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 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.

Sources of Growth in Non-Metro Appalachia 
Vol.3 Statistical Studies 

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.................................................................................35

3.4 USES AND LIMITATIONS OF THE FINDINGS ............................................................................40

3.5 SURVEY INSTRUMENT ...........................................................................................................41

4 POPULATION BASE & ACCESS TO AIRPORTS ...............................................................45

4.1 INTRODUCTION......................................................................................................................45

4.2 (A) POPULATION BASE: METHODOLOGY ..............................................................................45

4.3 (B) POPULATION BASE: RESULTS..........................................................................................48

4.4 (B) AIRPORT ACCESS: METHODOLOGY:................................................................................54

4.5 (B) AIRPORT ACCESS: RESULTS ............................................................................................55

4.6 USES AND LIMITATIONS OF THE FINDINGS ............................................................................60

5 SPATIAL INFLUENCES IN COUNTY ECONOMIC PERFORMANCE............................61

5.1 INTRODUCTION......................................................................................................................61

5.2 EXPLORATORY STATISTICAL ANALYSIS................................................................................61

5.3 MODELS TO PREDICT COUNTY ECONOMIC LEVEL.................................................................64

5.4 MODELING CHANGES IN ECONOMIC HEALTH........................................................................74

5.5 USES AND LIMITATIONS OF THE FINDINGS ............................................................................83

5.6 REFERENCES .........................................................................................................................86

Sources of Growth in Non-Metro Appalachia page i
Vol.3 Statistical Studies 

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. 

Sources of Growth in Non-Metro Appalachia page ii
Vol.3 Statistical Studies Ch.1 Introduction 

1 INTRODUCTION 

1.1 Background 

Role in the Sources of Growth Project. Volume #3 presents results of four empiri

research studies conducted as part of the Sources of Growth project. These

cal

studies

uild directly on the discussion of theory and prior research which are covered in 

ial

conomic growth and development. 

ll four also utilize some form of econometric modeling and/or geographic 

formation system to examine these relationships. 

ion an th Paths 

b

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 spat

location or access characteristics and its pattern of e

A

in

Exhibit 1-1. Locat d Access Factors Affecting Economic Grow

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) 

erminal, air or marine port 

tance

• Distance to highway, rail t

• Same day delivery dis

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

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 metho

in

ds for

Sources of Growth in Non-Metro Appalachia page 1
Vol.3 Statistical Studies Ch.1 Introduction 

on

and to advance our understanding of how ARC 

vestments promote economic development by reducing isolation and increasing 

local capacity for growth. 

examining spatial relationships among counties. This research thus aims to build up

prior ARC-funded research

in

1.2 Study Summaries and their Foundations

Extending Prior Research. It is important to note how these research efforts build 

upo p

•

djacent

n 

-Distressed Counties in Appalachia,” 2000) and revisits the 

evaluation of county-level “spatial regional multipliers” based on more recent 

 the 

y its

mic compositions that tend to 

inder each in benefiting from growth stimulated in a neighboring economy 

er

ongalia County’s metro status explains in large part why 

is county has a local spatial linkage multiplier that is more than double that 

ounties 

his result for Cherokee County can 

be attributed to the trade center role exerted by the City of Murphy on 

n rior studies.

The first study focuses on enhancing our understanding of relationships 

between counties that serve as rural trade centers (economic hubs) and a

counties that are served by them (economic spokes). This work by Ayma

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

employment data. 

The Pike County case study of Volume 2 can be better understood from

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 diversif

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 econo

h

(low total spatial linkage multiplier values). 

The Morgantown-Fairmont case study on the other hand now can be furth

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). Mon

th

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 c

which have competitive economic status. T

Sources of Growth in Non-Metro Appalachia page 2
Vol.3 Statistical Studies Ch.1 Introduction 

•

a

ch

y

ntly contribute to the

differential income and earnings growth experienced from 1969 to 2000 for 

eas

ies exert an adverse

rban backwash effect on Scioto County that challenges any geographic 

tter

palachian counties that 

terminate the corridor (Hamilton Co., TN and Buncombe Co., NC) it will not 

• of

g

try

EDR

the prior ARC report, “Handbook for Assessing 

Economic Opportunities from Appalachian Development Highways” by 

•

ts a pilot effort to demonstrate the 

value of spatial analysis to better understand factors affecting the economic 

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

long period of time. This work by Teresa Lynch of EDR Group utilizes time

series regression techniques. It updates and extends a direction of resear

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 highwa

capacity and access was tested and found to significa

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 ar

of Cincinnati, Columbus. These extra regional econom

u

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 be

market reach of the region’s eco-tourism and cultural heritage assets and/or 

eventual economic integration into the metro Ap

occur without better access through the region. 

The third study focuses on enhancing our understanding of the relationship

business mix to (a) the size of the local population base and to (b) accessin

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 indus

mix. This work by Teresa Lynch, Glen Weisbrod and Tyler Comings of

Group uses non-linear regression techniques and geographic information

systems. It builds upon

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 represen

Sources of Growth in Non-Metro Appalachia page 3
Vol.3 Statistical Studies Ch.1 Introduction 

ty

d by

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. 

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 Coun

KY and its neighbors in the Big Sandy Area, Scioto County OH embrace

two rivers, Corridor K’s Cherokee County NC as trade center to

Sources of Growth in Non-Metro Appalachia page 4
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

G OF

GROWTH 

PATTERNS

“Economic Base Modeling to Test Growth Patterns” 

Ayman Ismail, 

Massachusetts Institute of Technology 

2 ECONOMIC BASE MODELIN

HUB AND SPOKE 

by 

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 count

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 empl

y’s

oyment growth 

erformance (based on the strength of the regional employment multiplier) from 1992 

achian 

o as

ents in 

ter

-

 

p

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 Appal

Regional Commission (ARC) in 2000, Smirnov and Smirnova (hereafter referred t

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 establishm

which the county specializes, where a county’s employment in that industry is grea

than the average for the rest of the country (i.e., it has an employment location

quotient greater than one).2

 Second, they identify the industrial-mix of economic1

 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. 

Sources of Growth in Non-Metro Appalachia page 5
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

gy

mic-bases of 

istressed and near-distressed counties using regional employment multipliers and 

ased on their empirical analysis, S&S, identify several relationships and patterns that 



l 

een the distressed and

near-distressed counties against the more prosperous economies in Appalachia 

 e

1.79,

unties with higher values of regional 

multipliers tend to perform better and have higher economic growth potential than 



ntial for

onomic distress in affected counties. More 

dynamic and technologically advanced industries are virtually non-existent in the 



Distressed and near-distressed counties have very weak local and global economic 

 g.

lf-reinforcing

bases and regional spatial effects as important factors in shaping the regional 

economies of distressed and near-distressed counties. Third, they establish a typolo

for key parameters that characterize the economic potential of the econo

d

strength of spatial linkages; the latter measured by a spatial multiplier. 

B

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 transitiona

counties. The S&S comparison of economic-bases betw

reveals significant disparities in their key parameters. 

Regional employment multipliers show a direct (positive) relationship between th

level of economic distress and the strength of the economic-base. In 1996, the

average regional multiplier for distressed and near-distressed counties was

which is 11 percent lower than the average regional multiplier of 1.99 for all 

Appalachian counties. Distressed co

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 pote

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 ec

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. 

linkages that lead to their limited economic opportunities and slow growth rates.

The gap between distressed and prosperous counties in Appalachia is widenin

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 se

Sources of Growth in Non-Metro Appalachia page 6
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

-

rm

hich highlights the need for policies and initiatives that promote 

ify the

b or

Ex

C

Distres egional

s (13 counties) – Type

economic distress: (1) small size of rural economies, (2) non-diversified economic

bases, (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, fo

spatial clusters, w

closer economic ties between neighboring counties. Exhibits 2-1 and 2-2 ident

counties whose economies have been portrayed as functioning as a regional hu

regional spokes.

hibit 2-1. Potential Regional “Hubs” from among Select Appalachian 

ounties, Smirnov (2000)

sed and Near-Distressed Counties with Grow

Hub

th Potential as R

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. 

Sources of Growth in Non-Metro Appalachia page 7
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 8
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 9
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 10
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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); ( ) is the portion of the export-base employment attributed to 

exports to the neighboring counties, and ( ) is the portion of the export-base 

attributed to global exports (exports beyond neighboring counties). 

Es

Eg

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 is the size of the global export-base in the neighboring regions (counties), 

coefficient b (where 0 < b < 1)

( )

l Eg

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) 

Sources of Growth in Non-Metro Appalachia page 11
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 12
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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, is employment of industry j in county i ; is total employment in county i; is employment 

of industry j in the whole United States; 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.

Eij Ein Ej

En

Sources of Growth in Non-Metro Appalachia page 13
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 and for county A: Eg Es

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 and . Eg 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. 

Sources of Growth in Non-Metro Appalachia page 14
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 15
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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.

Sources of Growth in Non-Metro Appalachia page 16
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 17
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 18
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 19
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 20
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 21
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 22
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 23
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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 

Sources of Growth in Non-Metro Appalachia page 24
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

 

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. 

Sources of Growth in Non-Metro Appalachia page 25
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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.

Sources of Growth in Non-Metro Appalachia page 26
Vol.3 Statistical Studies Ch.2 Hub-Spoke Patterns

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. 

Sources of Growth in Non-Metro Appalachia page 27
Vol.3 Statistical Studies Ch.3 Twin Counties Update

TWIN COUNTIES STUDY UPDATE 

“The Impact of Highway Investments on Economic Growth in the Appalachian 

Region, 1 Study”

evelopment Research Group 

3

969-2000: An Update and Extension of the Twin County

By Teresa Lynch, Economic D

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

igher rates of income, population, and per capita income growth than otherwise 

timing 

o the blunt

on length, number of lanes, access type, number of signalized intersections,

nd number of interchanges. All thirteen ARC states participated fully in the survey

to

mance in the northern part of the ARC 

h

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

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 t

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, secti

a

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

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: perfor

Sources of Growth in Non-Metro Appalachia page 28
Vol.3 Statistical Studies Ch.3 Twin Counties Update

s

ent of highway 

vestments. Using the improved highway measures afforded by our survey, we were 

versus

poor

is suggests that when characteristics of the 

roposed highway investments are properly measured, there is empirical support for 

claims that highway investments--here in the form of the ADHS investments--

regions lagged its non-ARC twins and across the region, smaller metropolitan area

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 measurem

in

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

replacement investment; length of segment relative to county size) generated 

explanatory relationships that were statistically significant and robust, whereas

measures of investment did not. Th

p

contribute to economic growth. 

3.2 Appalachian Growth, 1969-2000 

A key question for national policy makers and ARC members, partners, and sta

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 prox

what would have otherwise occurred (in terms of growth) without ARC funding. T

authors posit that once identified, the difference in the mean cumulative growth rates

informs us whether ther

ff

y

he

e are real growth gains for the Appalachian county. To 

omplete the study’s objective, the authors attempted to identify the causal factors 

n county

in, and (b) assessing how suitable each “match” is before advancing the growth 

in our current update of their analysis which follows. 

Eligibl unties for selection as a possible match were predicated 

on

 s population centroid had to be at least 60 miles away from the 

 Comparable growth in personal income, earning by sector over the period 

1950-1959 

c

(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-Appalachia

tw

analysis. The set of 391 non-Appalachian twin counties identified by Isserman and 

Rephann are used

e non-Appalachian co

the following:

The county’

Appalachian border 

Sources of Growth in Non-Metro Appalachia page 29
Vol.3 Statistical Studies Ch.3 Twin Counties Update

he

factual” for Appalachian growth over the 1969-1991 period in the 

bsence of ARC investments was confirmed statistically by the authors albeit with a 

iod:

and

w

siness Patterns

 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 t

optimal pair matches. The validity of the 391 match counties to serve ultimately as 

the “countera

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 per

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)

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 ho

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 Bu ) 

o. We also use the same control group, namely the “twin county” matches developed 

the

most recent REIS data. (The latter estimates are expected to differ from those of 

 

d

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

15

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. 

Sources of Growth in Non-Metro Appalachia page 30
Vol.3 Statistical Studies Ch.3 Twin Counties Update

ifferences between Appalachian counties and their twins for the 1969-2000 period. 

od;

ce

e

wth (aggregate and per capita) and earnings growth than 

riginally measured.

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 

d

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 peri

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 performan

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 th

southern ARC region had more noticeably outperformed their twin counties with 

respect to income gro

o

Sources of Growth in Non-Metro Appalachia page 31
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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. 

Sources of Growth in Non-Metro Appalachia page 32
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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. 

Sources of Growth in Non-Metro Appalachia page 33
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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. 

Sources of Growth in Non-Metro Appalachia page 34
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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 

Sources of Growth in Non-Metro Appalachia page 35
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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 

Sources of Growth in Non-Metro Appalachia page 36
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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.

Sources of Growth in Non-Metro Appalachia page 37
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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 

Sources of Growth in Non-Metro Appalachia page 38
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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. 

Sources of Growth in Non-Metro Appalachia page 39
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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 

Sources of Growth in Non-Metro Appalachia page 40
Vol.3 Statistical Studies Ch.3 Twin Counties Update

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. 

Sources of Growth in Non-Metro Appalachia page 41
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

ACCESS TO

AIRPORTS 

“Spatial ccess”

Teresa Lynch, Tyler Comings and Glen Weisbrod 

Economic Development Research Group 

4 POPULATION BASE &

Geography: Effects of Population Base and Airport A

by 

4.1 Introduction 

This chapter describes findings from two related studies. One examines the impa

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

ct of

other

tudies discussed in this report in that they focus on identifying determinants of a 

siness sectors.

addition, these two research studies are intended to shed additional light on the role 

e

t

order

irport, which must also be of a

inimum size to provide sufficient scheduled service. Thus, the role of thresholds 

and non-linear responses becomes important. 

s

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 bu

In

and importance of highway access in supporting economic growth. 

A major element of both research studies is that they focus on examining the existenc

of “threshold” or other non-linear effects. In other words, it would be expected tha

some types of business require a minimum labor market or customer market in

to select a location for a new plant. Similarly, some types of business may require 

locations within a particular travel time to an a

m

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. 

Sources of Growth in Non-Metro Appalachia page 45
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

y

t

) data which is common for many specific industries 

small rural counties. Using this dataset, we define and calculate an indicator of 

atio (i,c)

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 Count

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, withou

the problem of missing (withheld

in

relative business concentration: 

 Business Concentration (i,c) = Employment Share (i,c) * Attraction R

 where: 

 Employment Share = Employment (i,c) / Employment (Σ i, c) 

 .Attraction Ratio = Employment (Σ i, c) / Population (c) 

unty

n 

of

uotient 

hich represents national norms for industry mix), we make use of an Attraction 

ter

uld

r that industry, though that may be due to many factors including a high a 

n

tration

ion in this way, we make it possible to analyze the role of

opulation size in affecting the business concentration, while avoiding correlation 

between the two. 

 and i = NAICS industry, Σ i = sum over all industries, c=co

 which simplifies down to: 

 Business Concentration (i,c) = Employment (i,c) / Population (c) 

The reason for constructing this composite measure of relative business concentratio

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

trade areas. However, instead of using the denominator of the Location Q

(w

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 cen

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 co

would normally be interpreted as an indicator that the county is not a center of activity

fo

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 i

county population size, so that a small county can in theory have a high concen

of a given industry just as easily as a large or populous county. By normalizing the

business concentrat

p

Sources of Growth in Non-Metro Appalachia page 46
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 47
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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.) 

Sources of Growth in Non-Metro Appalachia page 48
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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

Sources of Growth in Non-Metro Appalachia page 49
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 50
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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%

Sources of Growth in Non-Metro Appalachia page 51
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 52
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 53
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 54
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

• 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 

Sources of Growth in Non-Metro Appalachia page 55
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

industries (which seldom use air travel). 

Exhibit 4-7. Effect of Ground Access Time to Airport 

Exhibit 4-8. Effect of Airport Size 

Sources of Growth in Non-Metro Appalachia page 56
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 57
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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

Sources of Growth in Non-Metro Appalachia page 58
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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

Sources of Growth in Non-Metro Appalachia page 59
Vol.3 Statistical Studies Ch.4 Pop. Base & Airport Access

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. 

Sources of Growth in Non-Metro Appalachia page 60
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: 

Prof. Joseph Ferreira, Jr., Ayman Ismail and Zhijun Tan, 

ts Institute of Technology 

“Task 1, Part 4: Empirical Analysis”” 

by 

Massachuset

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 

ccess and highway links in affecting those results, and (d) identify how these factors 

d to 

 the 

eoDa, spatial statistics software developed by the Spatial Analysis Laboratory (SAL) 

in the Geography Department at the University of Illinois, Urbana-Champaign. 

a

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 relate

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

variables using statistical software and mapping and spatial analysis tools available in 

ArcGIS, geographic information systems, SPSS statistical analysis software and 

G

5.2 Exploratory Statistical Analysis 

The analysis conducted for this study focused on the development of various forms o

regression models to assess the role of explanatory factors 

f 

in explaining and predicting 

atterns and trends in the economic well-being of non-metro Appalachian counties. 

f county data that we use include: 

p

Specifically, the types o
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Depen

•

ied a

le as 

for

• f change over time in terms 

of (a) the rate of growth or decline in the ELI rating, and (b) the rate of 

nty as a whole.

Indepe

• ographic data from 2000 for such variables 

•

 the 

y

f

activeness of geographic features 

•

d

EIS data on

ting

•

easures of population-based rural-urban continuum 

codes and urban-influence codes; and the delineation of metropolitan and 

micropolitan areas. 

dent 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 classif

”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 variab

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

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 o

employment growth rate in the cou

ndent (explanatory) variables:

Demographic data: US Census dem

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

counties. The terrain and elevation data are from the US Geological Surve

(USGS), the transportation data are from ARC and the US Department o

Transportation’s Bureau of Transportation Statistics. The natural amenity

scale is an index of the density of attr

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, an

2000. We also develop entrepreneurship indicators from BEA/R

the diversity and value-added components of earned income. Commu

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 m

Sources of Growth in Non-Metro Appalachia page 62
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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)

Sources of Growth in Non-Metro Appalachia page 63
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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 

Sources of Growth in Non-Metro Appalachia page 64
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 65

(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 

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. 

Sources of Growth in Non-Metro Appalachia page 67
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 68
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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% 

Sources of Growth in Non-Metro Appalachia page 69
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 70
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 71
Vol.3 Statistical Studies Ch.5 Spatial Influences 

(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 

Sources of Growth in Non-Metro Appalachia page 72
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 73
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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 

Sources of Growth in Non-Metro Appalachia page 74
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 75
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 76
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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.) 

Sources of Growth in Non-Metro Appalachia page 77
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 78
Vol.3 Statistical Studies Ch.5 Spatial Influences 

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. 

Sources of Growth in Non-Metro Appalachia page 79
Vol.3 Statistical Studies Ch.5 Spatial Influences 

Sources of Growth in Non-Metro Appalachia page 80
Vol.3 Statistical Studies Ch.5 Spatial In

Sources of Growth in Non-Metro Appal

fluences 

achia page 81

Exhibit 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) 

T

heme 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 0.028Worker Access EMP40MINK " EMP50MINK 0.026 0.171 2.049 0.043" EMP60MINK 0.003 0.267 3.692 0.000Residence RADJ97_EMP Amenity ASCALE Infrastructure ROADWT Terrain SLOPE " AVG_SLOPE6 " AVG_SLOPE1 

Steps** 4 7 5Adjusted 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 A

RC 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

 

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, 

Sources of Growth in Non-Metro Appalachia page 82
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

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.

Sources of Growth in Non-Metro Appalachia page 83
Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

 

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. 

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Vol.3 Statistical Studies Ch.5 Models of Spatial Influence

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. 

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