

median 

Third quartile 

50% 

Bottom quartile 

25% 

Top half 

Median 

Type I Type II Type III Type III 

50% 

Bottom half Type III Type III Type III Type IV 

Source: MIT Multiregional Planning Research Group; based on Smirnov and Smirnova (2000). 

SREM = Spatial Regional Employment Multipliers; LSL = Local Spatial Linkages 

Data Methodology. While there are several sources of public and private 

employment data for the county-level economies, this updated analysis relies upon a 

current IMPLAN data set provided specifically for this analysis through the ARC. 

The data set covers all the 410 Appalachian counties as well as 137 contiguous nonAppalachian countries for 1997 and 2002. It covers 85 industries in each county, using 

the three-digit North American Industrial Classification System (NAICS) 

classification8

. 

There are substantial methodological differences in the nature of data sets used in this 

report and the 2000 S&S study, which relied upon Clean CBP & REIS data sets: 

 The IMPLAN data set is based on industry-level data with an algorithm to 

estimate suppressed data points, while the CBP data is the aggregate of 

establishment-level data (with data suppression issues). The result is 

slightly different notion of an ‘industry’ in both data sets. 

 Each data set uses a different level of industry aggregation. 

 IMPLAN uses NAICS codes, while CBP data used for the initial study was 

in terms of SIC codes. 

Additional data issues are presented after the current analysis’ results are compared to 

the original findings by Smirnov. 

 

8

 This data set does not include the inter-industry trade relationship or the county-to-county trade flows. These data 

would be useful in getting a deeper and more detailed understanding of the cross-county and inter-industry 

dynamics using techniques like input-output analysis. Some of these additional data sets may be available 

commercially, but were not available for this study. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 17

2.3 Overview of the Results 

Using Structured Query Language (SQL), the following four indices from the Smirnov 

analysis were recomputed for each Appalachian county for 1997 -2002: 

 Strength of economic-base using regional employment multipliers 

 Strength of spatial linkages 

 Degree of diversification in the economy 

 Classification of counties by growth potential 

Strength of the Economic-Base. The Spatial Regional Employment Multiplier 

(SREM) indicates the strength of the economic-base by measuring the number of new 

jobs generated in the county’s economy as a result of an additional job in the exportbase sector. To compare the SREM across the different types of economic-attainment 

counties, we calculate the average SREM for groups of counties based on their ARC 

designated economic status. 

By examining these aggregate results for the Appalachian region (Exhibits 2-7 and 2-

8), we identify two clear trends. First, there is a very limited (3%) difference between 

the SREM values across all counties between 1997 and 2002. Second, the SREM 

values increase linearly from the distressed counties to the attainment counties, 

indicating an increasing effect of the economic-base industries in the higher attainment 

counties. For example, in 2002, a new job in an export-based industry produced, on 

average, 2.1 jobs in a distressed county, compared to 3.6 jobs in an attainment county 

– a 58% difference. 

Exhibit 2-7: Strength of economic-base* by ARC Economic Status Class 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 18

SREM = spatial regional employment multiplier; Source: MIT Multiregional Planning Research Group 

Exhibit 2-8: Mapped Distribution of the 2002 spatial regional employment 

multipliers (SREM) 

Source: MIT Multiregional Planning Research Group. 

However, it is hard to specify the direction of the causality in this relationship. It could 

be that counties with industries that have higher SREM values have better 

opportunities for additional growth, as the export activity spurs forward and backward 

linkages. It could also be that counties that are economically developed have a more 

advanced and diversified economy such that the exporting firms can maximize local 

sourcing, rather than importing them from other counties. (The Appendix contains the 

complete SREM values for each of the 410 Appalachian counties for 1997 and 2002.) 

Strength of Spatial Linkages. “Local Spatial Linkage (LSL)” is a measure of how 

much employment in a county will increase as a result of a unit increase in the exportbase employment in the neighboring counties (Equation 11). LSL values (Exhibit 2-9 

and 2-10) are significantly higher in competitive and attainment counties, compared to 

the distressed and transitional counties. This indicates that a county has higher 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 19

economic linkages with neighboring counties. For example, in 2002, LSL = 0.05 in 

distressed counties, compared to LSL = 0.23 in attainment counties. Higher LSL 

values suggest that neighboring economies are more integrated and, therefore, more 

responsive to economic policies. This may be a result of the local geography, where 

attainment counties may contain residential neighborhoods next to an industrial 

county, where the impact of jobs in the industrial county trickles down to the 

neighboring suburban residential county. 

Exhibit 52-9: Strength of local spatial linkages (LSL) and total spatial linkages 

(TSL), 1997 and 2002 

Source: MIT Multiregional Planning Research Group. 

Total Spatial Linkage (TSL) is a measure of the similarities/differences in the 

industrial mix between a county and its spatial neighbor(s). TSL is a positive number 

between zero and one. A high TSL value indicates that the economy of the county is 

different and complements that of its spatial neighbor(s). A small value for TSL 

indicates similar economic-bases between the county and its spatial neighbors where 

they have limited interactions among each other (substitutes). TSL values (Figures 

5.5) are higher for attainment counties indicating more complementarities with their 

spatial neighbors, compared to distressed counties that have more similarities with 

their spatial neighbors, indicating less potential for economic integration. For 

example, in 2002, TSL = 0.21 for attainment counties, compared to 0.16 for distressed 

counties. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 20

Exhibit 2-10: Mapped Distribution of the Local Spatial Linkage, 2002

Source: MIT Multiregional Planning Research Group. 

Degree of Diversification in the Economy. The degree of diversification or 

concentration of employment in a county is measured by the percent of total 

employment in the county tied to the top five industries. For 2002, 42% of the 

employment in the competitive and attainment counties was concentrated in the top 

five industries (Exhibits 2-11 and 2-12). However, the transitional and distressed 

counties had more concentration, with 45% and 53%, respectively. These values have 

changed little between 1997 and 2002, except for distressed counties, in which the 

concentration in the top five industries increased by 6.9% from 0.494 to 0.528. The 

industrial concentration in distressed and transitional counties indicates more 

vulnerability to cyclical recessions in these individual industries. Most of the 

distressed and transitional counties have small economies, where these top industries 

often represent a small number of establishments with large employment (Smirnov 

and Smirnova 2000), thus the impact of factory closures or relocation can significantly 

affect employment in the county economy. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 21

Exhibit 2-11: Average values for Industry Diversification, 

by County economic-status

Economic-Status 1997 2002 

Percentage Change 

(1997-2002) 

Distressed 0.494 0.528 6.9% 

Transitional 0.437 0.451 3.2% 

Competitive 0.431 0.424 -1.5% 

Attainment 0.422 0.425 0.6% 

Source: MIT Multiregional Planning Research Group 

Exhibit 2-12: Mapped Distribution of County-level Industry diversification in

Appalachia, 2002

Source: MIT Multiregional Planning Research Group. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 22

Classification of Counties by Growth Potential. Based on the results for 2002, the 

ARC county distribution among the S&S typology (Exhibit 2-13 and 2-14) is overall 

consistent with the economic development status of the counties, e.g., a large number 

of the distressed and transitional counties are classified as Type III and Type IV, while 

most of the competitive and attainment counties are classified as Type I and Type II. 

We can use this classification to help identify counties that could serve as “anchors” or 

“hubs” for a regional economic development strategy, e.g., Type I counties that are 

distressed (1) and transitional (81) have strong employment multipliers and local 

spatial linkages. 

The transitional counties are more numerous than the set of distressed Appalachian 

counties and exhibit more heterogeneity (i.e., spread across the different types) in the 

composition of economic-base and therefore display a broader reaction to economic 

stimuli. Type I and II transitional economies are more likely to respond favorably to 

economic growth in the neighboring counties. In contrast, Type III and IV transitional 

and distressed counties will have less benefit. This suggests two policy implications. 

First, if officials target Type I and II counties for investments, they are likely to obtain 

a favorable growth response. Second, the overall effect in Appalachia of local 

initiatives will be higher for counties that are surrounded by Type I or II counties; and 

limited in the case of counties surrounded by Type III and IV counties, because all the 

linkages lead to outside-of-the-region interactions. 

Exhibit 2-13: Appalachian Counties by S&S Typology and Economic Status

Economic Status Type I Type II Type III Type IV 

Distressed 1 9 32 35 

Transitional 81 52 118 52 

Competitive 12 2 6 2 

Attainment 5 1 2 0 

Source: MIT Multiregional Planning Research Group. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 23

Exhibit 2-14 Appalachian Counties by County Typology, 2002

Source: MIT Multiregional Planning Research Group. 

2.4 Uses and Limitations of the Findings 

This analysis provides some useful insights into the development potentials of the 

distressed and transitional counties based on their export-base. However these indices 

should not be used in a vacuum when making county-level policy decisions or 

investment allocations. This section points to some of the strengths and limitations of 

the methods and data under-pinning this analysis which can serve to both (a) assist 

users of this report in interpreting the spatial economic-base implications for their 

county(ies) of interest, and (b) guide future follow-up work that may utilize a similar 

methodology.9

 

Strengths of the Economic-Base Model. The computed indices may be useful at the 

aggregate level to provide a picture of the economic capabilities in the Appalachian 

 

9

 This section benefited significantly from comments from Luc Anselin, Lisa Petraglia, Karen Polenske, Oleg 

Smirnov, and Glen Weisbrod. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 24

region. By examining the results of this analysis in comparison with the results of the 

original S&S analysis, we can highlight three key points that illustrate the strength of 

this approach. First, the quantitative characteristics of the counties did not change 

significantly from 1997 to 2002. Second, when we do a back-of-the-envelope 

comparison of the county typology for some counties between the original S&S 

computations for 1996 and the 2002 computations done for the present study, we find 

limited change in how a county is rated Type I through IV. This is an indication of 

both the consistency of the methodology despite the change in data sources and 

aggregation, and it also shows that there was little change in the Appalachian counties 

during that period. However, we would need to conduct a systematic comparison 

between the results of the two analyses to confirm this point. Third, the current results 

when applying the S&S typology may explain some of the differences between 

attainment and distressed counties vis-à-vis their economic-bases and spatial linkages. 

Limitations of this Analysis. The use of this analysis should be guided by the 

limitations of the theory, methodology, and data. 

• Economic-Base Model - In general, the key limitation of the economic-base 

model is its sensitivity to definitional issues in the computation. Analysts using 

the economic-base model must make two theoretical assumptions: (1) the 

reference region is a closed-economy, i.e., all economic activities happen within 

the region, and there is no trade activity between the reference region and outside 

the region; (2) all counties throughout the region have identical productivity and 

consumption levels (Kim 1995). 

For the first assumption, we use the United States as our reference region, 

assuming that all U.S. export/import activities happen within the country, and no 

one county exchanges goods with areas outside of the United States. The United 

States was chosen as the reference area in this study in order to compare 

Appalachian counties with other U.S. counties in terms of their economic-base 

performance. However, this may have limitations in counties (Appalachian or not) 

that have significant exports to areas outside of the United States. 

For the second assumption, we assume that, throughout the United States, labor 

productivity as well as consumers’ tastes and expenditure patterns, and 

households’ income levels are identical. This assumption implies evenly 

distributed demand and supply of each product in proportion to the population 

within the reference area. 

• Location-Quotient (LQ) Method - LQ is a useful technique to identify exportbased industries in a region; however, its accuracy depends on many factors 

including the reference area and level of data aggregation. 

First, for the reference area: we performed the same analysis twice using all of 

Appalachia and then the United States as the reference areas, and noticed a 

significant difference in the LQ values. This difference would trickle down 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 25

through the computations, and would produce a different picture for the exportbased industries. Although the results may remain similar across different time 

periods if the same computation method is used, analysts need to be careful in the 

interpretation of the results in either case. 

Second, for the level of data aggregation: we use three-digit NAICS codes (85 

industries) to calculate the LQs and identify export sectors. In theory, the results 

may vary depending on what data level of industrial classification an analyst uses 

to calculate the LQs. Using data at a more disaggregated level (larger number of 

industrial sectors), tends to produce more ‘accurate’ results.10 For example, with an 

analysis at the three-digit NAICS code level, a researcher will not detect some 

detailed export-based industries due to aggregation bias; but, at a four-digit level 

of analysis, one or two sub-sectors may appear as export sectors. However, when 

we compare the county-level aggregate outcomes and the resulting county 

typology from the S&S paper with the current results, there are no significant 

ordinal changes in the relationships among the counties (the ranking and 

typology), yet cardinal differences do exist, i.e., differences in the values of the 

LQs and multipliers. 

• Spatial Linkages Concept. Spatial linkages, as computed in this analysis, 

provides limited resolution as to the role of cross-sector linkages (input-output 

accounts would illuminate key inter-industry relationships capitalized upon in a 

county). Nor can one explicitly identify the role that a county’s personal income 

(predominantly made up of wage earnings) plays in the strength of the spatial 

linkages when household fulfill their demands for goods and services. It is also 

important to keep in mind that the terms multiplier and linkage represent different 

concepts than those terms connote in traditional input-output analysis. 

• County Classification. While the S&S county classification (Type I through IV) 

is a useful tool to avoid the variations in multiplier values due to the use of 

different data sets, two issues limit the usefulness of this classification. First, the 

two dimensions used in the county classification (SREM and LSL) are not 

orthogonal, i.e., they are correlated (SREM = (1/1-a) + LSL). The use of 

orthogonal dimensions is required for effective classification. Second, the 

classification does not take into account the standard error of the multiplier values. 

This would affect counties on the borderline between different types. 

• Data Issues and Comparability of Results. Since the current analysis used a 

different source of data than the one used in the 2000 S&S report (IMPLAN vs. 

Clean CBP/REIS data), there are issues with the comparability of the results. 

These issues stem from the different levels of aggregation in the 

establishment/industry data sets. Specifically one is limited in making a direct 

comparison of individual values for multipliers for county-industry pairings. To 

reliably overcome this and be able to make comparisons of the results between the 

 

10 Consequently, using more aggregated data tends to produce higher values for the regional multipliers. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 26

two analyses comparative ranking among the different counties/industries can be 

used. In doing so there is a strong correlation of the county classification results 

between the two reports, indicating a robustness of the results of the analysis at the 

aggregate level, despite the difference in the level of data aggregation, time period, 

industrial classification (SIC vs NAICs), and data source. This comparison also 

highlights the limited change in the (relative) economic structure in the 

Appalachian counties over the past decade. 

Applying the Results of this Analysis. Given the strengths and limitations of this 

analysis, we describe different approaches to make use of this analysis in the field and 

the potential for future research studies that would build on this analysis. 

First, using this analysis, we can create profiles for each county, highlighting the 

multipliers, the top industries, and the typology. However, the local county 

community cannot take solitary action based on these profiles since by definition they 

reflect the influences of neighboring counties. A second approach would be to use this 

analysis for a cross-county comparison to understand the relative characteristics of 

these counties. This may be useful to understand the relationships between economic 

attainment and the parameters computed in this analysis, e.g., the industry 

diversification or concentration, or the regional linkages. A third approach is to use 

the county typology to identify potential “growth hubs” at the regional level. This is 

similar to what S&S use in their paper (see Exhibit 5.1 above), where they identify 

counties with strong spatial linkages and economic-base as potential agents for 

triggering regional growth in their neighborhood. A fourth approach in using this 

analysis is to identify possible counties or groups of counties for future case studies to 

examine the spatial forces at work on each county in a neighborhood. 

The most important point to emphasize in using such an analysis at the individual 

county level is that it is not unusual that the computed figures would vary from the 

reality in the individual counties. This is due to several factors in the data collection, 

measurement errors, aggregation effects, and assumptions embedded in the 

computation process. When using these results in individual counties, an analyst needs 

to do a “reality check” to ensure that the results are not anomalies. A reality check 

should turn up consistency with existing economic changes/transactions. This 

becomes crucial when communicating these results to local communities, or when 

using them for county-level decisions. 

2.5 References 

Fingleton, Bernard, Danilo C. Igliori, and Barry Moore. 2003. “Cluster Dynamics: 

New Evidence and Projections for Computing Services in Great Britain, 

Regional Economics,” Paper presented at conference Economic Geography 

and Clusters (January). Cambridge, UK. 
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

Sources of Growth in Non-Metro Appalachia page 27

Kim, Hong-Bae. 1995. Urban and Regional Economics: Analysis and Estimation. 

Seoul: Kimundang. 

Smirnov, Oleg, and Alena Smirnova. 2000. “An Assessment of the Economic-bases of 

Distressed and Near-Distressed Counties in Appalachia: A Report to the 

Appalachian Regional Commission.” Washington, DC: ARC 

Smirnov, Oleg. 2002. “Measuring Self-Sustainability of Economic Development at the 

County Level,” The Annals of Regional Science. 36(4): 683-696. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 28

3 TWIN COUNTIES STUDY UPDATE 

“The Impact of Highway Investments on Economic Growth in the Appalachian 

Region, 1969-2000: An Update and Extension of the Twin County Study” 

By Teresa Lynch, Economic Development Research Group 

3.1 Introduction 

This chapter estimates the impacts of highway investments on economic growth in 

Appalachian counties between 1969 and 2000. The chapter has two objectives. The 

first objective is to update the 1995 study by Isserman and Rephann (I&R), which 

found statistically significant differences in economic growth rates of ARC counties 

when compared to their non-ARC counterparts in the 1965-1991 period, and that 

counties served by the Appalachian Development Highway System (ADHS) had even 

higher rates of income, population, and per capita income growth than otherwise 

similar (non-ARC) counties (1995; p.359). We extend this analysis to year 2000. 

The second objective is to determine whether the amount, characteristics, and timing 

of ADHS investments can explain some of the differences in economic outcomes. In 

the Isserman and Rephann study, “ARC program variables are almost never 

statistically significant” (p.362), a finding we hypothesized might be due to the blunt 

measures of ARC program variables used in that study. To improve the quality of the 

highway investment variable, we surveyed state DOTs on the timing and 

characteristics of ADHS segments in their states, including construction start and end 

dates, section length, number of lanes, access type, number of signalized intersections, 

and number of interchanges. All thirteen ARC states participated fully in the survey 

process. 

The critical empirical finding of this research is that (on average) the gap between 

ARC counties and their twins grew significantly in the 1990s. Relative to their nonARC county twins, income in ARC counties had grown 131% more over the 1969 to 

2000 interval; earnings growth was 96% higher; population growth was 9% higher; 

and per capita income was 36% higher. The performance of ARC counties with 

ADHS segments relative to their twins was even more impressive: income growth 

alone was over 200% higher for the 1969 to 2000 interval. The overall performance 

on the ARC region during this period, though, should not mask the struggles that 

pockets within ARC have experienced: performance in the northern part of the ARC 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 29

regions lagged its non-ARC twins and across the region, smaller metropolitan areas 

fell far behind their non-ARC counterparts. 

The critical methodological finding from this chapter is that one reason top-down 

methodologies approaches have often failed to establish a link between highway 

investments and Appalachian development is poor measurement of highway 

investments. Using the improved highway measures afforded by our survey, we were 

able to establish a statistical link between ADHS investments and differential income 

and earnings growth between ARC counties and their twins. 

We found that better measures of highway investment characteristics (e.g., new versus 

replacement investment; length of segment relative to county size) generated 

explanatory relationships that were statistically significant and robust, whereas poor 

measures of investment did not. This suggests that when characteristics of the 

proposed highway investments are properly measured, there is empirical support for 

claims that highway investments--here in the form of the ADHS investments--

contribute to economic growth. 

3.2 Appalachian Growth, 1969-2000 

A key question for national policy makers and ARC members, partners, and staff 

concerns the effectiveness of different ARC programs on improving economic 

outcomes in Appalachia. Isserman and Rephann’s 1995 study--which was subtitled 

“An Empirical Assessment of 26 Years of Regional Development Planning”--

compared economic growth in Appalachian counties to growth in a control group of 

non-Appalachian counties (“twins”). The purpose of the control group is to proxy 

what would have otherwise occurred (in terms of growth) without ARC funding. The 

authors posit that once identified, the difference in the mean cumulative growth rates 

informs us whether there are real growth gains for the Appalachian county. To 

complete the study’s objective, the authors attempted to identify the causal factors 

(through regression analysis) behind significant real growth differentials in favor of 

Appalachian counties. 

Clearly then, much rests upon (a) the methods to select a non-Appalachian county 

twin, and (b) assessing how suitable each “match” is before advancing the growth 

analysis. The set of 391 non-Appalachian twin counties identified by Isserman and 

Rephann are used in our current update of their analysis which follows. 

Eligible non-Appalachian counties for selection as a possible match were predicated 

on the following: 

 The county’s population centroid had to be at least 60 miles away from the 

Appalachian border 

 Comparable growth in personal income, earning by sector over the period 

1950-1959 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 30

 Comparable economic structure (earnings by sector) and population in 1959 

Since a qualifying non-Appalachian county might provide a match to more than one 

Appalachian county, the final matching was guaranteed to reflect the optimal set by 

applying a distance weighting on the proposed pairs in the set of 391 Appalachian 

counties. The solution that had the minimum Mahalanobian distance11 defined the 

optimal pair matches. The validity of the 391 match counties to serve ultimately as 

the “counter-factual” for Appalachian growth over the 1969-1991 period in the 

absence of ARC investments was confirmed statistically by the authors albeit with a 

slight bias.12

Isserman and Rephann (referred to here as “I &R”) found that on average, ARC 

counties outperformed their twins by significant margins over the 1969-1991 period: 

income and earnings growth in ARC counties was 48% higher (cumulatively) while 

per capita income growth was 17% higher. These differences were statistically 

significant (at the 10% level). The results were more ambiguous when county type 

was taken into account: large metropolitan (statistically insignificant however) and 

non-metropolitan counties (particularly those in the Central Appalachian subregion) 

fared much better than their twins, but smaller metropolitan areas (those with 

populations under 250,000) demonstrated a statistically significant finding of lower 

income, earnings, and per capita income growth than their twins. For non-metro areas, 

income, earnings, and per capita income differences were statistically significant. 

 

These findings only reflect performance through 1991, neglecting the question of how 

ARC counties fared during the 1990s. To answer this question, we use the same data 

and the same control group as Isserman and Rephann (I&R). The data are from the 

U.S. Bureau of Economic Analysis and provide information on population, personal 

income by source, and earnings by industry by county for 1969-2000. These data, 

termed the “REIS” data,13 provide a long time series and do not suffer from the data 

suppression issues that other potential data sources (e.g., County Business Patterns) 

do. We also use the same control group, namely the “twin county” matches developed 

by I&R and used in different studies of the Appalachian region.14 

Exhibits 3-1, 3-2 and 3-3 provide the relevant data on ARC growth since 1969. 

Exhibit 3-1 reproduces the mean growth rate differences between Appalachian 

counties and their twins for the period 1969-1991 reported by I&R. Exhibit 3-2 

presents updated estimates of the 1969-1991 mean growth rate differences using the 

most recent REIS data.15 (The latter estimates are expected to differ from those of 

 

11 Mahalanobian distance accounts for correlations between variables, as discussed in Isserman & Rephann (1995) 12 Over the 1950-1959 period the Appalchian counties exhibitied a slightly more moderate rate of growth than the 

391 non-Appalachian match counties – a manifestation that Appalachian counties pre-ARC investments (1965 

inception) were uniquely disadvantaged locations. This bias would only serve to understate the role of ARC 

investments over 1969-1991 should significant, positive growth differentials be observed. 

13 “REIS” is the acronym for “Regional Economic Information System.” 

14 We thank Andrew Isserman for providing a list of the county matches used in Isserman and Rephann, 1995. 15 REIS data used in this report were downloaded in late 2005. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 31

I&R in Exhibit 3-1 because of changes in methods used in REIS, as well as the 

periodic updating of data sets by BEA.) Exhibit 3-3 presents mean growth rate 

differences between Appalachian counties and their twins for the 1969-2000 period. 

Two matters stand out about the data. The first is that the more recent REIS data 

(shown in Exhibit 3-2) show a somewhat different picture of ARC performance for 

1969-1991 than presented by I&R (shown in Exhibit 3-1). Both data sets show that 

ARC counties outperformed their twins across all measures in the 1969-1991 period; 

that certain characteristics (e.g., presence of ADHS segment) are associated with 

strong economic performance and others (e.g., metropolitan status with less than 

250,000 in population) with weak performance; and great variability in performance 

of ARC counties by region and state. The more recent data, though, suggest that 

income growth was significantly higher in ARC counties than previously thought 

(68% higher than their twins between 1969 and 1991 compared to 48% in I&R); that 

the northern region of ARC outperformed its twins between 1969 and 1991; and the 

southern ARC region had more noticeably outperformed their twin counties with 

respect to income growth (aggregate and per capita) and earnings growth than 

originally measured.
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 32

Exhibit 3-1. Isserman & Rephann's Reported Mean Growth Rate Differences, 

1969-1991 

 Income Earnings Population 

Per 

Capita 

Income Manufacturing 

Retail 

Trade Services 

No. of 

Counties

Appalachia 48% 48% 5% 17% 87% 67% 138% 391

Northern -6% -11% -3% 7% -76% 13% 46% 143

Central 101% 92% 7% 51% 427% 99% 131% 86

Southern 68% 78% 10% 8% 63% 99% 222% 162

 

Alabama 8% 33% 1% -4% 94% 33% 127% 35

Georgia 199% 262% 35% 7% 101% 247% 689% 35

Kentucky 118% 105% 7% 68% 530% 112% 147% 49

Maryland 112% 95% 5% 72% 77% 173% 167% 3

Mississippi 27% 7% 7% -17% 55% 60% 95% 18

New York -2% -3% -2% 5% 1% -4% 0% 14

North Carolina 53% 21% 0% 40% -49% 101% 139% 29

Ohio -11% -2% 3% -23% -20% -29% 36% 28

Pennsylvania 6% -2% -2% 16% -70% 39% 58% 52

South Carolina 151% 130% 24% 12% 98% 191% 87% 6

Tennessee 68% 72% 10% 8% 277% 90% 119% 50

Virginia 36% -18% -3% 46% 191% -38% 79% 17

West Virginia -26% -26% -8% 15% -179% 9% 22% 55

 

Metropolitan 50% 64% 8% 4% 110% 70% 205% 95

<250,000 -65% -86% -11% -8% -160% -42% -11% 27

Non-metro 48% 43% 4% 22% 80% 66% 115% 296

 

Appalachian 

HWY 69% 49% 6% 32% 61% 78% 92% 110

Interstate HWY 41% 48% 4% 15% 125% 70% 148% 152

Growth Center 37% 40% 4% 14% 101% 62% 85% 90

Coal Producing 51% 41% 1% 38% 77% 47% 73% 148

Distressed 

County 48% 31% 2% 28% 168% 55% 92% 113

Note: Boldface indicates significance at the 10 percent level. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 33

Exhibit 3-2. Recent REIS Data Calculated Mean Growth Rate Differences, 

1969-1991 

 Income Earnings Population 

Per 

Capita 

Income MFG 

Retail 

Trade Services 

No. of 

Counties

Appalachia 68% 59% 6% 27% 79% 66% 170% 391

Northern 8% 3% -2% 16% -85% 29% 69% 143

Central 119% 89% 7% 57% 346% 69% 195% 84

Southern 94% 92% 11% 21% 93% 98% 248% 164

 

Alabama 51% 56% 2% 22% 105% 44% 179% 35

Georgia 221% 278% 38% 9% 64% 224% 741% 35

Kentucky 134% 96% 7% 61% 517% 69% 235% 49

Maryland 110% 107% 5% 67% 129% 158% 248% 3

Mississippi 0% -39% 5% -26% -1% 55% 9% 18

New York 6% 10% -25% -3% -6% 14% 23% 14

North Carolina 87% 40% -1% 60% -32% 78% 126% 29

Ohio 8% 12% 2% -3% 25% 2% -36% 28

Pennsylvania 14% 6% -1% 20% -65% 46% 78% 52

South Carolina 158% 148% 24% 17% 129% 196% 12% 6

Tennessee 113% 98% 11% 36% 257% 102% 167% 50

Virginia 7% -30% -3% 25% 212% -28% 110% 17

West Virginia -2% -3% -6% 26% -225% 23% 107% 55

 

Metropolitan 84% 80% 9% 21% 147% 71% 198% 76

<250,000 -53% -57% -10% 0% -90% -26% 63% 31

Non-metro 62% 51% 4% 29% 52% 65% 160% 284

 

Appalachian 

HWY 92% 69% 7% 42% 147% 81% 194% 139

Interstate HWY 63% 60% 5% 27% 125% 69% 153% 162

Growth Center 79% 87% 8% 28% 42% 121% 175% 124

Coal Producing 74% 67% 3% 40% 93% 53% 142% 134

Distressed 

County 69% 33% 3% 40% 139% 48% 169% 115

Note: Boldface indicates significance at the 10 percent level. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 34

Exhibit 3-3. Recent REIS Data Calculated Mean Growth Rate Differences, 

1969-2000 

 Income Earnings Population 

Per 

Capita 

Income MFG 

Retail 

Trade Services 

No. of 

Counties

Appalachia 131% 96% 9% 36% 132% 127% 424% 391

Northern -34% -48% -5% 8% -151% 0% 77% 143

Central 191% 84% 5% 93% 625% 131% 387% 84

Southern 245% 228% 22% 31% 146% 236% 757% 164

 

Alabama 4% -33% -1% -5% -35% -31% 183% 35

Georgia 780% 933% 79% 32% 583% 670% 2940% 35

Kentucky 205% 79% 6% 94% 1181% 128% 437% 49

Maryland 160% 101% 4% 88% -46% 123% 521% 3

Mississippi 34% -12% 6% -15% -26% 138% 67% 18

New York -54% -47% -8% 7% -75% -69% 19% 14

North Carolina 194% 70% 4% 116% -166% 177% 356% 29

Ohio -20% -8% -1% -15% -63% -14% -97% 28

Pennsylvania -7% -12% -1% 15% -130% 24% 46% 52

South Carolina 308% 236% 34% 15% 149% 465% 117% 6

Tennessee 239% 134% 19% 54% 203% 249% 319% 50

Virginia -35% -73% -9% 44% 15% -79% 205% 17

West Virginia -80% -98% -13% 18% -265% -3% 174% 55

 

Metropolitan 201% 186% 17% 15% 146% 157% 770% 76

<250,000 183% -200% -17% -40% -436% -164% 13% 31

Non-metro 105% 62% 5% 44% 127% 116% 292% 284

 

Appalachian 

HWY 202% 117% 12% 63% 96% 163% 516% 139

Interstate HWY 93% 117% 6% 23% 333% 108% 426% 162

Growth Center 133% 182% 9% 40% 102% 229% 510% 124

Coal Producing 96% 50% 1% 54% 92% 70% 284% 134

Distressed 

County 96% 3% 0% 72% 456% 76% 250% 115

Note: Boldface indicates significance at the 10 percent level. 

The second noteworthy finding concerns the performance of ARC counties in the 

1990s. As the data in Exhibit 3-3 show, by 2000, income in ARC counties had grown 

131% more since 1969 than in the non-ARC counties; earnings growth was 96% 

higher; population growth was 9% higher; and per capita income was 36% higher. 

Mean growth rate differences (relative to twins) in counties with ADHS segments 

grew from 92% for the 1969-1991 period to 202% for the 1969-2000 period. At the 

same time, the 1990s saw the northern region of ARC fall behind its non-ARC 

counterparts; and income and earnings growth in the 31 smaller metropolitan counties 

(populations less than 250,000) dropped from about 50% less than their twins through 

1991 to about 200% less than their twins by 2000. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 35

The performances of individual states also varied widely, ranging from 80% less than 

the twins to 780% more. Interestingly, the states that performed best (and 

significantly so) relative to their twins (Georgia, Kentucky, and South Carolina) 

seemed to do so in part on the strength of their performances in manufacturing. 

3.3 The Role of Highway Investments 

By adopting the “twin county” approach, itself a version of the comparison group 

methodology, we share an important assumption with I&R: that differences in growth 

rates between ARC counties and their twins represent “what would have happened in 

Appalachia without the ARC,” i.e., without ARC programs. Although I&R were 

unable econometrically to establish a robust relationship between ARC programs 

(growth centers, distressed counties, and highway investments) and economic 

outcomes in Appalachian counties, it is possible that their results reflect poor 

measurement of program variables rather than weak program effectiveness. The poor 

quality of program measures is evident in the treatment of highways in their regression 

model: I&R roll ADHS and interstate highway investments into one binary variable 

(“Highway in County”), which takes a value of “1” if the county is home to at least 3 

miles of ADHS or interstate and a “0” otherwise. The crudeness of this measure, we 

believed, might be the reason it was not possible to establish a statistical relationship 

between highway investments and economic growth. 

To improve the quality of the highway investment variable, we surveyed state DOTs 

regarding the timing and characteristics of ADHS segments in their states, including 

construction start and end dates, section length, number of lanes, access type, number 

of signalized intersections, and number of interchanges. (A sample survey is 

presented at the end of this chapter.) Each of the 13 ARC states participated fully in 

the survey. Survey data were added to the REIS data on economic performance to 

create a dataset of highway investments and economic outcomes. 

Before testing the new dataset for causal determinants of growth differentials between 

Appalachian counties and their twins, we attempt first to reproduce I&R’s findings for 

the 1969-1991 period, then extend their analysis to year 2000. The results are 

presented in Exhibit 3-4, which show reasonable consistency with I&R’s results. 

Specifically, for the analysis of income growth in ARC counties and their twins in the 

1969-1991 period (“INC 91”), the two sets of findings are in accordance on the sign 

and significance of 14 of 18 of the variables used in the original I&R model 

specification. For the analysis of earnings growth (“EARN 91”), the analyses are also 

in accordance on 14 of the 18 variables. Some of the differences that do exist can 

likely be attributed to how the variables were constructed. (For example, the 

economic structure variables used in I&R are defined as the contribution of farm, 

manufacturing, retail, and government sectors to county total income in 1959, while 

this analysis used 1969 data because of issues of timing and data availability.) Others 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 36

are likely due to differences in old and new REIS estimates of earnings and income for 

this period. 

Despite these differences, the current analysis reproduces the key finding of the 

original I&R analysis: that the presence of an interstate and/or ADHS highway 

segment cannot explain earnings or income growth patterns in ARC counties in the 

1969-1991 period. (However, unlike the findings of I&R, the “highway” variable is 

positively and significantly correlated with per capita income, a finding that should be 

further explored in a later study.) These results also hold when the analysis is 

extended to examine the difference in income or earnings growth between 1969 and 

2000 (“INC 00” and “EARN 00”). 

Exhibit 3-4. Regression Results Using Isserman and Rephann Specification 

(dependent variable is differential Income or Earnings growth by 1991 or 2000) 

Explanatory Variable INC 91 INC 00 EARN 91 EARN 00

(Constant) 1.273 4.243 .645 1.269

South Region 1.010 3.071 1.059 2.692

Central Region 1.154 2.308 1.019 1.630

Distance to City of 25,000 .014 .049 .015 .056

Distance to City of 100,000 -.006 -.023 -.008 -.023

Distance to City of 250,000 -.003 -.015 -.003 -.012

Distressed Counties 1990 -.159 -.660 -.663 -1.187

Growth Center -.108 -.596 -.059 -.217

Coal Producing .313 .278 .443 .359

Mahalanobis Distance -.039 -.078 -.004 -.011

Population Density, 1960 -.001 -.003 -.001 -.002

% Farm in Earnings, 1969 -.018 -.016 -.038 -.052

% Manu in Earnings, 1969 -.026 -.080 -.026 -.062

% Ret Trade in Earnings, 1969 -.001 .024 .025 .092

% FedGovCiv in Earnings, 1969 .025 -.041 .043 -.055

% FedMil in Earnings, 1969 -.090 -.282 -.079 -.199

% St/Local in Earnings, 1969 .014 .001 .039 .068

Population Growth Rate, 1950-60 .022 .060 .021 .059

ADHS or Interstate .204 .641 -.079 .359

Bold indicates the regression coefficient is significant at the 10 percent level in both analyses; 

Bold italics indicates variable is significant in current analysis but not in I&R analysis; 

Italics indicates variable is significant in I&R but not in current analysis 

For the second part of the analysis, we refined I&R’s single “highway” variable by 

decomposing it into its component parts, ADHS and interstate investments. Using a 

model specification that mimics the I&R model in all ways except that the “highway” 

variable is now disaggregated into separate “Interstate” and “ADHS” components, we 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 37

find that the presence of an ADHS segment in a county can in fact explain a portion of 

differential income growth for 1969-1991 (“INC 91”) and 1969-2000 (“INC 00”), as 

well as differential earnings growth in the 1969-2000 period (“EARN 00”). These 

results are presented in Exhibit 3-5. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 38

Exhibit 3-5. Regression Results Delineating Interstate and ADHS Investments 

( dependent variable is differential Income or Earnings growth by 1991 or 2000) 

Explanatory Variable INC 91 INC 00 EARN 91 EARN 00

(Constant) 1.355 4.669 .600 1.365

South Region 1.000 3.033 1.054 2.667

Central Region 1.129 2.210 1.009 1.575

Distance to City of 25,000 .013 .045 .015 .054

Distance to City of 100,000 -.008 -.028 -.009 -.027

Distance to City of 250,000 -.003 -.013 -.002 -.012

Distressed County -.149 -.627 -.654 -1.161

Growth Center -.113 -.603 -.076 -.243

Coal Producing .289 .209 .424 .294

Mahalanobis Distance -.040 -.086 -.005 -.013

Population Density, 1960 -.001 -.003 -.001 -.002

% Farm in Earnings, 1969 -.017 -.015 -.037 -.051

% Manu in Earnings, 1969 -.025 -.076 -.025 -.059

% Ret Trade in Earnings, 1969 -.003 .017 .024 .087

% FedGovCiv in Earnings, 1969 .025 -.039 .044 -.055

% FedMil in Earnings, 1969 -.080 -.245 -.068 -.170

% St/Local in Earnings, 1969 .015 .005 .041 .072

Pop. ulation Growth Rate, 1950-60 .022 .062 .021 .059

Interstate -.059 -.569 -.181 -.194

ADHS .421 1.552 .207 1.003

Bold indicates the regression coefficient is significant at the 10 percent level 

To get yet a better measure of highway investments, in the third part of the analysis we 

use survey results to refine the “ADHS” variable to reflect the size (in lane-miles) of 

the segment relative to the size of the county; and the type of investment (new, widen, 

or replace) represented by each segment. These data were combined to produce 

estimates of lane-miles per county for 1991 and 2000, which were then refined by 

dividing by the land area in each county. This calculation yielded an estimate of the 

size of each type of ADHS segment relative to county size for 1991 and 2000. 

Using these measures of highway investments confirms a relationship between ADHS 

investments and county-level income and earnings growth differentials relative to the 

non-ARC twin outcomes. However, as shown in Exhibit 3-6, the effect on earnings 

growth does not appear in the 1969-1991 growth rates but emerges for the 1969-2000 

growth rates, supporting the hypothesis that business sector response to highway 

improvements is slower than the residential sector. (Note: income measures are by 

place of residence, earnings are by place of work.) This interpretation gets further 

support from the results in Exhibit 3-7, which show that when the highway investment 

variable refers to investments in place by 2000 (rather than those in place by 1991, as 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 39

in Exhibit 3-6), the impact on income and earnings growth is smaller. 

Exhibit 3-6. Results Using 1991 ADHS Segment Length Relative to County Size 

 (dependent variable is differential Income or Earnings growth by 1991 or 2000) 

 INC 91 INC 00 EARN 91 EARN 00

(Constant) 1.397 4.631 .537 1.154

South Region 1.019 3.123 1.033 2.664

Central Region 1.227 2.663 .978 1.703

Distance to City of 25,000 .013 .042 .015 .053

Distance to City of 100,000 -.006 -.022 -.008 -.023

Distance to City of 250,000 -.003 -.014 -.002 -.012

Distressed Counties 1990 -.138 -.529 -.642 -1.075

Growth Center -.094 -.520 -.083 -.213

Coal Producing .407 .651 .479 .553

Mahalanobis Distance -.042 -.092 -.006 -.015

Population Density, 1960 -.001 -.003 -.001 -.002

% Farm in Earnings, 1969 -.020 -.024 -.037 -.053

% Manu in Earnings, 1969 -.026 -.078 -.024 -.058

% Ret Trade in Earnings, 1969 -.004 .010 .026 .087

% FedGovCiv in Earnings, 1969 .026 -.035 .046 -.048

% FedMil in Earnings, 1969 -.085 -.240 -.066 -.157

% St/Local in Earnings, 1969 .011 -.006 .039 .067

Population Growth Rate, 1950-1960 .023 .060 .021 .058

Interstate -.104 -.702 -.190 -.239

NewPerMileLandArea91 4.550 22.146 2.063 14.249

ReplacePerMileLandArea91 -2.125 -4.234 -2.204 -3.092

WidenPerMileLandArea91 -1.270 -5.317 2.334 2.987

The findings in Exhibit 3-6 also suggest that only some types of investments are likely 

to influence local economic activity. As the results in Table 6 show, the variable that 

measures lane miles of new highway construction (“NewPerMileLandArea91”) is 

positive and significant in the income and earnings growth equations for the 1969-

2000 period. The variables for “replaced” and “widened” lane-miles per land area, 

however, are not significant for income or earnings in either period. The 

“NewPerMileLandArea91” variable is also significant in the 1969-1991 period for the 

income variable, although the effect is larger for the 1969-2000 period. Because the 

vast majority (80+ %) of lane-mile investments in place in 2000 were actually made 

pre-1991, these findings also suggest that there is a considerable lag between highway 

investments and their full effect on economic growth. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 40

Exhibit 3-7. Results Using 2000 ADHS Segment Length Relative to County Size 

 (dependent variable is differential Income or Earnings growth by 2000) 

 Explanatory Variable INC 00 EARN 00 

(Constant) 4.727 1.147 

South Region 3.037 2.588 

Central Region 2.438 1.532 

Distance to City of 25,000 .045 .055 

Distance to City of 100,000 -.023 -.025 

Distance to City of 250,000 -.014 -.012 

Distressed Counties 1990 -.509 -1.077 

Growth Center -.527 -.256 

Coal Producing .552 .422 

Mahalanobis Distance -.092 -.017 

Population Density, 1960 -.003 -.002 

% Farm in Earnings, 1969 -.022 -.049 

% Manu in Earnings, 1969 -.080 -.057 

% Ret Trade in Earnings, 1969 .024 .098 

% FedGovCiv in Earnings, 1969 -.032 -.042 

% FedMil in Earnings, 1969 -.260 -.164 

% St/Local in Earnings, 1969 -.009 .071 

Population Growth Rate, 1950-1960 .068 .062 

Interstate -.740 -.220 

NewPerMileLandArea00 14.783 9.148 

ReplacePerMileLandArea00 -5.474 -1.394 

WidenPerMileLandArea00 -.832 8.422 

3.4 Uses and Limitations of the Findings

Whereas the prior study examined Appalachian economic growth over the 1965-1991 

period, this new study updates it to the year 2000. It confirms the general findings of 

the prior study that ARC is making a difference. The ARC counties are now 

outperforming comparable non-Appalachian counties in terms of income and earnings 

growth. It also confirms a general finding that economic performance is weaker and 

more problematic in the rural and micropolitan counties than in the larger metro 

counties. 

However, this new expanded analysis adds information not previously available. This 

research effort included development of a large base of data on Appalachian 

Development Highway system mileage, lanes, and construction years, by county. 

Using this more detailed dataset, the new study found statistically significant evidence 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 41

that the completion and presence of an ADHS segment in a county does lead to greater 

economic growth. It found that “lane miles of new highway construction” (mostly 

built prior to 1990) is a significant predictor of income and earnings growth occurring 

later during the 1990s but not in earlier years. This indicates that the economic 

development impact of new highways can take many years to unfold. It also supports 

the finding that business sector response to highway improvements can be slower than 

the residential sector response. The study also found that “new construction,” but not 

“replacement” or “widening,” led to a notable impact on economic growth. 

 

Beyond the highway impact, the study of long-term trends also showed that the states 

performing best relative to their non-Appalachian “twins” (i.e., Georgia, Kentucky, 

South Carolina, and Tennessee) appeared to do so in part on the strength of their 

performances in manufacturing. This reinforces the finding that manufacturing 

clusters are still an important source of economic growth. 

This research effort shows the importance of continual updating and analysis of 

economic trends in Appalachian counties, as economic growth patterns continue to 

evolve in new ways. It also shows the need for further study to better untangle: (a) 

interactions of ADHS and interstate highway system improvements, (b) differential 

impacts of highway expansion and new construction, and (c) impacts on per capita 

income vs. growth of aggregate income and earning power (which also reflects 

population changes). 

3.5 Survey Instrument 

The following three pages contain the survey letter and form. The survey was filled 

out by each of the thirteen state transportation departments, and provided information 

on Appalachian Development Highway sections in each state, including dates of 

construction of various highway sections, information on mileage, lanes, intersections, 

interchanges and traffic counts. 
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 42
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 43
Vol.3 Statistical Studies Ch.3 Twin Counties Update

Sources of Growth in Non-Metro Appalachia page 44
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 43

4 POPULATION BASE & ACCESS TO 

AIRPORTS 

“Spatial Geography: Effects of Population Base and Airport Access” 

by 

Teresa Lynch, Tyler Comings and Glen Weisbrod 

Economic Development Research Group 

4.1 Introduction 

This chapter describes findings from two related studies. One examines the impact of 

a county’s population base on its business mix. The other examines how highway 

access to airports also affects business mix. These two studies differ from other 

studies discussed in this report in that they focus on identifying determinants of a 

county’s business mix rather than its economic growth and well-being. 

The motivation is to help identify the conditions necessary for pursuing growth paths 

that target various types of manufacturing, trade, services or other business sectors. 

In addition, these two research studies are intended to shed additional light on the role 

and importance of highway access in supporting economic growth. 

A major element of both research studies is that they focus on examining the existence 

of “threshold” or other non-linear effects. In other words, it would be expected that 

some types of business require a minimum labor market or customer market in order 

to select a location for a new plant. Similarly, some types of business may require 

locations within a particular travel time to an airport, which must also be of a 

minimum size to provide sufficient scheduled service. Thus, the role of thresholds 

and non-linear responses becomes important. 

4.2 (A) Population Base: Methodology 

Measure of Business Mix. The first part of this study examines the relationship of a 

county’s business concentration and mix to its scale of population base or market. This 

relationship can be particularly important in establishing how the viability of various 

growth strategies and target industries may differ depending on the county population 

base or the degree to which it is urban or rural. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 44

For this study a dataset was used that provided year 2002 employment by 3-digit 

NAICS industry codes, for each of the 410 counties in the ARC region. A dataset 

prepared by IMPLAN was used because it provided measures of total employment 

including self-employed individuals and farm workers, who are not covered in County 

Business Patterns data. IMPLAN data are based predominantly on the REIS data, but 

has the advantage that it has values filled in for all industries in all counties, without 

the problem of missing (withheld) data which is common for many specific industries 

in small rural counties. Using this dataset, we define and calculate an indicator of 

relative business concentration: 

 Business Concentration (i,c) = Employment Share (i,c) * Attraction Ratio (i,c) 

 where: 

 Employment Share = Employment (i,c) / Employment (Σ i, c) 

 .Attraction Ratio = Employment (Σ i, c) / Population (c) 

 and i = NAICS industry, Σ i = sum over all industries, c=county 

 which simplifies down to: 

 Business Concentration (i,c) = Employment (i,c) / Population (c) 

The reason for constructing this composite measure of relative business concentration 

is to represent the combination of relative industry mix (represented by the 

Employment Share calculation) and relative industry attraction (represented by the 

Attraction Quotient). The Employment Share calculation is the numerator of the 

Location Quotient used in an earlier chapter to measure the economic base analysis of 

trade areas. However, instead of using the denominator of the Location Quotient 

(which represents national norms for industry mix), we make use of an Attraction 

Ratio which expresses industry employment per local population base. 

The Attraction Ratio can reflect the extent to which a county has a greater level of 

employment in the given industry than would be expected given its population. A 

high ratio is generally interpreted as an indicator that the county is a business center 

for the given industry and has a net inflow of workers coming in from surrounding 

areas for that industry. On the converse side, a low Attraction Coefficient could 

would normally be interpreted as an indicator that the county is not a center of activity 

for that industry, though that may be due to many factors including a high a 

unemployment rate or a low labor force participation rate (e.g., a retirement area). 

This per capita measure of business concentration nets out the effect of differences in 

county population size, so that a small county can in theory have a high concentration 

of a given industry just as easily as a large or populous county. By normalizing the 

business concentration in this way, we make it possible to analyze the role of 

population size in affecting the business concentration, while avoiding correlation 

between the two. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 45

Alternative Travel-Time Based Definition of Population Base. An alternative 

definition of population base was also constructed in which we used ESRI’s 

Geographic Information System to calculate the population base within a 30 or 40-

minute drive time of the population-weighted center of each county. That concept 

utilizes a more sophisticated form of spatial analysis than simply measuring the 

population located within each county. 

Unfortunately, preliminary analysis showed that this new measure actually had less

power in predicting business concentration. The reason was that defining a county’s 

trade area in this way fails to provide any leverage for distinguishing between (1) a 

county that is the center of activity in a multi-county region and (2) a fringe county 

that exports its workers and spending to the center of activity. In both cases, the 30 or 

40-minute drive time from each county would include the others, so that they would 

all appear to have an equally large trade area. The simpler metric of total county 

population, it is actually more accurate in distinguishing counties that are a center of 

population and activity from those that are more rural and serve as feeders to the 

activity centers. 

Modeling the effect of county population size. The analysis examines how the 

business concentration indicator for each industry differs by population level of the 

county (based on Year 2000 Census data). Two different techniques are used: (1) 

exploration of alternative regression functional forms, and (2) exploration of 

differences in the ratio among county population size groups. 

 The a priori assumption was that the ratio of employment over population for retail 

industries will stay generally constant as population increases. For specialized 

distribution and services industries, though, it would be expected that the ratio should 

increase with greater population base, as these industries are more sensitive to market 

size features. We would also expect those industries that thrive in rural areas—

agriculture and mining—to be negatively affected by increases in population density. 

The first technique involved using “curve estimation” to explore the relative 

significance and explanatory power of alternative functional forms for each industry. 

The slope of this regression at any given point will be the ratio of Business 

Concentration to population. Therefore, if a linear model fits the data best, then we 

can conclude that the Business Concentration changes directly as population increases. 

If a quadratic model is the best fit (wherein the sign of the coefficient of the quadratic 

term, [population]2

 , is positive) then the Business Concentration grows faster as 

population increases; if the sign is negative then the ratio growth will slow down as 

population increases. Similarly, if the best fit is a logarithmic model than the Business 

Concentration growth will also slow down and stop growing as population increases. 

These sets of models were run separately for 52 industries. In addition, an analysis of 

threshold effects was conducted by breaking down this relationship by six distinct size 

classes. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 46

4.3 (B) Population Base: Results 

Regression Results. The results of these regressions are close to a priori expectations. 

Retail industries have linear and quadratic regressions that fit very closely. Service 

industries have mostly positive quadratic coefficients. Agricultural industries exhibit a 

logarithmic or quadratic (with a negative sign) relationship. The latter is not surprising 

since agricultural industries usually thrive in rural areas. Manufacturing industries 

exhibit results that are generally mixed and insignificant, indicating that they are less 

sensitive to scale of the population base than retails and service businesses. 

The regressions results are shown in Exhibit 4-1 for those industries in which the 

model had statistically significant coefficients and an R2

 of 0.50 or better (indicating 

that the regression formula was explaining over 50% of the variance in the industry 

concentration measure.) Key findings are that: 

• The business sectors with a negative quadratic coefficient have an aversion to 

counties with a larger population base. These are generally agricultural 

sectors. 

• The business sectors with a positive quadratic coefficient show increasing 

growth of business concentration as population grows, though the point of 

inflection differs among industries. These are generally wholesale and retail 

trade sectors that have some market scale requirements. 

• The business sectors that had a logarithmic regression fit best are those that 

have some minimum population size requirement but no additional growth in 

business concentration as population increases further. These are industries 

that process crops and livestock, and hence need access to a minimal labor 

force 

• About half of all industries are not listed because there did not appear to be a 

statistically significant relationship between their concentration and county 

population in the regressions. They include mining, most manufacturing and 

freight transportation. For these industries, factors including the location of 

natural resources, topography and access to highway networks may be more 

important than just having a local population base. (Note that the role of access 

to highways is explored in the prior chapter, and highway access to airports is 

explored in a later part of this chapter.) 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 47

Exhibit 4-1 Business Concentration Regression Results for Selected Industries 

NAICS Parameter Estimates 

 B1(Pop)* B2(Pop^2)** R2

Quadratic w/ negative coefficient 

332 Fabricated metal prod 7933.4 -2200. .66 

447 Gasoline stations 5080.97 -2500. .90 

623 Nursing & residential care 12041.88 -6900. .86 

721 Accommodations 3313.56 -880. .50 

Quadratic w/ positive coefficient 

42 Wholesale Trade 7520.11 58500. .91 

92 Government & non NAICs 59961.5 34200. .85 

221 Utilities 1392.93 4150. .53 

230 Construction 25623.49 49400. .94 

323 Printing & Related 2268.82 2300. .58 

339 Miscellaneous mfg 1812.84 3560. .63 

441 Motor veh & parts dealers 7309.62 6050. .96 

442 Furniture & home furnishings 1459.61 2840. .88 

443 Electronics & appliances stores 1000.83 3450. .87 

444 Bldg materials & garden dealers 4180.55 4590. .90 

445 food & beverage stores 10155.42 5390. .95 

446 Health & personal care stores 3392.82 3140. .95 

448 Clothing & accessories stores 3156.62 9680. .79 

451 Sports- hobby- book & music stores 1819.98 4920. .88 

452 General merch stores 11004.37 4340. .94 

453 Misc retailers 4435.05 7810. .91 

454 Non-store retailers 4136.71 6150. .62 

484 Truck transportation 9186.41 2990. .50 

491 Postal service 1642.57 3830. .79 

492 Couriers & messengers 1371.51 3310. .63 

493 Warehousing & storage 1431.7 2030. .63 

511 Publishing industries 1523.2 4640. .83 

512 Motion picture & sound recording 278.92 1630. .72 

513 Broadcasting 539.11 21100. .89 

514 Internet & data process svcs -97.03 4630. .64 

521 Monetary authorities 3367.09 13000. .79 

523 Securities & other financial 497.98 9000. .77 

524 Insurance carriers & related 907.77 35400. .79 

525 Funds- trusts & other finan -340.7 2870. ,54 

531 Real estate 6331.76 17000. .85 

532 Rental & leasing svcs 1857.06 5280. .92 

541 Professional- scientific & tech svcs 13221.38 67400. .78 

551 Management of companies 1378.15 11600. .77 

561 Admin support svcs 13487.54 67000. .89 

621 Ambulatory health care 16961.05 12000. .89 

622 Hospitals 12300.35 9430. .67 

711 Performing arts & spectator sports 1744.07 5000. .90 

713 Amusement- gambling & recreation 4060.41 1730. .77 

722 Food svcs & drinking places 32363.29 20600. .95 

811 Repair & maintenance 9138.55 15100. .94 

812 Personal & laundry svcs 4926.94 8330. .90 

Logarithmic 

111 Crop Farming 27.909197 -- .55 

112 Livestock 36.6938123 -- .64 

 

The independent variable is POP. 

*actual coefficients multiplied by 1,000,000 

**actual coefficients multiplied by 

 1,000,000,000,000 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 48

Concept of Thresholds. The strong finding that retail and service industries had nonlinear relationships between industry concentration and population base indicates the 

likely presence of “threshold effects”—where a certain minimum population base is 

necessary to make a given industry viable and thus attracted to the area. Of course, 

the location and magnitude of this threshold effect may differ by industries. Exhibit 4-

2 portrays this relationship by contrasting a linear relationship, a quadratic relationship 

and a threshold relationship (where multiple thresholds are shown). 

Exhibit 4-2. Illustration of Linear, Quadratic and Threshold Relationships 

It would be expected that threshold effects are particularly important for specialized 

business functions such as professional and financial services. That is, we expect the 

size of an area affects its ability to attract certain (generally high-skilled) sectors, 

either because these sectors require large numbers of potential customers or require 

specialized skills that are more easily found in larger labor markets. 

To test this hypothesis, we calculated business concentration ratio for each 3-digit 

NAICS sector for the following county population sizes: <10,000, 10,000-24,999, 

25,000-49,999, 50,000-99,999, 100,000-249,999, and >250,000. As shown in Exhibit 

4-3, the industries that exhibit threshold effects can be categorized into two groups. In 

Group 1, Business Concentration (sector employment per capita) successively 

increases with county size, indicating that there may be increasing returns to 

increasing population base, and possibly also some threshold effects. In the Group 2, 

Business Concentration declines between county population size <10,000 and county 

population size 10,000-25,000, then increases with each increase in county population. 

This second group of industries may indicate that there are minimum requirements or 

scale effects at work that preclude those industries from locating in rural areas with a 

small population base. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 49

Exhibit 4-3. Evaluation of Threshold Effects 

% change in Business Concentration Ratio(Jobs-per-capita) from Previous (Smaller) Population 

Category

 

 ARC County population

NAICS - Industry Sector

10,000-

24,999

25,000-

49,999

50,000-

99,999

100,000-

249,999

over 

250,000

TOTAL 

Growth

Group 1: Positive growth across all size categories

42 Wholesale Trade 85% 36% 38% 24% 71% 640% 

230 Construction 26% 7% 22% 22% 27% 153% 

441 Motor vehicle/parts dealers 54% 30% 28% 8% 5% 190% 

442 Furniture stores 67% 35% 11% 38% 29% 345% 

443 Electronic & appl. stores 117% 23% 35% 61% 76% 919% 

445 food & beverage stores 21% 10% 5% 17% 4% 70% 

446 Health & pers care stores 15% 10% 10% 14% 10% 74% 

448 Clothing stores 77% 40% 39% 43% 56% 670% 

451 Specialty stores 48% 30% 65% 59% 60% 709% 

454 Non-store retailers 73% 9% 11% 9% 17% 166% 

481 Air transportation 14% 48% 208% 223% 270% 6108% 

485 Transit & ground passengers 88% 155% 2% 29% 18% 647% 

511 Publishing industries 17% 9% 43% 47% 152% 574% 

523 Securities & other financial 31% 12% 54% 125% 317% 2031% 

524 Insurance carriers & related 19% 13% 67% 61% 139% 767% 

541 Professional & tech. services 11% 52% 64% 31% 157% 829% 

561 Admin support services 82% 51% 41% 36% 88% 891% 

621 Ambulatory health care 26% 21% 23% 24% 29% 204% 

622 Hospitals 73% 2% 33% 12% 17% 208% 

722 Eating & drinking places 16% 36% 23% 15% 19% 166% 

811 Repair & maintenance 40% 34% 31% 14% 30% 265% 

812 Personal & laundry services 0% 48% 31% 27% 44% 254% 

Group 2: Sectors with Jobs-per-capita growth at population levels of 25,000+

92 Government etc. -10% 11% 19% 15% 20% 63% 

453 Misc retailers -2% 16% 38% 20% 29% 145% 

487 Sightseeing transportation -50% 88% 31% 9% 122% 195% 

514 Internet & data process svcs -70% 17% 104% 208% 79% 296% 

531 Real estate -22% 40% 20% 41% 122% 314% 

532 Rental & leasing svcs -45% 39% 19% 12% 84% 87% 

562 Waste mgmt & remediation -3% 62% 1% 0% 28% 102% 

711 Performing arts & sports -62% 9% 15% 72% 113% 76% 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 50

The results in prior Exhibit 4-3 show that the Business Concentration Ratio in various 

both retail and specialized services is much higher in larger counties than in smaller 

counties. This may also reflect the growth of big box retailers that invest primarily in 

areas with some minimum population size threshold. It is particularly interesting to 

note some of the most dramatic threshold jumps: 

• Growth in Transportation (NAICS 481) above 50,000 population, 

• Growth in Financial Securities (NAICS 523) above 100,000 population, 

• Growth in Publishing (NAICS 511) above 250,000 population, 

• Growth in both Professional-Technical-Scientific Services (NAICS 541) as 

well as Insurance Offices (NAICS 524) above 250,000 population, 

• Presence of Real Estate (NAICS 531) and Sightseeing Transportation (NAICS 

487) starting at 25,000 population with an additional jump above 250,000

The various types of threshold relationships are shown graphically in Exhibits 4-4 

through 4-6. Exhibit 4-4 illustrates the jump in professional and scientific services 

when the population exceeds 250,000. This finding makes sense as this kind of 

industry usually is found in very large population centers. This industry’s functional 

form was quadratic with a positive coefficient which indicated that relative activity in 

this industry increased with population. 

Exhibit 4-4. Threshold for Professional, Technical and Scientific Services

Exhibit 4-5 illustrates a pattern of continuing growth in business concentration as 

population increases. The functional form of the regression for this industry was 

quadratic with a negative coefficient which suggests that the rate of growth in activity 

peaks and then eventually tapers off as county population increases further. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 51

Exhibit 4-5. Threshold for Ground Transportation

Exhibit 4-6 shows an example of the relationship for many manufacturing industries, 

which may require a minimum of around 10,000 population but do not appear to grow 

in Business Concentration as area population increases further. As previously noted, 

this type of business is often dispersed along supply chains and depends on 

transportation network connections rather than population size as a locational 

determinant. In fact, one of the motivations of a dispersed supply chain is that it 

allows use of parts suppliers located in lower cost and smaller labor markets. 

Exhibit 4-6. . Threshold for Transportation Equipment Manufacturing

When comparing the regression results to the category-based threshold analysis, it was 

concluded that the latter form of analysis provided more precision. The regressions do 

give an indication of the type of relationship that employment-per-capita has for a 

given industry. However, they do not allow for precise identification of inflection 

points in a relationship that are due to minimum requirements for market or production 

scale economies. Further discussion of the use and limitations of this analysis, and 

directions for further research, are presented at the end of this chapter. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 52

4.4 (B) Airport Access: Methodology 

The second part of this study examines the relationship of a county’s business mix to 

airport proximity, where both highway drive time to the airport and the size of airport 

service are considered. For some manufacturing industries that have national and 

international customers, airport access can be particularly important. 

This analysis starts with the same dataset as the prior study of business concentration. 

It uses year 2002 employment by 3-digit NAICS industry codes, for each of the 410 

counties in the ARC region. It was supplemented by comparable data for another 228 

counties located outside of the ARC region, to enhance the coverage of outside metro 

centers. This data from IMPLAN represents total employment including selfemployed individuals and farm workers, who are not covered in County Business 

Patterns. The study also used a Geographic Information System to calculate each 

county’s population-weighted centroid, and the average drive time (in minutes) from 

that location to the closest airport with scheduled passenger service. Additional FAA 

data was used to represent the level of airport activity, represented as the number of 

commercial airline takeoffs and landings (known in the aviation field as “total 

operations”). 

It would be expected that industries that are more dependent on air transportation will 

seek locations convenient to an airport, and particularly locations convenient to an 

airport that is large enough to serve their needs. This may include businesses that rely 

on air service for incoming materials, customer visits, employee sales travel, or 

product delivery. In general, business sectors that are known to value air 

transportation include light manufacturing industries that rely on exporting and 

importing air cargo, and service industries that rely on employee business travel. 

To estimate this relationship, each industry’s share of total county-wide employment 

was calculated, and non-linear regression analysis was used to predict the roles of 

explanatory variables representing airport access time, airport size and the interaction 

of the two. There are several salient considerations that guided this specification: 

• The measure of Employment Share was used to represent the relative portion 

of countywide employment each industry. This measure was used in order to 

focus on how airport access affects the economic specialization of counties. 

This measure was used instead of employment size or business concentration 

measures to avoid correlation with population size of the county, which is 

another factor analyzed separately in the preceding part of this chapter. 

• The analysis of explanatory factors focused on interactions between airport 

size and airport distance or travel time in order to illuminate the role of 

highway connections in improving access for air-dependent industries. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 53

• Various forms of “curve fitting” regression formulations were used to calculate 

the relationships and shape of curves. But unlike the preceding study of 

population base, there was no separate analysis of threshold effects for airport 

distance because many types of business value airport proximity but few if any 

would find additional value in being some minimum distance away. 

The statistical analysis tested various linear, quadratic and logarithmic curve forms to 

explain the roles of airport size and ground access travel time on industry employment 

shares. They all generally involved three explanatory variables: size of 

airport(number of operations), distance to airport (access time), and the interaction 

between the two (number of operations*access time). The functional form for the 

linear model was: 

 Employment Share (i,c) = B1*time(c) +B2*Size(c) +B3* [time(c)* size(c) 

 where i = NAICS industry and c=county 

The statistical analysis also tested a “gravity model” formulation that represented the 

interaction between a positive weighting factor of airport size and a negative factor of 

airport access time (squared). 

 Employment Share (i,c) = B1*time(c) / size2

(c) 

4.5 (B) Airport Access: Results 

Roles of Airport Access Time and Size. Results of the regressions can best be 

illustrated by showing how various industries respond differently to the effect of 

airport access time (holding airport size constant), and to the effect of airport size 

(holding airport access time constant). Accordingly, we present a pair of graphics for 

a typical county. 

Exhibit 4-7 illustrates how the predicted number of jobs in a typical county would 

differ as ground access time to a typical size airport increases. It shows a steep dropoff of jobs in professional and technical services as airport access time increases from 

1 to 80 minutes, with lesser impact beyond that point. The role of access time is 

significant but less dramatic for transportation equipment manufacturing and 

essentially non-existent for logging industries (which seldom use air travel). 

Exhibit 4-8 illustrates how the predicted number of jobs would differ as airport scale

(annual operations) increases. It shows a steep rise of jobs in professional and 

technical services as airport size rises above 50,000 annual commercial operations, 

tapering off as annual commercial operations increase beyond 100,000. The role of 

airport scale is significant but less dramatic for transportation equipment 

manufacturing (increasing most steeply as annual commercial operations rise to at 

least 10,000). Again, the role of airport size is essentially non-existent for logging 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 54

industries (which seldom use air travel). 

Exhibit 4-7. Effect of Ground Access Time to Airport 

Exhibit 4-8. Effect of Airport Size 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 55

The regression coefficient estimates, shown in Exhibits 4-9 and 4-10, also show how 

some industries have a negative (or positive, diminishing) reaction to airport access 

and size. For instance, in Exhibit 4-9 the effects in agricultural industries have a 

positive coefficient for time. This means that they benefit from being further away 

from an airport. Other industries that have a significant positive coefficient for time 

include: mining, apparel manufacturing, and furniture manufacturing. 

Some of those industries in Exhibit 4-9 that have a negative, significant coefficient for 

time (indicating that they value a reduction in airport travel time) are: wholesale trade, 

paper manufacturing, insurance, and professional services. These are the types of 

industries that we would expect to situate near airports, since they all rely on worker 

air travel for meetings with either clients or other office locations of their business. 

Exhibit 4-10 shows industries that have a positive but diminishing effect as airport 

access time decreases or operations increase. Some of these industries showed a 

negative effect in Exhibit 4-9 (i.e. crop production); this is due to the use of a different 

functional form. The estimates obtained for Exhibit 4-10 used a logarithmic model 

which gave many significant parameters estimates yet was not the best fit across all 

industries. 

Testing of Urbanization Effect. We might expect these coefficients to also be 

affected by the degree of urbanization of a county. While the measure of Employment 

Share is standardized so that it is not affected directly by population size, it is known 

that some technology and service industries congregate in high population areas, as 

demonstrated in the preceding Population Base analysis. To see if this has an effect on 

the airport analysis, we tested whether the core model coefficients changed sign or 

significance when adding the “urban effect.” This effect was incorporated by adding 

a dummy variable for counties with population of 200,000 or more (Urban dummy = 

1). All other counties were assigned a value of 0” representing non-urban areas. 

The implications of adding a test of urbanization to Model #1 are embedded in Exhibit 

4-9. The urban effect is categorized in the columns to the right of each independent 

variable: (A) represents cases where the urban dummy variable stays significant and 

has the same sign as the original variable, (B) represents cases where the original 

variable is insignificant but the urban variable is significant, and (C) represents cases 

where there is no significant urban effect; i.e., where the urban variable is not 

significant even though the original variable was significant. 

The results show most of the effects lie in the “C” category. For the time variable, 

there is either no urban effect or adding the urban variable only makes the time 

variable insignificant—the exception being the wood products industry. For the size 

and interaction variables, there are several industries where the effect of these 

variables is reinforced in urban counties. These industries include: insurance, real 

estate, professional services, administrative services, and publishing. However, for 

most industries there seems to be no urban effect. 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 56

Exhibit 4-9. Regression Results for Airport Access Model #1 

(shown for selected industries with statistically significant coefficients) 

Sector B1(Time) B2(Size) B3(Time*Size) 

111 Crop Production 181.38 C -2981 C 23.731 - 

112 Animal Production 210.41 C -5443 C 69.797 C 

113 Forestry & Logging 68.704 C -317.7 - -2.519 - 

115 Support for Agriculture & Forestry -25.778 - -1649 C 34.547 C 

212-213 Mining & Support Activities 281.68 - 886.9 - -25 - 

230 Construction -53.341 - 3171.5 C -4.689 - 

313 Textile Mills 1.162 - -1654 - 24.611 C 

315 Apparel Manufacturing 60.604 - -959.2 - 11.457 - 

321 Wood Products 120.15 B -1509 - 10.069 - 

322 Paper Manufacturing -38.529 C -601.9 - 4.229 - 

324 Petroleum & Coal Products -6.944 C -45.09 - 0.262 - 

325 Chemical Manufacturing -44.866 C -577.6 - 3.71 - 

336 Transportation Equipment -1.818 - -1558 - 16.915 - 

337 Furniture & Related Products 104.76 - -929.2 - 8.37 - 

420 Wholesale Trade -85.138C 3051.8 C -32.57 C 

441-454 Retail Trade -53.926 - -348.1 - 0.209 - 

491-493 Mail, package delivery & warehousing -51.904 C 636.49 - -8.7 - 

511 Publishing Industries (except Internet) -7.676 - 595.39 A -7.997 A 

512 Motion Picture & Sound Recording -4.165 C 248.7 C -2.874 C 

513 Broadcasting -1.182 - 1345.2 C -18.366 C 

514 Internet & data process svcs -3.325 - 595.67 C -8.048 C 

524 Insurance Carriers & Related Activities -39.451 C 1145.7 A -15.307 A 

525 Funds, Trusts, & Financial 1.755 - 335.42 A -4.53 C 

531 Real Estate -28.396 - 2846.1 A -31.769 A 

532 Rental & Leasing Services 4.199 - 449.11 A -6.451 C 

541-551 Prof. Scientific, Technical, Services -176.12 C 7735.9 A -93.673 A 

561 Administrative & Support Services -147.74 C 4584 A -46.247 A 

711-713 Amusement & Recreation -49.522 C 1569.8 C -17.369 C 

721-722 Accommodations, Eating & Drinking -124.69 C 1887.8 C -18.107 - 

811-812 Repair, Maint, Personal Services -84.957 C 1428.1 C -11.626 C 

814 Government 58.482 - -2413 - 37.496 C 

bold indicates that coefficient is statistically significant

A=urban variable reinforces effect 

B=only an urban effect 

C=no urban effect when variable is already significant 

"-"=no significant effect in either case or incorrect sign 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 57

Exhibit 4-10. Regression Results for Airport Access Model #2 

(shown for selected industries with statistically significant coefficients) 

NAICS Industry Parameter Estimates 

 b1(ln(oper/time)) 

111 Crop Production 0.003992 

112 Animal Production 0.004882 

113 Forestry & Logging 0.00068 

115 Support for Agriculture & Forestry 0.000756 

212-213 Mining & Support Activities 0.001431 

230 Construction 0.010472 

313 Textile Mills 0.001388 

315 Apparel Manufacturing 0.001057 

321 Wood Products 0.001964 

322 Paper Manufacturing 0.000789 

325 Chemical Manufacturing 0.00091 

336 Transportation Equipment 0.001901 

337 Furniture & Related Products 0.001897 

420 Wholesale Trade 0.004073 

441-454 Retail Trade 0.018785 

491-493 Mail, package delivery & warehousing 0.001503 

511 Publishing Industries (except Internet) 0.000559 

512 Motion Picture & Sound Recording 0.000142 

513 Broadcasting 0.001072 

514 Internet & data process svcs 0.000224 

524 Insurance Carriers & Related Activities 0.001345 

531 Real Estate 0.00256 

532 Rental & Leasing Services 0.000731 

541-551 Professional Scientific, Technical, Services 0.006102 

561 Administrative & Support Services 0.004967 

711-713 Amusement & Recreation 0.002314 

721-722 Accommodations, Eating & Drinking 0.010516 

814 Government & non NAICs 0.00226 

Note: bold coefficients are statistically significant 
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

Sources of Growth in Non-Metro Appalachia page 58

4.6 Uses and Limitations of the Findings 

The findings shown in this chapter can be directly embedded in the Local Economic 

Assessment Package which ARC provides to its Local Development Districts. The 

findings on threshold effects associated with local population base can be used to 

identify likelihood of attracting various industries to a local area. The findings on the 

role of access time and facility service level factors on business attraction can be 

incorporated in the diagnosis of barriers associated with insufficient access to airport 

services. At the time of this publication, these improvements have already been made 

to the LEAP model. 

There are, however, clear ways in which this line of analysis can be improved. There 

is a need to explore whether or not a measure of trade center strength, such as the 

spatial multiplier used in the Chapter 2 study, may be as good or better than the 

current population base as a predictor of market area strength for attracting retail, 

wholesale and related service businesses. There is also need for further analysis of the 

business attraction relationship to airport access – separating improvements in access 

time, distance, type of highway access and/or airport service levels. Finally, there is a 

need to further explore the ways in which the impacts of market scale and airport 

access features may be better measured in terms of industry employment shares, 

concentration ratios or total size of the industries. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

5 SPATIAL INFLUENCES IN COUNTY 

ECONOMIC PERFORMANCE

This chapter is extracted and edited from the original document: 

“Task 1, Part 4: Empirical Analysis”” 

by 

Prof. Joseph Ferreira, Jr., Ayman Ismail and Zhijun Tan, 

Massachusetts Institute of Technology 

5.1 Introduction 

To better understanding what causes some non-metro Appalachian counties to make 

economic strides forward, while others remain distressed, a set of empirical studies 

were conducted with the aim of elucidating the role exerted by economic areas linked 

to a county. Our objective here is to (a) identify the nature of that linkages among 

counties, (b) define the geographic extent and features (contiguous/ non-contiguous) 

of this spatial neighborhood, (c) assess the roles of mountain topography, market 

access and highway links in affecting those results, and (d) identify how these factors 

affect levels of economic distress and changes in those levels over time. 

In this section, we present an exploratory analysis of the factors affecting the current 

economic conditions and trends in Appalachia’s non-metropolitan (non-metro) 

counties. We extract four types of variables that we consider to be closely related to 

the USDA/ERS typology of Appalachian Region counties, because regional analysts 

generally consider county type to play a significant role in determining county 

economic performance. We explore the statistical features and spatial patterns of the 

variables using statistical software and mapping and spatial analysis tools available in 

ArcGIS, geographic information systems, SPSS statistical analysis software and 

GeoDa, spatial statistics software developed by the Spatial Analysis Laboratory (SAL) 

in the Geography Department at the University of Illinois, Urbana-Champaign. 

5.2 Exploratory Statistical Analysis 

The analysis conducted for this study focused on the development of various forms of 

regression models to assess the role of explanatory factors in explaining and predicting 

patterns and trends in the economic well-being of non-metro Appalachian counties. 

Specifically, the types of county data that we use include: 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 60

Dependent variables: 

• Measures of economic health: As a dependent variable, to be explained 

through the empirical analysis, we examine several measures (current levels or 

growth change) of each county’s economic health. One key measure is the 

ARC county economic-status classification, whereby counties are classified a 

”attainment,” ”competitive,” ”transitional,” or “‘distressed” for each (fiscal) 

year. This classification is based on employment, income, and poverty 

measures (relative to the US average). The “Pickard Index” combines the 

three measures into a single, continuous index of economic level. In order to 

distinguish these two variables, we name the four-level, categorical variable as 

the ARC county Economic Status Class (ESC), and the continuous variable 

(the Pickard Index) as the county Economic Level Index (ELI). Another 

measure of economic health that we utilize is the county employment growth

between 1990 and 2000, adjusted (using shift-share analysis) to control for 

national trends. This measure is obtained from IMPLAN based on Bureau of 

Economic Analysis and their Regional Economic Information System. 

• Change in economic health: We assess patterns of change over time in terms 

of (a) the rate of growth or decline in the ELI rating, and (b) the rate of 

employment growth rate in the county as a whole. 

Independent (explanatory) variables: 

• Demographic data: US Census demographic data from 2000 for such variables 

as the age, education, minority status, mobility, and urban/rural residential 

location of the county population, 

• Geographic characteristics: terrain, elevation, natural amenity, and highway 

data describing the geographic features and transportation infrastructure of the 

counties. The terrain and elevation data are from the US Geological Survey 

(USGS), the transportation data are from ARC and the US Department of 

Transportation’s Bureau of Transportation Statistics. The natural amenity 

scale is an index of the density of attractiveness of geographic features 

developed by the Economic Research Service (ERS) of the United State 

Department of Agriculture (USDA) 

• Industrial mix and commuting patterns: measures of industrial mix, types 

and business, and commuting patterns within the Appalachian counties. 

BEA/REIS data break down earned income by industry for 1980, 1990, and 

2000. We also develop entrepreneurship indicators from BEA/REIS data on 

the diversity and value-added components of earned income. Commuting 

patterns are based on 1990 US Census ‘’journey-to-work’ data. 

• Density and Urban Influence: measures of population density and 

urbanization for each county and for sub-county regions. These indicators 

include USDA/ERS measures of population-based rural-urban continuum 

codes and urban-influence codes; and the delineation of metropolitan and 

micropolitan areas. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 61

Exhibit 5-1 shows the frequency distributions of ARC’s Economic Status Classes 

(ESC). Exhibit 5-2 plots the frequency distribution of the ELI index. The ELI 

measure (labeled IND_FY06 for Fiscal Year 2006) is a continuous function of the 

three measures (unemployment, income, and poverty) used to determine the ESC 

category. Compared with the four discrete ESC categories, the continuous ELI 

variable provides more differentiation among counties and, hence, an increased 

opportunity to explain variations in economic health across counties in terms of the 

independent variables that we have identified. 

Exhibit 5-1: Distribution of County Economic Status Class (ESC) 

(Labeled as “DISTFY2006”) 

Source: ARC’s Economic Status Classification. 

Exhibit 5-2: Distribution of the county Economic Level Index 

(ELI, Labeled as “IND_FY06” for Fiscal Year 2006)
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 62

5.3 Models to Predict County Economic Level 

A number of researchers have used econometric methods to model economic health 

(at county levels) as a function of various demographic and socio-economic factors, 

and industrial mix. However, relatively little work has been done to understand how 

geography and transportation infrastructure affect the interaction among counties and 

population centers and, as a result, the pattern and pace of economic development. 

We focus our efforts on investigating measures of geographic and infrastructure 

features that might influence economic health through facilitating, or hindering, the 

interconnectedness of Appalachian counties – and the resulting speed at which 

economic growth might occur. GeoDa software allows us not only to run classic 

ordinary least-squares (OLS) regression models, but also to estimate “spatial-lag” and 

“spatial-error” regression models that account for additional spatial “spillover” effects 

that reflect the influence of economic neighbors. 

Explanatory Variables. In order to see how much of the variation in ELI across the 

Appalachian counties can be explained by demographic, geographic, and market 

segmentation factors, we begin with the following set of measures for various factors 

that the literature suggests are correlated with economic health. Listed below are the 

basic explanatory variables used in regression models to predict county ELI levels. 

Demographics 

 PCTHSGRAD Percentage of people with high school diploma 

 PER_MINORI Percentage of people who are minority 

 PER_POP65P Percentage of people over 65 years old 

Mobility 

 PCTSAMCNT Percentage of people who resided in the same county 5 years earlier 

Amenities 

 ASCALE Natural amenity scale 

Entrepreneurship 

 BREADTH Economic breadth = # non-farm proprietors / total non-farm emp 

 DEPTHINC2 Non-farm proprietor income/# non-farm proprietors 

 DEPTHVALAD Non-farm proprietor income, BEA/non-employer receipts 

Industrial mix 

 AGRIC00 Percentage of income from agriculture in 2000 

 MIN00 Percentage of income from mining in 2000 

 CNSTR00 Percentage of income from construction in 2000 

 MANFC00 Percentage of income from manufacturing in 2000 

 TRNSP00 Percentage of income from transportation in 2000 

 WHTRD00 Percentage of income from wholesale trade in 2000 

 RETRD00 Percentage of income from retail trade in 2000 

 FIRE00 Percentage of income from finance, insurance, real estate in 2000 

 SERV00 Percentage of income from services in 2000 

 GOV00 Percentage of income from government employment in 2000 

County interdependence 

 RADJ97_EMP Income adjustment to account for workers’ county of residence 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 63

(normalized by employment) 

 

In this section, we develop two basic forms of regression model. The first one 

estimates the role of various county attributes (previously listed) on the Economic 

Level index (ELI) of each ARC county as of FY2006. The second one adds 

geography and infrastructure factors to increase explanatory power. For both forms of 

regression model, a set of four variations is estimated. (Additional regression models 

of changes in county economic health are discussed in the section which follows.) 

Exhibit 5-3 summarizes results for the first set of regression models under three 

different formulations: Ordinary Least Squares, Spatial Lag and Spatial Error. 

Findings from each of these model variations are summarized below: 

Model 1-A. Ordinary Least Squares Regression. Using GeoDa softrware, the ELI 

rating of each county was regressed onto each of the 18 variables. The R-squared of 

0.71 indicates that a linear combination of the independent variables explains 71% of 

the variance in ELI across counties – a modestly good fit. Most estimated coefficients 

have the expected sign. For example, the coefficient for education (PCTHSGRAD) 

implies a predicted decrease of 2.32 in the ELI indicator (i.e., an improvement in 

economic health because ELI measures the extent of poverty and unemployment) for 

every percentage point increase in the county’s adults who have at least a high school 

graduate level of education. One other demographic variable was highly significant 

(with a positive relationship), the percentage of the population who are minority 

(PER_MINORI). The mobility indicator (PCTSAMCNT) was also significant. This 

measure is the percentage of persons who lived in the same county five years earlier. 

High values suggest an immobile population. Both these variables had positive signs 

indicating that higher percentages were correlated with higher ELI values –i.e. 

distressed economic conditions. 

The ASCALE index measures the quantity and quality of scenic natural features and 

recreation areas in each county. It was not statistically significant as an explanatory 

factor. It could be that the economic benefits of natural amenities are accrued not so 

much by the county in which they reside, but by particular, proximate counties that are 

key points of access to the amenities, e.g., the valley along a major highway 

connecting population centers to scenic mountains and national parks. Likewise, the 

mere presence of a natural amenity does not imply that the county or proximate 

counties are able to leverage their assets into a thriving tourism economy. 

The three entrepreneurship measures show mixed results. The breadth of 

proprietorship measure (BREADTH) is not significantly different from zero, and the 

two proprietorship “depth” measures (DEPTHINC2 and DEPTHVALAD) are 

significant but have opposite signs. Increases in DEPTHINC2 are associated with 

improved economic health (lower IND_FY06) and increases in DEPTHVALAD are 

associated with declines in economic health (higher IND_FY06). The standardized 

beta coefficients indicate that their effects are opposite in sign. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Exhibit 5-3: Coefficient Comparison of MODEL-1 Statistical Variations  Model 1-A (OLS) Model 1-B (Spatial Lag) Model 1-C (Spatial E

Variable Coeff. T-Stat Prob. Coeff. Z-Val. Prob. Coeff. Z-Val. 

CONSTANT 246.707 10.346 0.000 156.208 24.141 .0000 302.088 11.998 

PCTHSGRAD -2.326 -14.528 0.000 -1.629 0.169 0.000 -2.636

-

13.082 

PER_MINORI 0.555 5.198 0.000 0.513 0.094 0.000 0.507 3.901 PER_POP65P -0.615 -1.172 0.242 0.004 0.461 0.993 0.387 0.768 PCTSAMCNT_ 1.374 5.800 0.000 1.078 0.208 0.000 0.696 3.089 

ASCALE -1.338 -1.243 0.215 -0.302 0.948 0.750 0.560 0.523 

BREADTH -8.386 -0.476 0.634 5.057 15.508 0.744 -2.680 -0.180 

DEPTHINC2 -2.399 -5.865 0.000 -1.674 0.368 0.000 -1.631 -4.182 

DEPTHVALAD 71.183 4.958 0.000 42.311 12.948 0.001 42.494 3.042 RADJ97_EMP -0.682 -3.440 0.001 -0.389 0.176 0.027 -0.411 -2.453 

AGRIC00 -297.529 -1.385 0.167 -493.950 188.851 0.009 -482.146 -2.861 

MIN00 -7.076 -0.397 0.691 -9.769 15.634 0.532 -6.054 -0.355 

CNSTR00 -63.832 -1.768 0.078 -69.330 31.674 0.029 -50.697 -1.631 MANFC00 -79.504 -7.133 0.000 -61.381 9.890 0.000 -52.132 -5.346 TRNSP00 -37.585 -1.379 0.169 -39.687 23.914 0.097 -52.058 -2.368 WHTRD00 -154.474 -2.825 0.005 -150.903 47.999 0.002 -105.494 -2.312 RETRD00 34.988 0.899 0.369 1.825 34.172 0.957 -10.766 -0.346 

FIRE00 -115.927 -1.617 0.107 -102.956 62.869 0.102 -82.349 -1.387 SERV00 -45.419 -2.558 0.011 -46.495 15.577 0.003 -32.775 -2.158 LAMBDA 0.647 13.998 

Log-likelihod -1823 -1786 -1777 

R-Squared 71.0% 77.6% 80.1% 

 Dependent variable is the economic level index for FY2006 (ind_fy06).  Coefficients significant at the 0.05 level or better are in bold face. 

Source: MIT-DUSP ARC Research Team. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 65

Three of the nine industrial mix variables in MODEL-1A were statistically significant. 

They are manufacturing, wholesale trade, and services. All three have coefficients 

with negative signs indicating that sector size increases are associated with reductions 

in ELI scores which represent improvements economic well-being. The industrial mix 

coefficients are larger than those for the demographic variables, but that is because the 

industrial mix measures are fractions ranging from zero to 1.0 while the demographic 

factors range from 0 to 100%. The standardized coefficients adjust for differences in 

measurement units and show the much weaker effect. 

The negative residential income adjustment (RADJ97_EMP) coefficient indicates that 

a county is better off (lower IND_FY06) if its residents bring in more wage income 

from out-of-county than the county’s non-resident workers export to their home 

counties. This is one type of “spatial multiplier” effect whereby counties tend to have 

improved ELI scores if they experience net gains when earned income accounting is 

shifted from place of work to place of residence. That is, earned income tends to be 

spent closer to one’s home than to one’s workplace, so counties gain an economic 

stimulus if they house more out-commuters than they employ non-resident workers. 

Model 1-B: Spatial-lag Regression. This model regresses ELI on the same 18 

variables as before, but now using a “spatial-lag model.” That type of regression 

model assumes that the value of an independent variable in one county spills over to 

affect the corresponding values in adjacent counties (Anselin, 2003). The model is a 

weighted regression where the weights are non-zero for counties that are adjacent to 

one another and the coefficients are estimated using maximum likelihood estimation. 

The likelihood ratio test indicates that accounting for spatial-lag is worthwhile, and the 

effective R-squared increases to 78%. We are not surprised that the estimated 

coefficients for the most significant variables are somewhat reduced in the spatial lag 

model. For example, consider the education effect. Spillover effects from better 

education in neighboring counties could account for what otherwise might be lumped 

into a larger same-county coefficient in the ordinary least squares regression. 

One change is that the size of the agricultural sector (AGRIC00) is now significant, 

and inverse in its effect, which is counterintuitive. A separate histogram shows that 

this variable is highly skewed with most values at or near zero and a right tail reaching 

only to 3 %. We would be better off treating AGRIC00 as a dummy variable 

indicating which counties had a measurably large agricultural sector. 

Model 1-C: Spatial-Error Regression. This model regresses ELI on the same 18 

variables as before, but now using “a spatial-error model” in place of the spatial-lag 

model. The “spatial-error” regression model assumes that the county-to-county 

spillover occurs indirectly through spatial correlation in the error terms for 

neighboring counties. That is, the independent variables have only local effects, but 

factors missing from the model specification are spatially correlated. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 66

The signs and significant variables for the spatial-error model are similar to those for 

the spatial lag, although the residential persistence variable (pctsamcnt) is now 

marginal and the transportation sector size becomes significant. Overall, the loglikelihood is slightly higher and the effective R-squared is increased slightly (to 80%). 

Both the spatial lag and spatial error runs use simple measures of proximity – spillover 

effects are assumed to come exclusively from neighboring counties and each adjacent 

county contributes in the same manner. Even with these simple assumptions, we see 

evidence of significant spillover effects. The RADJ97_EMP variable adjusts income 

earned by workers in a county in order to account for the county of residence of the 

employee. The fact that the RADJ97_EMP (expressed on a per-employee basis) is 

significant in the OLS regression indicates that income earned elsewhere can matter. 

The variable is less significant with a much smaller coefficient in the spatial lag and 

spatial error models, because some of the county-to-county influence is explicitly 

captured in the spatial lag or spatial error term. 

Model 1-D. Consolidating the Industrial Mix. The industry specific variables in all 

of the preceding models had “multicollinearity” (meaning that a high share of 

employment in any one industry would tend to bring a lower share of employment in 

other industries). That makes their coefficient estimates subject to error. To address 

that, we used factor analysis to identify linear combinations of industrial sector 

percentages that capture most of the variation across counties. 

Exhibit 5-4 show the component score coefficients for the extracted factors. For 

example, a county’s 2000 factor score for Factor 1 would be computed by multiplying 

the coefficients in the Factor 1 column by the corresponding industry mix percentages 

for agriculture, mining, construction, etc. We see that Factor 1 has a large negative 

coefficient for manufacturing and large positive coefficients for wholesale and retail 

trade, fire, and services. So, counties with a high share of employment in services or 

trade and little manufacturing (relative to the other ARC counties) will have a high 

score on Factor 1. Alternatively, Factor 2 deemphasizes manufacturing and 

emphasizes mining, government, and transportation. So, counties with a high share of 

employment in mining and government, and little in manufacturing and wholesale will 

have a high score on Factor 2. Similarly, Factor 3 emphasizes government, 

agriculture, and construction without wholesale trade; and Factor 4 emphasizes 

construction, transportation, agriculture without government, or services. 

Exhibit 5-5 (left side) shows the results of rerunning Model-1C (the spatial error 

model) with the four composite industry factors substituted in place of the nine 

industrial sector percentages (labeled as Model 1-D). We see that the fit is slightly 

better than before, with five fewer variables. Note that the most significant factor 

among the four is Factor-2 (which is higher where there is more reliance on mining or 

government activities and less on manufacturing or wholesale trade activities). The 

large positive coefficient (7.75) indicates that a one standard deviation increase in a 

county’s Factor-2 value correlates with a 7.75 point increase (that is, diminished 

economic condition) in the ELI score for that county. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 67

Exhibit 5-4, Factor Analysis Results 

(Component Score Coefficient Matrix) 

Component 

Factor-1 Factor-2 Factor-3 Factor-4 

agric00 .107 -.031 .443 .519

min00 -.168 .260 -.201 .359

cnstr00 .191 .005 .494 .233

manfc00 -.148 -.472 -.041 -.113

trnsp00 .002 .168 -.401 .433

whtrd00 .264 -.210 -.234 .200

retrd00 .295 .075 -.053 -.331

fire00 .358 -.005 -.035 -.061

serv00 .292 .162 -.218 -.083

gov00 -.099 .365 .278 -.340

Factor Interpretation: 

 Factor-1: service/trade without manufacturing 

 Factor-2: mining/government without manufacturing/wholesale 

 Factor-3: government/agriculture/construction without wholesale trade 

 Factor-4: construction/transportation/agriculture without government/services 

Exhibit 5-5: Coefficient Comparison for Models Using Industry Factors 

 

Model 1-D 

(spatial error model 

using industry factors) 

Model 1-E 

(commuting shed model 

using industry factors) 

Variable Coeff. Z-Val. Prob. Coeff. Z-Val. Prob. 

CONSTANT 243.932 9.425 0.000 268.711 10.030 0.000 

PCTHSGRAD -2.534 -13.048 0.000 -2.781 -14.409 0.000 

PER_MINORI 0.443 3.683 0.000 0.616 5.370 0.000 

PER_POP65P 0.291 0.609 0.542 0.740 1.593 0.111 

PCTSAMCNT_ 0.904 4.159 0.000 0.608 2.807 0.005 

ASCALE 0.577 0.552 0.581 1.376 1.411 0.158 

BREADTH 3.227 0.227 0.820 16.695 1.207 0.227 

DEPTHINC2 -1.306 -3.470 0.001 -1.017 -2.752 0.006 

DEPTHVALAD 33.612 2.486 0.013 28.460 2.125 0.034 

FAC1_2000 -4.173 -3.582 0.000 -4.403 -3.827 0.000 

FAC2_2000 7.753 6.992 0.000 6.495 5.605 0.000 

FAC3_2000 1.639 1.591 0.112 1.279 1.275 0.202 

FAC4_2000 -3.487 -3.884 0.000 -3.317 -3.798 0.000 

RADJ97_EMP -0.507 -3.120 0.002 -0.356 -2.270 0.023 

LAMBDA 0.625 13.066 0.000 0.900 109.477 0.000

Log-likelihod -1769 -1754 

R-Squared 80.7% 80.0% 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 68

Model 1-E. Alternative Measures of County Connectivity – Commuting Zones. 

Both the spatial-lag and spatial-error models presented so far employ a simple notion 

of spillover, which assumes that each county is only affected by its “nearest 

neighbors” – with equal weight given to each neighbor. Given the mountainous 

terrain over much of Appalachia, we might expect that hills, rivers, interstates, and 

other major obstacles, and convenient infrastructure, could distort the meaning of 

“adjacency.” For example, counties with highly inter-connected development paths 

might be those along a major interstate running through a valley. 

The economic interdependence of counties can amplify the beneficial impact of 

economic development. If we know how counties are interdependent, then we can 

devise more effective economic development strategies. Prior versions of Model 1 

provided some evidence of significant spillover effects among immediately adjacent 

counties. The best way to measure county connectivity is likely to depend on the type 

of development being considered. Analysts who use traditional economic growth 

models focus on residence/workplace linkages, and they might use commute-sheds to 

identify well-connected counties. But we envision other development strategies that 

may use a different notion of connectivity. Consider, for example, asset-based 

development, such as tourism or mining. In such cases, connectivity and 

interdependence might involve convenient highway and rail infrastructure connecting 

the local site to population centers or resource users. Alternatively, a knowledgebased development strategy may require an understanding of alumni networks and 

university connections. For example, the zip code frequency for home addresses of 

university students may be a good measure of where a university’s education and 

technology transfer efforts are most likely to be felt. 

To explore the usefulness of alternative connectivity measurement beyond 

“adjacency,” we examine the commute-sheds (or commute-zones) for Appalachian 

counties. The USDA has developed commute-shed data for Appalachia based on US 

Census Bureau Year 2000 journey-to-work data. Each of the 410 counties is clustered 

into a commute-shed with other counties that most often share commuters who work 

in one county and live in the other. GeoDa software can use “commute sheds” to 

calibrate spatial weights that offer an alternative to the “adjacent county” approach. 

Exhibit 5-5 (Model 1-E) shows the results of rerunning the prior model with spatial 

weights based on the commute-sheds, rather than on county adjacency. The results 

show little change in the model’s explanatory power. Given the significant overlap of 

commute-sheds and “nearest neighbor” adjacent counties, we are not surprised that the 

results are similar for these two ways of identifying proximate counties that have 

intertwined economies. Also, the commute-shed results would probably be improved 

if we included counties at the edge of Appalachia that fall within commute-sheds that 

include one or more Appalachian counties. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 69

Model 2-A and 2-B: Adding Geography and Access Factors. The final variation of 

the economic health models adds considerations of terrain slope, road density and 

worker accessibility. 

• Terrain Ruggedness – Slope Computations. Because much of Appalachia is 

mountainous terrain, we might expect that hills, rivers, interstates, and other 

major obstacles (and convenient infrastructure) could warp the meaning of 

“connectedness” to be quite different from “as the crow flies.” To investigate 

such possibilities, we computed a measure of terrain ruggedness based on 

slope computations. We obtained USGS elevation data, projected it to the 

Alber’s area-preserving coordinate system used by ARC, and then converted it 

to a raster-elevation model in ArcGIS. We overlaid the grid cell slope 

(rise/run) estimates with the county boundaries, to estimate average slopes 

within each county (variable name SLOPE). 

• Nearby Terrain Slopes. We also computed average slopes for all counties 

whose centroid fell within 66 kilometers of the target county (variable name 

SLOPE66). Exhibit 5-6 is a thematic map of the estimated slope of the 

Appalachian Region with lighter colors indicating locations with steeper 

slopes. Note the sharp change between the Cumberland Plateau and the Great 

Smoky Mountains where the Tennessee River Valley corridor runs Northeast 

and Southwest of Knoxville. 

• Transportation Infrastructure – Road Density. Our team obtained National 

highway data from 2004 National Highway Planning Network (NHPN), 

Federal Highway Administration, U.S. Department of Transportation. We also 

obtained additional, more detailed, Appalachian Development Highway 

System (ADHS) data from the ARC. With these data, we developed estimates 

of road density within each county (variable name ROADWT) 

• Worker Accessibility. Using data compiled for the Local Economic 

Assessment Package, we obtained a data set estimating the number of workers 

who live within 50 minutes driving time of each county. We use this data as a 

measure of each county’s labor market accessibility (variable name EMP50M). 

We first ran a new regression model in which we added the access measures and 

geography measures as cited above. Both a standard OLS regression (Model 2-A) and 

as a spatial error regression (Model 2-B) were run. However, the results showed that 

none of the access and geography measures was statistically significant in explaining 

county-level economic health. It was believed that the reason for this result is that the 

effect of access and geography is likely to differ for metro and non-core counties. 

Accordingly, a new variation on the model was run in which coefficients for the 

explanatory variables were interacted with dummy variables for metropolitan and nonmetro areas. That attempt, using metro/ non-metro interaction variables, was more 

successful. It is referred to as Models 2-C and 2-D, and is discussed and shown next. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 70

(Results for the earlier Models 2-A and 2-B are not shown in this summary although 

they are shown in the full report.) 

Exhibit 5-6: Slope Estimate for the Appalachian Region (Based on USGS 90m 

Elevation Data from the National Map) 

Source: MIT-DUSP ARC Research Team using ArcGIS. 

Model 2(C-F): Interaction of Metro Status with Geography and Access. The 

alternative model specifications included interactions between type-of-county and the 

other explanatory variables. The interaction of labor market and non-metro status was 

added in Models 2-C (OLS model version) and 2-D (spatial error model version). The 

further interaction of slope factors and non-metro status was added in Models 2-E 

(OLS model version) and 2-F (spatial error model version). In both cases, the spatial 

error version provided a better fit than the OLS version, although the coefficient 

estimates were generally consistent across both model types. For brevity, results are 

shown only for the spatial error versions in Exhibit 5-7 (though results for the other 

model variations are shown in the full report.) 

The spatial error results for Models 2-D and 2-F confirm that the effects of several 

variables do differ depending on whether a county’s status is metro or non-metro. 

Results are shown in Exhibit 5-7 just for the statistically significant variables. Note 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 71

that variables interacted with the metro dummy variable are denoted by an “M_” 

prefix and those interacted with a non-metro dummy variable are denoted by an “N_” 

prefix. 

The results show that slope and labor force access measures do have statistically 

significant effects in predicting economic health level, but only in the non-metro 

counties (indicated by coefficients for variables N_SLOPE, N_SLOPE66, and 

N_EMP50). We are not surprised by the overlapping effects of employee access and 

terrain, because we expect that employee accessibility will be lower in mountainous 

areas and that non-core counties might benefit if the counties that surround them are 

relatively mountainous and inaccessible. 

The coefficient values for the slope variables also show that above average slopes 

within a non-core county (N-SLOPE) are associated with weaker economic levels, 

while above average slopes in surrounding areas (N_SLOPE66) are associated with 

stronger economic levels. Those findings are plausible. In metro areas, density and 

infrastructure make the slope and employee access measures less relevant. Also, 

place-of-residence and place-of-workplace are more likely to span counties in metro 

areas16. 

Exhibit 5-7: Coefficient Comparison of MODEL-2 Variations 

 Model 2-D Model 2-F 

 Spatial-error model with worker 

access and road density 

Spatial-error with 

local and nearby slopes 

Variable Coeff. Z-Val. Prob. Coeff. Z-Val. Prob. 

CONSTANT 5.67570 35.3827 0.00000 5.66271 34.8558 0.00000 

PCTHSGRAD -0.01688 -13.9391 0.00000 -0.01702 -14.0408 0.00000 

PER_MINORI 0.00324 4.5158 0.00001 0.00343 4.6660 0.00000 

PCTSAMCNT_ 0.00590 4.9752 0.00000 0.00594 5.0046 0.00000 

DEPTHINC2 -0.00860 -3.6808 0.00023 -0.00895 -3.8275 0.00013 

DEPTHVALAD 0.19684 2.2972 0.02161 0.20793 2.4263 0.01525 

FAC1_2000 -0.02636 -3.6787 0.00023 -0.02727 -3.8094 0.00014 

FAC2_2000 0.04371 6.1400 0.00000 0.04153 5.7560 0.00000 

FAC4_2000 -0.02371 -4.3558 0.00001 -0.02379 -4.3710 0.00001 

M_RADJ97 -0.00545 -5.7599 0.00000 -0.00488 -4.3712 0.00001 

M_ROADWT -0.00814 -3.7559 0.00017 -0.00626 -2.3412 0.01922 

N_EMP50M -0.00825 -2.6881 0.00719 

N_SLOPE 0.00584 2.6972 0.00699 

N_SLOPE66 -0.00588 -2.1887 0.02862 

LAMBDA 0.884 92.985 0.000 0.89497 104.1315 0.00000 

Log-likelihod 320.9 320.2 

Akaike info -617.8 -614.5 

R-Squared 83.3% 83.2% 

Source: MIT-DUSP ARC Research Team. 

 

16 An alternative explanation is that the commute-sheds do a better job of capturing high economic impact regions 

within metro areas since the weights matrices are not sensitive to the number of cross-county employees. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 72

5.4 Modeling Changes in Economic Health 

We have made several attempts to measure changes in economic status, so that we 

could have a stronger econometric underpinning for modeling economic growth over 

time and space (Anselin, 2003; Feser, 2005). We consider changes in the ELI measure 

during the last decade, and attempt to estimate and analyze the change in value added 

(per employee) as a dependent variable between 1997 and 2002 using IMPLAN data. 

In both cases, the results were limited with, for example, R-square values in the teens. 

Although we expect lower R-square values when modeling differences, a closer look 

at the data suggested deeper problems. The time series of annual income and poverty 

data underlying the ELI measure are based on sample sizes and estimation methods 

that vary somewhat from year to year. Large samples, such as for the decennial 

census, are not repeated annually. Hence, year-to-year changes tend to track simple 

trends. Then, when the next large data sample becomes available, big changes occur 

all at once in those places that have not followed the fitted curve. The measurement 

noise that is thereby added to the data can be significant when studying small counties 

or developing indices that fuse data from different sources or analysis subsectors of 

the economy. 

The most success that we have had with modeling temporal changes in economic 

indicators for Appalachia has been in studying employment growth during the 1990s 

after controlling for labor-market conditions and other factors, such as labor mobility, 

natural amenities, and market size. One member of the research team, worked on this 

analysis for her Master of City Planning Thesis, “Industrial Structure and Employment 

Growth in the 1990s in Appalachian Counties.” 

Before presenting the economic change models, we will explain and summarize the 

measures that we use to characterize economic growth of Appalachian counties during 

and since the 1990s. 

Changes in ELI. It is important to note that the Economic Level Index (ELI) was 

developed by averaging the county unemployment rate, poverty rate, and per capita 

market income levels (all expressed as a percentage of the US average). These 

components are developed from different samples taken at different points in time. 

When selecting two points in time for use in modeling change, we should be cognizant 

of the sampling and accuracy issues in the datasets. The ELI estimate for 2004 is the 

most recent estimate that could be computed using datasets available at the time (in 

2005) that we assembled the data – and is the first 2000+ estimate that includes the 

results of the 2000 US Census. Analysis of the changes in ELI (variable NEW_DELI) 

showed that the larger improvements tended to be along the edge of Appalachia east 

of Cincinnati and Louisville or northwest of Atlanta. 

Changes in Employment during the 1990s. Because the ELI measure is a composite 

index of poverty, employment, and income outcomes, it is difficult to construct an 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 73

economic model of growth that can directly account for spatial and temporal impacts 

on ELI. As an alternative measure of changing economic conditions, we examined 

changes in employment in Appalachia counties during the 1990s. We used the 

percentage change in employment and adjusted the results (using shift-share analysis) 

to account for national trends in industrial sectors. The variable CMPT_CAP 

measures each county’s percent change in employment during the 1990s above and 

beyond whatever change might have occurred if the county followed national trends. 

These competitively adjusted changes in employment levels represent a measure of 

economic growth that can be regressed against demographic, industrial mix, 

geographic, and other factors in order to identify the conditions that resulted in faster 

(or slower) growth and to estimate the extent of spatial spillover effects whereby 

neighboring counties amplified (or, possibly, diminished) the local rate of growth17. 

Tan (2005) explains the methodology in detail. 

Exhibit 5-8 contains the histogram plots of 1990-2000 employment changes for ARC 

counties. Part A shows the unadjusted percent changes, GR00_90, and Part B shows 

the competitively-adjusted changes in employment levels, CMPT90_00. The 1990s 

were a period of economic growth for the entire nation so the 22% mean percentage 

increase in employment is no surprise. However, the large range and standard 

deviation is noteworthy. The distribution of competitively adjusted employment 

changes is similar in shape and standard deviation but shifted negative (with a mean of 

-15.9%) because Appalachia counties did not fare as well as the nation on the whole. 

Exhibit 5-9 plots these changes in employment thematically across the 410 Appalachia 

counties. The map on the left shows the competitively adjusted employment changes 

whereas the map on the right shows the unadjusted employment-change results. A 

cluster of high-growth counties is evident in the Southeast (that is, northwest of 

Atlanta). Another group of low-growth counties is visible in the Eastern Kentucky 

and West Virginia area, but the competitive adjustment tends to temper the magnitude 

of these changes. 

 

17 Anselin (2003) has explained how weighted regression fits of such models can estimate first-order spatial-lag 

and spatial-error effects and Boarnet (1994), Feser and Isserman (2005), and others have developed simultaneousequation models of employment and population size that can be used to model economic growth and estimate 

spatial-spillover effects. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 74

Exhibit 5-8: Histogram of 1990-2000 Percent Change in Employment 

(A) Unadjusted Change, GR00_90 

(B) Adjusted: Change Relative to National Average, GR00_90 

Source: MIT-DUSP ARC Research Team. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 75

Exhibit 5-9: Employment Change within Appalachian Counties (1990 -2000) Source: MIT-DUSP ARC Research Team using ArcGIS. 

Model 3 and Model 4-A: ELI Rating Change vs. Employment Change. We begin 

the discussion of economic change models by considering the same right-hand-side 

variables that we used earlier to estimate effects on recent economic health levels in 

Models 1 and 2. Some minor changes are in order, however, because we want the 

measures of the right-hand side variables at or near the start of the period for which 

change is observed – 1997 for change in Economic Level Index (NEW_DELI) or 1990 

for the competitively adjusted and capped employment change (CMPT_CAP). 

Initially, parallel OLS regressions were run to estimate effects on ELI change (in 

Model 3) and effects on employment change (in Model 4A). The results indicated a 

poor fit, particularly for the ELI change, where the R2

 indicated that only 16% of the 

variance was being explained by the model. A substantially better R2

 of 32% was 

achieved for the model of employment change. Actually, this difference was 

expected, given the coarse and discrete nature of the ELI rating changes and the 

smoother nature of variation in the employment change measure. Based on these 

findings, it was decided that better results could be obtained by focusing on the 

determinants of employment change, and that the spatial lag and spatial error model 

forms were likely to yield better fits to the data. Those results are presented and 

discussed next. (For brevity, results of Model 3 and 4-A are not shown here though 

they are presented in the full report.) 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 76

Model 4(B-C): Change in Employment. Exhibit 5-10 shows the results of models to 

predict employment change, using both spatial lag approach (Model 4-B includes the 

rhs variable W_CMPT_CAP) and spatial error approach (Model 4-C). Both models 

attempt to predict the employment change variable using the same right-hand side 

variables (or their early-90 equivalent) that were previously used in Model 2-A to 

predict current ELI levels. The results for both new models show better explanatory 

power (R2 = 38%) than the previously discussed OLS results. However, the model fit 

for explaining economic change is still far lower than the explanatory power of similar 

regressions that explained current economic performance levels. That is not 

unexpected, since there is greater variation in the dependent variable depicting a 

growth rate and the explanatory variables have some updating limitations that were 

previously discussed. 

There are some surprising findings shown in the employment change results. 

Educational attainment (PCTHSGRAD) is now showing a significant but counterintuitive relationship on employment growth (this interpretation was acceptable when 

the dependent variable was current ELI). Adjusted employment growth outcomes in 

neighboring counties will exert a significant influence on a county’s employment 

changes in the same direction. The key importance of prior industry mix also remains 

strong, though there are some differences. In the earlier Model 2-A of ELI levels, 

industry factors 1, 2, and, 4 were significant. For the new models of employment 

changes, factors 3 and 4 are significant. They both exert positive effects on the 

adjusted employment growth that occurred between 1990 and 2000. 

Some of the other results are less expected. The industry concentration measure 

(BEAGINI_9) appears insignificant, as do the economic breadth (BREADTH) and 

amenity (ASCALE) variables. However, all of these unexpected results can be 

attributed to correlation with other variables and equally importantly, differences in 

their impacts within metro vs. non-metro areas. 

Model 4(D-F) Metro and Non-Metro Differences. To test this last hypothesis, 

separate model runs were made for those counties designated by USDA as 

metropolitan, micropolitan, and non-core counties. The explanatory variables 

included the demographic variables measuring education, minority and senior citizen 

presence, and mobility (percent of population living in the same county for at least 5 

years); the four industrial mix factors from the factor analysis plus a measure of 

industry concentration (BEAGINI_9); the three worker access measures counting 

(counting workers within 40, 50, and 60 minutes) plus the place-of-residence 

adjustment of worker-based-county income, RADJ97_EMP; and the various 

geography and infrastructure measures: ASCALE for the USDA amenity index, 

ROADWT for the weighted percentage of land used for major roads, SLOPE for the 

average slope, and AVG_SLOPE6 and AVG_SLOPE1 for the average slope of 

neighboring counties within 66 and 100 km. The results are shown in Exhibit 5-11, 

and they reveal that the impact of the same explanatory variables differed considerably 

across the three types of counties. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 77

EXHIBIT 5-10 Model 4-B,C: Models of Employment Change Over Time 

 Model 4-B Model 4-C 

 Spatial-lag model Spatial-error model 

Variable Coeff. Z-Val. Prob. Coeff. Z-Val. Prob. 

W_CMPT_CAP 0.497 14.405 0.000 

CONSTANT 1.213 5.123 0.000 1.241 4.460 0.000 

PHSGRAD90 -0.005 -4.254 0.000 -0.006 -3.678 0.000 

PMINORI90 -0.004 -3.872 0.000 -0.004 -3.218 0.001 

PSAMECNT90 -0.011 -6.033 0.000 -0.010 -5.310 0.000 

BEAGINI_9 -0.092 -0.470 0.638 -0.036 -0.190 0.849 

F1_1990 -0.023 -1.741 0.082 -0.016 -1.196 0.232 

F2_1990 0.008 0.709 0.478 0.005 0.361 0.718 

F3_1990 0.026 2.238 0.025 0.031 2.697 0.007 

F4_1990 0.034 3.307 0.001 0.030 3.075 0.002 

RADJ97_EMP -0.002 -1.327 0.184 -0.004 -2.199 0.028 

SLOPE -0.002 -0.702 0.483 -0.006 -1.836 0.066 

ROADWT 0.000 -0.104 0.917 -0.001 -0.150 0.881 

EMP50MINK 0.002 1.857 0.063 0.001 0.984 0.325 

AVG_SLOPE6 -0.001 -0.267 0.790 -0.002 -0.408 0.683 

LAMBDA 0.698 29.578 0.000 

Log-likelihod 121.0 121.1 

Akaike info -211.9 -214.1 

R-Squared .38 .38 

The results in Exhibit 5-11 show that the best fit was obtained for the metropolitan 

counties (Model 4-D), with 57 percent of the variability in employment growth 

explained by the model. For micropolitan counties (Model 4-E), the explanatory 

power dropped to 33%, and for non-core counties (Model-4-F), the explanatory power 

dropped to 18.5%. 

Not only did the goodness of fit vary, but the selected variables and coefficients vary 

as well. High school graduation rates (PHSGRAD90) matter for metro and non-core 

counties (not for micropolitan counties) yet the sign once again is negative as seen 

above in results for Models 4-B and 4-C – indicating slower growth rates in counties 

with more educated populations. The minority share of the population does not matter 

in metropolitan counties, matters most in micropolitan counties, and matters somewhat 

less in non-core counties. In both cases, the sign is negative indicating that counties 

with higher minority shares grow at slower rates. The adult population share, 

PROP65_90, matters only for micropolitan counties and also has a negative 

coefficient. The mobility measure, PSAMECNT90, is significant for all three county 

types but is estimated to have less than half the impact in non-core counties. Once 

again, the sign is negative. 
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 78
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 79Exhibit 5-11: MODEL-4 Stepwise OLS Fits for Metro/Micro/Non-Core Submarkets 

Model 4-D Model 4-E 

 Metropolitan Counties Micropolitan Counties  (109 as of 1993) ( 118 as of 1993) Theme Variable B* Beta* T Sig. B* Beta* T Sig. B* B Constant 1.848 7.312 0.000 1.265 2.920 0.004 1.071 Demographics PHSGRAD90 -0.009 -0.263 -4.025 0.000 -0.007 -" PMINORI90 -0.007 -0.287 -3.261 0.001 -0.002 -" PPOP65_90 -0.020 -0.169 -2.142 0.034 " PSAMECNT90 -0.018 -0.536 -7.729 0.000 -0.019 -0.347 -4.234 0.000 -0.007 -Concentration BEAGINI_9 0.825 0.233 2.665 0.009 -0.439 -Industry Mix F1_1990 " F2_1990 0.053 0" F3_1990 0.081 0.318 3.615 0.000 " F4_1990 0.053 0.212 3.196 0.002 0.044 0.180 2.223 0.028 Worker Access EMP40MINK " EMP50MINK 0.026 0.171 2.049 0.043 " EMP60MINK 0.003 0.267 3.692 0.000 Residence RADJ97_EMP Amenity ASCALE Infrastructure ROADWT Terrain SLOPE " AVG_SLOPE6 " AVG_SLOPE1 

Steps** 4 7 5 Adjusted R2 0.570 0.332 0

* B = the coefficient estimate and Beta = the standardized coefficient estimate ** Stepwise ordinary least squares regression of CMPT_CAP (capped, competitively-adjusted employment percent growth 1990-2000) 

for 410 ARC Counties on the eighteen variables. Separate runs by 1993 USDA County type: Metropolitan, Micropolitan, Non-Core. 

Source: MIT-DUSP ARC Research Team. 
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

Sources of Growth in Non-Metro Appalachia page 80

 

The industry concentration GINI measure (BEAGINI_9) is not significant for metro 

counties but was significant – with different signs – for micro and non-core counties. 

In micropolitan counties, increased industry concentration correlates with faster 

growth, but in non-core counties, increased industry concentration correlates with 

slower growth (and the coefficient estimate was half as large). The results for the 

industry mix factors are also interesting. Only the fourth factor, F4_1990, matters in 

metro counties. This factor emphasizes construction/transportation/agriculture 

without government/services and higher factor scores correlates with faster growth. 

For micropolitan counties factor 4 still matters (a little less), but factor 3 is even 

stronger (and also positive). Factor 3 emphasizes government/agriculture/construction 

without wholesale trade. On the other hand, for non-core counties, only factor 2 

matters (positively). Factor 2 emphasizes manufacturing and wholesale trade without 

mining and government. 

The worker access measures matter most for micropolitan counties and not at all for 

non-core counties. The worker count within 50 minutes, EMP50MINK, performs best 

for micro counties, but the 60-minute count, EMP60MINK, performs best for metro 

counties. Note that the coefficient is much smaller for metro counties (0.003 vs. 

0.026) but, based on the standardized Beta coefficient, is more influential for metro 

counties (0.267 vs. 0.171). The worker access distribution is skewed with a long right 

tail for counties close enough to large metropolitan areas. Hence, the smaller 

coefficient will tend to be applied to a much larger worker access count, 

EMP60MINK, for metro counties than for the micropolitan counties that are further 

from the large metro centers and where the best fitting variable is the 50-minute count, 

EMP50MINK. 

The place-of-residence adjustment, RADJ97_EMP, was not significant for any of the 

three county types and neither were the amenity, infrastructure, and terrain measures. 

Because these models predict employment growth by place of employment, we are not 

surprised that the place-of-residence income adjustment is not relevant (even though it 

was for earlier ELI models that focused on unemployment, poverty, and income by 

place of residence). The amenity variable, ASCALE, focuses (as explained earlier) on 

the scenic and recreational features of a county and other counties might be the ones 

that benefit economically from these features (e.g., a county along the highway that 

leads to a national park located in the next county). The terrain measures could well 

have less effect on 10-year growth than they did for the earlier cross-sectional models. 

For example, there could be a long-standing advantage to counties in the valley vs. in 

the hills that explains the much lower density, income, etc. in the hills, even if the 

recent 10-year employment growth rate is similar. 

Another possible explanation for the limited effects of geography in Exhibit 5-11 is 

that the OLS fits do not account for spatial-spillover effects. The spatial-lag and/or 

spatial-error models that account for spillover effects within commuting zones 

consistently outperform the OLS fits. From earlier runs, we see that these spatial 

models alter the significant variables as well as the coefficient values. Unfortunately, 
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

Sources of Growth in Non-Metro Appalachia page 81

the models and estimation algorithms needed to handle both county stratification and 

spatial effects are beyond the scope of this study. For example, commuting zones 

often include a mix of metro, micro, and non-core counties. We cannot meaningfully 

run the GeoDa models separately for metro, micro, and non-core counties.18 

Nevertheless, our analyses have provided useful insights into both the factors (and 

county differences) that influence growth rates and the spatial relationships that 

influence county interactions. In this section, we summarize these findings and draw 

conclusions regarding decision tools that can assist in identifying promising 

development strategies. 

5.5 Uses and Limitations of the Findings 

The analyses demonstrate the importance of demographic, industry mix, and spatial 

interactions in explaining differences across ARC counties in their economic health 

and growth rates. The most interesting results relate to the explicit inclusion of 

detailed geography, infrastructure, and spatial dependencies in models of economic 

health and growth. We demonstrated that useful measures of geographic influence 

could be computed, using modern GIS tools, from readily available data in a manner 

that is practical and consistent across an area as large as Appalachia. Use of GeoDa 

has also demonstrated the importance of modeling spatial dependencies explicitly in 

order to avoid fitting miss-specified ordinary least-squares models that can overstate 

individual factor coefficients as a result of ignoring spatial dependencies. We have 

also demonstrated circumstances (the commute shed) in which the nearest-neighbor 

adjacency was not the best way to model spatial dependency. 

Nevertheless, despite the progress with improved spatial-analysis tools, the model 

specifications do not go as far as we would like in linking policy options and 

development strategies to predicted outcomes. The employment growth model does, 

indeed, use change data to calibrate the parameters. However, we have not explicitly 

modeled the development process responsible for observed employment changes. We 

have not, for example, specified an underlying “economic-growth” model that 

postulates primary industries, demand for ancillary services, import and export flows, 

and the like, in order to identify which public investments are most likely to yield the 

biggest returns through exports and local multiplier effects. 

Acquiring the data (e.g., freight flows) needed to calibrate such models is impractical 

at present, and, in the parts of Appalachia that are most in need of assistance, 

traditional economic-base analysis is likely only a piece of the tool-kit needed to help 

inform the right development questions. In the small, non-metro counties that are 

transitional, the size of the multiplier effect associated with project investment 

 

18 In order to use tools such as GeoDa to estimate spatial spillover effects for mixed models that allow differing 

variable coefficients by county type with clusters of ‘connected’ counties, we would have to transform all the 

variables and include county-type interaction terms that measured deviations from the main (non-interacting) 

effects. This is beyond the scope of the current study. 
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

Sources of Growth in Non-Metro Appalachia page 82

depends on many local factors that are not readily observed and estimated. How much 

of the new money will recycle locally may not be evident or easily modeled from 

standard data sources. Also, the “connectivity” mechanism that facilitates spillover 

and other multiplier effects may not be visible and may be relatively different from a 

”next-door” adjacency model. A “tourism” strategy, for example, might involve 

spillover effects along the transportation corridors to the tourist sites, whereas a 

“knowledge economy” strategy might build social networks that leapfrog counties or 

even states. The appropriate connectivity matrix for studying (and forecasting) spatial 

dependencies in these cases could look very different from either the nearest-neighbor 

or the commute-shed examples that we considered. 

Consider, for example, that the employment growth models worked best for 

metropolitan counties (57% explained) and least well in non-core counties (18% 

explained). Upon reflection, these variations are not surprising because the traditional 

export-base model of economic growth is likely to work better for metropolitan areas 

with sizeable economies, and well developed infrastructure and commute sheds. A 

further analysis of the Appalachian commute sheds also showed that most include a 

mix of at least two county types.19 Many of the more distressed counties are in 

commute sheds that include no metropolitan county. 

Rather than try to identify a single, complex model for explaining growth across all 

county types, it may be more useful to turn the question around and ask which of 

several types of models is most appropriate for a county depending upon the 

characteristics of that county and its neighbors. If, for example, a county has 

favorable demographics and is in a commuter shed that includes a metropolitan area, 

then a traditional economic development strategy aimed at the commuter shed may be 

beneficial and able to capitalize on favorable spillover effects for that county. 

However, if the commuter shed includes only non-core counties without favorable 

demographics and industry mix, then traditional development strategies may not be 

effective, and growth in neighboring commuter sheds might even have unfavorable 

“backwash” effects.20 For these counties, more promising development strategies 

might focus less on commuter-shed ‘neighbors’ and more on supply-chain possibilities 

or amenity-driven development. Would it make sense for the county to grow its 

warehouse facilities, is the county along the path from a population center to 

potentially attractive amenities, etc.? 

Research our team conducted for the white papers and other aspects of the project 

suggests that, for many transitional counties, the development choice is not a matter of 

fine-tuning the investment strategy and choosing the one with the biggest multiplier. 

Instead, it is likely to involve sizing up whether one or another of a few plausible 

growth paths is practical, given the current circumstances for the county and its 

 

19 The map also highlights the need to include non-ARC border communities in further analysis because many oneor two-county commute sheds at the edge of the Appalachia region are really part of a larger commute shed, 

including sheds oriented toward metropolitan centers outside ARC. 

20 A recent study by Feser and Isserman (2005) of employment and population growth in all US counties provides 

evidence of both favorable spillover and unfavorable backwash effects for non-metro counties. 
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

Sources of Growth in Non-Metro Appalachia page 83

neighbors. In order to make tourism work, a county needs access to tourists, desirable 

venues, highways and motels, etc. For a retirement community, or industrial park to 

work, a different set of questions would be asked. The most effective use of empirical 

analyses may be to support these evaluations with good (electronic) bookkeeping and 

visualization. How many people are less than two hours driving distance away from 

their work? Which counties will benefit from (or contribute to) a new development in 

a county if the county undertakes certain type of strategies? What gaps exist in the 

supply or demand for services, infrastructure, skilled workers, etc. What questions 

should a county ask in order to see if one or another growth model is plausible for the 

county? Is the county near a metropolitan area, along a transportation corridor, etc.? 

Modern web-mapping tools and online services are making it practical to acquire data 

and develop visualization tools and indicator systems that can greatly facilitate “what 

if” dialogues with citizens and local agencies. Fieldwork and case studies will help 

when combined with the kind of empirical analysis we have done to measure 

geographic constraints, neighborhoods, and opportunities. Also, analysts might use 

outlier counties identified by models, such as the ones we calibrated, to identify places 

to look for success/failure examples. 

Such an approach suggests a policy-oriented decision strategy that: 

(a) identifies different sets of potential partners for each county based on the growth 

model that might be emphasized (for example, counties in the same commuting 

zone for traditional export-base growth, but counties along the TVA riverway for 

particular supply-chain analyses, or counties along a highway corridor for certain 

amenities strategies), 

(b) compares the characteristics of the county (and its “neighbors”) with those 

suggested by the relevant right hand side variables for the growth model that 

matches the particular development strategy being contemplated to see whether 

one or more of these strategies has the factor levels needed to suggest a high 

likelihood of success (e.g., do not use an export-base strategy for an isolated 

county with poor transportation infrastructure), 

(c) checks whether the type of economic development that is anticipated will be 

structured in a way that leaves value-added in the county (e.g., mining can benefit 

locals a lot or a little depending on whether most of the value-added is recirculated 

in the community or shifted to remote shareholders), and 

(d) identifies complementary investments (e.g., in other “neighboring” counties) that 

would help the group of “neighbors” assemble the factors needed to tap local 

synergy and enhance the likelihood of success. 
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

Sources of Growth in Non-Metro Appalachia page 84

5.6 References 

Anselin, Luc, Attila Varga, and Zoltan Acs. 1997. “Local Geographic Spillovers 

between University Research and High Technology Innovations.” Journal of Urban 

Economics. 42: 422-448. 

Anselin, Luc, Attila Varga, and Zoltan J. Acs. 2000. “Geographic and Sectoral 

Characteristics of Academic Knowledge Externalities.” Papers in Regional Science. 

79: 435-443. 

Anselin, Luc. 2003. “Spatial Externalities, Spatial Multipliers, and Spatial 

Econometrics.” International Regional Science Review. 26 (2): 153-166. 

Domar, Evsey. 1946. “Capital Expansion, Rate of Growth, and Employment.” 

Econometrica 14 (April): 137-147. 

Feser, Edward, and Andrew Isserman. 2005. Urban Spillovers and Rural Prosperity, 

University of Illinois, Urbana-Champaign. 

Goodchild, Michael 1986. Spatial Autocorrelation, CATMOG 47, Norwich, UK: Geo 

Books. 

Henry, Mark S., David L. Barkley, and Shuming Bao. 1997. “The Hinterland’s Stake 

in Metropolitan Growth: Evident from Selected Southern Regions.” Journal of 

Regional Science, 37(3): 479-501. 

Isserman, Andrew M., and John D. Merrifield. 1987. “Quasi-Experimental Control 

Group Methods for Regional Analysis: An Application to an Energy Boomtown and 

Growth-Pole Theory.” Economic Geography, 63 (1): 3-19. 

Jaffe, Adam 1989. "The Real Effects of Academic Research," American Economic 

Review, 79(957-70). 

Karkalakos, Sotiris. 2004. “The Spatial Boundaries of Regional Technological 

Productivity in European Union.” Working paper. 

Leichenko, Robin. 2000. “Exports, Employment, and Production: A Causal 

Assessment of US States and Regions.” Economic Geography, 76 (4): 303-326. 

Martin, Ron, and Peter Sunley. 1998. “Slow Convergence? The New Endogenous 

Growth Theory and Regional Development.” Economic Geography, 74(3): 201-227. 

Ramajo, Julián, Miguel Márquez, and María del Mar Salinas. 2004. “Spatial Patterns 

in EU Regional Growth: New Evidence about the Role of Location on 

Convergence.” (mimeo, en evaluación) 
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

Sources of Growth in Non-Metro Appalachia page 85

Sala-i-Martin, Xavier. 1996. "Regional Cohesion: Evidence and Theories of Regional 

Growth and Convergence," European Economic Review. 40: 1325-1352.. 

Schaffer, William A. 1998. A Survey of Regional Economic Models. Atlanta, GA: 

Georgia Institute of Technology. 

http://www.rri.wvu.edu/WebBook/Schaffer/chap02.html

Smirnov, Oleg, and Alena Smirnova. 2000. “An Assessment of the Economic Base of 

Distressed and Near-Distressed Counties in Appalachia.” Report to the Appalachian 

Regional Commission, report from Bruton Center for Development Studies, School 

of Social Sciences, The University of Texas at Dallas. 

Solow, Robert. 1956. “A Contribution to the Theory of Economic Growth,” Quarterly 

Journal of Development Economics 70(1): 65-94. 

Stirboeck, Claudia. 2004. “A Spatial Econometric Analysis of Regional Specialisation 

Patterns across EU Regions.” ZEW Discussion Paper No. 04-44. 

http://ssrn.com/abstract=560884

Tan, Zhijun, 2005. “Industrial Structure and Employment Growth in the 1990s in 

Appalachian Counties,” MIT MCP Thesis (August) Cambridge, MA: Department of 

Urban Studies and Planning, Massachusetts Institute of Technology. 

Vias, Alexander C., and Gordon F. Milligan. 1999. “Integrating Economic Base 

Theory with Regional Adjustment Models: The Nonmetropolitan Rocky Mountain 

West.” Growth and Change, Vol. 30 (Fall): 507-525. 

Widner, Ralph R. 1990. “Appalachian Development After 25 Years: An 

Assessment.” Economic Development Quarterly, 4(4): 291-312. 

 
 

Sources of Regional Growth in Non-Metro Appalachia 

Vol. 4 Economic Development Assessment Tools and 

Study Conclusions for Identifying Sources of Growth 

Prepared for the Appalachian Regional Commission 

Prepared by: 

Economic Development Research Group, Inc. 

Revised 2007 
Vol.4 Economic Development Assessment Tools

Sources of Growth in Non-Metro Appalachia 

SOURCES OF GROWTH PROJECT

The Sources of Growth project is part of a series of research efforts funded by the Appalachian 

Regional Commission to improve our understanding of factors affecting economic growth in 

rural and distressed areas. As stated in the Volume 1 Introduction, “the starting premise of 

this project is that there can multiple paths that an area can pursue in successfully enhancing 

job and income creation. They may build on natural resources, cultural resources, human 

resources, local amenities, institutional facilities or location advantages. The resulting 

direction of economic growth may involve manufacturing or supply chain development, 

resource extraction or tourism development, educational development or trade center 

development.” This research is intended to provide a basis of information that can ultimately 

be useful for enhancing the effectiveness of policies and tools aimed at improving the region’s 

economic development. 

This is Volume 4 in a series of reports prepared as part of this project: 

• Executive Summary –synthesis of findings from all work products related to the 

study’s four main research components. 

• Volume 1, Project Background and Prior Research on Economic Growth Paths – 

study objectives, characteristics of non-metro Appalachian counties, classification of 

economic development growth paths, and synopsis of white paper findings on theory 

relating to economic development growth paths. 

• Volume 2, Case Studies of Local Economic Development Growth Processes –

findings related to growth paths as observed for selected case studies covering 

manufacturing industry specialization clusters, supply chain-based development, 

tourism-based development, advanced technology development, and diversification 

from resource-based economies. 

• Volume 3, Statistical Studies of Spatial Economic Relationships – findings from a 

series of econometric modeling and GIS-based analyses, focusing on roles of spatial 

adjacency, market access and transportation in determining economic growth and 

development of trade centers. 

• Volume 4, Economic Development Assessment Tools & Study Conclusions –

description of new and updated tools available to ARC and its Local Development 

Districts to assess economic development opportunities and potential directions for 

economic growth. 

• Appendices – (A) Spatial Analysis of Economic Health, (B) Economic Analysis of 

Hub-Spoke Relationships, (C) White Papers on Economic Growth Theories, (D) 

Literature Review of Empirical Studies on Spatial Influences in Economic 

Development. 
Vol.4 Economic Development Assessment Tools 

Sources of Growth in Non-Metro Appalachia 

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i 

1 INTRODUCTION..........................................................................................................................1

1.1 OVERVIEW: FROM RESEARCH TO ACTION...............................................................................1

1.2 ARC ROLE IN DEVELOPING ECONOMIC TOOLS.......................................................................2

1.3 NEED FOR ECONOMIC ASSESSMENT TOOLS.............................................................................3

1.4 PITFALLS IN USING ECONOMIC TOOLS ....................................................................................4

2 OVERVIEW OF LEAP.................................................................................................................7

2.1 LEAP STRUCTURE ..................................................................................................................7

2.2 ELEMENTS OF INTEGRATED EVALUATION...............................................................................9

2.3 LEAP USES FOR APPALACHIAN GROWTH.............................................................................13

3 USE OF FINDINGS FROM SOURCES OF GROWTH PROJECT ......................................16

3.1 ENHANCING ANALYTIC CAPABILITIES ..................................................................................16

3.2 TRADE CENTER DEVELOPMENT PATHS .................................................................................18

3.3 INDUSTRY AGGLOMERATION CLUSTER PATHS......................................................................19

3.4 SUPPLY-CHAIN (DISPERSAL) PATHS .....................................................................................21

3.5 AMENITY & CULTURAL ASSET GROWTH PATHS...................................................................22

3.6 LEARNING AND TECHNOLOGY GROWTH PATHS ....................................................................24

3.7 NATURAL RESOURCE & OTHER GROWTH PATHS..................................................................26

3.8 SUPPORTING LOCAL ECONOMIC DEVELOPMENT ...................................................................27

4 CONCLUSIONS FROM THE STUDY OF SOURCES OF GROWTH.................................28

4.1 LESSONS LEARNED FROM THE COMPREHENSIVE EXAMINATION ...........................................28

4.2 LESSONS FROM THE CASE STUDIES .......................................................................................28

4.3 KEY FINDINGS FROM EMPIRICAL STUDIES ............................................................................29

4.4 IMPLEMENTING FINDINGS FOR STRATEGY PLANNING ...........................................................33

4.5 FUTURE STUDY DIRECTIONS ON NON-METRO GROWTH PROCESSES .....................................34
Vol.4 Economic Development Assessment Tools

Sources of Growth in Non-Metro Appalachia page i

ACKNOWLEDGEMENTS

This volume was authored by Economic Development Research Group, Inc. Other volumes in 

this series were also co-authored by staff of Regional Technology Strategies, Inc. and by 

faculty and graduate students in the Dept. of Urban Studies & Planning at the Massachusetts 

Institute of Technology. 

The Sources of Growth project involved a team of researchers including: 

• Economic Development Research Group, Inc. (EDRG) – Lisa Petraglia (Project 

Director), Glen Weisbrod (Principal-in-Charge) and Teresa Lynch, with research 

support from Tyler Comings, Brett Piercy and Susan Moses; 

• Regional Technology Strategies, Inc. (RTS) –Stuart Rosenfeld, Phil Psilos and Dan 

Broun; 

• Massachusetts Institute of Technology, Department of Urban Studies & 

Planning (MIT-DUSP) – Prof. Karen R. Polenske, Prof. Joseph Ferreira, Jr. 

and Ayman Ismail, with research support from Tan Zhijun, Isabelle Xin Li, 

Yi Xu and Leigh Graham. 

The project also benefited from the expertise of outside policy and research experts who 

reviewed documents, participated in project meetings and provided technical guidance: Deb 

Markley (Co-Director of the Center for Rural Entrepreneurship, a Rural Policy Research 

Institute), Joseph Cortwright (Vice-President of Impresa Consulting), Ken Poole (Executive 

Director of ACCRA: The Council for Community and Economic Research), David Freshwater 

(Professor of Agricultural Economics and Public Policy at the University of Kentucky), David 

McGranahan and Luc Anselin (Professor. Dept. of Agriculture & Consumer Affairs and 

Regional Economics Applications Laboratory at the University of Illinois, UrbanaChampaign). 

Overall project direction and oversight was provided by Dr. Greg Bischak of the Appalachian 

Regional Commission, whose wide range of research experience served to focus the project 

team on the development of policy applications. Important insight and suggestions were also 

provided by officials of the Appalachian Regional Commission who participated in a day-long 

symposium with the project team, including Thomas Hunter (executive director of ARC, Ann 

Pope (federal co-chair of ARC) and Rick Peltz (alternate federal co-chair). In addition, Ken 

Wester and Jason Wang of ARC assisted the project team in collecting and assembling 

transportation and geographic data. 
Vol.4 Economic Development Assessment Tools Ch.1 Introduction

Sources of Growth in Non-Metro Appalachia page 1

1 INTRODUCTION 

1.1 Overview: From Research to Action 

The Appalachian Regional Commission (ARC) is recognized in the field of economic 

development, not only for its program investments that have been shown to create 

jobs, but also for its research aimed at improving the effectiveness of its economic 

development efforts. The Sources of Growth project is part of that research effort 

aimed at improving our understanding of factors affecting economic distress and 

identifying strategies that can enhance economic growth in the region. 

One of the most important elements of the Sources of Growth project is that it aims to 

illuminate the range of potential economic growth paths that can be relevant for rural 

areas (Exhibit 1-1). It avoids the “urban bias” that exists when people focus just on invogue concepts such as technology-driven clusters. Instead, it lays out multiple paths 

that areas can potentially pursue to create jobs and income. For any specific area, 

though, some growth paths are more likely to succeed than others. Hence, successful 

economic development requires analysis to identify the relevant growth paths. 

Exhibit 1-1 Alternative Growth Paths 

Assetbased 

Strategy

Performance Evaluation – gauging progress on a growth path

Given Local Conditions – Resources, Constraints and Opportunities

Decision Criteria on Most Appropriate Growth Path(s) to Pursue

Goal – Improved Economic Development

Supply 

Chain 

Strategy

Learningbased 

Strategy

Agglomeration 

Strategy

Trade 

Center 

Strategy

The prior three volumes describe theory, prior research, case studies and empirical 

analysis of economic growth factors -- all generating insight and implications for local 

policy initiatives in Appalachia’s non-metro counties, where economic distress is 

greatest. This volume discusses our ability to use those findings to improve tools that 

can be used by ARC’s Local Development Districts (LDDs) for assessing their 

economic development opportunities and developing growth path strategies. 
Vol.4 Economic Development Assessment Tools Ch.1 Introduction

Sources of Growth in Non-Metro Appalachia page 2

1.2 ARC Role in Developing Economic Tools 

ARC-Opps Spreadsheet Tools. The ARC started providing tools for enhancing 

economic development targeting and strategy when it released the highway 

opportunities model: ARC-OPPS in March 2001.1

 That system of analysis tools was 

designed to help ARC’s Local Development Districts identify the type of business 

growth opportunities that come along when areas gain new or improved highway 

access. It was motivated by concern that local economic development agencies were 

often not fully prepared to identify or pursue new opportunities created when 

segments of the Appalachian Development Highway System (ADHS) were 

completed. ARC-OPPS was successfully used for various ADHS links such as 

Appalachian Corridor “V” in Mississippi and Appalachian Corridor “T” in New York 

State. However, this system also created interest in expansion of developing broader 

tools to assess economic development targeting opportunities for regions that did not 

have new highway openings. 

ARC-LEAP Spreadsheet Tools. In January 2004 the Appalachian Regional 

Commission (ARC) issued the report and software tool known as ARC-LEAP, the

Local Economic Assessment Package.

2

 Building on the demands of the ARC’s Local 

Development Districts (LDDs), this product provided the LDDs with a robust package 

of economic development assessment tools that could assist development practitioners 

in their local economic planning efforts. This package superseded ARC-OPPS by 

covering the economic and employment impacts of other types of development 

projects, including water and sewer projects, industrial site development, workforce 

development, and transportation improvements. 

ARC-LEAP was widely distributed among Appalachian state and local government 

economic development programs. The Southern Tier West Regional Planning Council 

in New York was an early adopter of the LEAP package to evaluate how 

transportation accessibility affected economic development opportunities in his region, 

and to assess development options for distribution centers and the lodging sector. 

Another example was the First Development District of Tennessee, which engaged in 

a strategic planning process utilizing the capabilities of LEAP to identify key 

development opportunities for the region. The Middle Georgia Regional Development 

Center used LEAP as the foundation for a larger effort to develop a regional economic 

diversification strategy plan. It was also used for Tennessee DOT’s evaluation of the 

potential economic development benefits of completing Appalachian Corridor “J”. 

 

1 Handbook for Assessing Economic Opportunities from the Completion of Appalachian Development Highways, 

by Economic Development Research Group with the assistance of Cambridge Systematics, March 2001. 

Available at http://www.arc.gov/index.do?nodeId=709 2 Handbook: Assessing Local Economic Development Opportunities with ARC-LEAP, Appalachian Regional 

Commission Local Economic Assessment Package, by Economic Development Research Group, January 2004. 

Available at http://www.arc.gov/index.do?nodeId=2203 
Vol.4 Economic Development Assessment Tools Ch.1 Introduction

Sources of Growth in Non-Metro Appalachia page 3

Recognition Awards. During the 2005-2006 period, LEAP began earning national 

recognition and awards from the IEDC - International Economic Development 

Council and ACCRA – the Council for Community and Economic Research. Both of 

these recognition awards noted the unique capabilities of LEAP in enabling local 

economic development agencies to effectively assess their targeting strategy options, 

and both were given jointly to ARC and Economic Development Research Group 

(EDRG) in recognition of their partnership in its development. 

Web-Tools: EDR-LEAP®. While the LEAP spreadsheet-based toolbox was gaining 

critical success, its use was limited to agencies that had the staff time and resources to 

collect all of the information required to use it. In response to this need, EDRG 

developed a new system that overcame this problem by having: (a) essentially all of 

the data already collected and immediately available via a dynamic database and 

geographic information system, and (b) the entire system available on-line and directly 

usable through any web browser, with help screens for new users. ARC made the 

system available to Appalachian Local Development Districts and Appalachian State 

Economic Development Departments. (It is available to other users through EDRG.3

 ) 

The initial version of EDR-LEAP® assessed local economic performance gaps, 

barriers holding back further development, business attraction target opportunities and 

effects of program or policy initiatives. It also included some evaluation of business 

cluster opportunities. However, it did not fully distinguish the alternative growth 

paths that can be important for any region, but particularly for rural districts where 

there is not necessarily a critical mass of population and employment to support 

business clusters. Findings from the Sources of Growth project -- including literature 

review, case studies and empirical studies – now provide a base for further enhancing 

the breadth of analysis and use of this tool. 

1.3 Need for Economic Assessment Tools 

The concept of local economic assessment is not new. It goes back at least forty 

years, with “economic base analysis” and its set of ratio calculations to identify their 

economic performance strengths and weaknesses.4

 These methods started appearing 

in guides for economic development agencies in the 1970s. 

In the later 1980s and most of the 1990s, there was also a flurry of research ranking 

business site location factors. Today, there is now a strong consensus on the nature of 

the key business location factors, which represent local competitiveness factors for 

economic developers. Those factors are shown in Exhibit 1-2. 

Together, the evaluation of economic performance (via economic base analysis) and 

the evaluation of economic competitiveness factors (via analysis of site location 

 

3

 See www.edrgroup.com/leap for further information and links to contact information. 

4

 This includes LQ (Location Quotient), S (Shift-Share) and VAMP (value added minus payroll per employee). 
Vol.4 Economic Development Assessment Tools Ch.1 Introduction

Sources of Growth in Non-Metro Appalachia page 4

factors) provided a foundation for guides such as Economic Development Planning, 

International Economic Development Council (2002). 

Exhibit 1-2. Business Site Location Factors 5

 

 Suitability of Business Parks, Land and Buildings 

 Scale and Skills of the Labor Market --Workforce 

 Scale and Socioeconomic Characteristics of the Consumer Base 

 Availability and Quality of Infrastructure -- roads, power, water/sewer, broadband 

telecom, intermodal transportation terminals and connections 

 Access to Markets, as well as to airports, marine ports and intermodal rail terminals 

 Business Support services & business climate – job training, regulations, business 

organizations 

 Quality of life -- including climate, arts and culture, recreation, and school quality 

 Cost of doing business – including labor, utilities, infrastructure and taxes 

1.4 Pitfalls in Using Economic Tools 

Note: Most of this part 1.4 text is drawn from a separate article, “New Tools for 

Economic Development Targeting and Strategy: Applying a Local Economic 

Assessment Package” by Glen Weisbrod and Brett Piercy, 2006. publication pending. 

The full value of an integrated evaluation and targeting system such as LEAP comes 

from its ability to offer a coordinated toolkit that effectively support economic 

development targeting and strategy development. As a coordinated toolkit, it avoids 

the common limitations and pitfalls that come from reliance on simpler methods or 

bundles of separate tools. Examples of these problems include the following: 

• While area-wide industry mix patterns and trends are easy to assess, most 

economic developers understand that such information is of limited value 

unless it can be compared to relevant neighbor and competitor areas to identify 

performance gaps, and then linked to business competitiveness factors to help 

explain those results. 

The problem of over-reliance on industry patterns and trends is that they can 

lead to a naïve conclusion that already strong industries represent clusters that 

 

5

 Industrial site location factors are widely recognized in the field of economic development today, though most of 

the research to identify them took place over the prior decade. Sources include: (1) Portland 2002: Strategy for 

Economic Vitality, Appendix 2-3: “Location Factors,” 2002, (2) Sloagett, Gordon and Mike Woods. “Critical 

Factors in Attracting New Business and Industry in Oklahoma. Oklahoma Cooperative Extension Service; (3) 

Kotler, Philip et al. Marketing Places. The Free Press, 1993; (4) Lyne, Jack, “Quality of Life Factors Dominate 

Many Facility Location Decisions,” Site Selection Handbook, August 1988, and (5) Finkle, Jeffrey. “Developing 

Strategies for Economic Stability and Growth,” Council for Urban Economic Development, 1997. For quality 

of life, also see (6) Segedy, James. “How Important is Quality of Life in Location Decisions and Local 

Economic Development” in Bingham and Mier (Eds.) Dilemmas of Urban Economic Development, Sage, 1997. 
Vol.4 Economic Development Assessment Tools Ch.1 Introduction

Sources of Growth in Non-Metro Appalachia page 5

should be the top priorities for further recruitment. More appropriately, 

economic development strategies should focus on identifying existing gaps and 

missed opportunities, desired growth paths and the steps needed to overcome 

barriers now holding back achievement of those opportunities. 

• Measuring cost differences among regions is a straightforward process, and the 

nature of those differences forms a core of economic simulation and 

forecasting models. Those models focus on estimating dollar flows and cost 

differences to explain how industry growth and investment moves among 

areas. However, most economic developers understand that business location 

requirements also depend on various non-cost (size, quality and access) factors 

that are at least as important as cost in determining competitiveness and 

resulting industry growth and investment shifts. 

The problem of over-reliance on cost comparisons is that they can lead to a 

naïve conclusion that local economic development strategy should focus just 

on cost incentives to attract economic growth. Often, economic development 

strategies need to focus more on identifying opportunities to overcome gaps in 

transportation facilities, job training, industrial park facilities and/or business 

support services as ways to enhance quality. 

• Economic forecasting and impact models can show how a given type of new 

business will generate additional flows of dollars to suppliers. However, most 

economic developers understand that part of their job is to make economic 

forecasting and impact models be wrong. That is because economic 

forecasting models usually assume no change in competitiveness factors aside 

from costs, while economic developers may be working hard to make quality 

improvements in local facilities, job training or support services. In addition, 

economic impact calculations assume that dollars will “leak” out of the area if 

there are currently no local suppliers to serve a major new industry, while 

economic developers may be working hard to develop local supply chains that 

can keep those dollars in the local economy.6

The problem of reliance on economic forecasts and impact models is that they 

 

6

 An economic impact model applied before the opening of the BMW assembly plant in South Carolina 

would normally have calculated that the flow of dollars to auto parts suppliers would go mostly out of 

state, since there was no major auto parts industry in the state at that time. It would not have known 

that the cooperative efforts of BMW and the state would subsequently lead to the attraction of 49 new 

auto parts suppliers, creating thousands of additional jobs. 
Vol.4 Economic Development Assessment Tools Ch.1 Introduction

Sources of Growth in Non-Metro Appalachia page 6

can lead to a pessimistic view of future prospects for local economic 

development, and wrong priorities for industry growth and attraction targets. 

More appropriately, economic developers need to take advantage of 

opportunities to enhance local supplier networks as a way of enlarging the 

indirect benefits of business expansion and attraction efforts. 
Vol.4 Economic Development Assessment Tools Ch.2 Overview of LEAP

Sources of Growth in Non-Metro Appalachia page 7

2 OVERVIEW OF LEAP 

2.1 LEAP Structure 

In recognition of these shortcomings, the Appalachian Regional Commission 

supported development of the “Local Economic Assessment Package” as a bundle of 

tools to give economic developers the ability to diagnose local competitive position, 

select appropriate targets and design economic development targeting strategies that 

build on strengths and minimize weaknesses. The resulting package of tools follows 

the evaluation process supporting IEDC’s Economic Development Planning guide and 

recommended targets and policy priorities. It is designed specifically to avoid the 

pitfalls just discussed. 

The structure of this approach is shown in Figure 2. It revolves around three steps or 

modules, shown by the shaded three-dimensional boxes: (1) Economic Assessment, 

(2) Targeting Diagnostics and (3) Policy Analysis. They implement the three-phase 

evaluation process that was previously discussed to provide information for the IEDC 

economic development planning process. Most importantly, it avoids or minimizes the 

pitfalls of incomplete and inappropriate conclusions by making the critical connection 

between (a) local economic performance results to date and (b) local competitiveness 

factors (costs, quality, access and market scale differences). That provides a basis for 

determining (c) potentially feasible business growth/attraction targets and actions 

needed to make them possible. The steps are as follows: 

• Economic Base Assessment – This step develops profiles of business mix and 

performance trends by industry, and benchmarks them against adjacent or 

competing areas to identify leading & lagging industries, performance gaps 

and business types with the greatest local growth or attraction potential. 

• Targeting Diagnostics – This step rates competitive strengths and weaknesses 

of the area in terms of various costs (e.g., utilities, housing g, land, labor, 

taxes), qualities (worker skills, industrial/office park amenities), access (to 

airports, highways, railroads) and supporting infrastructure (broadband, 

business resources). It uses a knowledge base of industry requirements, 

thresholds for business location, and inter-industry relationships to identify the 

key factors that are constraining local attractiveness for each industry, and 

potentially achievable business attraction targets. 
Vol.4 Economic Development Assessment Tools Ch.2 Overview of LEAP

Sources of Growth in Non-Metro Appalachia page 8

• Policy Analysis – This step allows users to assess how changes in economic 

development conditions can affect the size and nature of potential future 

business attraction. It estimates changes in job growth associated with positive 

or negative changes in labor skills training, industrial/office park amenities, 

land availability, broadband access, and/or transportation accessibility. It 

provides a basis for prioritizing future economic development initiatives. 

Figure 2. LEAP Structure 

An interesting aspect of this integrated system design is that it is flexible in the choice 

of economic development targeting objectives, as the assessment of gaps, 

opportunities and targets can be viewed in terms of (a) job creation, (b) income 

generation, (c) maximizing local value added or (d) increasing business sales. The 

choice can make a big difference in findings and recommendations, as some industries 

Define Study 

& Comparison 

Areas 

EDR-LEAP Local Economic 

Assessment Package

Change in Primary & Secondary 

Opportunities for Business Attraction

Define 

Alternative 

Scenarios 

Industry

Trends

Economic Performance Gap Screener

Primary Opportunities for Business 

Development & Attraction

Industry 

Requirements

Define Local 

Ratings: 

Cost, Availability,

Access, Quality 

Industry 

Forecasts

Inter-Industry

Relationships

suppliers, cluster,

dispersion Secondary Opportunities for Supporting 

Business Attraction 

Economic Base: Patterns & Trends

Diagnosis: Local Adequacy 

workforce, materials, industry facilities, 

infrastructure, technologies, int. trade, 

transport terminals, reliability 

Factors Holding Back Business Growth

Scenarios: Change in Local Ratings
Vol.4 Economic Development Assessment Tools Ch.2 Overview of LEAP

Sources of Growth in Non-Metro Appalachia page 9

are growing in business sales while jobs or effective salaries are being cut. It is also 

flexible in the choice of comparison areas for benchmarking, which can be adjacent 

areas, national or regional competitors, or other areas that will be linked by new 

transportation corridor connections. That decision also depends on the purpose and use 

of the analysis. 

Recognizing its flexibility, this system has now been adopted by the Appalachian 

Regional Commission and distributed to its Local Development Districts in 13 states 

to support and enhance their economic development targeting efforts. Applications of 

it have won national recognition awards from IEDC - the International Economic 

Development Council and ACCRA – the Council for Community and Economic 

Research.7

 

2.2 Elements of Integrated Evaluation 

Assessment of the Economy. As noted by economic development textbooks, the three 

principal tools that form the starting basis for economic base analysis are Location 

Quotient (business mix analysis), Shift Share (business trend analysis), and SWOT 

(Strengths, Weaknesses, Opportunities and Threats) analysis.8 These techniques are 

not new and they often form part of Comprehensive Economic Development Strategy 

(CEDS) documents funded by the US Economic Development Administration. 

Nor are these techniques inherently complicated. In fact, they can be done quickly 

with spreadsheets following instructions in regional economic textbooks. The 

difficulties lie in (a) collecting data on dozens of industries at the appropriate level of 

detail, and then (b) making the right comparisons to extract findings on local strengths 

and weaknesses. 

This is one area where LEAP diverges from traditional analysis approaches. The 

traditional approach for economic base analysis has been to compare a local area 

against national patterns and trends. Economic models similarly also compare local 

costs against national costs. The problem, of course, is that a rural region does not 

necessarily expect to compete against big metro regions for the same industries, nor 

does a lake recreation area expect to compete against mining or industrial centers. 

That is why a benchmarking approach, which compares local industry mix patterns 

and growth trends against relevant competing areas, will lead to totally different types 

of findings on local gaps than a comparison to state or national averages. Figure 3 is a 

graph generated by LEAP that illustrates a comparison of business cost factors in a 

study area relative to a user-defined comparison area. 

 

7

 ACCRA 2006 National Award for Applied Research; IEDC Honorable Mention for Research Studies, 2005 

8

 Bendavid-Val, Avrom. Regional and Local Economic Analysis for Practitioners, fourth edition. 1991. 
Vol.4 Economic Development Assessment Tools Ch.2 Overview of LEAP

Sources of Growth in Non-Metro Appalachia page 10

Figure 3. Relative Cost Factor Comparison 

Targeting Diagnostics. The diagnostic phase of LEAP includes an assessment of local 

advantages and disadvantages for each industry in which there is a potential for further 

business growth and attraction, as identified in the assessment phase. This set of 

diagnostics identifies “critical” and “important” weaknesses that need to be addressed 

if the area is to fulfill some of the growth potential identified in the local area 

assessment. 

A major problem holding back systematic analysis of economic development 

opportunities in the past has been difficulty pulling together information on just how a 

local area stacks up against competing areas in terms of various “competitiveness 

factors” -- which can range from very specific (such as tax and utility rates) to very 

vague (such as business climate and quality of life). Traditional economic models 

sidestep the problem by ignoring those non-dollar factors and concentrating instead on 

the more easily measured business output trends and costs. Yet economic developers 

know that these scale, quality and access factors can be at the core of economic 

competitiveness and addressing them can be critical to achieving success in business 

growth and attraction. 

The LEAP approach takes this issue of information assembly head on, as it attempts to 

recognize all of the major business location considerations that are important to 

economic developers. The solution is two pronged: 

• Use of Broader Data Sources. Information on many factors that are not readily 

available can in fact be obtained through an up-front research effort to tap 

proprietary databases, with costs greatly reduced if they are spread over many 

users. That is done with an on-line version of LEAP, which includes measures 

for every US county of: (a) costs factors including labor, utilities, taxes and 

buildings, (b) size and quality factors including delivery markets and education 

characteristics of the workforce accessible within a 40 minute drive, (c) access 
Vol.4 Economic Development Assessment Tools Ch.2 Overview of LEAP

Sources of Growth in Non-Metro Appalachia page 11

times and size of available commercial airports, marine ports and intermodal 

truck/rail terminals, and (d) availability and magnitude of broadband facilities, 

recreation activities and international exports. Figure 4 illustrates this type of 

comparison. 

• Use of Local Information Worksheets. To assess local conditions for some 

important factors that are not readily available, it is necessary to rely on locally 

completed worksheets. These include ratings based on detailed criteria for 

judging the quality features of local business parks and buildings, quality 

ratings for local training, business support services and business climate, and 

quality rating for local tourism support facilities and services. Practitioners 

have shied away from such measures in the past because they require judgment 

in assessing business facilities and supporting resources. However, the LEAP 

approach is based on an understanding that these factors cannot be fully 

measured by available public or proprietary databases, but they also cannot be 

ignored. By providing and allowing for optional use of local assessment 

worksheets, the system can provide a more robust and complete picture of 

local competitiveness factors. 

Figure 4. LEAP Comparison of Area Access Characteristics 

(note: access data obtained via ESRI GIS system) 

Opportunities and Barriers. The crux of the matter, then, is to connect an area’s 

economic performance gaps (unfulfilled opportunities) to its shortfalls in the various 

competitiveness (cost, scale, quality and access) factors. To diagnose which of the 

competitiveness factors are acting as barriers to business growth and attraction, LEAP 

relies on a base of information concerning detailed industries, their relative business 

requirements for these factors, and the how industries respond to changes in these 

factors. This approach recognizes that industries must meet thresholds for some 

factors in order to make their business operations economically viable at a given 
Vol.4 Economic Development Assessment Tools Ch.2 Overview of LEAP

Sources of Growth in Non-Metro Appalachia page 12

location. For instance, the thresholds can be minimum market size requirements 

(common for financial and business services), maximum access times to airports 

(common for electronic products), and/or delivery time and reliability requirements 

along supply chain corridors (common for just-in-time automotive parts). Additional 

elements of the information base include baseline industry growth forecasts and interindustry supplier and buyer relationships, which together provide information on how 

attracting one industry can create spatial cluster opportunities to also attract additional 

growth through complementary industries. 

In this way, LEAP identifies sets of industries that are good targets for economic 

development based on the match of local characteristics and the operating 

requirements of each industry. For those industries that are currently lagging but could 

offer future growth opportunities, it identifies the nature of current disadvantages that 

need to be overcome in order to effectively promote more local business activity. 

Figure 5 shows an example of a LEAP diagnostic report and the resulting 

identification of factors responsible for current industry performance gaps. 

Figure 5. LEAP Diagnosis of Factors Holding Back Economic Growth 

in a Sample Study Area 

Armed with these diagnostics, LEAP identifies industry targets with the greatest 

opportunities for direct business attraction, the magnitude of potentially achievable 

growth, and the factors that must be addressed to realize those results. It also helps 

practitioners consider opportunities for building upon inter-industry linkages – in other 

words, sets of industries that build on common needs and buyer-supplier relationships. 

Complementary industries are types of business which are not primary target 

industries, but which may nevertheless represent growth opportunities because they 

are suppliers of goods and services to the primary target industries or otherwise 

interact with them. In this case, any direct opportunities for business growth may also 

indirectly create opportunities for growth in complementary industries that do not 

directly depend on highway access. 
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Sources of Growth in Non-Metro Appalachia page 13

Policy Analysis considers how some barriers to business growth and attraction can be 

minimized or overcome by the programs and projects of local planners and economic 

developers. Local public policies and programs and projects can include 

improvements in the availability and adequacy of local education, workforce skills 

training, infrastructure enhancement, business site development, access to airports, sea 

ports, and rail; and improvements to highways or initiation of improved support 

services. By applying the base on information on industry growth factors, the system 

can then identify the potential impact of proposed policies or projects on business 

attraction, and present estimates of the range of resulting impact on jobs, income, 

value added or business output. The impacts are expressed in terms of range estimates, 

based on risk factors including industry volatility and sensitivity to business cycles. 

Follow On Actions. Economic development targets identified from LEAP will only be 

achieved if a strategy plan is put in place to address remaining needs and to actively 

entice such business growth and attraction. Once potential opportunities for targeting 

future business growth and attraction have been identified, along with needs for 

addressing existing barriers, the economic developer must devise a process to work 

with other area agencies and leaders in forging a strategy plan. This includes 

agreement on targets and goals, and a program of action steps covering organizational, 

staffing and financing plans to pursue the goals, as well as some form of monitoring 

and evaluation of results. 

2.3 LEAP Uses for Appalachian Growth 

Performance Indicators. For regional and state economic development agencies, 

LEAP was designed to be used in several ways. First, it provides economic 

development performance indicators, reflecting the area’s economic performance, 

trends and growth opportunities, as well as comparison of those performance 

indicators to surrounding areas or counterparts elsewhere. LEAP provides a large 

volume of key information that would take considerable effort for local economic 

development agencies to assemble and process themselves. This includes the 

following 

• The core data on employment and business output (patterns and shifts over 

time) is provided through a cooperative agreement with Minnesota IMPLAN 

Group, Inc. (MIG). It is developed from information compiled by the US 

Bureau of Economic Analysis, US Bureau of Labor Statistics, Zip and County 

Business Patterns, and the Economic Census, with additional enhancement to 

provide full details for small areas without suppression. We update aggregate 

estimates using information from more recent Dun & Bradstreet. 

• Additional information on local concentration of international exports and 

local purchasing patterns is also provided by MIG under cooperative 
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agreement. The export estimates are based on International Trade 

Administration data, while further analysis of local source purchasing is 

derived from calibrated IMPLAN models. 

• Local travel times, accessibility measures, delivery markets and labor markets

are derived from highway network drive times along with business and 

demographic data, using a Geographic Information System (GIS) from ESRI. 

• Other local information includes: (a) workforce characteristics, educational 

attainment and housing costs derived from the US Census; (b) Utility costs 

derived from the Energy Information Administration, Edison Electric Institute 

and Energy User News; (c) Local taxes and government revenues compiled 

from the Census of Government; (d) Airport, marine port, and freight 

intermodal facility locations and activity levels are based on inventories 

maintained by the US Dept of Transportation. 

Support for Strategic Review. A second use of LEAP results is use of its evaluation 

reports to support both internal organization strategy and outside information 

dissemination. This includes the following: 

• Profile of business attraction strengths and weak nesses, such as the size of the 

labor market and delivery market, availability of transportation and broadband 

telecommunications facilities, and workforce education. 

• Tracking change in the local area economy is changing over time, in terms of 

gains and loses in employment and/or business sales in various local industries. 

Also identify local industries where local business sales are gaining while jobs 

are dropping as a result of increasing outsourcing and/or mechanization). 

• Comparison of local performance relative to adjacent or competing areas (or 

the state as a whole), to identify how the local area has been over- or underperforming in terms of its business mix and business growth performance. 

• Benchmarking of local competitiveness for various growth paths, by showing 

relative differences in workforce skills, educational attainment, transportation 

access, broadband penetration, and quality of industrial parks. 

Strategy Development. Finally, LEAP can provide information that can help guide 

strategy for pursuing various growth paths. This includes the following elements: 

• Identification of barriers holding back local current success in promoting 

business growth and attraction economic performance: LEAP identifies the 

specific factors that are constraining local business growth and attraction, and 

the specific industries that are being constrained. 
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• Target Industries that represent opportunities for business attraction. LEAP 

identifies the best industries to focus on, the magnitude of the potential 

opportunity and the extent of uncertainty associated with it. It also identifies 

mutually supporting and complementary industries that can be an indirect 

element of a growth strategy. 

• Policy Actions that can affect the type and size of industry growth 

opportunities and targets. LEAP allows you to estimate how proposed 

scenarios for enhancing industrial parks, transportation infrastructure, job 

training and business support services may change target opportunities.
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3 USE OF FINDINGS FROM 

SOURCES OF GROWTH 

PROJECT

3.1 Enhancing Analytic Capabilities 

The Sources of Growth project provided a synthesis of theory and prior research on 

growth paths, a day-long workshop of experts in the field, and empirical studies of 

factors affecting economic growth in Appalachia. As summarized in the prior three 

volumes, these efforts highlighted critical considerations in defining alternative 

growth paths and their determinants. That has enabled us to identify ways in which 

LEAP tools can be improved to better serve the needs of Appalachian LDDs and State 

Economic Development Agencies. These improvements fall into three categories: 

• Defining Growth Paths. Until now, LEAP has focused on evaluating and 

identifying appropriate target industries for local areas. However, the research 

conducted for this study suggests that there can be additional value in 

explicitly identifying classes of target growth paths (rather than just target 

industries) that local areas can pursue in their economic development 

strategies. An example of such a classification is shown in Exhibit 3-1. 

• Rating Existing Situation. Until now, LEAP has focused on characterizing 

existing conditions through ratings of local economic performance and trends. 

However, the research conducted for this study enables us to also develop 

ratings of localized growth specialization. Such ratings can reflect the extent to 

which a local area is already specializing as a resource-based, learning-based, 

tourism-based, supply-chain based or trade center based economy. 

• Rating Potentials for Alternative Growth Paths. Until now, LEAP has 

focused on recommendations of economic growth opportunities in terms of 

potential industries. However, the research conducted for this study provides 

us with a further capability for recommending potential growth path directions

based on potential competitive advantages such as labor skills, road networks, 

climate or technology infrastructure features. 
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Exhibit 3-1. Definition of Six Major Classes of Economic Growth Paths 

Basis for Area’s 

Economy Growth 

Description 

Trade Center Growth pattern emanating from a small urban cluster that 

provides goods and services to the exurban communities & 

rural hinterlands 

Agglomeration 

(e.g. cluster economy)

Growth resulting from geographic concentrations of 

interconnected businesses and institutions that enhance the 

productivity of the core industries. 

Supply-Chain 

(e.g. dispersal economy) 

Remote location is chosen over the central metropolitan 

area to host a node of economic activity (distribution or 

assembly) that is part of a larger (geographic) production 

chain. 

Natural Amenity or 

Cultural Assets 

Growth as a result of either quality-of-place attracting new 

households –or – efforts to actively develop & promote 

cultural, recreation, eco-tourism venues and their 

supporting visitor services. 

Knowledge (Learning) 

Assets 

Growth opportunities leveraged from the collective 

knowledge embodied in the region, including social 

capital, technical applications / commercialization, 

institutional assets (educational and financial), 

entrepreneurial start-ups. 

Other Growth Paths: 

Natural Resources and 

Government 

Growth made possible by the existence of long-standing 

mineral, lumber or agricultural resources, or by the 

decision of government agencies to site major regional or 

national facilities in an area. 

The remainder of this chapter describes various measures that can be constructed to 

assess the current growth path status of an area or the factors affecting local potential 

for various growth paths. The measures that are listed and discussed in the rest of this 

chapter are not intended to represent a complete list of desired or possible metrics. 

Rather, they are intended to represent what is known to be currently possible given 

(a) publicly available data sources and (b) proven metrics that have been demonstrated 

in the literature of prior studies (described in Volume 1) or recent empirical studies 

(described in Volume 3). 

These various measures represent potential additions to enhance the value and use of 

LEAP for assessing growth path opportunities. At the time of this report’s 

publication, some have been implemented, some are planned for implementation in the 

near future and others are still in the proposal or development process. Updates on the 

status of these changes will be posted for participating registered users of the LEAP 

system, including Appalachian Local Development Districts and Appalachian State 

Economic Development Agencies at www.edr-leap.com .. 
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3.2 Trade Center Development Paths 

A micropolitan trade center provides goods and services to a surrounding rural 

“hinterland.” It depends on having a small but critical base of population and 

employment, a nature junction of traffic routes serving surrounding areas, and distance 

or topographical features that encourage residents and employees in those surrounding 

areas to visit this trade center location rather than other adjacent or nearby areas as 

their center for buying retail goods and consumer services. The plan is to improve 

LEAP by providing each county or group of counties (comprising a region or Local 

Development District) with trade center indicators rating for both existing conditions 

and future potential opportunities. 

Rating Existing Situation. A description of trade center-based economies is provided 

in Volume 1. It indicates that a trade center is characterized by having a larger than 

normal concentration of retail stores and consumer and professional services (barbers, 

doctors, loan companies) than would be expected, given its population base. Of 

course, a given area may be a strong trade center that is well-serving a large 

surrounding area, or it may be a weak trade center that only partially services outside 

areas. This leads to the following proposed indicators of existing conditions: 

• Economic Base Indicator: Consumer Trade-Based Concentration – This 

indicator is defined as the ratio of local employment in retail + consumer 

services + professional services, divided by local population. 

• Trade Center Micro/Metro Rating – This indicator is defined as the county or 

place that has a high rating for the above-cited consumer-based concentration 

and is also designated by the US Census as a “metropolitan center” or 

“micropolitan center” on the basis of net inflows of workers coming in from 

surrounding counties. 

Rating Potentials for Future Trade Center Growth. Discussion of the determinants 

of trade center-based economies are provided in Volume 1 as well as case studies in 

Volume 2. In addition, relevant research on economic hubs, spokes, and market area 

effects are described in Volume 3. These documents focus on transportation, 

topography and population clustering patterns that create centers of consumer trade 

activity serving surrounding trade areas that do not have similarly strong centers of 

activity. This leads to the following diagnostic indicators: 

• Economic Base: Trade Linkages– Technically known as a “spatial lag 

multiplier,” this measure is an indicator of the extent to which economic 

activity for each industry in a given county is supported by demand generated 

in neighboring counties. It is calculated by considering the industry mix of 

each county and that of neighboring counties and information on inter-industry 

relationships. It effectively captures situations where one county is serving as 
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the hub of economic activity for other surrounding counties. Details of this 

measure are provided in the Volume 2, Chapter 2 report where it was 

successfully used to explain trade center growth. It is based on work by Ismail 

at MIT, updating original work by Smirnov. 

• Labor Market Area (Scale) – This is an indicator of the size of the workforce 

or population base that lives within a given (40, 50 or 60 minute) drive time of 

the population center of a county. It thus reaches into neighboring counties to 

calculate a “market area”, which can be interpreted as an indicator of the 

relative size of both the labor market for any industry and the shopper 

customer market for retail and consumer service industries. This measure is 

constructed on the basis of geographic information systems and highway 

network drive times. It is a variant of the population base used for analysis in 

Volume 2, Chapter 4 of the Sources of Growth Study. This indicator is now 

implemented in EDR-LEAP. 

• Composite Trade Center Indicator – This indicator combines spatial lag 

multipliers or trade area indicators with information on distance to the closest 

larger city or urban county. It is intended to overcome a problem confronting 

both of the preceding indicators, which is that they assume a strong central city 

surrounded by a rural hinterland, and can provide misleading results when 

there are actually multiple cities of significance in a multi-county region. For 

instance, the spatial lag multiplier seemed to indicate that Scioto County, OH 

was a strong trade center when it found economic strength in that county and 

evidence that residents of surrounding counties were going outside their home 

counties for purchasing of goods and services. However, the case study (in 

Volume 2, Chapter 2) found that the residents of those surrounding counties 

were actually shopping in extra-regional metro centers due to new highway 

access thereby creating an adverse backwash effect on Scioto County. By 

measuring the distance to next larger cities or urban counties, this error can be 

minimized. 

3.3 Industry Agglomeration Cluster Paths 

Agglomeration-based economic growth is based on development of geographic 

concentrations of interconnected businesses and institutions that enhance the 

productivity of the core industries. It most often depends on achieving some form of: 

(a) economies of scale in operations of a single industry, or (b) economies of vertical 

integration associated with clustering industries that buy from and sell to each other, 

or (c) economies associated with several industries sharing a common skill or resource 

base in a given region. The plan is to improve LEAP by providing each county or 

group of counties (comprising a region or Local Development District) with 

agglomeration cluster indicators rating for both existing conditions and future 

potential opportunities. 
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Rating Existing Situation. A description of agglomeration -based economies is 

provided in Volume 1. It indicates that an industry agglomeration cluster is 

characterized by having a larger concentration of individual production-based sectors 

and their directly complementary sectors, relative to the rest of the economic base mix. 

This leads to the following proposed indicators of existing conditions 

• Economic Base: Manufacturing Concentration – This indicator is an index 

reflecting the extent to which any one or more manufacturing industries have a 

higher concentration (location quotient) in the study area that the statewide 

average. 

• Economic Base: Vertical Integration of Suppliers – This indicator is an index 

reflecting the extent to which the dominant manufacturing industries also have 

a strong relative concentration of their suppler industries within the region. 

That is determined by using the tables of inter-industry purchasing patterns 

(technology coefficients) within BEA national input-output tables. These 

input-output relationships are already in use within LEAP for the identification 

of indirect business attraction opportunities; the proposed new measure would 

use that information for also assessing existing industries. 

Rating Potentials for Future Industry Cluster Growth. Discussion of the 

determinants of trade center-based economies is provided in Volume 1, along with the 

manufacturing case study (Alabama’s auto manufacturing cluster) in Volume 2. These 

documents focus on the scale and density of industries, workforce skills and 

supporting facilities, as well as their cost and quality. This leads to the following 

diagnostic indicators: 

• “Effective Density” of Opportunities Rating – This is a composite indicator of 

the productivity gain associated with increasing the effective density of 

activities reachable from a center of industrial activity. It is calculated on the 

basis of population based within 40 minutes of the population-weighted center 

of the core county, divided by the land area of the county. It effectively 

represents density of the county population, modified to add extra “effective 

density” if additional outside population is close by. This measure is based on 

research results of “Productivity and Metropolitan Density,” by Timothy 

Harris and Yannis Ioannides, Tufts Univ. Dept of Economics, 2000. This 

measure is now implemented in LEAP. 

• Gap Analysis: Vertical Integration of Suppliers – This is the flip side of the 

economic base measure cited earlier. It reflects that proportional magnitude of 

the gap between (a) level of local employment in suppliers to the dominant 

manufacturing industry, and (b) the theoretical maximum employment if all 

suppliers were locally present. 

• Barrier Analysis: Cost Competitiveness – This is a composite measures of the 
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local cost of labor, energy and transportation inputs for each industry, relative 

to competing areas or other comparison areas. It is calculated using relative 

weights for the degree to which each industry makes use of labor, energy and 

transportation factors in is production process. This calculation is now 

implemented in LEAP. 

3.4 Supply-Chain (Dispersal) Paths 

Supply-chain based economic growth is based on development of suppliers and 

distributors strung along a highway corridor. This arrangement makes use of dispersal 

economies for keeping labor costs low, and it makes use of transportation connection 

efficiencies associated with same day delivery. In some cases, it also makes use of 

multi-modal delivery connections (e.g., intermodal truck-rail or truck-air connections). 

The plan is to improve LEAP by providing each county or group of counties 

(comprising a region or Local Development District) with supply chain ratings for 

both existing conditions and future potential opportunities. 

Rating Existing Situation. A description of supply chain economic growth is 

provided in Volume 1. It indicates that a supply chain-based economy is most often 

characterized by having a larger than normal concentration of distribution facilities 

and/or parts suppliers to assembly plants (e.g., metal or plastic product fabricators 

serving auto plants). This leads to the following proposed indicators of existing 

conditions 

• Economic Base: Logistics Concentration – This is an index reflecting the 

extent to which warehousing/distribution, wholesaling, and trucking industries 

have a higher concentration in the study area that the statewide average. This 

is reflected in a composite “Location Quotient” for those logistics-related 

industries. 

• Economic Base: Fabricated Parts Suppliers – This is an index reflecting the 

extent to which metal, plastic or glass fabrication industries have a higher 

concentration in the study area that the statewide average. This is reflected in a 

composite “Location Quotient” for those fabrication industries. 

Rating Potentials for Future Supply Chain Growth. Discussion of the determinants 

of supply chain -based economies is provided in Volume 1 and the auto alley case 

study in Volume 2. These documents focus on the roles of labor cost, industry mix and 

location relative to highways, in addition to highway and connections with intermodal 

rail and air terminals. This leads to the following diagnostic indicators: 

• Major Highway Access – This is constructed as a measure of distance from the 

county population-weighted center to the nearest four-lane or interstate level 

highway. Alternatively, the county’s mileage of four lane highways can be 
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used to reflect the extent of highway access occurring in the county, as done in 

the “Twin County” study described in Volume 3, Chapter 3. Either way, the 

highway measure can be used with the measures of logistics and fabricated 

parts suppliers to improve the indicator of supply chain supporting activity. 

• Drive time to Commercial Airport – This is a measure of the highway drive 

time from the county population-weighted center to the nearest public airport 

with regular commercial scheduled airline service. It is combined with 

information on the magnitude of service provided at that airport (measured in 

terms of the number of annual commercial airline takeoffs and landings), so 

that both airport proximity and airport size (service level) are reinforcing 

positive factors. This interaction reflects the new empirical analysis described 

in Volume 2, Chapter 4. The measure also requires a database of commercial 

airport facilities from the FAA, and a highway network with shortest time path 

travel times. This has now been implemented in LEAP. 

• Drive time to Commercial Rail Intermodal Terminal – This is a measure of the 

highway drive time from the county population-weighted center to the nearest 

freight truck-rail intermodal terminal with regularly scheduled commercial 

scheduled freight train service. It requires a database of commercial truck-rail 

intermodal interchange facilities (from USDOT) and a highway network with 

shortest time path travel times. This has now been implemented in LEAP. 

• Drive time to Commercial Marine Port – river to sea – This is a measure of the 

highway drive time from the county population-weighted center to the nearest 

marine (river or sea) port with regularly scheduled commercial marine ship or 

barge service. It requires a database of commercial marine terminals with 

regular service (from USDOT) and a highway network with shortest time path 

travel times. This has now been implemented in LEAP. 

• Labor Force Scale Rating – This is a measure of the population or workforce 

living within 40 minutes drive time from the county population-weighted 

center. A minimum level of workforce is needed to attract warehousing, 

wholesaling and related logistics-related industries, as shown in Volume 3, 

Chapter 4. Thus this measure can be interacted with the preceding four 

transportation access measures to develop more refined measures of potential 

area attractiveness for growing supply-chain based activities t an area. 

3.5 Amenity & Cultural Asset Growth Paths 

Amenity and cultural assets are “quality of place” features that can serve to attract new 

households to an area for a tourist visit or as a retirement destination. The attractions 

can be climate, interesting mountains or water features, and/or developed cultural 

activities or recreation venues. The plan is to improve LEAP by providing each 
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county or group of counties (comprising a region or Local Development District) with 

amenity and cultural asset activity ratings, for both existing conditions and future 

potential opportunities. 

Rating Existing Situation. A description of amenity and cultural asset-based 

economies is provided in Volume 1. It indicates that this type of growth path is 

characterized by a concentration of lodging, meal and/or recreation activities, serving 

either day visitors or overnight visitors. This leads to the following proposed 

indicators of existing conditions: 

• Economic Base: Lodging, Restaurants and Recreation Concentration – This is 

an index reflecting the extent to which local lodging (hotel, motel and 

camping), meals (restaurants, bars and takeout establishments) and recreation 

services have a higher concentration of employment in the study area than the 

per capita statewide average for those activities. This is reflected in a 

composite “Location Quotient” for those visitor-serving industries. This has 

now been implemented in LEAP. 

• Housing/Population Base: Retirees – This is an index reflecting the extent to 

which the local area has a higher share of population that is retired and living 

in the region shorter than five years. It draws information from the US Census 

Bureau and their Current Population Survey. 

Rating Potentials for Future Amenity & Cultural Asset-Based Growth. 

Discussion of the determinants of amenity and cultural asset -based economies are 

provided in Volume 1, as well as the case study of asset-based growth (e.g 

Chautauqua County, NY and the Corridor K region) in Volume 2. These documents 

indicate that this economic growth path depends on having some combination of: (1) 

desirable climate, (2) interesting water, mountain or other scenery features, (3) 

interesting cultural, creative or recreational visitation sites and (4) access to a nearby 

population market for day trips. In the case of some large regional or national draws, 

it may also depend on (5) highway and airport for long-distance trips. 

Unfortunately, there is no good database that can identify the locations of man-made 

sites of interest, and particularly places where there is a potential for future 

development of cultural, creative or recreational attractions (factor #3 above). 

However, it is still possible to obtain or derive data relating to factors #1-2 and #4-5 

above, and those factors lead to the following diagnostic indicators: 

• Climate Rating – Composite ratings of outdoor temperature comfort levels can 

be used as a factor affecting the potential for outdoor activities including 

tourism and recreation. This information can be drawn from NOAA data on 

temperature conditions in weather stations across the country, covering all 

states and metropolitan areas. For each location, their database provides 

monthly data on temperature averages and ranges, as well as heating and 

cooling “degree-days” (indicators of comfort levels for outdoor activities and 
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need/cost of running heaters and air conditioners if indoors). This database has 

now been assembled for potential use in LEAP. 

• Physical Amenity Rating – Composite ratings of physical amenities can be 

used to represent the attraction of an area as a place to live. The ERS-USDA 

“Natural Amenities Scale” is an index reflecting the extent to which each 

county offers topographic variation (hills and mountains) and water areas 

(lakes, rivers and seacoasts), as well as temperate weather and low humidity. 

It is provided by the Economic Research Service (ERS) of the US Dept. of 

Agriculture (USDA). This dataset was obtained and used in the Volume 3, 

Chapter 5 study by MIT, though that use was in the context of predicting 

economic distress (where it was not a significant explanatory factor) rather 

than the context of assessing amenity-based tourism and retirement activities 

(where it would be a much more relevant factor). It is now available for 

potential use in LEAP. 

• Major Highway Access – Good highway access is needed for development of 

most tourism and visitor attraction sites. This can be measured in terms of 

distance from the county population-weighted center to the nearest four-lane or 

interstate level highway. That is the same measure discussed earlier for 

evaluation of supply chain growth potentials. 

• Urban Rating – Urbanized areas could provide a larger density of population 

and higher likelihood of multiple attractions for some types of day-trip 

tourism. Degrees of urbanization can be measured in terms of the ERS/USDA 

“Rural-Urban Continuum Codes” -- a classification scheme of nine steps that 

distinguishes metropolitan counties by population size and non-metropolitan 

counties by degree of urbanization and adjacency to metropolitan areas. The 

urbanization measure was used in the Volume 3 study of general spatial 

influence factors. However, the role of this factor particularly for tourism and 

amenity based development is not yet proven. 

3.6 Learning and Technology Growth Paths 

Learning and technology growth paths are forms of economic development that 

leverage the collective knowledge of specialized technologies and/or the 

entrepreneurial base that is embodied in the residents and workforce of a region. 

These features are typically the result of two factors: (1) specialized workforce 

training, including experience with technical applications and/or commercialization 

processes, and (2) strength of specialized supporting systems such as colleges, 

research & development facilities, financial institutions and high levels of broadband 

availability and usage. The plan is to improve LEAP by providing each county or 

group of counties (comprising a region or Local Development District) with learning 

and technology-based ratings for both existing conditions and future potential 
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opportunities. 

Rating Existing Learning-based Setting. A description of learning and technologybased economic growth is provided in Volume 1. It indicates that areas with this type 

of economic growth path are often characterized by having a larger than normal 

concentration of activity in supporting institutions, including graduate level higher 

education institutions, private research laboratories, and science-based industries (such 

as pharmaceuticals and computer electronics). This leads to the following proposed 

indicator of existing conditions: 

 

• Economic Base: Education, Research and Development –This is an index 

reflecting the extent to which higher education institutions, research and 

development laboratories and science-based industries account for a higher 

portion of employment in the study area than the statewide average for those 

activities. This is reflected in a composite “Location Quotient” for education, 

research and technology industries. 

Rating Potentials for Future Learning-Based Growth. Discussion of the 

determinants of learning and technology-based economies are provided in Volume 1, 

as well as the case study of technology development (Morgantown-Fairmont WV) in 

Volume 2. They indicate that key factors affecting success in learning-based 

economic development are: workforce training and entrepreneurship, supported by 

concentrations of educational institutions and research centers, availability of 

broadband technology, and availability of financing options. Those factors lead to the 

following diagnostic indicators: 

• Four Year Colleges (spatial lag) – This measure is defined as the number of 

students attending four year colleges and graduate programs in the specified 

county and surrounding counties. It is constructed parallel to the “spatial lag” 

variable, defined earlier to measure local concentrations of activity that serve a 

broader “hinterland” region. A database listing the names and addresses of all 

colleges is provided by the US Dept. of Education. This measure is being 

processed and will soon be programmed as an addition to LEAP operations. 

• College Graduates – This measure is defined as the portion of the active 

workforce in a given county that has completed at least four years of college, 

relative to the national average. It is derived from US Census Bureau data. 

The rating can also be scaled by workforce size, if desired. The basic measure 

has now been implemented in LEAP. 

• Broadband Access – This measure is defined through a 0 – 4 scale reflecting 

the number of competing service companies offering broadband access in a 

given area. It is compiled from FCC and telecom/cable industry sources. The 

premise behind this measure is that greater availability and more competition 

lead to increased coverage and reduced prices compared to places where such 

competition is not available. This measure has now been implemented in 
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Sources of Growth in Non-Metro Appalachia page 26

LEAP. 

• Entrepreneurship – This measure reflects the fact that some areas have 

attracted a population base that exhibits notably higher than normal rates of 

entrepreneurship. This may be due to some combination of location isolation, 

cultural traditions, local institutions or historical factors. The Federal Reserve 

Bank of Kansas City has developed “Entrepreneurship Indicators” on the basis 

of BEA and Census data about local employment and income generated by 

non-farm proprietors. 

3.7 Natural Resource & Other Growth Paths 

There are other economic growth paths that need to be acknowledged, though we do 

not develop measures of their status or growth factors for reasons that are explained 

below. 

• Natural resource-based economic growth is that made possible by the 

existence of mineral, lumber or agricultural resource assets. Historically, the 

American economy through the mid nineteenth century was based primarily on 

natural resource development, and many rural areas across America still 

depend on it. A problem that plagues many rural areas, but particularly rural 

Appalachia, is that the coal mining and lumber/wood resources that were 

previously the mainstay of local economies is no longer a source of job growth 

(while industry output has grown by investing in technology-enhancements.) 

Therefore, most of these areas are making attempts to diversify away from a 

resource-based economy. Accordingly, we do not focus heavily on natural 

resources among the various forms of asset-based growth in this study (the 

exception is the case study in Volume 2 for Pike County, KY) for the 

Appalachian Regional Commission. 

• Government and institution-based economic growth is made possible by the 

external decision of government agencies (federal and/or state) and private 

institutions to site major regional or national facilities in an area. There are 

some notable examples of military bases, government office facilities, colleges 

and research labs that have chosen to locate in rural, isolated areas within 

Appalachia and elsewhere. Some of these decisions were made in part to help 

“jump-start” a local economy. However, the political, personal and 

institutional preference factors underlying these decisions are usually outside 

of local control. Hence, we do not develop measures to reflect the potential for 

this form of economic growth. 
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Sources of Growth in Non-Metro Appalachia page 27

3.8 Supporting Local Economic Development 

Use of LEAP. The Local Economic Assessment Package (LEAP) is being used by 

the Appalachian Regional Commission as a means of providing its Local 

Development Districts (LDDs) and the Appalachian state-level economic development 

agencies with both relevant information and diagnostic tools for improving economic 

development. Adding measures of the current growth path status of local areas, as 

well as ratings of potential opportunities for pursuing various growth paths will impart 

new capabilities to the LEAP analysis. Instead of just offering diagnostics and targets 

in terms of industries, it will also be capable of offering diagnostics and targets in 

terms of growth paths. By having both capabilities, ARC, the LDDs and the state 

agencies can be empowered to make more informed and better targeted economic 

development strategies for local development. 

Further Research and Enhancement of Tools. The improvements laid out in this 

chapter are an initial attempt to expand the economic development analysis tools and 

assessment methods beyond the industry-based cluster targeting that has been a 

mainstay of the economic development field for many decades. The research 

discussed in Volume 1, the case studies in Volume 2 and the new empirical analysis 

covered in Volume 3 all point to a common conclusion -- that local economic 

development success comes from the confluence of many factors, and further work is 

clearly necessary to further untangle their roles and effects. 

In particular, the work completed in those three earlier volumes also moves forward 

our state of knowledge and understanding of economic development growth factors. 

In particular, it confirms the importance of understanding “spatial linkages” – factors 

that tie the economic development success of an individual county or a region to the 

broader economic development patterns and trends of their neighbors – proximal and 

/or economic. That work also confirms the key role that accessibility and Appalachian 

transportation improvements can make in affecting all of the various economic 

development growth paths. As new research is completed, further improvements can 

be made in our diagnostic measures of growth path opportunities and targets.
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Sources of Growth in Non-Metro Appalachia page 28

4 CONCLUSIONS FROM THE 

STUDY OF SOURCES OF 

GROWTH

4.1 Lessons learned from the Comprehensive 

Examination 

This multi-year study effort on non-metro Appalachian growth prospects combined 

several research techniques to examine various hypotheses on the growth processes 

that may be most compatible with local conditions and assets (both physical and 

human-made). The working hypothesis of this inquiry is that when local economic 

development efforts are better informed by the use of the new tools and insights about 

what they have to work with – including the attributes of neighboring 

communities/economies – such efforts will yield better suited opportunities for growth 

than when planned in isolation. 

That being said, it is not always transparent to even local economic developers what 

the explanation is for one rural county’s success story. Even if that evidence can be 

articulated retrospectively, it is likely that the perspectives would differ in terms of the 

identifying the most critical factor(s) to the economic turnaround and in the sequence 

of socio-economic/policy events – whether local, regional, national or global. 

4.2 Lessons from the Case Studies 

In the few instances where case study results did not entirely agree with the expected 

model generated patterns of growth, we gained a new understanding of how 

neighboring economies’ spatial influence exert adverse backwash effects on the case 

study economies (such as the influence of the Cincinnati metro area on the 

development path of Scioto County, OH). In essence we learned about the limitations 

of the spatial economic base modeling diagnostics and reinforced the validity of 

findings from other research, such as by Feser (2005). This discovery process added 

another dimension to our understanding of the processes influencing the current and 

desired economic performance in non-metro Appalachia. In short, while timing and 
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Sources of Growth in Non-Metro Appalachia page 29

patience are key features of every strategy plan, and its associated outcomes, these 

results underscore the need for a periodic reassessment of how the local economy and 

labor market are changing in relation to neighboring economies. Market access 

opportunities are rarely evenly distributed which makes it all the more imperative to 

(a) improve what you can, and then with the remaining access limitations (b) plan 

regionally so that the growth opportunities that have emerged for one county (e.g. Pike 

County, KY) may exert more of a beneficial spillover to distressed, neighboring 

counties. 

Engaging the educational system – from K-12 to leveraging certificate programs and 

community colleges – has to be one of the first steps to re-conditioning the existing 

workforce and preparing the county’s future working age residents for meeting 

regional employment demand – especially if job growth is slow to ignite in the home 

county. This also requires that the workforce has access to the transport infrastructure 

to connect to the employment center. This evolution of events was most clearly 

demonstrated in the case of Alabama’s success in building its current auto assembly 

cluster. 

Both the educational resources and transportation infrastructure of a county can evolve 

to take on more dynamic roles to shaping local and regional growth outcomes. As the 

case of the Morgantown-Fairmont high-tech development demonstrated, the R&D 

investment and population that are drawn to locations with higher-education 

institutions and government research facilities are rewarded with broader networks 

(e.g. social capital), commercialization of research, business start-ups, and 

opportunities for higher wage job formation. These are the benefits conferred on 

learning-based economies. 

In addition to Alabama’s responsive educational system, auto assembly manufacturing 

took hold throughout the state as a result of plentiful development sites (many as 

greenfields and flat terrain) and the fact that Alabama’s ample highway network 

allowed in-state auto manufacturing firms to participate in several national supply 

chains as well. 

4.3 Key Findings from Empirical Studies 

These studies shed new light on what causes some non-metro Appalachian counties to 

make economic strides forward, while others remain distressed. Key objectives of 

these studies were an examination of the role of economic linkages among counties, 

and the effects of demographic factors, industry mix, mountain topography, market 

access and highway improvements, among other factors, in affecting relative 

economic performance. Of particular note is the exploration of new techniques to 

examine spatial and economic linkages in a region to help diagnose complementary 

development prospects for the economic base of neighboring counties. Another 

contribution is the empirical study of the economic development impact of the ADHS, 

which provides evidence on the significant impact of new corridors in the system, as 
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Sources of Growth in Non-Metro Appalachia page 30

well as the continued importance of manufacturing in accounting for the growth 

differences of Appalachian counties as compared to their socioeconomic nonAppalachian twins. The set of empirical studies provide important insights into how 

spatial measures interact with demographic, industry, geographical and transportation 

variables to influence economic performance and growth rates. Taken together these 

findings provide better calibrated economic analytical techniques that can help to 

identify relevant development paths given the assets, linkages and constraints of the 

counties within their regional neighborhood. 

Economic Base Studies: 

The spatial linkage economic base model provides new tools to diagnose the economic 

development prospects of counties relative to their neighbors and the larger 

surrounding region. The principal distinction between the classical export-base model 

and the modified spatial model is that in the spatial model, the export-base is 

segmented into two components where the “local” oriented export-base is linked 

directly to “global” export activities in the neighboring counties. In addition, this 

approach introduces the concept of regional neighborhood which can be understood as 

the sphere of immediate economic influence of a county’s economy exerted via 

common infrastructure, economic linkages, shared labor pools, etc. Because most of 

these effects diminish with geographical distance, it is reasonable to assume initially 

that most of these cross-county border interactions affect neighboring counties. 

The spatial export base method provides insights into the development potentials of 

the distressed and transitional counties’ export-base, but the methodology is perhaps 

best used on a regional and sub-regional basis rather than on a county basis. While this 

analysis can be used to create profiles for each county, highlighting the multipliers, the 

top industries for each county, etc., users should not construe these county profiles as 

policy prescriptions since by definition the profiles reflect the influences of 

neighboring counties. Instead this approach should be used for a cross-county 

comparison to understand the relative characteristics of these counties such as the 

degree of industry diversification or concentration, or the regional linkages. This 

application may be useful in identifying potential “growth hubs” that possess strong 

spatial and economic linkages with their neighbors and the potential to generate 

regional growth, but caution is recommended given important data and modeling 

limitations as evidenced by the case study of Scioto County, OH in Volume 2. 

Moreover, this model has analytical limitations in applications to remote, rural 

counties. In this context it is recommended that this method be applied to groups of 

counties for case studies that examine the spatial forces at work on each county in a 

specific neighborhood. 
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Transportation Access Studies: Several facets of these studies examine the impact of 

different types of transportation accessibility in affecting the economic performance 

and prospects of counties. 

The Impact of the ADHS: The key empirical finding of twin county study on the 

impact of ADHS is that by 2000, the performance of ARC counties with open ADHS 

segments had higher income growth relative to their non-Appalachian twins, with the 

ADHS counties posting 200% more income growth over the 1969-2000 period. This 

finding can be compared to the growth rate gap between all ARC counties and their 

twins. By 2000 income in all ARC counties had grown 131% more since 1969 than in 

the non-Appalachian counties; earnings growth was 96% higher; population growth 

was 9% higher; and per capita income growth was 36% higher. Thus, this study 

showed that using survey-based data overcame shortcomings in earlier analyses to 

demonstrate a robust statistical link between ADHS investments and differential 

income and earnings growth between ARC counties and their twins, particularly for 

new construction. These findings also suggest that there is a considerable lag between 

highway investments and their full effect on economic growth. 

The twin county study also provides insight into the uneven performance in the ARC 

region during this period: performance in the northern part of the ARC region not only 

lagged its non-Appalachian twins but also the rest of Appalachia, and smaller 

metropolitan areas fell far behind their non-Appalachian counterparts. By contrast, the 

study of long-term trends also showed that the states performing best relative to their 

non-Appalachian “twins” (i.e., Georgia, Kentucky, and South Carolina, and 

Tennessee) appeared to do so in part on the strength of their performances in 

manufacturing. This reinforces the finding that manufacturing clusters are still an 

important source of economic growth. 

Airport Accessibility: This study found that there are the types of industries that can be 

expected to situate near airports because they rely on business air travel for meetings 

with either clients or other office locations of their business. Businesses that appear to 

particularly value reductions in travel time to airports include wholesale trade, paper 

manufacturing, insurance, and professional services. While these findings on airport 

access make sense, there is need for further analysis of the business attraction 

relationship to airport access – separating improvements in access time, distance, type 

of highway access and/or airport service levels. Furthermore, there is a need to further 

explore the ways in which market scale and airport access may be better measured by 

industry employment shares, concentration ratios or total size of the industries. 
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Demographic and Spatial Influences on County Economic Performance: These 

econometric studies provide new insights into how spatial influences interact with 

demographic, industry, geographical and transportation attributes of a county to 

influence its economic performance and rate of growth. First, the studies demonstrated 

the importance of explicitly modeling spatial dependencies among counties in order to 

avoid overstating the influence of other non-geographical factors that account for 

growth differences within the Region. In addition, using adjacency to measure spatial 

dependency may not be the best way to account for spatial spillover effects among 

counties, particularly knowledge-based spillovers such as the diffusion of information, 

innovations, and technical collaboration which are not as simply contained by 

adjacency.

Second, the analyses confirm the importance of other measures of connectivity and 

interdependence, particularly major highway and rail infrastructure connecting the 

localities to population centers or resource users. Work force accessibility as 

measured by commuting times vary in their impact on economic performance 

according to county types, with commuter accessibility mattering most for 

micropolitan counties, registering as somewhat important for Metro counties, and as 

not significant for non-core, non-metro counties. With respect to geographical factors, 

the most salient finding is that metro areas’ economic performance are least influenced 

by geography (the result of infrastructure and population-economic density having 

diluted the constraints of topography). In contrast non-metro areas, particularly noncore counties with neighboring counties that have relatively more rugged terrain, may 

benefit economically from accessibility improvements as shown in the case study in 

Volume 2 of Pike County, KY as a trade center, and Cherokee County (Murphy, NC) 

as a trade center in the Corridor K region. 

Third, rather than trying to identify a single, complex model for explaining economic 

performance and growth differences across all county types, a potentially more useful 

inquiry was to identify the most relevant type of model for a county depending upon 

the characteristics of that county and its neighbors. Indeed, while the results from the 

general models developed in this report underscored the relevance of spatial modeling, 

the findings also indicate the need to disaggregate counties into metro, micropolitan 

and non-core, non-metro types. 

Once the cross sectional analysis was disaggregated by county type, the separate 

analyses demonstrated that metro, micropolitan and non-core counties exhibit 

considerable variation in economic performance and growth, with varying responses 

to demographic, industrial, geographic and transportation accessibility factors. Yet, 

analyses based on county types pose new challenges in modeling spatial relationships, 

as the researchers indicated, and leave open to question certain findings since spillover 

effects are not being explicitly modeled, particularly for demographic variables such 

as educational attainment (which are probably considerable for micropolitan counties). 

The finding of the positive employment growth effect of industry concentration within 

the micropolitan county makes intuitive sense in that it may reflect the influence of 

cluster-type development, while the negative employment effects of industry 
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Sources of Growth in Non-Metro Appalachia page 33

concentration in non-core, non-metro counties undoubtedly reflects the effect of a 

narrow economic base due to the high dependence on one industry. The lack of 

influence of industry concentration in metro counties follows from the higher 

diversification of the economies of such counties. Nevertheless, the findings on 

industry mix (based on factor analysis) raise more questions than they answer, and 

clearly require more exploration, as do the specifications of these models which lack 

explanatory power, particularly for explaining employment growth in micropolitan 

and non-core, non-metro counties. 

Fourth, a few conundrums were uncovered by these analyses, particularly the lack of 

explanatory power of natural amenities to account for economic performance 

differences based on the natural assets of the counties. The lack of explanatory power 

for natural amenities suggest the need for other types of spatial modeling, perhaps 

based on transportation networks and improvements between metro and non-metro 

counties. Since the presence of natural amenities is largely invariant over time, it 

makes sense to model relevant changes in infrastructure that may affect the 

accessibility or value of these assets to the non-resident population. The influence of 

knowledge-based spillovers may require an understanding of networks that link, for 

example, higher education institutions with research and industry centers. 

Fifth, entrepreneurial measures performed reasonably well in the economic health 

models indicating that increases in income per non-farm proprietor were positively 

correlated with lower distress levels, while an increase in dependence on proprietors’ 

income relative to wage and salary income were correlated with increased distress. 

These results seem to reflect the differences stemming from greater entrepreneurial 

opportunities in counties where proprietors income in growing, while increases in 

proprietors income relative to wage and salary incomes suggests entrepreneurship of 

necessity due to a lack of wage and salary employment growth. 

Finally, a separate analysis of the relationship of the size of the population base on the 

business mix of a county shows that population thresholds matter, particularly for 

transportation, financial services, publishing, professional and technical services, and 

real estate. These findings are useful in framing and targeting local strategies for both 

business recruitment and entrepreneurial strategies that non-metro counties might 

pursue. 

4.4 Implementing Findings for Strategy 

Planning 

Many of these modeling findings provide an analytical foundation for applying better 

calibrated economic techniques to identify relevant development paths given the 

assets, linkages and constraints of the counties within their regional neighborhood. 

Counties in micropolitan areas, and perhaps those adjacent to micropolitan areas or 
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Sources of Growth in Non-Metro Appalachia page 34

linked via major transportation corridors and supplier chains, should be viewed as the 

prime candidates for applying many of these techniques and insights. Many of the 

growth factors that were identified in the various facets of this study are amenable to 

further refinements by augmenting the diagnostic capabilities of the EDR-LEAP 

model which is available to all local development district entities as an on-line 

research tool. Many of the growth path specific attributes are already now 

implemented in the EDR_LEAP tool and the current market access logic of EDRLEAP implicitly begins to address spatial linkage potential, though this could be done 

with greater detail as the data resources become available. Having evolved from the 

first ARC-Highway Opportunities model, the EDR –LEAP model is an accessible 

economic development analysis framework that accounts for the role of overcoming 

market isolation and points towards different opportunities for an area’s working age 

residents and businesses. The result of including better understood metrics that depict 

the spatial influences exerted on a county, or its growth path propensity would seem 

promising to improve how opportunities are both understood and identified. There 

may be opportunities to complement such applications with additional case study work 

that applies spatial econometric analysis and regional input-output analysis to better 

explicate the nature of these spatial relationships among non-metro counties and the 

implications on how economic activity is organized. 

4.5 Future Study Directions on Non-metro 

Growth Processes 

The following areas have emerged for future study as a result of where this current 

research effort has concluded. The impact analysis of the ADHS suggests the need for 

more detailed examination of the time lags between the completion of corridors and 

the economic impacts, including applying spatial analysis to assess any backwash or 

relocation effects. The augmented export base model could be revisited to improve the 

level of resolution regarding the nature of cross-sector interactions under-pinning the 

spatial linkages currently detected. Further spatial modeling techniques should be 

developed to explore the spillover effects for different county types, as well as 

developing new spatial modeling approaches for amenity and knowledge-based 

spillovers. Finally, nowhere in the current study undertaking was the role of fiscal 

capacity in growth outcomes explored. To do so will require overcoming the current 

data constraints and harnessing a good cross-section of fiscal data.