Prepared on behalf of the

 Appalachian Regional Commission

Strengthening Economic 

Resilience in Appalachia

Technical Report
ACKNOWLEDGEMENTS

Report	Authors	and	Contributors	

We	thank	the	following	Advisory	Team	members	for	

participating	in	calls	and	providing	valuable	feedback:	

Jim	Baldwin,	Cumberland	Plateau	Planning	District	Commission

Shannon	Blevins,	University	of	Virginia’s	College	at	Wise

Dee	Davis,	Center	for	Rural	Strategies	

Eric	Dixon,	Appalachian	Citizen	Law	Center

Peter	Hille,	Mountain	Association for	Community	Economic	

Development

Debra	Horne,	Mayor	of	Town	of	Dungannon,	VA

Leasha	Johnson,	Mingo	County	Redevelopment	Authority

Justin	Maxson,	Mary	Reynolds	Babcock	Foundation

Leslie	Schaller,	Appalachian	Center	for	Economic	Networks

Jennifer	Simon,	Ohio	University

Barbara	Wycoff,	One	Foundation

We	 thank	 the	 following	 Focus	 Group	 participants	 for	 their	

valuable	insight:	

Jimmie	Adkins,	LENOWISCO	PDC	

Philippa	Belsches,	Just	Transition	Fund	

Shannon	Blevins,	UVa-Wise	Office	of	Economic	Development

Dee	Davis,	Center	for	Rural	Strategies

Eric	Dixon,	Appalachian	Citizens	Law	Center

Peter	Hille,	MACED

Ginny	Kidwell,	Tennessee	Institute	of	Public	Health

Jennifer	Simon,	Ohio	University

Ada	Smith,	Appalshop	

Kent	Spellman,	Rails	to	Trails	Conservancy

						We	thank	the	following	interviewees:	

Jonathan	Belcher,	Virginia	Coalfield	Economic	

Development	Authority	

Dee	Davis,	Center	for	Rural	Strategies	

Ta	Enos,	Pennsylvania	Wilds	Center	

Anthony	Flaccavento,	Rural	Scale	

Michelle	Foster,	The	Greater	Kanawha	Valley	Foundation	

Peter	Hille,	Mountain Association	for	Community	

Economic	Development	

William	Isom,	Sustainable	and	Equitable	Agricultural	

Development	Task	Force	

Leasha	Johnson,	Mingo	County	Redevelopment	Authority	

Jake	Lynch,	West	Virginia	Community	Development	Hub

Deborah	Markley,	Center	for	Rural	Entrepreneurship	

Erik	Pages,	EntreWorks	Consulting	

Leslie	Schaller,	Appalachian	Center	for	Economic	

Networks

Ada	Smith,	Appalshop	

Kent	Spellman,	Rails-To-Trails	Conservancy	

Bonnie	Swinford,	Tennessee	Chapter	of	the	Sierra	Club	

Tom	Torres,	Appalshop	Board	Executive	Committee	

Stephanie	Tyree,	W.V Community	Development	Hub	

Thomas	Watson,	Rural	Support	Partners	

Barbara	Wycoff,	One	Foundation

Christine	Gyovai	

Emily Carlson,	

Alexandria	Sentilles

Dialogue	+	Design	Associates

2573	Pea	Ridge	Road

Charlottesville,	VA	22901

www.dialogueanddesign.com

Fritz	Boettner,	Evan	Fedorko,	

Evan	Hansen

Downstream	Strategies

911	Greenbag	Road

Morgantown,	WV	26508

www.downstreamstrategies.com

Alan	Collins,	Brianne	Zimmerman

West	Virginia	University

Downstream	Strategies	

consultants

Stephan	J.	Goetz,	Yicheol	Han	

The	Northeast	Regional	Center	for	

Rural	Development

The	Pennsylvania	State	University

7	Armsby	Building

University	Park,	PA	16802-5602

https://aese.psu.edu/nercrd
TABLE	OF	CONTENTS

ACKNOWLEDGEMENTS .........................................................................................................................................I

LIST	OF	TABLES.....................................................................................................................................................II

LIST	OF	FIGURES...................................................................................................................................................II

ABBREVIATIONS..................................................................................................................................................III

EXECUTIVE	SUMMARY .........................................................................................................................................1

1. INTRODUCTION............................................................................................................................................5

2. THE	COUNTY-LEVEL	DETERMINANTS	OF	ECONOMIC	RESILIENCE ..................................................................7

2.1 INTRODUCTION................................................................................................................................................. 7

2.2 PROCEDURES.................................................................................................................................................... 7

2.3 THE	DETERMINANTS	OF	RESILIENCE: MODEL	SPECIFICATION....................................................................................... 7

2.4 VARIABLES, DATA, AND	THEIR	SOURCES................................................................................................................ 11

2.4.1 Demographic/Mobility	variables............................................................................................................ 11

2.4.2 Economic/Industry	variables.................................................................................................................. 12

2.4.3 Community/Health	variables ................................................................................................................. 15

2.5 RESULTS ........................................................................................................................................................ 15

2.5.1 Demographic/Mobility	variables............................................................................................................ 15

2.5.2 Economic/Industry	variables.................................................................................................................. 15

2.5.3 Community/Health	variables ................................................................................................................. 16

2.5.4 The	entire	model .................................................................................................................................... 16

2.6 SENSITIVITY	ANALYSES ...................................................................................................................................... 18

2.7 ANALYSIS	OF	RESIDUALS.................................................................................................................................... 19

3. THE	COUNTY-LEVEL	SPILLOVER	EFFECTS	OF	ECONOMIC	RESILIENCE ........................................................... 22

3.1 SPATIAL	ECONOMETRIC	ANALYSIS: PREDICTING	THE	SPILLOVER	EFFECTS ...................................................................... 22

3.2 SPATIAL	MODEL	RESULTS................................................................................................................................... 23

3.3 RESULTS: IMPORTANT	INDIRECT	EFFECTS	AND	TOTAL	EFFECTS	ESTIMATES.................................................................... 25

3.3.1 Demographic/Mobility	variables............................................................................................................ 25

3.3.2 Economic/Industry	variables.................................................................................................................. 27

3.3.3 Community/Health	variables ................................................................................................................. 28

3.4 COUNTY	RELATIONSHIPS	AND	POLICY	STRATEGIES................................................................................................... 29

3.4.1 Local	or	strictly	county-level	impacts..................................................................................................... 29

3.4.2 Cooperative	regional	policy	strategies................................................................................................... 29

3.4.3 Competitive	regional	policy	strategies................................................................................................... 30

3.5 IDENTIFICATION	OF	HIGHLY	RESILIENT	COUNTIES .................................................................................................... 30

4. QUALITATIVE	ANALYSIS OF	RESILIENCE ...................................................................................................... 32

5. CASE	STUDY	SELECTION.............................................................................................................................. 35

6. CONCLUSION.............................................................................................................................................. 43

REFERENCES....................................................................................................................................................... 44

APPENDIX	A:	FOCUS	GROUP	MATERIALS............................................................................................................ 48

APPENDIX	B:	INTERVIEW	SUMMARY.................................................................................................................. 66

APPENDIX	C:	ADVISORY TEAM	CALL	SUMMARIES............................................................................................... 89

APPENDIX	D:	PRACTITIONER	SURVEY ................................................................................................................. 98

APPENDIX	E:	REGRESSION	RESULTS	WITH	QUADRATIC	TERMS ......................................................................... 132
ii |	Page

APPENDIX	F:	REGRESSION	RESULTS	WITH	INTERACTION	TERMS....................................................................... 133

APPENDIX	G:	EMPLOYMENT	FOR	COUNTIES	WITH HIGH	RESIDUALS	IN	THE	OLS	MODEL .................................. 136

APPENDIX	H:	EMPLOYMENT	FOR	TOP	COUNTIES	BASED	ON	RESILIENCE	IN	THE	OLS	MODEL ............................ 139

APPENDIX	I:	TOP	COUNTIES	BASED	ON	RESIDUALS	IN	THE	OLS	MODEL THAT	ARE	SELF-EMPLOYMENT	DOMINANT

142

LIST OF	TABLES

Table	1:	Demographic/mobility	variables,	definitions,	data	sources,	and	summary	statistics.......................... 13

Table	2:	Economic/industry	variables,	definitions,	data	sources,	and	summary	statistics................................ 13

Table	3:	Community/health	variables,	definitions,	data	sources,	and	summary	statistics................................ 14

Table	4:	Regression	results	for	the	three	subcategories	of	variables................................................................ 17

Table	5:	Top	24	counties	ranked	based	on	regression	residuals ....................................................................... 21

Table	6:	Comparison	of	direct	effects	and	OLS	results	with	beta	estimates ..................................................... 24

Table	7:	Spatial	econometric	regression	estimate	results	for	economic	resilience........................................... 26

Table	8:	Ranked	order	of	positive	and	negative	impacts	of	statistically	significant	SDM	beta	coefficients ...... 27

Table	9:	Policies	categorized	by	impact	and	implications	for	surrounding	counties......................................... 30

Table	10:	Top	15	highly	resilient	counties	based	on	residual	values................................................................. 31

Table	11:	Most-resilient	counties	in	the	Appalachian	Region............................................................................ 31

Table	12:	Top	24	counties	based	on	resilience	in	the	OLS	model...................................................................... 37

Table	13:	Potential	case	study	areas	identified	in	the	qualitative	research ...................................................... 38

Table	14:	The	ten	case	study	counties............................................................................................................... 42

Table	15:	Regression	results	with	quadratic	terms,	Models	1	through	6 ........................................................ 132

Table	16:	Regression	results	with	interaction	terms,	Models	1	through	4...................................................... 133

Table	17:	Regression	results	with	interaction	terms,	Models	5	through	10.................................................... 134

Table	18:	Regression	results	with	interaction	terms,	Models	11	through	14.................................................. 135

Table	19:	Top	counties	based	on	residuals	and	self-employment	dominant .................................................. 142

LIST	OF	FIGURES

Figure	1:	Coal	production	in	Appalachia	and	Central	Appalachia........................................................................ 5

Figure	2:	Regional	economic	change	from	a	major	shock	and	concepts	of	drop	and	rebound........................... 8

Figure	3:	Resilience	values	for	counties	across	the	United	States....................................................................... 9

Figure	4:	Resilience	values	for	counties	in	the	Appalachian	Region .................................................................. 10

Figure	5:	Summary	of	qualitative	research	activities......................................................................................... 34

Figure	6:	The	112	candidate	case	study	counties .............................................................................................. 36

Figure	7:	The	ten	case	study	areas..................................................................................................................... 41
iii |	Page

ABBREVIATIONS

ARC Appalachian	Regional	Commission

CH community/health

DM demographic/mobility

EI economic/industry

OLS ordinary	least	squares

POWER Partnerships	for	Opportunity	and	Workforce	and	Economic	Revitalization

SDM Spatial	Durbin	Model

USCB United	States	Census	Bureau

USDA United	States	Department	of	Agriculture

USDC United	States	Department	of	Commerce

USDL United	States	Department	of	Labor
1 |	Page

EXECUTIVE	SUMMARY

The	economies	of	many	counties	and	subregions in	the	Appalachian	Region	have	historically	depended	on	a	

few	 dominant	 industries,	 such	 as	 mining	 or	 manufacturing.	 In	 recent	 years,	 Appalachian	 coal	 production—

particularly	 production	 in	 the	 Central	 Appalachian	 coal	 basin—has	 plummeted.	 Coal	 companies	 have	 gone	

bankrupt	and	jobs	have	been	lost,	with	devastating	impacts	on	families,	communities,	counties,	and	states.	

The Strengthening Economic Resilience	in Appalachian Communities project, sponsored	by the Appalachian	

Regional	Commission,	was	conducted	by Downstream	Strategies,	Penn	State,	Dialogue	+	Design	Associates,	

and	other	partners.	The	purpose	of	this	project	was	to	quantify	economic	resilience	and	understand	the	

underlying	factors	that explain	how	communities	can	buffer	economic	shocks.	Using	these factors,	our	team	

explored	and	documented	the	strategies	and	policies	local	leaders	can	use	to	enhance	the	future	economic	

prospects	of	coal-impacted	communities	throughout	the	Appalachian	Region.	The	four	key	components	to	

this	research	project	were	to:	1)	develop	a	comprehensive,	quantitative	framework	to	explore	economic	

resilience;	2)	identify	a	series	of	best-practice	strategies	for	strengthening	local	economic	resilience;	3)	

conduct	10	in-depth	case	studies;	and	4)	produce	a	concise	guidebook	that	interprets	and	integrates	findings	

of	the	research,	written	specifically	for	local	economic	development	practitioners.	Our	multidisciplinary	

research	team	performed	several	linked	analyses.

These	analyses	explore	economic	resilience:	a	local	community’s	capacity	to	absorb,	resist,	and	recover	from	

an	economic	shock,	such	as	an	economy-wide	recession	or	industry	restructuring. We	performed	two	

statistical	analyses	that	illustrate	the	independent	effects	of	alternative	determinants	of	resilience.	

The	first,	a	conventional	ordinary	least	squares	analysis,	assesses	each	county	across	the	United	States	in	

isolation.	A	total	of	35	variables	are	included	in	this	analysis,	which	are	divided	into	demographic/mobility,	

economic/industry,	and	community/health	variables.	Several	statistically	significant	variables	provide	insights	

into	the	types	of	policies	and	practices	that	can	be	implemented	in	local	communities	to	promote	economic	

resilience.

For	the	demographic/mobility	variables,	greater	resilience	is	found	in	counties	with	a	larger	proportion	of	

young	adults,	where more	people	migrate	in	and	fewer	people	migrate	out	of	the	county,	where	more	

people	commute	out	of	the	county,	where	the	county	has	a	higher	population,	and	where	a	greater	portion	

of	the	county’s	residents	have	earned	college	degrees.

For	the	economic/industry	variables,	several	variables	are	found	to	be	positively	associated	with	economic	

resilience,	including	labor	force	participation,	innovation	potential,	and	industry	diversity.	Other	variables	are	

found	to	be	negatively	associated.	These	include	business	density;	poverty;	knowledge	diversity;	and	farming,	

coal,	and	manufacturing	employment.

Finally,	many	community/health	variables are	found	to	be	positively	associated	with	economic	resilience,	

including	availability	of	educational	facilities,	broadband,	and	recreational	opportunities	(employment	in	arts,	

entertainment, and	recreation),	as	well	as	agritourism	and	direct	farm	sales.	In	contrast,	social	capital	

unexpectedly	has	a	strong	and	significant	negative	effect.

The	second statistical	analysis,	a	spatial	econometric	model,	extends	the	first	model	by	allowing	for	spillovers	

of	economic	processes	across	county	lines.	This	represents	a	sensitivity	analysis	that	allows	for	more	nuanced	

relationships.	The	same	set	of	35	variables	are	investigated.	Again,	several	statistically	significant	variables	

provide	insights	into	the	types	of	policies	and	practices	that	can	be	implemented	in	local	communities	to	

promote	economic	resilience.	

When	making	policy	recommendations	based	on	the	observed	variables,	one	must	consider	that	a	policy	

implemented	in	a	given	county	may	impact	surrounding	counties.	While certain	policies	are	best	dealt	with	as	
2 |	Page

single-county	issues,	others	may	be	more	efficiently	implemented as	a	joint	effort	with	surrounding	counties	

(or	even	at	the	state	level).	The	variables	impacting	resilience	may	also	point	to	different	strategies	needed	to	

address	a	policy	issue.	Based	on	the	results	of	the	spillover	analysis,	policies	may	be	complementary	

(cooperative)	or	competitive	across	county	lines.	Cooperative	strategies	strengthen	the	resilience	of	

surrounding	counties,	while	competitive	strategies	weaken	their	resilience.	This	analysis	also	allows	policies	

that	support	statistically	significant variables	to	be	categorized	as	strictly	county-level,	cooperative,	altruisticcooperative,	competitive,	and	ultra-competitive.	

For	example,	as	illustrated	in	Table	ES-1,	policies	that	increase	the	diversity	of	industries	and	innovation	

potential,	and	policies	that	increase	participation	of	the	workforce,	were	found	to	have	strictly	county-level	

impacts.	These	approaches	neither	help	nor	hurt	resilience	of	neighboring	counties.

Cooperative	and	altruistic-cooperative	policies,	such	as	those	that	increase	county	population	size or	protect	

or	enhance	natural	amenities,	would	help	neighboring	counties—not	just	the	county	implementing	the	

policy.

On	the	other	hand,	competitive	and	ultra-competitive policies	in	a	county	tend	to	reduce	the	resilience	of	

surrounding	counties	and	the	total	region.	Examples	of	such	policies	include	those	that	attract	collegeeducated people	and	those	that	increase	the	portion	of	the	population	aged	65	and	over.

Table	ES-1:	Policies	categorized	by	impact	and	implications	for	surrounding	counties

Strictly	county-level Regional-level	impacts

impacts Cooperative Altruistic-cooperative Competitive Ultra-competitive

Policies	that	increase	

the	diversity	of	

industries	and	

innovation	potential

Policies	that	increase	

participation	of	the	

workforce

Policies	that	increase	

county	population	size

Policies	that	decrease	

the	poverty	rate

Policies	that	reduce	

the	out-migration	of	

county-level	

population

Policies	that	protect	or	

enhance	natural	

amenities

Policies	that	increase	

the	number	of	

businesses	per	capita	

within	a	county

Policies	that	increase	

farm	employment

Policies	that	attract	

college-educated	

people

Policies	that	attract	

younger	workers	(25	

to	44)

Policies	that	increase	

the	portion	of	the	

population	age	65+

Policies	that	

encourage	direct	farm	

sales

Policies	that	increase	

the	employment	in	the	

arts,	entertainment,	

and	recreation

Policies	that	increase	

community	college	

employment

Policies	that	increase	

manufacturing	

employment	
3 |	Page

We	also	performed	a	qualitative	analysis	by	conducting	interviews,	a	focus	group,	and	a	survey	to	gather	

feedback	from	economic	development	practitioners,	elected	officials,	and	others	on	best	practices	and	

strategies	for	growing	economic	resilience	in	the	Appalachian	Region.	We	also	solicited	feedback	from	an	

Advisory	Team.	Results	were	synthesized	into	eight	best	practices,	with	associated	strategies:	

1. Invest	in	education,	technology,	infrastructure, and	broadband.

2. Engage	the	community	over	the	long	term.

3. Create	communities	where	people	want	to	live.

4. Grow	youth	engagement	and	next-generation	leadership.

5. Identify	and	grow	the	assets	in	the	community	and	region.

6. Build	networks	and	foster	collaboration.

7. Move	multiple	sectors	forward	for	economic	development	and	grow	value	chains.

8. Cultivate	entrepreneurs	and	develop	resources	for	business	start-ups.

Ten	case	study	areas—seven	inside the	Appalachian	Region	and	three	in	other	parts	of	the	country—were	

selected	to	provide	further	insight	into	how	communities	are	evolving	their	approach	to	economic	

development	(See	Figure	ES-1).	These	case	studies,	along	with	more	details	on	the	best	practices	and	

strategies,	are	provided	in	an	accompanying	practitioner	guidebook,	which	is	designed	for	use	by	economic	

development	practitioners,	local	government	officials,	nonprofit	organizations,	and	others	working	in	the	

Appalachian	Region.
4 |	Page

Figure	ES-1:	The	ten	case	study	areas
5 |	Page

1. INTRODUCTION

The	economies	of	many	counties	and	subregions	in	the	Appalachian	Region	have	historically	depended	on	a	

few	dominant	industries,	such	as	mining	or	manufacturing.	In	recent	years,	Appalachian	coal	production—

particularly	production	in	the	Central	Appalachian	coal	basin—has	plummeted (see	Figure	1).	Coal	companies	

have	gone	bankrupt	and	jobs	have	been	lost,	with	devastating	impacts	on families,	communities, counties,	

and	states (See,	for	example,	Hodge, 2016;	Hansen	et	al.,	2016;	Maher	and	Frosch,	2015;	Farrell,	2016;	Office

of	the	Governor,	2015;	Hals	and	Rucinski,	2016).

Figure	1:	Coal	production	in	Appalachia	and	Central	Appalachia

Source: United States Energy Information Administration (2017). Note: Appalachian coal production includes Central 

Appalachian coal production.

Recognizing	the	recent	decline	in	the	coal	mining	industry	and	broader	energy	transitions,	the	United	States

Economic	Development	Administration	and	the	Appalachian	Regional	Commission	(ARC)	are	collaborating	to	

assist	coal-impacted	communities	in	Appalachia	through	the	federal	Partnerships	for	Opportunity	and	

Workforce	and	Economic	Revitalization (POWER)	Initiative.	Grants	support efforts	to	increase	economic	

diversity,	enhance	job	training,	create	new	jobs,	and	attract	new	sources	of	investment (ARC,	2017).

One	critical	aspect	of	the	POWER	Initiative	is	understanding	how	coal-impacted	communities	can	transform	

and	diversify	their	economies and	build	resilience	against	future	economic	shocks. Economic	resilience	is	

defined	as	a	local	community’s	capacity	to	absorb,	resist,	and	recover	from	an	economic	shock,	such	as	an	

economy-wide	recession	or	industry	restructuring. In	this	research,	we	examine	county-level	data	to	assess	

local	community	resilience. This	project	investigates	best	practices,	strategies, and	policies	that	local	leaders	

can	use	to	enhance	the	future	economic	prospects	of	coal-impacted	communities	throughout	the	

Appalachian	Region.

Our	multidisciplinary	research	team—which	includes	experts from	Downstream	Strategies, Dialogue	+	Design	

Associates,	the	Northeast	Regional	Center	for	Rural	Development	at	the	Pennsylvania	State	University,	and	

West	Virginia	University—performed	several	linked	analyses. We	conducted	two	statistical	analyses	that	

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Million	tons

Appalachia

Central	Appalachia
6	|	Page

illustrate the independent effects	of alternative determinants	of resilience. The first, a conventional ordinary

least	squares (OLS)	analysis, assesses each county in isolation (see	Chapter 2). The second, a spatial

econometric model, extends the	OLS	model by allowing	for spillovers of economic processes across county

lines.	This represents a sensitivity analysis that	allows for	more nuanced relationships (see	Chapter 3).

We also performed	a qualitative analysis by conducting interviews, a	focus group, and a	survey to gather

feedback from economic	development	practitioners, elected	officials, and	others on	best practices and	

strategies	for fostering economic resilience	in the	Appalachian Region. We also solicited feedback from an

Advisory Team,	consisting	of	local	development	practitioners	and	representatives	from	community	groups,

philanthropic organizations, and	universities. Chapter 4	documents these efforts and identifies eight best

practices for increasing economic resilience	in the	Appalachian	Region.	

In	addition,	we	used	the	results	from	our	analyses,	along	with	feedback	from	ARC,	to	select	10	case	study	

areas	(see	Chapter	4).	These	case	studies	provide	further	insight	into	how	communities	are	evolving	their	

approach	to	economic	development	both	inside	and	outside	the	Appalachian	Region.	

This technical report,	one of two	reports for	this project,	focuses	on	the	process	used	by	the	research	team	to

conduct the qualitative and quantitative analyses.	The second report,	a	guidebook,	provides best practices,

strategies, and	case studies	for use by economic development practitioners, local government officials,

nonprofit organizations, and	others working	in the Appalachian Region.

Several recent ARC	reports set	the stage for	this analysis of	economic	resilience. Hodge (2016)	provides an	

overview of the coal industry ecosystem,	including production, power	generation, and supply chain linkages,

and presents the	most current data	and trends on coal-related industries. A	previous analysis of economic

diversity in	the Appalachian	Region	calculated	economic diversity trends in	Appalachian	counties and	subregions using four diversity indices:	industrial,	functional, occupational, and knowledge.	Ten case studies

were presented that identified successful diversification	approaches (University	of	Illinois at	UrbanaChampaign’s Regional Economics Applications Laboratory and	the Center for Regional Economic

Competitiveness, 2014).	A	2012 analysis of 10 distressed	and	formerly distressed	Appalachian	counties	

presented	strategies for economic improvement, based	on	both	qualitative and	quantitative research	(Ezzell	

et al., 2012).	

Other recent ARC	reports investigated trends in the Region since	1965 (Center for Regional Economic

Competitiveness	and	West Virginia University, 2015), household wealth and financial security (Jacobsen et	

al., 2013), access to capital and credit	(Silver et al., 2013), and entrepreneurial opportunities (Rural Support

Partners,	2013). While these are all important variables related to different aspects of economic resilience,

our study focuses specifically on	resilience related to total number of jobs.	We suggest that, although	it is not

without limitations, employment is a better measure than unemployment, which in a dynamic economy is

more sensitive to workers entering and dropping out of the workforce than unemployment. In	a	recent	study,	

Brown	and	Greenbaum (2017) used unemployment rate	stability to reflect resilience.

Our study introduces a novel set of potential explanatory variables into	the regression	analysis and	uses

state-of-the-art spatial econometrics to capture	local spillover effects.	We focus on	the measurement and	

explanation of factors impacting	economic resilience	at the	county level. Our measurement of economic

resilience includes three factors: (1)	time	of	impact	or	beginning	of	recession and how it	was spread and

experienced, (2)	the	response of affected	counties, and	(3)	the	relationship between recession and recovery.

To explain economic resilience,	three	sets	of	explanatory	variables	were	utilized:	demographic/mobility,	

economic/industry, and community/health. These	variables provide	alternative, potentially	competing

predictors of resilience in	a community, identified	both	from the literature and	informed	by our interviews

with key knowledgeable community-based	stakeholders. By explaining economic resilience with	these

variables, we can assess the	impacts of potential policies on local as well as regional resilience.
7 |	Page

2. THE	COUNTY-LEVEL	DETERMINANTS	OF	ECONOMIC	RESILIENCE

2.1 Introduction

This	section	of	the	report	describes	the	statistical	analysis	showing	the	independent	effects	of	alternative	

determinants	of	resilience. Here, we	examine	the	relationship	between	different	variables	that	are	

hypothesized	to	be	associated	with	the	ability	of	counties	not	only	to	resist	but	also	to	rebound	from	shocks.

By	using	statistical	methods,	we	isolate	the effects of	individual	variables	while	holding	others	constant,	and	

therefore	place	counties	on	the	“same	playing	field.” For	example,	we	can	study	the	effect	of	education	on	

resilience,	while	holding	constant	the	county’s	industrial	structure.

In	this	chapter,	we	begin	with	a	conventional	ordinary	least	squares	(OLS)	analysis,	whereby	individual	

counties	are	viewed	in	isolation. The	following	chapter	presents	a	spatial	econometric	model,	which	extends	

the	OLS	model	by	allowing	for	spillovers	of	economic	processes	across	county	and	state	lines. This	represents	

a	sensitivity	analysis	that	allows	for	more	nuanced	relationships	among	the	explanatory	and	dependent	

(resilience)	variables.

2.2 Procedures

We	proceed	as	follows	with	the	analysis. First, we	specify	and	then	estimate	a	regression	model	with	

economic	resilience	as	the	dependent	variable. This	reveals	the	general,	average	determinants	of	resilience	

across	the	United	States in	response to	the	2008	Great	Recession,1 which	in	turn	provides	insights	into	

strategies	that	local	communities	may	be	able	to	follow	to	increase	their	economic	resilience. We	chose	the	

regressors	from	existing	literature	and	by	applying	what	we	have	learned	from	the	interviews	and	surveys	of	

knowledgeable community	leaders	(see	Appendices A-D). This	serves	both	to	“ground truth”	our model	and	

to	verify	whether	the	factors	mentioned	by	practitioners	in	the	field	have	independent	measurable	effects	in	

the	statistical	model. Second,	following	this	analysis,	the	results	of	which	we	also	review with	community	

leaders	surveyed,	we	use	residuals	from	the	regression	equation	to	identify	those	Appalachian	counties	that	

are	performing	better	in	terms	of	resilience	than	they	should	be,	based	on	their	local	conditions.

In	addition	to	comparing	our	list	of	top	residual	counties	with	the	list	emerging	from	the	interviews,	we	

examine	the	top	24	such counties	in	greater	detail	to	develop	a	narrower	list	of	candidate	case	study	

counties. Some	of	these	were	eliminated immediately	from	further	consideration	if	their	higher-thanexpected	resilience	is	due	to	some	unique	circumstance. For	example,	counties that	are	home	to	a	state’s	

land-grant	university	or	that	benefited	from	the	recent	shale	gas	development	boom are	not	of	interest,	

because these conditions	are	not	likely	to	be	easily	replicated	elsewhere.

2.3 The	determinants	of	resilience:	Model	specification

A	literature	review	finds	very	few	studies	that	have	attempted	to	model	resilience	using	an	econometric	

model	(Sherrieb	et	al.,	2010;	Hill	et	al.,	2012;	Augustine	et	al.,	2013;	Tsai	et	al.,	2015). In	part, this	may	be	due	

to	the	fact	that	a	systematic	measure	of	resilience,	defined	as	the	employment	rebound	in	response	to	a	

shock,	has	been	developed	only	relatively	recently	(Han	and	Goetz,	2015). Even	the	recent	study	by	Brown	

and	Greenbaum	(2017),	published	after	this	project	was	initiated,	uses	the	notion	of	unemployment	stability	

rather	than	the	idea	of	a	recovery	following	a	shock. Han	and	Goetz	(2015)	calculated	resilience	as	the	

amount	of	decline	relative	to	the	expected	employment,	and	the	amount	of	recovery	relative	to	the lowest	

post-shock	employment	(see Figure	2). One	earlier	study	of	potential	relevance	was	prepared	by	Experian	for	

the	United	Kingdom;	it	identified	a	set	of	four	factors	hypothesized	to	underlie	resilience. Also	potentially	

																																								 																				 1 The Great Recession started in December 2007 and ended in June 2009 (National Bureau of Economic Research, 2017).
8 |	Page

relevant to	this	work	are	Pender’s	studies	(Pender	et	al.,	2014) on	rural	wealth	and	a	recent	ARC	report	(Feser	

et	al.,	2014)	on	diversity.	

Figure	2:	Regional	economic	change	from	a	major	shock	and	concepts	of	drop	and	rebound

Source: Han and Goetz (2015).

For	our	dependent	variable, we	use	the	resilience	measure that	was	developed	by	Han	and	Goetz	(2015),	

with	the	slight	modification	that	we	also	consider	the	rate	of	growth	in	the	period	leading	up	to	the	peak	and	

subsequent	decline. In	addition,	we	update	this	measure	with	data	through	March	2016. As	in	the	original	

work,	we	apply	a	seasonal	adjustment	filter	to	the	monthly	data	to	smoothen	the	series. Figure	3 illustrates	

these	resilience	values	for	counties	across	the	United	States,	and	Figure	4 focuses	on	the	counties	in	the	

Appalachian	Region.
9 |	Page

Figure	3:	Resilience	values	for	counties	across	the	United	States
10 |	Page

Figure	4:	Resilience	values	for	counties in	the	Appalachian	Region

12 |	Page

Communities	that	attract	more	in-commuters	are	expected	to	be	more	resilient,	and	the	same	is	expected	of	

communities	with	more	out-commuters	(Goetz	et	al.,	2010;	Goetz	and	Han,	2015). The	hypothesized	reason	

is	that	commuters	are	attracted	to	places	that	offer	greater	and	more	robust	employment	opportunities.	By	

the	same	token, the	in-flow	of	earnings	by	commuters	from	other	places	also	has	a	stabilizing	portfolio-type	

effect. Commuting	also	facilitates information	flows	into	and	out	of	counties,	with	potential	ideas	for	solving	

local	problems	that	have	public	good	characteristics.	Commuting	can	also	facilitate	starting	new	businesses.

Holding	land	area constant,	we	expect	communities	with	greater	resident	populations	to	be	more	resilient	

because	of	greater	agglomeration	benefits, as	well	as	more	opportunities	to	generate	new	ideas. For	the	

same	reason, we	expect	college	graduate	rates	to	exert	the	same	positive	influence	on	resilience	levels	in	a	

community.

2.4.2 Economic/Industry	variables

Among	the	set	of	Economic/Industry	variables,	we	include	distance	from	major	population	centers	of	at	least	

250,000	persons.	To	the	extent	that	the	housing-related	recession	of	2008	was	largely	an	urban	phenomenon	

(Chernick	et	al.,	2011),	greater	distance	is	expected	to	have	insulated	more	rural	communities	from	the	

shock.	The	density	of	businesses	per	working	age	population	as	well	as	self-employment	rates	are	both	

expected	to	be	associated	with	greater	resilience,	because	of	a	greater	capacity	to	cope	with	and	adapt	to	an	

economic	shock	(Stephens	and	Partridge,	2011).	Communities	with	lower	poverty	rates,	those	with	higher	

labor	force	participation	rates, as	well	as	innovation	potential	(Goetz	and	Han,	2017)	and	industry	diversity	

(Feser	et	al.,	2014), are	each	expected	to	exhibit	greater	levels	of	resilience	in	response	to	a	shock.	The	

innovation	potential	measure	uses	the	input-output	table	and	spatial	locations	of	firms	in	different	industries	

to	measure	opportunities	both	for	process	(supplier-based)	and	product	(buyer-based)	innovation;	we	expect	

this	to	have	a	positive	effect.	In	the	case	of	industry	diversity,	we	expect	a	stabilizing	portfolio-type	effect.

Less	obvious	is	the	expected	effect	of	greater	knowledge	diversity	(holding	college	graduate	shares	constant).	

Such	communities	may	be	better	able	to	address	the	fallout	from	a	recession,	on	the	one	hand,	but	they	may	

also	be	more	vulnerable	to	a	shock	that	affects	the	extremes	of	the	knowledge	distribution,	on	the	other,	all	

else	equal.3

We	also	include	in	this	sub-vector	the	employment	shares	in	key	blue-collar	type	industries,	ranging	from	

farming,	oil	and	gas,	coal	mining,	and	manufacturing.	These	are	important	industries	within	the	Appalachian	

Region	and	they	have	been	profoundly	affected	both	by	(environmental)	policy	and	by	globalization	as	well	as	

labor	saving	technological	change	(Partridge,	Stephens,	and	Goetz,	2017).	We	measure	these	variables	in	

2005,	which	would	not	yet	reflect	the	current	shale	gas	boom.

																																								 																				 3 In our analysis, we also consider two other diversity measures: functional and occupational diversity (Feser et al., 2014). Because they are highly correlated 

with the industry and skills diversity measures, we cannot use them in the model.
13 |	Page

Table	1:	Demographic/mobility	variables,	definitions,	data	sources,	and	summary	statistics

Variable Definition Source Mean Std.	Dev. Min Max

Age	25	to	44 %	of	population	25-44	years	in	age	20+	population,	2005 USCB,	2005b 34.45 4.99 17.6 54.5

Age	65	and	up %	of	population	65	years	or	more	in	age	20+	population,	2005 USCB,	2005b 20.57 5.04 3.9 42.9

Ethnic	Diversity Ethnic	diversity	of	minorities	(except	white),	2000 USCB,	2000a 0.04 0.1 0 0.83

Mobility	In-Migration %	of	in-migration	(#in-migrants/#population),	2000 USCB,	2000a 18.54 6.57 0 65.12

Mobility	Out-Migration %	of	out-migration	(#out-migrants/#population),	2000 USCB,	2000a 18.05 6.21 0 100

Mobility	In-Commuting %	of	in-commuting	(#in-commuting/#employment),	2000 USCB,	2000a

USCB,	2005a 31.21 16.58 2.02 100

Mobility	Out-Commuting %	of	out-commuting	(#out-commuting/#employment),	2000 USCB,	2000a

USCB,	2005a 50.12 32.14 1.47 100

Residence	Born %	of	population	who	born	in	state	of	residence,	2000 USCB,	2000a 69.66 14.79 14.32 96.51

Population Population,	2005,	log USCB,	2005b 4.45 0.62 1.85 6.99

Land	Area Land	area	in	square	miles,	2000 USCB,	2000b 953.7 1,309.41 1.99 20,052.5

College	 %	of	population	age	25+	with	bachelor	degree or	higher,	2000 USCB,	2000a 16.54 7.84 4.9 63.7

Table	2:	Economic/industry	variables,	definitions,	data	sources,	and	summary	statistics

Variable Definition Source Mean Std.	Dev. Min Max

Distance	to	City Distance	(miles)	to	a	county	with	more	than	250,000	population,	2005 Author 83.46 76.35 0 565.86

Business	Density #	establishments per	1,000	age	20-64	population,	2005 USCB,	2005a 42.09 16.06 0 181.05

Self-Employment %	Nonfarm	proprietors	employment,	2005 USDC,	2005 21.48 7.75 3 61.8

Poverty %	of	poverty	all	ages,	2005 USCB,	2005c 15.33 6.52 2.5 51

Participation %	of	participation,	2000 USCB,	2000a 60.59 7.04 22.6 86.1

Innovation	Potential Potential	for	economic	innovation,	2005 Author 0.32 0.1 0.01 0.77

Industry Diversity Industry-based	economic	diversity Feser	et	al.,	

2014 4.53 0.53 0.91 5.60

Knowledge Diversity Knowledge-based	economic	diversity Feser	et	al.,	

2014 2.44 0.007 1.88 2.69

Farming %	of	employment	in	Farming,	2005 USDC,	2005 7.92 7.92 0 57.67

Oil	&	Gas %	of	employment	in	Oil	and	Gas	extraction,	2005 USCB,	2005a 0.26 1.89 0 69.87

Coal %	of	employment	in	Coal	mining,	2005 USCB,	2005a 0.33 2.4 0 49.65

Manufacturing %	of	employment	in	Manufacturing,	2005 USCB,	2005a 16.53 12.59 0 69.33
14 |	Page

Table	3:	Community/health	variables,	definitions,	data	sources,	and	summary	statistics

Variable Definition Source Mean Std.	Dev. Min Max

Primary	and	Secondary Employment	in	Elementary	and	Secondary	Schools	(NAICS	6111)	

per	1,000	age	5-19	population,	2005,	log

USCB,	2005a

USCB,	2005b 0.64 0.56 0 2.78

Community	College Employment	in	Community	college	(NAICS	611210)	per	1,000	age	

20-64	population,	2005,	log

USCB,	2005a

USCB,	2005b 0.13 0.42 0 3.34

Broadband %	of	population	who	live	within	one	or	more	broadband	service	

areas,	2010 USDC,	2007 76.94 19.04 0 100

Child/Elder	Care

Employment	in	child	day	care	services	(NAICS 62441) per	1,000	

age	5- population	or	the	elderly	and	persons	with	disabilities	

(NAICS	61412)	per	1,000	age	65+	population,	2005,	log

USCB,	2005a

USCB,	2005b 1.66 0.5 0 3.22

Health	Practitioner Employment	in	offices	of	physicians,	dentists,	and	other	health	

practitioners	(NAICS	6211-3)	per	1,000	population,	2005

USCB,	2005a

USCB,	2005b 8.31 6.55 0 99.02

Recreational Opportunity Employment	in	arts,	entertainment,	and	recreation	(NAICS	71)	

per	100K	population,	2005,	log

USCB,	2005a

USCB,	2005b 0.61 0.36 0 2.76

Natural	Amenity Natural	amenity USDA,	2005 0.07 2.29 -6.4 11.17

Social	Capital Social	capital	index,	2005 NERCRD 0 1.39 -3.9 14.38

Federal	Funds Total	amount	of	Federal	funds	per	capita,	2005,	log USDA,	2005 3.39 0.39 2.24 5.21

Voting	Competitiveness %	of	margin	in	U.S.	Presidential	elections	voting,	2008 Leip,	2008 25.82 18.07 0.01 87.73

Agritourism Agritourism	receipts	($)	per	Agritourism	operations USDA,	2007 7,095.9 21,088 0 428,000

Direct	Farm	Sales Direct	farm	sales	($)	/	Farms	with	direct	sales,	2007 USDA,	2007 5,903 8,244 0 130,688
15 |	Page

2.4.3 Community/Health	variables

For	the	final	group	of	variables,	we	include	among	the	Community/Health	variables	employment	in	primary	

and	secondary	educational	institutions, as	well	as employment	in community	colleges	and	broadband	access.

Each	of	these	is	expected	to	be	associated	with	greater	resilience. We	also	include	availability	of	both	elder	

and	childcare	facilities; healthcare	practitioners; and	employment	share	in	arts,	entertainment, and	

recreation,	along	with	the	U.S.	Department	of	Agriculture/Economic	Research	Service	natural	amenities	

index. Recently	there	has	been	considerable	excitement	about	the	effect	of	such	creative/recreation	

activities,	and	these	were	also	identified	in	the	interviews	of	key	community	leaders	and	stakeholders.

We	also	include	social	capital	stocks,	which	have	been	shown	to	be	important	to	economic	success	

(Rupasingha et	al.,	2000),	federal	funding	per	capita, and	voting	competitiveness. The	latter	measures	the	

absolute	value	of	the	difference	in	the	percent	of	the	population	voting	Republican	or	Democratic	in	the	2008	

federal	election. The	hypothesis	here	is	that	the	greater	this	number,	the	less	the	degree	of	political	

competition	in	the	county,	and	the	less	likely	it	is	that	incumbents	seeking	reelection	will	work	hard	for	their	

constituents.	Finally,	to	measure	community	cohesion	as	related	to	local	agricultural	activities,	we	include	

agritourism	and	direct	farm	sales	activities. While	evidence	on	the	net	benefit	of	these	in	the	literature	is	

mixed	(Brown	et	al.,	2014),	they	also	have	been	mentioned	in expert	interviews	as	leading	to	greater	

resilience.

2.5 Results

We	first	discuss	regression	results	for	the	three	individual	sub-categories	of	variables;	in	all	cases	we	include	

state	fixed	effects	(see	Table	4).

2.5.1 Demographic/Mobility	variables

Among	the	demographic	and	mobility	measures, the	younger	age	cohort, as	well	as	both	in- and	outmigration	measures, have	the	expected	signs	and	are	statistically	significant	at	the	1 percent level. Outcommuting,	or	the	ability	to	rely	on	other	nearby	communities	for	employment,	likewise	has	a	positive	effect.

Places	with	larger	populations	and	greater	shares	of	college	graduates	also	are	more	resilient,	as	expected.

Remarkably,	ethnic	diversity	has	no	statistically	significant	association	with	resilience,	when	we	consider	only	

these	demographic	and	mobility	variables,	which	is	counter	to	expectations	and	other	findings in	the	

literature.

2.5.2 Economic/Industry	variables

Turning	next	to	the	economic	and	industry	variables,	distance	from	larger	population	centers	has	no	effect,	

while	greater	business	density	has	an	unexpected	negative	effect	in	these	initial	sub-regressions.	For	poverty	

and	labor	force	participation,	the	direction	of	effects	is	as	expected,	and	statistically	different	from	zero	at	

the	1 percent level.	Greater	innovation	potential	and	industry	diversity	are	associated	with	greater	resilience,	

while	greater	diversity	of	knowledge	has	a	negative	association,	for	reasons	worthy	of	further	exploration.	In	

particular,	the	finding	that	places	with	a	greater	diversity	of	skills	sets	are	less	resilient	is	unexpected.	This	

suggests	that	counties	with	more	low- and	high-skilled	workers	are	more	vulnerable	to	shocks	or	have	more	

difficulty	rebounding	in	terms	of	employment.	This	may	be	related	to	layoffs	of	scientists	and	other	Ph.D.	

degree	holders	in	research	and	development	departments	during	downturns,	with	spillovers	to	lower-skilled	

workers	such	as	janitors	and	entry-level	clerks.	Communities	that	rely	relatively	more	on	farming,	coal,	and	

manufacturing	employment	are	less	resilient,	as	expected,	while	no	statistical	effect	is	detected	for	oil	and	

gas	employment	which,	again,	is	measured	in	the	period	before	the	shale	gas	boom	and,	as	such,	does	not	

measure	actual	participation	in, and	benefits	from, the	boom.
16 |	Page

2.5.3 Community/Health	variables

In	the	third	block	of	variables,	availability	of	educational	facilities,	broadband,	recreational	opportunities	

(employment	in	arts,	entertainment	and	recreation),	as	well	as	agritourism	and	direct	farm	sales, are	all	

associated	positively	with	resilience,	and	in	a	statistically	significant	manner.	In	contrast,	social	capital	

unexpectedly	has	a	strong	and	significant	negative	effect,	while	child/elder	care,	healthcare	practitioners,	

natural	amenities,	federal	funding, and	political	competitiveness	have	no	effect.

2.5.4 The	entire	model

Turning	to	the	last	column	of	results,	which	includes	all	regressors	at	the	same	time,	we	see	that	the	

community/health	variables	are	impacted	to	the	greatest	degree	by	including	the	other	regressors.	There	is	

insufficient	independent	variation	in	a	number	of	these	variables,	once	we	include	the	demographic	and	

economic	factors,	to	allow	them	to	exhibit	statistically	significant	effects	on	resilience.	Beyond	that,	the	

following	variables	remain	statistically	significant	in	the	overall	model	and	allow	us	to	make	initial	policy	

recommendations	about	factors	underlying	economic	resilience	in	communities.

Having	a	proportionately	younger	(age	25-44)	workforce,	more	ethnic	diversity,	more	in- and	less	outmigration	as	well	as	more	in-commuting,	and	a	larger	population	with	greater	shares	of	college	graduates	are	

all	important	factors	associated	with	greater	resilience.	Ethnic	diversity	as	well	as	in-commuting	shares	

become	statistically	significant	when	we	add	the	economic/industry	and	community/health	variables	(while	

out-commuting	shares	are	no	longer	significant).	Some	of	these	statistically	significant	factors	may	more	

readily	be	influenced	by	economic	development	strategies	than	others.	For	example,	while	the	availability	of	

child	and	elder	care	is	not	statistically	a	factor,	communities	could	seek	to	develop	conditions	that	support	

younger	career	couples	(those	age	25-44,	who	are	more	likely	to	be	raising	small	children)	by	providing	more	

of	the	kinds	of	resources	that	would	appeal	to	them	(e.g.,	excellent	schools)	as	well	as	their	children	(e.g.,	

opportunities	for	recreation	and	sports).	

Being	welcoming	of	different	ethnicities,	including	individuals	from	other	countries,	is	helpful,	as	is	trying	to	

recruit	former	residents	who	moved	away.	This	may	include	those	who	may	now	seek	to	retire	(although	the	

population	share	of	65	years	plus	is	not	significant),	as	well	as	younger	workers	who	may	have	picked	up	

important	business	skills	elsewhere	and	could	perhaps	move	back	to	start	their	own	businesses.	Likewise,	

either	attracting	college	graduates	or	encouraging	local	residents	to	pursue	college	training	and	remain	in	the	

county,	to	the	extent	that	is	feasible,	would	be	an	important	strategy.

In	the	full	model,	distance	from	major	metropolitan	areas	is	associated	with	greater	economic	resilience,	

whereas	neither	self-employment	shares	nor	business	density	matter	in	this	specification.	Before	dismissing	

these	latter	two	factors	as	central	strategies	for	achieving	resilience,	however,	it	is	important	to	test	for	nonlinearities	as	well	as	potential	interactions.	We	do	this	in	a	sensitivity	analysis,	with	results	reported	below.	

While	lowering	poverty	rates	to	achieve	greater	resilience	is	important	but	not	easily	accomplished,	

recognizing	the	importance	of	labor	force	participation	and	innovation	potential	in	the	community	as well	as	

seeking	greater	diversification	of	the	industrial	base	would	be	important	strategies.	In	fact,	many	leaders	in	

the	Appalachian	Region	are	starting	to	recognize	the	advantages	of	a	diversified	economic	portfolio	over	

reliance	on	a	single	industry	such	as	coal	mining	(e.g.,	Gunn,	2017).	Such	strategies	could	include	more	local	

tourism-type	activities,	for	example.	Somewhat	surprisingly,	greater	diversity	of	types	and	levels	of	

knowledge	is	associated	with	lower	resilience.4 This	should	be	explored	in greater	detail	in	future	research.	

Our	regression	analysis	also	confirms	that	coal	counties	(as	measured	by	a	high	share	of	employment	in	the	

coal	industry)	were	less	resilient	in	the	recent	downturn.	The	effects	of	farming	and	oil	and	gas	are	positive,	

																																								 																				 4 The ARC report defines this as (Feser et al., 2014, p. 8) “...employment in twelve broad groups of occupations, with the groups reflecting the different types and 

levels of knowledge required for success in various professions. This produces a measure of knowledge-based economic diversity (or knowledge diversity).”
17 |	Page

while	manufacturing	dominance	had	negative	effects	on	resilience;	however,	these	variables	are	not	

statistically	significant	in	our	model.

Table	4:	Regression	results for	the	three	subcategories	of	variables

Demographic/

mobility

Economic/

industry

Community/

health Entire model

Variable beta t beta t beta t beta t

Demographic/mobility

Age	25	to	44 0.194*** 5.12 0.099** 2.43

Age	65	and	up 0.055 1.39 -0.033 -0.69

Ethnic	Diversity 0.006 0.27 0.086*** 3.13

Mobility	In-Migration 0.107*** 3.75 0.119*** 4.02

Mobility	Out-Migration -0.073*** -2.94 -0.089*** -3.7

Mobility	In-Commuting 0.013 0.51 0.058** 2.04

Mobility	Out-Commuting 0.097*** 4.07 0.014 0.43

Residence	Born -0.002 -0.09 0.004 0.15

Population 0.213*** 6.38 0.279*** 4.58

Land	Area -0.002 -0.1 -0.032 -1.5

College	 0.207*** 7.04 0.107*** 2.85

Economic/industry

Distance	to	City 0.037 0.87 0.147*** 3.22

Business	Density -0.169*** -7.19 0.036 1.02

Self-Employment -0.004 -0.22 0.007 0.27

Poverty -0.103*** -3.48 -0.111*** -3.03

Participation 0.188*** 6.27 0.085** 2.1

Innovation	Potential 0.149*** 6.27 0.06** 2.2

Industry	Diversity 0.153*** 4.94 0.092* 1.93

Knowledge	Diversity -0.092*** -4.4 -0.051** -2.38

Farming -0.066** -2.01 0.059 1.44

Oil	&	Gas 0.022 0.66 0.044 1.29

Coal -0.069*** -4.21 -0.063*** -3.82

Manufacturing -0.113*** -6.08 -0.03 -1.36

Community/health

Primary	and	Secondary 0.068*** 3.28 -0.007 -0.34

Community	College 0.033** 2.02 -0.026* -1.76

Broadband 0.134*** 5.49 -0.035 -1.35

Child/Elder	Care 0.018 0.87 0.004 0.17

Health	Practitioner -0.039 -1.60 -0.095*** -4.51

Recreational	Opportunity 0.109*** 4.62 -0.022 -0.9

Natural	Amenity 0.003 0.09 0.006 0.17

Social	Capital -0.102*** -4.07 0.004 0.1

Federal	Funds -0.033 -1.46 -0.008 -0.3

Voting	Competitiveness 0.028 1.17 -0.007 -0.29

Agritourism 0.056*** 3.48 0.016 1.13

Direct	Farm	Sales 0.076*** 4.00 0.027* 1.7

Constant *** 2.81 *** 11.28 *** 19.66 *** 2.71

N 3,054 3,053 3,050 3,049

Adjusted R2 0.2250 0.2112 0.1173 0.2577

Note: Table shows robust standardized coefficients with state fixed effect. Significance levels: different from zero at *10%, **5%, and ***1% or lower.
18 |	Page

Among	the	community	and	health	factors,	only	the	expansion	of	direct	farm	sales	remains	as	one	potential	

local	strategy	for	enhancing	resilience. However,	beyond	making	farmers	and	consumers	aware	of	this	

possibility	through	education	and	marketing	or	advertising,	it	is	not	obvious	how	to	do	this. Instead,	this	

variable	likely	is	picking	up	some	kind	of	community	cohesion	effect	that	is	unique	to	the	individuals	living	

there	and	could	be	difficult	to	replicate	or	expand	through	policy. Even	so,	it	is	worth	highlighting	these	kinds	

of	successful	communities	if	only	to	show	others	what	is	possible	through	effective	leadership. It	is	also	

noteworthy	from	our	last	equation	that	the	effect	of	community	colleges	(still	positive	in	the	standalone	

regression	for	community/health	factors)	turns	negative	once	we	control	for	the	other	intervening	factors.

This	would	suggest	that	promoting	the	number	of	community	colleges	is	not	necessarily	a	workable	strategy	

for	increasing	resilience.

Likewise,	while	regional	medical	hubs	can	be	important	drivers–especially	in	remote	communities,	as	our	

expert	panel	also	has	suggested–our	model	suggests	that	greater	employment	of	healthcare	practitioners	is	

detrimental	to	resilience. Before	dismissing	the	development	of	healthcare	hubs	as	a	potential	strategy,	this	

would	warrant	further	investigation. It	is	also	possible	that	we	are	picking	up	a	reverse	causation	in	that	a	less	

healthy	population	or	workforce,	which	requires	more	medical	care,	is	inherently	less	likely	to	be	able	to	

support	a	robust	and	resilient	economy. We	also	note	that employment in	the	primary/secondary	education	

sector,	broadband	availability,	child/elder	care	availability,	recreational	opportunities,	natural	amenities,	

social	capital,	federal	funds	secured,	voting	competitiveness, and	agri-tourism	have	no	effect	on	resilience	in	

this	model.5 We	examine	some	of	these	variables	in	greater	detail	in	subsequent	sensitivity	tests	discussed	

below.

2.6 Sensitivity	analyses

Before	dismissing	altogether	some	of	the	key	variables	that	were	hypothesized	to	be	significant	but	turned	

out	not	to	be,	or	had	unexpected	signs,	we	conduct	a	series	of	sensitivity	tests involving	non-linear	effects	

(quadratic	terms)	as	well	as	interactions	between	selected	variables	(see	Appendix	E and	F for	detailed

results).

When	we	include	a	quadratic	term	for	self-employment,	the	linear	term	is	positive	(0.158)	while	the	

quadratic	term	is	negative	(−0.145),	with	both	being	statistically	significant. This	indicates	that	the	effect	of	

self-employment	(our	proxy	for	entrepreneurship)	is positive	at	lower	levels	of	self-employment,	increases	up	

to	a	maximum	self-employment	rate	of	54.5 percent,	and	then	becomes	negative	at	even	higher	levels	of	

self-employment	(indicating	too	much	of	a	“good	thing”). Only	nine	counties	out	of	the	over	3,000	currently	

have	self-employment	shares	above	this	maximum	(>54.5 percent). Thus,	a	strategy	of	promoting	selfemployment	to	enhance	economic	resilience	would	be	most	effective	in	communities	starting	out	with	lower	

levels	of	self-employment. This	emphasis	on	homegrown	entrepreneurship	is	important,	because	it	was	

stressed	repeatedly	by	the	panel	of	experts.

We	also	explore	interactions	among	key	variables. For	example,	a	positive	effect	of	social	capital	on	countylevel resilience	occurs	with	smaller	shares	of	25-44-year-olds;	with	smaller	shares	of	in-commuters;	with	

smaller	total	populations;	and	with	greater	distance	from	large	population	centers. Each	of	these	results	is	

plausible	and	can	be	explained	as	follows. Individuals	in	the	25-44-year-old	category	are	more	likely	to	be	

launching	careers	and	raising	young	families. Thus,	the	effects	of	any	social	capital	in	the	community	are	less	

likely	to	be	amplified	or	leveraged	(compared	to	communities	with	proportionately	larger	shares	of	older	

workers,	who	may	have	more	free	time)	in	terms	of	enhancing	resilience. While	commuting	(and	migration)	

can	entail	benefits	in	terms	of	greater	information	flows	(Goetz	and	Han,	2015),	commuters	also	have	less	at	

stake	in	terms	of	the	general	social	wellbeing	of	the	communities	to	which	they	commute;	they	are	more	

likely	to	expend	their	social	capital	in	the	communities	where	they	live. As	a	result,	the	weakened	effect	of	

																																								 																				 5 Digression on model results for ARC region only: when we estimate the regression equations for only the 412 counties of the ARC region, only in-migration 

rates, distance from major metros, and voting competiveness show up as statistically significant, with the latter carrying an unexpected positive sign.
19 |	Page

social	capital	in	communities	with	large	in-commuter	shares	is	not	surprising. In	communities	with	smaller	

total	populations,	and	that	are	more	distant	and	isolated	from	larger	cities,	the	feeling	of	cohesion	and	selfreliance	(“we	have	to	solve	our	problems	on	our	own	and	cannot	rely	on	help	from	elsewhere”)	may	be	

greater, and	thus	existing	social	capital	can	be	leveraged	to	greater	effect. The	general	conclusion	from	this	

supplemental	analysis	is	that	social	capital	does	matter	for	resilience,	but	only	under	the	right	conditions.

This	finding	is	important	because	a	number	of	the	experts	interviewed	for	this	project	indicated	that	social	

capital	was	key	to	economic	resilience	in	a	community.

Similarly,	we	find	statistically	significant	interactions	between	self-employment	rates	and	a	number	of	

variables. Notable	among these	is	that	self-employment	becomes	more	effective	as	a	resilience-enhancing	

strategy	in	counties	with	larger	populations	(perhaps	because	these	businesses	can find	more	customers).

Conversely,	it	becomes	less	successful	as	distance	to	major	metropolitan	areas	declines,	perhaps	because	

they	have	fewer	opportunities	to	sell	goods	and	services	into	cities. Also,	it	is	less	successful	as	the	labor	force	

participation	rate	and	employment	in	farming	each	increase. The	reasons	for	this	latter	finding	could	be	

explored	further. Again,	rather	than	dismissing	the	promotion	of	local	entrepreneurship,	another	key	factor	

identified	by	the	experts,	policy	makers	and	planners	need	to	probe	more	deeply	to	assess	whether	the	right	

conditions	exist	locally.

Federal	funding	is	more	effective	in	furthering	resilience	in	communities	that	have	smaller	shares	of	incommuters,	perhaps	because	the	benefits	of	such	funding	do	not	remain	in	the	community	if	more	workers	

leave	at	the	end	of	every	day. Having	more	child/elder	care	facilities	improves	prospects	for	resilience	in	

communities	with	lower	levels	of	industry	diversity. Voting	competitiveness	matters	more	for	resilience	as	

the	rate	of	in-migration	declines. This	could	reflect	the	fact	that	it	is	more	difficult	for	in-migrants	who	are	

new	to	an	area	to	connect	with	elected	leaders	(and	those	seeking	office)	to	express	their	preferences.

Recreational	opportunities	and	business	density	also	interact	negatively,	indicating	that	having	a	greater	

business	density	together	with	greater	recreational	opportunities	is	detrimental	to	economic	resilience;	a	

plausible	explanation	is	that	the	businesses	have	too	many	distractions,	or	that	if	there	are	fewer	businesses,

the	recreational	opportunities	get	used	more,	thereby	contributing	to	greater	resilience.6

This	discussion	has	raised	more	questions	for	further	research	and	does	not	provide	definitive	answers. Our	

main	purpose	for	including	these	results	of	the	sensitivity	analyses	here	is	to	caution	against	dismissing	any	

one	of	the	strategies	identified	simply	because	the	variable	was	not	statistically	significant	in	the	regression	

model. Instead,	a	more	nuanced	and	subtle	analysis	is	needed	to	understand	the	full	range	of	effects	and	

conditions	under	which	they	work	best.

An	additional	form	of	sensitivity	analysis	is	included	in	Chapter	3,	which	presents	a	spatial	econometric	

analysis of	economic	resilience	variables	and	their	spillover	influences	beyond	county	boundaries.

2.7 Analysis	of	residuals

The	results	of	the	regression	analysis	reported	above	are	valuable	for	understanding	the	independent	effects	

of	various	factors	hypothesized	to enhance	economic	resilience. However,	the	regression	is	also	valuable	in	

that	it	highlights	those	counties	that	perform	much	better	(or	worse)	in	terms	of	resilience	than	they	are	

expected	to,	controlling	for	the	list	of	explanatory	factors. These	counties	lie	far	above	(or	below)	the	

regression	line	when	the	predicted	value	of	the	dependent	variable	is	plotted	against	the	actual	value	

(resilience);	these	counties	have	very	large	positive	(or	negative)	residuals.

Residuals	are	useful	in	identifying	counties	for	further	study,	because	those	with	the	highest	residuals	may	

hold	lessons	in	strengthening	economic	resilience	for	counties	across	the	Appalachian	Region.	When	we	

																																								 																				 6 Fan et al. (2016) find that human capital and natural amenities individually and jointly increase wage growth, but in our study, such an effect is not observed for 

resilience.
20 |	Page

select	these	counties	for	further	study, we	want	to	rule	out	those	that	have	a	unique	condition	that	cannot	

be	easily	replicated	elsewhere. For	example,	a	county	may	have	experienced	a	temporary	construction	boom	

due	to	an	infusion	of	outside	money. After	the	boom	is	over,	employment	returns	to	the	previous	level,	and	

this	county	is	not	on	a sustainable	growth	path. Or	a	county	may	be	home	to	the	state’s	land-grant	university,	

which	is	not	easily	replicated	elsewhere	in	the	state.

The	list	of	top-ranked	residuals	generated	by	the	regression	model	identifies	those	counties	that	performed	

better	than	expected after	the	Great	Recession,	considering	the	list	of	variables	included	as	explanatory	

factors	(see	Table	5 and	Appendix	G). For	example, where	one	would	expect	counties	with	greater	shares	of

college	graduates	to	be	more	resilient	than	those	with	smaller	shares,	some	counties	may	be	even	more	

resilient	than	they	should	be	given	their	share	of	college	graduates. In	this	example,	something	is	happening	

in	these	communities	that	makes	them	more	resilient than	expected. Whatever	that	factor	is,	it	is	not	

captured	in	the	regression	model,	and	by	identifying	it	(or	them)	we	may	gain	additional	insights	into	the	

determinants	of	resilience. For	example,	some	communities	may	have	even	stronger	civic	leadership	capacity	

than	is	reflected	in	our	social	capital	measure,	and	other	communities	may	have	more farmers	markets.

Table	5 shows	the	top-ranked	counties	in	terms	of	the	resilience	residual,	calculated	from	the	regression	

equation.	Kemper,	MS	has	the	highest	residual.	Further	investigation	shows	that	this	county	benefited	from	

construction	of	a	lignite	power	plant	around	2008-10, and	the	high	residual	reflects	the	temporary	boom	in	

the	construction	sector,	as	employment	subsequently	fell	back	to	the	earlier	period.	Employment	growth	in	

construction	accounted	for	three-quarters	of	the	post–Great	Recession	job	boost	in	this	county.	Thus,	we	

exclude	this	county	from	further	consideration.

Using	a	similar	argument,	we	exclude	Doddridge,	WV,	which	benefited	from	the	shale	gas	boom,	and	we	

exclude	Monongalia,	WV,	which	is	home	to	West	Virginia	University	and	has	enjoyed	steady	growth	thanks	to	

that	institution.	On	the	other	hand,	Taylor,	WV,	which	borders	Monongalia,	WV,	has	experienced	an	

interesting	rebound	not	just	associated	with	spillover	housing	development.	Therefore,	understanding	what	

has	happened	here	through	community	leadership	may	prove	fruitful	in	that	it	can	be	replicated	elsewhere.	

For	each	of	the	other	counties,	we	list	the	sector	that	contributed	the	most	to	the	higher-than-expected	

resilience	in	that	county,	as	well	as	the	percent	of	the	overall	county	job	growth	that	is	accounted	for	by	

expansion	in	this	sector(s).	In	three	cases	(Taylor,	WV;	Morgan,	TN;	and	Bledsoe,	TN),	multiple	sectors	played	

important	roles.	We	investigate	this	further	with	the	goal	of	identifying	a	possible	strategy	that	builds on	

diversification.	It	is	also	noteworthy	that	four	counties	saw	increases	in	manufacturing	as	the	source	of	their	

higher-than-expected	resilience	levels.
21 |	Page

Table	5:	Top	24	counties	ranked	based	on	regression	residuals

Rank County

Population

(2015) Resilience Residuals Dominant	sector in	recovery

Percent of	job	

growth	

accounted	for	

by	this sector

1 Kemper,	MS 10,551 1.081 0.490 Construction 75.4%

2 Swain,	NC 13,463 0.908 0.379 Local	government 88.3%

3 Doddridge,	WV 7,808 0.901 0.323 Natural	resources	&	mining 45.5%

4 Taylor,	WV 16,318 0.828 0.254 N/A N/A

5 Monongalia,	WV 88,424 0.844 0.253 Education	&	health	services 25.7%

6 Jackson,	GA 50,607 0.895 0.227 Trade,	transportation,	&	utilities 44.2%

7 Harrison,	WV 67,989 0.794 0.221 Education	&	health	services 45.9%

8 Union,	MS 26,569 0.807 0.204 Manufacturing 62.0%

9 Chenango,	NY 51,154 0.75 0.204 Manufacturing 59.4%

10 Choctaw,	MS 9,026 0.708 0.198 Local	government 62.8%

11 Morgan,	TN 20,959 0.784 0.195 N/A N/A

12 Holmes, OH 41,630 0.812 0.181 Manufacturing 51.3%

13 Bledsoe,	TN 12,685 0.852 0.176 N/A N/A

14 Washington,	PA 205,359 0.762 0.165 Professional	&	business	services 38.4%

15 Harrison,	OH 15,907 0.652 0.154 Trade,	transportation,	&	utilities 50.5%

16 Pickett,	TN 4,997 0.737 0.153 Trade,	transportation,	&	utilities 58.1%

17 Cortland,	NY 49,330 0.678 0.145 Leisure	&	hospitality 51.6%

18 Bradley,	TN 93,024 0.806 0.130 Professional	&	business	services 22.8%

19 Madison,	KY 78,732 0.762 0.123 Manufacturing 32.7%

20 Itawamba,	MS 23,254 0.71 0.122 Manufacturing 93.1%

21 Buncombe,	NC 221,642 0.745 0.118 Education	&	health	services 26.9%

22 Grayson,	VA 16,153 0.694 0.116 State	government 81.2%

23 Oktibbeha,	MS 43,985 0.746 0.110 Leisure	&	hospitality 40.5%

24 Butler,	PA 179,522 0.756 0.109 Professional	&	business	services 30.5%

Source: USDL (2015). Note: The data is not seasonally adjusted.

23 |	Page

the	spatial	lag	in	the	dependent	variable,	and	ρWY represents	the	spatial	autoregressive	term.	WX measures	

the	spatial	lag	in	the	explanatory	variables.	The	spatial	weight	matrix,	W,	is	defined	by	j nearest	neighbors.

Our	SDM	produces	coefficient	estimates	at	three	levels:	direct,	indirect,	and	total	effects.	These	estimates	

provide	an	average	effect	for	the	entire	study	region.	The	direct	effect	measures,	on	average,	how	a	change	

in	an	explanatory	variable	in	county	i affects	resilience	in	county	i.	The	interpretation	of	the	direct	effect	

follows	standard	OLS	coefficient	interpretation.	The	indirect	effect	is	the	difference	between	the	total	effect	

and	the	direct	effect.	It	measures	the	average	impact	from	an	explanatory	variable	in	county	i on	resilience	in	

j counties	surrounding	county	i,	also	known	as	the	spillover	effect.	The	total	effect	is	the	sum	of	the	direct	

and	indirect	effects.	The	total	effect	can	be	interpreted	as	the	average	total	impact	on	resilience	in	every	

county	resulting	from	a	change	in	an	explanatory	variable	within	an	individual	county	i.	The	scalar	ρ estimates	

the	level	of	spatial	dependence	in	these	data.	Interpretation	of	the	indirect	and	total	effects	is	provided	for	

the	statistically	significant	variables	below.

3.2 Spatial	model	results	

Initially,	we	compare	the	direct	effect	coefficient	estimates from	the	SDM with	the	coefficients	from	the	

entire	model	of	the	OLS	regression.	The	OLS	coefficients	are	shown in	Table	4,	and	they	are	compared	with	

the	direct	effect	coefficients	in Table	6. State	fixed	effects	(coefficients	not	shown)	and	robust	standard	error	

estimates	are	included	as	part	of	the	OLS	regression.	Both	models	have	a	similar	level	of	explanatory	power	

with	adjusted	R2 values	of	approximately	0.25,	explaining	one-quarter	of	the	variation	in	the	dependent	

variable. The	OLS	model	does	have	a	slightly	higher	adjusted	R2 value.

For	most	explanatory	variables,	the	β	coefficient	estimates	are	similar	between	the	OLS	model	and	the	direct	

effects	of	the	SDM. Based	on	coefficient	estimates	from	either	model,	the	largest	positive	influences	on	

resilience	come	from	population	size,	distance	to	cities,	mobility	in-migration,	and	percentage of	the	

population	above age 25	with	a	college	degree. Percentage	of	population	in	poverty	has	the strongest

negative	influence	on	resilience in	both	models.

A few	variables,	however,	have	different	signs between	the	OLS	regression	and	SDM	direct	effects:	child	and	

elder	care,	social	capital,	and	voting	competitiveness.	None	of	these	variables	have	coefficient	estimates	that	

are	statistically	different	from	zero	in	either	model. The	ethnic	diversity,	diverse	knowledge,	community	

college,	and	direct	farm	sale	variables	have	statistically	significant	coefficients	in	OLS	model,	but	their	

coefficients	are	not	statistically	significant	in	the	SDM. However,	the	community	college	and	direct	farm	sale	

variables	are	only	statistically	significant	at	the	10	percent	level	in	the	OLS	model.	Conversely,	the	farming	

and	broadband	variables	have	statistically	significant	direct	effects	in the	SDM	regression,	but	are	not	

statistically	significant	in	the	OLS	model.	The	SDM	controls	for	spatial	dependence	(bias)	in	the	explanatory	

variables,	but	the	OLS	model	cannot.

The	direct	effect	coefficients in	Table	6 suggest	several	conclusions,	including,	for	example:

• The	distance to	a	city	of	more	than	250,000	has	a positive	direct	effect	on	resilience,	significant	at	

the	99	percent	level.	As	this	distance	increases	by	one	standard	deviation,	economic	resilience	within	

the	county	will	also	increase	by	0.146	standard	deviations	from	the	mean.

• Poverty	has	a	negative	direct	influence	on	resilience,	at	the	99	percent	level.	As	the	percentage of	

the	population	who	live	in	poverty	increases	by	one	standard	deviation,	the	economic	resilience	of	

the	county	will	decrease	by	-0.086	standard	deviations	from	the	mean.	
24 |	Page

Table	6:	Comparison	of	direct	effects	and	OLS	results	with	beta	estimates

Variables OLS	results Direct	effect

Demographic/mobility

Age	25	to	44 0.099** 0.098**

Age	65	and	up -0.033 -0.013

Ethnic	Diversity 0.086*** 0.070

Mobility	In-Migration 0.119*** 0.124***

Mobility	Out-Migration -0.089*** -0.082***

Mobility	In-Commuting 0.058** 0.061***

Mobility	Out-Commuting 0.014 0.017

Residence	Born 0.004 0.021

Population 0.279*** 0.291***

Land	Area -0.032 -0.037

College	 0.107** 0.127***

Economic/industry

Distance	to	City 0.147*** 0.146***

Business	Density 0.036 0.032

Self-Employment 0.007 0.014

Poverty -0.111*** -0.086***

Participation 0.085** 0.077**

Innovation	Potential 0.060** 0.068**

Industry Diversity 0.092* 0.081***

Knowledge Diversity -0.051** -0.045

Farming 0.059 0.061***

Oil	&	Gas 0.044 0.048

Coal -0.063*** -0.051***

Manufacturing -0.030 -0.010

Community/health

Primary	and	Secondary -0.007 -0.011

Community	College -0.026* -0.020

Broadband -0.035 -0.031*

Child/Elder	Care 0.004 -0.017

Health	Practitioner -0.095*** -0.083***

Recreational Opportunity -0.022 -0.013

Natural	Amenity 0.006 0.010

Social	Capital 0.004 -0.012

Federal	Funds -0.008 -0.002

Voting	Competitiveness -0.007 0.014

Agritourism 0.016 0.012

Direct	Farm	Sales 0.027* 0.009

Constant *** -

Adjusted	R2 0.2577 0.2410

Note: Significance levels: different from zero at *10%, **5%, and ***1% or lower.

The	ρ	value	for	the	SDM,	which	identifies	spatial	dependence	within	these	county-level	data, is	statistically	

significant	at	the	99	percent	level (see	Table	7). This	suggests	that	estimation	with	the	spatial	econometric	

model	is	appropriate.	Because	ρ is	positive,	our	model	identifies	that	economic	activities	or	policies	in	county	

i	that	influence	resilience	have	a	similar	spillover	in	county	j.

A spatial	weight	matrix	of	six	nearest	neighbors	produced	the largest	maximum	likelihood	value.	This	means	

the	spatial	dependence	of	a	county	extends	to	the	six	closest,	neighboring	counties.	Distance	between	

counties	is	based	upon	the	distance	between	the	central	locations	of	each	county,	known	as	a	centroid. The	
25 |	Page

SDM	incorporates	a	global	effect	by	allowing	for	a	“ripple	effect”	from	changes	at	a	single	county	level, which	

impacts	six	neighboring	counties,	each	of	which	also	impacts	six	neighboring	counties,	and	so	on.

For	the	estimation	of	county-level	resilience,	spatial	econometric	models	provide	a	number	of	advantages	in	

interpretation	and	discussion	compared	to	the	OLS	regression	model.	Economic	resilience	is	not	just	a	

county-level	phenomenon. For	example,	a	change	in	one	county’s	ability	to	generate	economic	resilience

through	policies	that	impact	the	magnitude	of	a	variable	(such	as	an	increased	college-educated	population)	

will	have	a	multi-county	response.	Spatial	econometric	models	account	for	the	observed	spatial	dependence	

within	these	data.	In	addition,	based	on	the	direct,	indirect,	and	total	effect	estimates,	we	can	identify	policy	

issues	that	are	most	appropriately	addressed	as	local	issues	(those	with	minor	spillover	effects)	versus	those	

that	are	best	addressed	as	regional	or	statewide	issues (those	with	large	spillover	effects).	

Based	on	the	signs	of	the	direct	and	indirect	coefficients,	we	can	also	identify	variables	that	provide	either	

competitive	or	cooperative	strategies. Competitive	strategies	impact	independent	variables	that	have	the	

opposite	signs	for	direct	and	indirect	coefficient	estimates.	In	other	words,	the	strategy	in	county	i would	

promote	resilience	in	county i, while	detracting	from	resilience	in	county	j. Cooperative	strategies	impact	

independent	variables	that	have	the	same	coefficient	signs	for	both	direct	and	indirect	coefficients.	These	

strategies	have	the	same	influence	on	resilience	both	within	and	outside	a	county.

3.3 Results:	Important	indirect	effects	and	total	effects	estimates	

Table	7 presents	the	results	of	the	spatial	econometric	model.	The	magnitude	of	these	coefficient	estimates	

(positive	and	negative)	are	ranked	in	Table	8.	The	following	discussion	provides	our	interpretation	of	the	

estimated	coefficients	for	the	statistically	significant	variables	in	each	category.	

3.3.1 Demographic/Mobility	variables

The	characteristics	of	a	county’s	population	influence	its	resilience.	Population	size	has	the	largest	positive	

direct	and	total	effects	on	resilience	plus	the	second-largest	indirect	effect	(both	statistically	significant	at	the	

99	percent	level).	As	the	population	increases	within	a	county,	the	county	itself,	as	well	as	counties	in	the	

surrounding	region,	will	increase	in	resilience.	Thus,	on	average,	larger	populations	promote	greater	

resilience.	

The	age	distribution of	the population	also	influences	a	county’s	resilience	level.	The	percentage of	

population age	25	to	44	has	a	positive	direct	effect,	while	the	indirect	and	total	effects	are	negative,	although	

the	total	effect	is	the	smallest	of	all	negative	coefficients.	As	this	portion	of	the	population	increases	within	a	

county,	the	resilience	of	that	county	will	increase.	However,	attraction	of	younger	workers	to	county	i causes	

declines	in	resilience	within	surrounding	counties	as	well	as	the	total	regional	effect.	This	impact	might	be	

explained	by	a	competitive	effect	of	businesses	and	firms	being	more	likely	to	locate	in	proximity	to	the	25-

to-44-year-old	segment	of	the	workforce.	The	percent	of	population	age	65	and	up	has	the	largest	negative	

indirect	and	total	effects	(significant	at	the	99	percent	level);	as	the	older	population	increases	within	a	

county,	resilience	of	nearby	counties	and	the	total	region	decreases.	Lastly,	the	educational	attainment	of	the	

population	also	influences	a	region’s	resilience.	College	education	among	the	population	has	a	positive	direct	

effect	but	a	negative	indirect	effect	on	resilience	(both	significant	at	the	99	percent	level).	As	the	percent	of	

the	population	with	a	bachelor	degree	or	higher	increases	within	a	county,	the	resilience	of	that	county	

increases	while	the	resilience	of	surrounding	counties	decreases.	Thus,	county-level	strategies	that	attract	

more	of	a	college-educated	population	are	competitive	with	the	economic	resilience	of	surrounding	counties.	
26 |	Page

Table	7:	Spatial	econometric	regression	estimate	results	for	economic	resilience

Direct Indirect Total

Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat

Demographic/mobility

Age	25	to	44 0.098 6.308*** -0.148 -6.427*** -0.050 -1.661*

Age	65	and	up -0.013 -0.333 -0.296 -2.906*** -0.309 -2.834***

Ethnic	Diversity 0.070 1.507 0.074 0.616 0.144 1.142

Mobility	In-Migration 0.124 4.724*** 0.054 0.946 0.178 3.077***

Mobility	Out-Migration -0.082 -3.147*** -0.127 -2.058** -0.209 -3.211***

Mobility	In-Commuting 0.061 2.767*** 0.118 1.979** 0.179 2.724***

Mobility	Out-Commuting 0.017 0.683 -0.290 -4.376*** -0.273 -3.694***

Residence	Born 0.021 0.712 0.064 0.727 0.085 0.887

Population 0.291 10.425*** 0.394 6.471*** 0.685 11.015***

Land	Area -0.037 -0.628 -0.084 -0.580 -0.122 -0.774

College	 0.127 4.989*** -0.125 -2.599*** 0.002 0.042

Economic/industry

Distance	to	City 0.146 4.462*** 0.016 0.190 0.162 1.848*

Business	Density 0.032 0.662 0.103 1.524 0.135 3.129***

Self-Employment 0.014 0.398 0.010 0.115 0.025 0.258

Poverty -0.086 -3.627*** -0.201 -2.758*** -0.287 -3.622***

Participation 0.077 2.177** 0.021 0.236 0.098 1.051

Innovation	Potential 0.068 2.047** -0.051 -0.568 0.017 0.180

Industry Diversity 0.081 3.410*** -0.002 -0.031 0.079 1.031

Knowledge Diversity -0.045 -1.006 0.109 0.885 0.064 0.482

Farming 0.061 2.909*** 0.521 8.572*** 0.582 8.606***

Oil	&	Gas 0.048 1.356 0.053 0.633 0.101 1.116

Coal -0.051 -2.844*** -0.042 -1.025 -0.092 -2.161**

Manufacturing -0.010 -0.590 -0.078 -1.758* -0.089 -1.850***

Community/health

Primary	and	Secondary -0.011 -0.529 0.029 0.507 0.018 0.289

Community	College -0.020 -1.007 -0.104 -2.033** -0.124 -2.236**

Broadband -0.031 -1.723* -0.045 -0.889 -0.075 -1.367

Child/Elder	Care -0.017 -0.750 -0.051 -0.917 -0.069 -1.166

Health	Practitioner -0.083 -4.419*** -0.102 -1.877* -0.185 -3.175***

Recreational Opportunity -0.013 -0.539 -0.118 -1.647* -0.132 -1.657*

Natural	Amenity 0.010 0.454 0.105 1.691* 0.116 1.698*

Social	Capital -0.012 -0.341 -0.063 -1.104 -0.075 -1.439

Federal	Funds -0.002 -0.052 0.040 0.511 0.038 0.469

Voting	Competitiveness 0.014 0.584 -0.090 -1.574 -0.076 -1.276

Agritourism 0.012 0.582 0.042 0.947 0.053 1.195

Direct	Farm	Sales 0.009 0.539 -0.190 -3.795*** -0.181 -3.256***

Note: Significance levels: Different from zero at 10%*, 5%**, and 1% or lower***. Spatial Durbin Model, six nearest neighbors. Dependent variable = Resilience. 

R2 = 0.2585. Rbar-2 = 0.2410. sigma2 = 0.7222. log-likelihood = -2786.655. Nobs, Nvars = 3,049, 71. ρ = 0.220 ***.

Four	variables	measuring	mobility	are included	within	this	analysis:	in-migration,	out-migration,	incommuting,	and	out-commuting.	In-migration	has	positive	direct	and	total	effects	(significant	at	the	99	

percent	level).	As	more	people	migrate	to	a	county,	resilience	of	the	county	and	the	surrounding	counties	

increases. As	communities	create	places	where	people	want	to	live,	the	populations	of	these	communities	

will	increase, as	will	the	resilience.	Conversely,	out-migration	has	negative	direct,	indirect,	and	total	effects	

on	resilience	(direct	and	total	significant	at	the	99	percent	level).	As	people	leave	a	county,	presumably	for	

jobs	and	economic	opportunities	elsewhere,	resilience	declines	throughout	the	entire	region.	Thus,	outmigration	has	negative	impacts	on	the	resilience	for	the	county,	surrounding	counties,	and	the	region	as	a	

whole.	
27 |	Page

Table	8:	Ranked	order	of	positive	and	negative	impacts	of	statistically	significant	SDM beta	coefficients

Direct Indirect Total

Positive Negative Positive Negative Positive Negative

Population

Distance	to	City	

College

Mobility	InMigration

Age	25	to	44

Industry	

Diversity	

Participation

Innovation	

Potential

Farming

Mobility	InCommuting

Poverty

Health	

Practitioner

Mobility	OutMigration

Coal

Broadband

Farming

Population

Mobility	InCommuting

Natural	Amenity

Age	65	and	up

Mobility	OutCommuting

Poverty

Direct	Farm	

Sales

Age	25	to	44

Mobility	OutMigration

College

Recreational	

Opportunity

Community	

College

Health	

Practitioner

Manufacturing

Population

Farming

Mobility	InCommuting

Mobility	InMigration

Distance	to	City

Business	Density

Natural	Amenity

Age	65	and	up

Poverty

Mobility	OutCommuting

Mobility	OutMigration

Health	

Practitioner

Direct	Farm	

Sales

Recreational	

Opportunity

Community	

College

Coal

Manufacturing

Age	25	to	44

Note: Variables are listed from highest to lowest impact.

In-commuting	mobility	has	positive	direct,	indirect,	and	total	effects	on	economic	resilience	(significant	at	the	

99	percent	level). As	the	number	of	people	who	commute	into	a	county	for	employment	increases,	the	

resilience	of	the	entire	region	also	increases.	An	increase	in	the	number	of	in-commuters	will	increase	

participation	in	the	local	economy.	High	in-commuting	levels	also	indicate a	“wealthy”	region,	full	of	

employment	opportunities	and	other	economic	resources.	Out-commuting	has	statistically	significant	

negative	indirect	and	total	effects	on	resilience	(significant	at	the	99	percent	level).	As	out-commuting	

increases from	the	county,	the	resilience	of	the	surrounding	counties	and	the	total	region	decreases.	One	

explanation	for	this	outcome	may	be	that	employment	levels	in	counties	surrounding	those	counties	with	

high	percentages	of	out-commuting	did	not	rebound	to	the	same	level	after	the Great Recession	because	

people	commuting	from	out	of	the	county	for	employment	are	less	likely	to	return	to	employment	in	

surrounding	counties	after	the	recession	receded.

3.3.2 Economic/Industry variables

The	regional	placement	of	the	county	as	well	as	its	poverty	rate	and	business	density	all	influence	the	

resilience	of	a	county.	As	the	distance	to	a	large	city	increases,	the	resilience	of	a	county	and	the	total	region	

increases. This	result	contrasts	with	the	estimated	positive	coefficient	for	the	population	variable. An	

increase	in	business	density	in	a	county	has	a	positive	impact	on	the resilience	of	the	entire	region	

(statistically	significant	at	the	99	percent	level). The	indirect	effect	is	three	times	larger	than	the	direct	effect,	

so	that	this	variable	has	a	larger	impact	on	a	regional	basis	compared	to	a	county	basis. Poverty	has	the	

largest	negative	direct	effect, the	third-largest	indirect	effect, and	the	second-largest	total	effect	(each	
28 |	Page

statistically	significant	at	the	99	percent	level).	The	portion	of	the	population	in	poverty	within	a	county	has	

an	important	influence	on	the	resilience	of	the	entire	region.	As	poverty	increases	in	a	county,	the	resilience	

of	the county,	its	surrounding	counties,	and	the	entire	region	will	decrease.

The	industry	composition	of	a	county	also	reflects	its	resilience.	Farming,	coal, and	manufacturing	

employment	each	have	statistically	significant	effects. Employment	in	farming	has	positive	direct,	indirect,	

and	total	effects	(each	statistically	significant	at	the	99	percent	level).	As	farming	employment	grows	in	a	

county,	resilience	of	that	county, surrounding	counties,	and	the	total	region	will	increase.	Farming	

employment	has	a	unique	effect	on	resilience.	Farming	has	a	very	small	positive	direct	effect	at	the	county	

level; however,	it	increases	resilience	in	the	counties	of	the	entire	region	by	a magnitude	of	over	nine	times	

greater	than	for	the	county	itself	(the	largest	positive	indirect	effect).	Employment	in	the	coal	industry	has	a	

negative	direct	effect	(statistically	significant	at	the	99	percent	level)	and	total	effect	(statistically	significant	

at	the	95	percent	level).	As	employment	in	the	coal	industry	increases	in	a	county,	resilience	of	the	entire	

region	will	decrease.	Manufacturing	employment	has	slightly	negative	indirect	and	total	effects	(statistically	

significant	at	the	90	percent	level).	As	manufacturing	employment	increases	within	a	county,	resilience	for	

the	surrounding	counties	and	total	region	decreases	slightly, as	the	coefficients	are	small.	

3.3.3 Community/Health variables

Community	and	health	variables	influence	resilience	at	both	the	county	and	regional	levels.	Variables	

reflecting	employment	opportunities	that	support	the	community	and	the	natural	resource	base	within	a	

community	have	statistically	significant	effects. Employment	in	community	colleges	has	negative	indirect	and	

total	effects	(statistically	significant	at	the	95	percent	level). Employment	increases	in	community	colleges	

may	reflect	the	fact	that	more	of	the	population	in	surrounding	counties	is	continuing	their	education	instead	

of	entering	the	workforce.	Employment	in	healthcare	has	negative	direct,	indirect,	and	total	effects	

(statistically	significant	at	the	99	percent	level	for	direct	and	total	effects).	If	a	county	has	a	higher	level	of	

employment	in	the	healthcare	practitioner	field,	this	may	reflect	that	the county	and	surrounding	county	

populations	are	less	healthy,	creating	a	lower	percentage	of	the	population	being	employed. Thus,	while	

counties	may	observe	the	economic	gains	of	providing	health	care	services,	the	employment	losses	

associated with more	people	using	health	care	services is	not	as	readily	observed.

Employment	in	arts,	entertainment,	and	recreation	has	negative	indirect	and	total	effects	on	resilience	

(significant	at	the	95	percent	level).	As	recreation	opportunities	increase	in	a	county,	resilience	in	the	

surrounding	counties	and	the	entire region	decreases.	While	these	results	are	difficult	to	explain,	recreational	

employment	tends	not	to	be	particularly	stable	due	to	the	seasonality	of	these	occupations.	After	a	

recession,	employment	levels within	this	industry	may	not	rebound	as	well	as	other	industries.	Conversely,	

natural	amenities	have	positive	indirect	and	total	effects	on	resilience.	A	county	with	high	quality	natural	

amenities	provides	positive	spillover	effects	on	the	surrounding	counties	and	total	region	resilience.	Thus,	

preservation	of	natural	amenities	within	a	county	is	important	to	resilience	of	a	larger	region.	Direct	farm	

sales	have	negative	indirect	and	total	effects	(statistically	significant	at	the	99	percent	level).	As	direct	farm	

sales	increase	within	a	county,	resilience	of	surrounding	counties	and	the	total	region	decrease.	Thus,	any	

resiliency	benefits	of	direct	farm	sales	tend	to	stay	within	the	host	county,	though	not	at	a	statistically	

significant	level.

Direct	farm sales	do	have	negative	spillover	effects	in	surrounding	counties.	However,	this	variable	does	not	

indicate	the	size	of	the	farming	industry	in	a	county.	This	variable	reflects the	average	farm	sales	value	per	

farm.	Counties	surrounding	large	metropolitan areas	tend	to	have	a	higher	value	of	direct	farm	sales	than	

counties	in	predominantly	rural	areas. Thus,	these	coefficient	estimates	may	reflect	metropolitan	location	

rather	than	direct	farm	sales	themselves.
29 |	Page

3.4 County	relationships	and	policy	strategies	

The OLS	results	only	examine	the	impact	of	county-level	variables	on	economic	resilience	within	their	own	

county. However,	a	spatial	econometric	model	provides	a	multi-dimensional	analysis	that	explores	countylevel	variable	impacts	on	their	own	plus	neighboring	counties’	resilience.

When	making	policy	recommendations	based	on	the	observed	variables,	one	must	consider	that	a	policy	

implemented	in	a	given	county	may	impact	surrounding	counties.	While certain	policies	are	best	dealt	with	as	

single-county issues,	others	may	be	more	efficiently	implemented	as	a	joint	effort	with	surrounding	counties	

(or	even	at	the	state	level).	The	variables	impacting	resilience	may	also	point	to	different	strategies	needed	to	

address	a	policy	issue.	Based	on	the	results	of	the	spillover	analysis,	policies	may	be	complementary	

(cooperative) or	competitive across	county	lines.	Cooperative	strategies	strengthen	the	resilience	of	

surrounding	counties,	while	competitive	strategies	weaken	their	resilience.

3.4.1 Local	or	strictly	county-level	impacts

Variables	such	as	the	labor	market	participation rate,	potential	for	economic	innovation,	and	industrial	

diversity	all	have	positive	direct	effects	on	resilience	at	the	county	level	only	(see	Table	9).	These	approaches	

neither	help	nor	hurt	resilience	in neighboring	counties.	These	variables	support	the	best	strategy	and	

practice	of	moving	multiple	sectors	forward	for	economic	development discussed	in	the	qualitative	analysis.	

Policymakers	can	take	county-level	approaches	without	the	concern	of	how	these	policies	will	affect	the	

surrounding	regions.	

3.4.2 Cooperative	regional	policy	strategies

Based	on	our	results,	we	identify	two	types	of	multi-county	policy	approaches: cooperative	and	altruisticcooperative	(see	Table	9).	Cooperative	policy	strategies	include	variables	with	statistically	significant	

coefficients	for	direct	and	indirect	effects	that	have	the	same	signs.	Policies	in	this	classification	include those

that increase	population,	decrease	poverty,	and	reduce	out-migration.	If	we	take	the	best	practice from	the	

qualitative	analysis	of	engaging	the	community	over	the	long-term,	as	poverty	is	alleviated,	resilience	will	be	

promoted	both	within	a	county and	in	surrounding	counties.	

Altruistic-cooperative	policies	include	variables	with	statistically	insignificant	direct	effects	but	statistically	

significant	positive	coefficients	for	the	indirect	and/or	total	effects.	Policies	in	this	classification	include those	

that protect	or	enhance	natural	amenities,	increase	the	number	of	businesses	per	capita,	and	increase	farm	

employment.	Identifying	and	growing	the	assets	in	the	community and	region is listed	as	a	best	practice	

within	the	qualitative	analysis; however, amenities	have	a	stronger	regional	impact	on	economic	resilience	

than	a	county-level	impact. Thus, uniqueness	should	be	viewed	more	broadly	than	solely	at	the	county	level.	

Cultivating	entrepreneurs	and	developing	resources	for	business	start-ups is also	listed	as	one	of	the	best	

practices	in	the	qualitative	analysis; however,	cultivating	businesses	in	a	community	is	beneficial	for	

economic	resilience	of	surrounding	counties,	too.	Farming	is	included	in	the	altruistic-cooperative	category	

due	to	its very	small	(although	statistically	significant)	direct	effect, but	very	large	indirect	and	total	effects.	

Growing value	chains is	one	aspect	of	another	best	practice identified	in	the	qualitative	analysis.	Farming	

employment	has	a	stronger	effect	as	a	regional	market	than	as	a market	just	at	the	county	level.

If	county-level	policy	makers	take	an	isolated	approach	to	these	altruistic-cooperative	policy	issues,	the	

county	will	not	receive	measurable	benefits	from	these	policies,	according	to	our	model.	Thus,	regional	or	

statewide	approaches	are	most	appropriate	when	focusing	on	these	strategies	to	improve	resilience.
30 |	Page

3.4.3 Competitive	regional	policy	strategies

Based	on	our	model	results,	we	can	identify	two	levels	of	competitive	strategies: competitive	and	ultracompetitive	(see	Table	9).	Competitive	policy	strategies	include	variables	with	statistically	significant	

coefficients	for	direct	and	indirect	effects	with	opposite	signs.	Policies within	this	classification	include those	

that	relate	to	or	affect the	percent	of	population	age	25	to	44	as	well	as	the	percent	of	population	with	a	

college	degree.	Growing youth	engagement	and	next	generation	leadership is	another	best	practice

identified	in	the	qualitative	analysis.	As	counties	seek	to	attract	these	people,	who	are	highly	engaged	in	the	

workforce,	the	resilience	of	the	county	will	increase.	However,	the	resilience	of	surrounding	counties	and	the	

total	region	will	decline,	according	to	our	model.	

Ultra-competitive	strategies	include	variables	with	statistically	insignificant	coefficients	for	direct	effects	but	

statistically	significant	negative	coefficients	for	indirect	or	total	effects.	Strategies	within	this	classification	

include	those	that	increase	the	amount	of	elderly	population,	increase	direct	farm	sales,	increase	recreational	

opportunity,	and	increase	employment	at	community	colleges	and	manufacturing	facilities.

Table	9:	Policies	categorized	by	impact	and	implications	for	surrounding	counties

Strictly	county-level Regional-level	impacts

impacts Cooperative Altruistic-cooperative Competitive Ultra-competitive

Policies	that	increase	

the	diversity	of	

industries	and	

innovation	potential

Policies	that	increase	

participation	of	the	

workforce

Policies	that	increase	

county	population	size

Policies	that	decrease	

the	poverty	rate

Policies	that	reduce	

the	out-migration	of	

county	level	

population

Policies	that	protect	or	

enhance	natural	

amenities

Policies	that	increase	

the	number	of	

businesses	per	capita	

within	a	county

Policies	that	increase	

farm	employment

Policies	that	attract	

college-educated

people

Policies	that	attract	

younger	workers	(25	

to	44)

Policies	that	increase	

the	portion	of	the	

population	age	65+

Policies	that	

encourage	direct	farm	

sales

Policies	that	increase	

the	employment	in	the	

arts,	entertainment,	

and	recreation

Policies	that	increase	

community	college	

employment

Policies	that	increase	

manufacturing	

employment	

Note: Strictly county-level impacts include variables with statistically significant coefficients for direct effects but no indirect or total effects. Cooperative impacts 

include variables with statistically significant coefficients for direct and indirect effects with the same signs. Altruistic-cooperative impacts include variables with 

statistically insignificant coefficients for direct effects but statistically significant positive coefficients for indirect or total effects. Competitive impacts include 

variables with statistically significant coefficients for direct and indirect effects with opposite signs. Ultra-competitive impacts include variables with statistically 

insignificant coefficients for direct effects but statistically significant negative coefficients for indirect or total effects. “Policies that increase farm employment” is 

included in the altruistic-cooperative category due to its very small (although statistically significant) direct effect but very large indirect effect.

3.5 Identification	of	highly	resilient	counties

Highly	resilient	counties	are	identified	based	on	the	residual	values	estimated	for	each	county	from	the	

analysis	of	the	SDM. Residual	values	are	calculated	as	the	difference	between	observed	resilience	value	and	

the	predicted	resilience	from	the	SDM.	Counties	with	a	positive	residual	greater	than	2.5	standard	deviations	

from	the	mean	are	identified	as	highly	resilient.	The	SDM	identified	72	counties	across	the	United	States	that	

over-performed	with	high	levels	of	resilience;	Table	10 ranks	the	top	fifteen	of	these	72	counties. Over	half	of	

these counties	are	from	North	Dakota	and	Texas, where	employment	was	significantly	impacted	by	the	

expansion	of	shale	oil	and	gas	production.
31 |	Page

Of	the	72	counties	identified	as	highly	resilient,	five	are	located	within	the	Appalachian	Region.	Table	11

ranks	these	counties	based	on	the	highest	residual	value.	Three	of	the	five	counties	are	located	within	West	

Virginia.	

Table	10:	Top	15	highly	resilient	counties	based	on	residual	values

County FIPS Resilience Residual

Appalachian	

Region

Dunn,	ND 38025 1.48 6.76 No

Grant,	OK 40053 1.30 6.44 No

Fairfield,	SC 45039 1.28 6.02 No

Twiggs,	GA 13289 1.25 5.99 No

Karnes,	TX 48255 1.31 5.92 No

Williams,	ND 38105 1.42 5.60 No

Dimmit,	TX 48127 1.17 5.20 No

Mountrail,	ND 38061 1.27 5.10 No

Rockwall,	TX 48397 1.31 4.85 No

Lyon,	IA 19119 1.16 4.80 No

McKenzie,	ND 38053 1.32 4.77 No

Custer,	NE 31041 1.12 4.74 No

Kendall,	TX 48259 1.27 4.63 No

Cameron,	LA 22023 1.11 4.35 No

Kemper,	MS 28069 1.08 4.24 Yes

Table	11:	Most-resilient	counties	in	the	Appalachian	Region	

County	 FIPS Resilience Residual

Kemper,	MS 28069 1.08 4.24

Doddridge,	WV 54017 0.90 3.10

Bledsoe,	TN 47007 0.85 2.24

Monongalia,	WV 54061 0.84 2.22

Taylor,	WV 54091 0.83 2.22
32 |	Page

4. QUALITATIVE	ANALYSIS OF	RESILIENCE

Our	qualitative	analysis	of	resilience	included	interviews,	a	focus	group,	an	Advisory	Team,	and	a	survey (see	

Figure	5).

An	extensive	literature	review	for	the	qualitative	analysis	was	conducted	from	November	2016	through	

January	2017	to	highlight	potential	best	practices,	strategies,	and	possible	case	studies	for	the	project,	which	

were	shared	with	project	team	members.

These	suggestions	were	then	presented	to	the	Advisory	Team	for	the	project	on	its	first	call	on	February	6,	

2017	for	feedback	and	idea	generation,	which	is	available	in	Appendix	C.	Feedback	was	also	shared	via	email

both	before	and	after	the	Advisory	Team	call.	

Initial	interviews	were	held	by	phone	between	January	and	March	2017. While	interviews	were confidential

and	all	results	kept	anonymous,	summaries	were	drafted	from	each	interview. Then,	initial	best	practices	and	

strategies	were	developed	as	key	themes	emerged	from	the	interviews	and	were	highlighted	in	the	Interview	

Summary.	The	Interview	Summary	was	shared	with	interviewees	and	Advisory	Team	members	at	the	

conclusion	of	the	interview	process.	Appendix	B	summarizes	the	ideas	from	the	interviews	under	the	themes:

• best	practices	and	related	strategies,	

• tools,	

• resources,	

• points	of	inspiration,

• challenges,	

• guidebook	format	Ideas,	

• ideas	of	others	to	reach	out	to,	and	

• potential	case	study	locations.	

Appendix	B	also	includes	a	list	of	interviewees	and	interview	questions.	Initial	interviewees	were	selected

with	project	team	and	ARC	feedback	to	represent	a	balanced	variety	of	backgrounds,	affiliations,	

geographies,	experiences,	and	knowledge	sets.	Interviewees	for	the	second	round	of	interviews	were	

selected	based	on	focus	group	feedback,	with	the	goal	of	rounding	out	the	initial	list	of	interviewees	for	

background	and	sector	representation.

A	focus	group	meeting	was	then	held	with	key	thought	leaders	in	Appalachia,	including	Advisory	Team	

members,	in April	2017	to	solicit	feedback	on	the	draft	best	practices	and	strategies,	with	both	video	and	inperson	meeting	participation	options	(the	meeting	was	held	in	Duffield,	Virginia,	at	the	LENOWISCO	Planning	

District	Commission	office).	Appendix	A	includes	a	summary	of	the	focus	group	meeting	and	a	copy	of	the

presentation	that	was	shared	during	the	focus	group,	with	initial	qualitative	and	quantitative	findings.	Focus	

group	participants	provided	extensive	feedback	during	the	meeting	as	well	as	following	the meeting	via	email	

and	phone,	which	largely	confirmed	the	draft	best	practices	and	strategies.	One	new	best	practice	was	

added,	and	two	were	combined. In	addition,	focus	group	participants	suggested	that	additional	interviews	be	

conducted	with	key	sectors.

Next,	a	second	round	of	interviews	were	held	in	April	and	May	2017	to	vet	the	draft	practices	and	strategies,	

gather	additional	feedback,	and	continue	to	refine	the	initial	best	practices	and	strategies.	

Finally,	a	practitioner	survey	was	shared	in	late May	through	early	June	2017	with	practitioners	across	the	

Appalachian	Region	to	prioritize	the	draft	best	practices	and	strategies	and	solicit	new	ideas.	A	total	of	151	

responses	were	received	from	across	the	region.	The	survey	is	shown	in	Appendix	D.	Survey	responses

confirmed	the	draft	best	practices	and	strategies	identified	by	the	qualitative	team	and	highlighted	several	

specific	ideas	and	needs.
33 |	Page

A	final	Advisory	Team	call	was	held	in	June	2018	to	share	final	best	practices,	strategies,	and	quantitative	

analysis	findings,	and	ideas	were	shared	about	how	to	synthesize	the	quantitative	and	qualitative	findings.	

Appendix	C	summarizes	this	Advisory	Team	call.

Results	from	the	aforementioned	steps	were	synthesized	into	eight	best	practices	for	growing	economic	

resilience	in	the	Appalachian	Region:	

1. Invest	in	education,	technology,	infrastructure, and	broadband.

2. Engage	the	community	over	the	long	term.

3. Create	communities	where	people	want	to	live.

4. Grow	youth	engagement	and	next-generation	leadership.

5. Identify	and	grow	the	assets	in	the	community	and	region.

6. Build	networks	and	foster	collaboration.

7. Move	multiple	sectors	forward	for	economic	development	and	grow	value	chains.

8. Cultivate	entrepreneurs	and	develop	resources	for	business	start-ups.

The	guidebook,	a	companion	report	to	this	technical	report,	expands	upon	each	of	these	best	practices	with	

prioritized	strategies,	highlights,	case	studies,	and	economic	strategies.
34 |	Page

Figure	5:	Summary	of	qualitative	research	activities

Literature	

Review,	Nov.	

2016	-	Jan.	2017	

•Inital	best	practces,	

strategies,	and	

possible	case	studies		

Advisory	Team	

call,	Feb.	2017	

•Documents	sent	in	advance	for	review;	feedback	

shared	during	call	and	auer	via	email	

First	Round	of	Interviews,

Jan.	-	March	2017,	with	key	

thought	leaders	across	

Appalachia	

•Drau	best	practces,	strategies,	and	case	study	ideas	

are	synthesized	from	interviews	in	an	interview	

summary	document	and	prepared	for	a	

presentaton	to	a	focus	group	

Focus	Group	Mee^ng,	April	

2017,	with	key	thought	leaders	

in	Appalachia	and	Advisory	

Team	members		

•Suggeston	for	change	of	one	best	

practce	and	for	additonal	interviews	to	

be	conducted	by	the	foucs	group		

Second	Round	of	

Interviews,	April	-	May	

2017,	to	vet	drau	best	

practces	and	strategies	

•Final	best	practces	and	strategies	

were	synthesized	from	additonal	

interviews	and	focus	group	feedback	

via	email	

Survey,	late	May	-	early	

June	2017,	to	share	drau	

best	practces,	strategies,	

and	additonal	questons	

•151	responses	were	

received	and	responses	

confirmed	drau	best	

practce	and	strategy	

identficaton	by	

qualitatve	team	

Final	Advisory	Team	call,	June	

2018,	to	share	final	best	

practces,	strategies,	and	

quanttatve	analysis	findings.	

•Ideas	were	

shared	about	

how	to	

synthesize	the	

quanttatve	and	

qualitatve	

findings	
35 |	Page

5. CASE	STUDY	SELECTION

The	guidebook	produced	for	this	project	includes,	among	other	things,	10	case	studies.	Selection	of	these	

case	studies	was	informed	by	the	qualitative	and	quantitative	analyses,	as	well	as	best	professional	judgment

from	the	project	team	and	ARC	staff.

Case	study	selection	was	an	iterative	process.	In	total,	the	three	prongs	of	the	resilience	study	resulted	in	a	

list	of	112	candidate	counties7 for	case	studies.

The	team	then	developed	a	system	of	review	through	which this	number	could	be	reduced.	The	research	

team,	with	input	from	ARC	staff,	established	general	criteria	for	case	study	selection.	Ideal	case	study	areas	

would:	

• be	identified	as	highly	resilient	from	one	or	both	of	the	quantitative	economic	analyses;

• be	suggested	by	multiple	interviewees	in	the	qualitative	analysis;

• have	a	high	degree	of	applicability	to	the	Appalachian	Region;

• ideally,	exhibit	collaborative	planning	for	economic	development	and/or	community	

development;	and/or	

• relate	to	multiple	best	practices	and/or	strategies;	

The	quantitative	research	teams	independently	evaluated	their	respective	results	and	engaged	in	cursory	

research	to	select	candidates	from	their	top	results.	Many	counties	were	removed	at	this	stage	based	on	best	

professional	judgment.	These	counties	were	removed	for	several	reasons,	but	largely	if	the	high	resilience	of	

the	area	was	due	to:

• factors	largely	inapplicable	to	Appalachia,	

• non-repeatable	factors,	or	

• a	single,	large	economic	development	project	that	occurred	at	precisely	the	correct	point	in	time	to	

influence	that	county’s	outcome	in	the	analysis.	

Twenty-four	highly	resilient	counties	were	identified	based	on	their	resilience	value	result	from	the	OLS	

model	(see Table	12).	These	counties	were	considered	for	inclusion	as case	studies.	Appendix	H	shows

employment	charts	for	these	24	counties.

As	shown	above	in	Table	5,	another	set	of	24	counties	were	identified	based	on	their	high	residuals	in	the	OLS	

model.	The	resilience	exhibited	by	these	counties	was	greater	than	expected	by	the	model.	Appendix	G	

shows	employment	charts	for	these	24	counties.	These	counties	were	also	considered	for	inclusion	in	the	

case	studies.	Some	of	the	highly	resilient	counties	identified	in	Table	12 also	have	high	residuals	and	are	

included	in	Table	5.

																																								 																				 7 Multiple scales—towns, counties, or groups of counties—were considered for case study selection. Ultimately, nine of the selected case studies are counties, 

and one is a two-county area.
36 |	Page

Figure	6:	The	112	candidate	case	study	counties
37 |	Page

Table	12:	Top	24	counties	based	on	resilience in	the	OLS	model

County

Population

(2015) Resilience

Net

growth Dominant	sector in	recovery

Sector	

growth

Percent	of	job	

growth	

accounted	for	

by	this sector

Kemper,	MS 10,551 1.08 1,206 Construction 909 75.4%

Swain,	NC 13,463 0.91 2,801 Local	government 2,473 88.3%

Doddridge,	WV 7,808 0.90 402 Natural	resources	&	mining 183 45.5%

Jackson,	GA 50,607 0.90 7,675 Trade,	transportation,	&	utilities 3,389 44.2%

Bledsoe,	TN 12,685 0.85 1,056 N/A N/A N/A

Monongalia,	WV 88,424 0.84 9,033 Education	&	health	services 2,318 25.7%

Taylor,	WV 16,318 0.83 459 N/A N/A N/A

Holmes,	OH 41,630 0.81 3,781 Manufacturing 1,939 51.3%

Union,	MS 26,569 0.81 3,127 Manufacturing 1,939 62.0%

Bradley,	TN 93,024 0.81 9,133 Professional	&	business	services 2,084 22.8%

Harrison,	WV 67,989 0.79 2,088 Education	&	health	services 959 45.9%

Morgan,	TN 20,959 0.78 424 N/A N/A N/A

Forsyth,	GA 137,643 0.77 19,190 Professional	&	business	services 3986 20.8%

Madison,	KY 78,732 0.76 6,349 Manufacturing 2,077 32.7%

Washington,	PA 205,359 0.76 9,491 Professional	&	business	services 3,642 38.4%

Butler,	PA 179,522 0.76 7,908 Professional	&	business	services 2,412 30.5%

Sevier,	TN 81,275 0.76 9,022 Leisure	&	hospitality 5554 61.6%

Chenango,	NY 51,154 0.75 2,773 Manufacturing 1,648 59.4%

Oktibbeha,	MS 43,985 0.75 3161 Leisure	&	hospitality 1,278 40.4%

Buncombe,	NC 221,642 0.75 18,048 Education	&	health	services 4,856 26.9%

Cherokee,	GA 182,128 0.75 12,677 Trade,	transportation,	&	utilities 3,314 26.1%

Hall,	GA 160,979 0.74 14,859 Manufacturing 4,697 31.6%

Pickett,	TN 4,997 0.74 248 Trade,	transportation,	&	utilities 144 58.1%

St.	Clair,	AL 72,596 0.73 3331 Manufacturing 1,504 45.2%

Source: USDL (2015). Note: The data is not seasonally adjusted.

Other	candidate	counties	for	further	study	from	the	OLS	model	are	those	that	have	a	high	residual	and	also	

have	high	self-employment	shares.	The	top	six	such	counties	are	shown	in	Appendix	I.	Four	of	these	six

counties	were	already	identified	as	having	high	resilience	and/or	high	residuals.	Two	counties—Fannin,	GA	

and	Alcorn,	MS—were	not	previously	identified.

Table	10,	above,	lists	the top	15 highly	resilient	counties	identified	in	the	SDM	based	on	residual	values.

As	illustrated	in	Table	13,	53	areas	were	identified	in	the	qualitative	research.	Most	of	these	areas	include	

single	counties,	but	several,	such	as	the	Pennsylvania	Wilds	area	in	Pennsylvania,	are	multi-county	areas.
38 |	Page

Table	13:	Potential	case	study	areas	identified	in	the	qualitative	research

County

1 Elkhart,	IN

2 Jacksonville,	FL

3 British	Columbia

4 East	Central	VT (Addison,	Orange,	Rutland	(particularly),	Windsor	counties)

5 Southwest	VA	(Wise,	Russell,	Scott, Tazewell counties;	towns	of	Wise	and	Norton)

6 Athens	County,	OH

7 Chattanooga,	TN

8 Coastal	Fisheries,	ME (York,	Cumberland,	Sagadahoc,	Lincoln,	Knox,	Waldo,	

Hancock,	Washington	counties)

9 Pennsylvania	Wilds,	PA (counties	of	Cameron,	Centre,	Clarion,	Clearfield,	Clinton,	

Elk	Forest,	Jefferson,	Lycoming,	McKean,	Potter,	Tioga,	and	Warren)

10 Rutland,	VT

11 Beckley,	WV

12 Morganton,	NC

13 Mingo	County	WV

14 Gilbert,	WV

15 Asheville,	NC

16 Greenbrier,	WV

17 Eastern	KY	Counties (especially	the	town	of	Whitesburg)

18 Fayetteville,	WV

19 Charleston,	WV

20 Huntington,	WV

21 Tri-Cities:	Johnson	City,	TN,	Bristol,	TN, and	Kingsport,	VA

22 Danville,	PA

23 Bloomsburg,	PA

24 Columbia,	PA

25 Ohiopyle,	PA

26 Fayette	County,	PA

27 Tupelo,	MS

28 Oxford,	MS

29 Florence,	MS

30 Mussell	Shoals,	MS

31 Bucksport,	ME

32 Dickinson	County,	IA

33 Cleveland,	OH

34 Sparksville,	MO

35 Columbus,	MO

36 Roanoke,	VA

37 Blacksburg,	VA

38 Louisville,	KY

39 Marion,	VA

40 Moorehead,	MN

41 Belfast,	ME

42 Damariscotta,	ME

43 Pittsburgh,	PA

44 Rock	Castle	County,	KY

45 Lawrence,	KS

46 Beckwith	County,	KS

47 Buckhannon,	WV

48 Grafton,	WV

49 Wheeling,	WV

50 Williamson,	WV

51 Princeton,	WV

52 Fairmont,	WV

53 Bellingham,	WA
39 |	Page

We	also	categorized	each	county	according	to	its	pattern	of	total	employment	across	the	time	period	of	

concern	(2000-2016),	with	an	emphasis	on	how	employment	responded	to	the	Great	Recession.	This	created	

a	consistent	baseline	upon	which	to	consider	the	resilience	of	these	communities	and,	paired	with	the	

quantitative	definition	of	resilience,	offered	an	opportunity	to	quickly	remove	those	counties that did	not	

meet	the	criteria	of	being	identified	as	resilient.	Our	categorical	breakdown	of	counties	was	as	follows:

A. No	employment	shock	or	very	minimal	employment	shock	in	2008

B. Employment	decline	pre-dating	2008

C. Shock	in	employment	evident	in	2008,	no	employment	recovery	since

D. Shock	in employment	evident	in	2008,	recovery	evident	since

Those	counties	in	Category	D	were	of	particular	interest	to	the	research	team,	because they	most	clearly	

exhibited evidence	of	recovery	following	a	shock.	Those	counties	with	serious	employment	decline	and	no	

subsequent	recovery	(Category	C)	were	effectively	removed	as	case	study	candidates	at this	point,	as	were	

those	counties	in	Category	B. At	this	time,	no	Category	A	counties	were	included as	prospective	case	study	

counties.	

After	narrowing	down	the	list	of	counties	based	on	the	factors	described	above, 40	of	the	original	112	

candidate	counties	remained.	We	then	engaged	in	a	deeper	research	effort	on	the	remaining	40	counties,	

wherein	we	more	carefully	examined	all	available	data.	We	reviewed	the	model	variable	data	and,	where	

available,	draft	case	study	information	drafted	by	the	qualitative	team.	We	researched conditions	that	might	

support	or	negate	the	inclusion	of	these	counties,	based	on	the	case	study	criteria.	This	effort	allowed	us	to	

quickly	discount	a	number	of	counties	based	on	outlier	conditions, such	as	evidence	that	economic	recovery	

was	tied	to	the	opening	of	a	single,	large	employer	such	as	a	prison	or	power	plant.	These	entities,	while	

valuable	for	employment,	are	not	generally	repeatable	or	applicable	to	other	geographies	and	are	outside	

the	aim	of	the	best	practices.	We	looked	specifically	for	evidence	of	intentional	economic	and/or	community	

planning	that	reflected	recommended	best	practices,	as	well	as	existing	conditions	that	reflected	those	best	

practices.	

The	research	team	convened	to	review	this	analysis	and	make	recommendations	on	next	steps.	A	strong	case	

was	made	by	several	research	team	members	to	more	closely	examine	a	number	of	other	counties—

especially a	number	of	those	within	Category	A (no	employment	shock	or	very	minimal	employment	shock	in	

2008).	For	all	categories,	if	the	independent	research	of	any	team	members	resulted	in	a	strong	case	for	

inclusion	of	counties	from	Categories	A,	B,	or	C,	those	counties	were	included	in	subsequent	research.	A	small	

number	of	Category	B	and	C	counties	were	also	added	in	this	manner.	Team	members	recommended	specific	

counties	as	well	as	some	revisions	to	the	review	methodology	and	we	engaged	in	a	secondary	round	of	

research.	

The	second	round	of	research	specifically	addressed many	of	the	case	study	selection	criteria and included	41	

counties;	this	included 14	additions	to, and	13	deletions	from, the	previous	list	of	40	counties.	

In	this	phase,	certain	counties	were	included	if	we	thought	that	the	county	was	poised	to	become	

economically	resilient.	These	counties,	which	we	refer	to	as	“cusp	communities”	in	the	selection	criteria,	

were	also	considered	as	candidates	for	place-based	examples	in	the	guidebook.	These	areas	may	have	all	the	

factors	in	place	to	become	resilient	in	the	coming	4-6	years,	but	may	not	have	achieved	a	level	of	resilience	

that	warrants	considering	them	as	full	case	study	locations.	

Based	on	best	professional	judgment,	the	project	team	then	narrowed	the	list	to	the	strongest	32 candidate	

counties	and	provided	all	review	information	as	well	as	interactive	web	map	to	ARC staff	for	review.	
40 |	Page

ARC	staff	selected	the	final	group	of	case	studies	after	careful	consideration	of	materials	provided	to	them	by	

the	research	team	about the	candidate	counties,	which	included	results	of	the	quantitative	analysis	as	well	as	

background	information.	The	staff	members	at	ARC	weighed	the	following	factors	while	determining	the	

locations	of	the	case	studies:

1) results	of	the	quantitative	model	measuring	economic	resiliency;

2) historical	context	about	the	community,	such	as	information	about	the	specific	employment	shock	

and	recovery	efforts;

3) socioeconomic	characteristics	of	the	communities;

4) geographic	coverage	throughout	the	Appalachian	Region;	and

5) if	outside	of	Appalachia,	whether	the	community	is	relatable—and	whether	lessons	learned	would	

be	applicable—to	the	Region.

Based	on	these	quantitative	and	qualitative	considerations,	ARC	staff	selected	a	diverse	and	balanced	group	

of	case	study	locations	(see	Figure	7 and	Table	14).	Because	the	Appalachian	Region,	which	spans	13	states	

and	is	home	to	more	than	25	million	people,	is	heterogeneous	in	nature,	the	goal	was	to	select	a	set	of	

locations	diverse	enough	so	that	most—if	not	all—communities	throughout	the	Region	would	be	able	to	

identify	with	the	experiences	of	the	case	studies.
41 |	Page

Figure	7:	The	ten	case	study	areas
42 |	Page

These	case	study	areas	include	four	counties	considered	due	to	their	modeled resiliency	from	the	OLS	model	

and	five	counties	considered	due	to	their	residuals	from	the	OLS	model.	None	of	these	final	case	study	areas	

were sourced	from	the	SDM. Four of	the	final	case	study	counties	were	identified	in	the	qualitative	research,	

and	one	other	(Flathead,	MT)	was	added	based	on	input	from	ARC staff.

Table	14:	The	ten	case	study	counties

County(ies) FIPS

High	

resilience	

(OLS)

High	

residual	

(OLS) Qualitative? Cusp?

Dickinson,	IA 19059 X No

Fannin,	GA 13111 X Maybe

Flathead,	MT 30029 Maybe

St.	Clair,	AL 01115 X No

McCracken,	KY 21145 X Yes

Holmes,	OH 39075 X X No

Chenango,	NY 36017 X X No

Harrison,	WV 54033 X X No

McKean,	PA 42083 X Maybe

Lee	and	Itawamba,	MS 28081,	28057 X X Yes

The	guidebook,	which	is	the	second	report	for	this	project,	includes	detailed	descriptions	of	each	of	the	10	

case	study	areas.
43 |	Page

6. CONCLUSION

In	this	project,	we	applied	integrated	quantitative	and	qualitative	research	to	identify	policies, best	practices,

and	associated	strategies	for	growing	economic	resilience	in	the	Appalachian	Region.	

The	first	quantitative	analysis,	a	conventional	OLS	analysis,	assesses	each	county	across	the	United	States	in	

isolation.	The	second,	a	spatial	econometric	model,	extends	the	first	model	by	allowing	for	spillovers	of	

economic	processes	across	county	lines.	Both	of	these	analyses	investigate	the	same	set	of	35	variables.

The	qualitative	research	included	interviews,	a	focus	group,	an	Advisory	Team,	and a	survey.	It	grounded	the	

statistical	results	with	information	from	practitioners.	

Results	were	synthesized	into	eight	best	practices	for	growing	economic	resilience	in	the	Appalachian	Region:	

1. Invest	in	education,	technology,	infrastructure, and	broadband.

2. Engage	the	community	over	the	long	term.

3. Create	communities	where	people	want	to	live.

4. Grow	youth	engagement	and	next-generation	leadership.

5. Identify	and	grow	the	assets	in	the	community	and	region.

6. Build	networks	and	foster	collaboration.

7. Move	multiple	sectors	forward	for	economic	development	and	grow	value	chains.

8. Cultivate	entrepreneurs	and	develop	resources	for	business	start-ups.

Through	our	analysis,	we	also	identified	10	case	study	areas from	which	we	gained	further	insight	into	how

communities	are	evolving	their	approach	to	economic	development and	resilience, both	inside	and	outside	

the	Appalachian	Region.

While	this	technical	report	documents	details	of	our	analysis,	the	other	report,	a	guidebook,	provides	best	

practices,	strategies,	and	case	studies	for	economic	development	practitioners,	local	government	officials,	

nonprofit	organizations,	and	others	working	in	the	Appalachian	Region.
44 |	Page

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APPENDIX	A:	FOCUS	GROUP	MATERIALS

Strengthening	Economic	Resilience	in	Appalachian	

Communities	project	– Focus	Group	meeting

April	11,	2017 

Meeting	Summary

Focus	Group	overview	and	highlights

A Focus Group meeting was held on April 11, 2017 at the LENOWISCO Planning District 

Commission office in Duffield, VA to review the draft findings of the Strengthening Economic 

Resilience in Appalachian Communities project. The agenda can be found at this link, and a list 

of participants may be found at the end of the summary. A series of initial interviews were held 

in January - March of 2017 to develop the draft best practices and strategies, in tangent with the 

economic analysis for the project [and another round of interviews have taken place since the Focus 

Group meeting on the draft findings as well]. The initial Interview Summary document is available 

at this link, and will be updated with another round of interviews that will be held after the 

Focus Group meeting. The interviews captured key questions on how communities can grow 

economic resilience in Appalachia and ideas for best practices, strategies and case studies. The 

initial findings for both the qualitative and quantitative analyses were shared during the Focus 

Group meeting to look at what is missing, to hear priorities as well as feedback from 

participants. 

The project team shared their initial findings thus far, asking Focus Group members for 

feedback, in a presentation that is available at this link. Key areas of feedback are bolded 

throughout the document, with the following emerging as key points of Focus Group feedback:

• Address equality and engaging underrepresented populations should be a core focus of 

the project. 

• Identify more precisely the audience for the guidebook in order to direct focus and 

language.

• Focus more directly on issues including investing in education, technology, and public 

water/sewer infrastructure and their effects on economic resilience.

Project	background	

The Strengthening Economic Resilience in Appalachian Communities project, sponsored by the 

Appalachian Regional Commission, is being conducted by Downstream Strategies, Penn State, 

Dialogue + Design, and other partners, will explore and document strategies and policies local 

leaders can use to enhance the future economic prospects of coal-impacted communities 

throughout the Appalachian Region. There are four key components to this research project: 1) 

develop a comprehensive, quantitative framework to explore economic resilience; 2) identify a 
49 |	Page

series of best-practice strategies for strengthening local economic resilience; 3) conduct up to 

10 in-depth case studies; and 4) produce a concise guidebook that interprets and integrates 

findings of the research, written specifically for local economic development practitioners.

Purpose	and	Introductions	

After participant introductions, Christine Gyovai of Dialogue + Design Associates reviewed the 

Focus Group purpose. Fritz Boettner of Downstream Strategies then introduced the project. 

Fritz noted that the project team has been employing both qualitative and quantitative methods 

to create an economic analysis, conduct a literature review and interviews, and create synthesis 

of factors for strategies, best practices and case studies around economic resilience, with the 

guidebook and report complete by September 2017. Questions and responses about the 

project overview included: 

• A participant asked about the audiences for the guidebook. Would it be for officials? 

o Fritz replied that the intended audience would be a cross-section from officials 

to economic development leaders to community organizers, but its intended 

audience is for economic development practitioners across Appalachia. 

o Christine offered that the team has been asking for feedback on formats for the 

website from interviewees with ideas on how to make the guidebook as 

accessible and relevant as possible to economic development practitioners in 

Appalachia. 

• Another participant asked what format will the guidebook take? 

o Print and digital version, as well as linking to videos, tools, and resources, and a 

strong focus on storytelling have all been considered (as well as other tools). 

• A participant asked if there would be enough time to review the potential case studies 

during the Focus Group meeting, and asked about equity issues, noting that at the 

Appalachian Funders Network, they are trying to be sensitive to equity, race, and 

genders issues. She has been wondering if the team is looking at resilience as part of 

economic balance in measures. 

o The team noted that the best practice 8 includes engaging the underengaged 

community members currently, but this is the type of feedback they are looking 

for around what 

needs to be 

reflected in this 

project. 

Draft	Best	Practices	and	

Strategies

Christine then introduces the draft 

best practices and strategies, 

beginning with a quick look at the 
50 |	Page

word cloud produced from the interview summary where words like: “people,” “communities,” 

“grow,” “work,” “possible,” and “stories” emerge as prominent elements of discussion. The 

draft eight best practices, at right, emerged from a combination of the literature review, the 

Advisory Team call, and interviews, and there may need to prioritize or develop guiding 

principles and further refine develop the best practices and strategies. 

Stephan Goetz of Penn State explained the asterisks next to the best practices represent the 

overlap between qualitative research and their economic analysis, looking at what is measurable 

yet important. Before diving into strategies, Christine asked if Focus Group participants had any 

initial feedback from list of draft best practices (BP). Feedback included (with the list of Best 

Practices are listed first):

Best Practices for growing economic resilience in Appalachia

1. Cultivate entrepreneurs and develop resources for business start-ups

2. Identify and grow the assets in the community and region

3. Connect to markets regionally and develop value chains 

4. Move multiple sectors forward for economic development 

5. Grow youth engagement and next generation leadership 

6. Build networks and foster collaboration

7. Create communities where people want to live 

8. Engage the community over the long-term

o Surprise that no BPs address technology. BPs 1 and 7 could include technology, but 

investing in information/technology infrastructure such as broadband, is not explicit. 

Christine replied that it didn’t emerge recently, but that the feedback was good. Three 

participants agreed that they were also surprised by the absence of technology in the 

BPs.

o Pre-K through higher education are the key to everything: health, economic 

development, opioid addiction. It needs to be front page, BP1. BP 5 hints at addressing 

these issues. 

o Are the BPs for communities or for whom? This could tilt the balance of what’s on the 

list, which should be based on the audience. We should figure out the audience, and 

then prioritize accordingly.

o Entrepreneurship is a big term seen in different ways. There is national decline in the 

retail sector, and we might want to make a distinction between business start-ups and 

traditional retail. Traditional retail is consolidated, and we might need a deeper 

explanation of this. For example, it would be kind of crazy to encourage retail start-ups 

without looking at the market risk. 

o One participant noted that when they work with traditional economic developers and 

officials, their definition of economic development does not include entrepreneurial 

development. Part of the job is making that a priority and communicating the value 

of entrepreneurship.

Christine then offered that the BPs and strategies don’t necessarily reflect the implicit 

challenges, but recognize that challenges are present. Christine then reviewed the draft best 

practice and affiliated list of draft strategies. Feedback included: 
51 |	Page

• A participant noted that examples may need to be given for BP 3 around value chain to 

clarify its meaning as it currently seems more abstract than the others. 

• Another strategy should be added under BP 4 around energy efficiency and renewables.

• Under BP 7, consider looking at Jonesborough, TN which is well-known for storytelling. 

Its proximity to Johnson City and its education challenges could make it useful to 

examine (noting that it is like Asheville but hidden).

• Megatrends include that technology is eliminating a lot of jobs in urban areas, but this 

technology also poses opportunities to attract people that are losing these jobs to the 

region.

• Under BP 8, one participant noted that we should look at where change comes from 

and use community listening as a key strategy and best practice. 

Economic	Analysis	Discussion

Stephan then led the presentation of economic analysis findings. They had examined data from 

300 counties looking at what factors they could measure such as, ability to rebound in terms of 

employment, which is more important to policymakers than income. The economics team 

hadn’t looked at specific population’s ability to rebound (alluding to equality sensitivity), but 

they do control for these variables.

Economic	Analysis	Findings	

• The literature has indicated three categories: demographic/mobility, economic/industry, 

and community/health. 

• They found that communities with a lot of people ages 25-44 are more resilient. 

• College education seems to always be significant, that education IS key to everything. 

• Other indicators of resilience are workforce participation. 

• Poverty certainly makes it difficult to be resilient. 

• Proximity to firms does not increase resilience.

• Ability to innovate has a strong positive effect on resilience.

• Diversity of industry is important.

• Some variables seem to cancel each other out such as effects of community colleges and 

broadband access lose significance when dominated by college effects.

• Child and elder care does not appear significant, but recreational opportunity is a 

positive. 

• Social capital seems to be a surprising negative, which Stephan attributed to the higher 

rate of retirees that might add social capital, but might not be active in the economic 

arena. 

• Self-employment may be a proxy for entrepreneur; it turns out that self-employment 

only increases resilience at low levels, but diminishes the county’s resilience at higher 

levels. Stephan pointed back to the earlier point made about retail: we need to be 

careful about what they’re doing and competing with. 

Stephan described how they control for variables, for example looking into the effect of age or 

college education into the economic analysis. They are looking at correlation not causation. For 

example, migration could be important before resilience is measured; looking at the structure 
52 |	Page

of the economy before measuring resilience to claim causation. However, some are harder to 

separate, and are more associations than causations. 

One participant suggested that it is important to look at how the 2008 economic recovery 

was supported. It may have more to do with whether the community was well-positioned to 

receive recovery assistance. Bigger industries were the bigger winners. Stephan offered that not 

all counties experienced the downturn, but that this is a good point. 

In conclusion, Stephan shared a slide with the core findings. More resilient counties are:

• bigger, with more young workers and greater ethnic diversity, more in-migration and 

less out- migration, and more college-educated; 

• more remote from metros, have less poverty, more labor force participation, innovation 

potential, more industry- but less knowledge diversity, less coal dependence; and 

• less dependent on employment in community colleges, fewer health practitioners, more 

active in terms of direct farm sales. 

He then shared some of the subtler findings: 

• Self-employment matters but at a diminishing rate; 

• Self-employment effect stronger in places with more population, or that are more 

remote; 

• Social capital has stronger positive effects if the 25-40 year old cohort is smaller; 

• Federal funding is more effective when there are fewer in-commuters; and

• Recreational opportunities matter when the density of existing business is lower. 

Alan Collins then discussed the econometric analysis that his team has been working on as part 

of the economic analysis. Their team has looking at regional impacts and resilience where they 

found that some variables have a larger impact than others, and these will be part of the 

economic analysis. 
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Case Studies and Focus Group feedback 

The project team then introduced online mapping tool that included the quantitative variables 

and helped Focus Group participants identify possible case studies. The team then discussed the 

process of how case studies are being selected. 113 locations have been explored as possible 

case study locations that have emerged from both the qualitative and quantitative analyses. The 

team noted that ARC has asked that they look outside of Appalachia as well as in the region, 

and at various scales, noting that data is most easily accessible at the county-level.

After reviewing map with possible case study locations, the project team asked Focus Group 

participants to consider two primary questions:

• What are we missing?

• What arises as a priority? 

Closing questions and comments 

• One participant noted that said she was interested in the comment about the regression 

analysis related to self-employment, and wanted to look at how we could relate that 

back to value-chain strategies. In Ohio they worked with auto suppliers, and notice that 

as they take on more employees, they become more tied into the value chain, which can 

affect them negatively. She was surprised by the potential effect of retirees affecting 

social capital. Maybe we could look at more positive stories such as youth in West 

Virginia. She gave the example of a youth-oriented Rotary Club that’s young and active, 

and an important anchor in Athens, OH. She also suggested that we look more 

specifically at innovation since economic resilience is such a broad term to expand and 

clarify it, further noting that recreation opportunities may improve the quality of life, 

attract younger people, but also can come with low wages. She wants to understand 

these wages more, and advocated that wages are an indicator of economic resilience. In 

Athens, OH, companies have focused on technology (from GED to PhD levels). 

• Another participant noted that capital/angel investment is important for small 

businesses, but is not flowing to rural regions. How is this accounted for in the analysis 

(if it is accounted for)? 

• Another Focus Group member noted that more time for the Focus Group meeting 

would be helpful as 1 1⁄2 hours wasn’t enough time to get into depth or discuss many of 

the points in depth. The project team welcomed comments by phone or email after the 

Focus Group meeting as well. There were also concerns about the interviewees who 

were all white, and additional suggestions for interviewees were made. 

• A Focus Group participant noted that all these solutions are exciting, and could help 

some members of the community but could create other problems. How do we 

create solutions without creating more problems?

• Another participant would like to see more women in the workforce. There are huge 

efforts at putting coalminers back into the workforce that have largely floundered. We 

should be looking at more opportunities for younger people and women. 

• A Focus Group member added that we need to be making a connection between Best 

Practices 1, 3, and 4 connecting markets with opportunities for entrepreneurs. 
54 |	Page

After the closing feedback from Focus Group participants, the in-person participants continued 

to meet for a brief discussion around the suggestions for next steps. Feedback included: 

• One participant noted that we might want to see what is already being done. Two 

participants have worked on a grant for the National Public Health Institute where they 

recently concluded seven Focus Groups in Virginia and Tennessee which were data-rich 

and focused on multi-sectors, gathering a lot of background on economic development 

policy. They were funded again and are setting up a population health network focusing 

on diabetes, bringing economic developers to the table and using community health 

rankings as a competitive incentive. 

• Another attendee added that they could help make connections with targeting 

underrepresented folks to broaden perspective if helpful. 

• Another participant noted that having more diverse interviewees would be important to 

ensure that a broad range of knowledge is represented. Christine responded, “Will the 

digital engagement help? (some nods), pointing to the need to expand the engagement 

period of the project. 

• A participant suggested Erik Pages as an interviewee, who Christine noted has been 

interviewed. 

• One participant commented that the research team had sent out the Best Practices 

before the Focus Group; we didn’t need to necessarily go over them. The economic 

material was useful to discuss. She added that public water and sewer are 

significant factors in the ability to attract business, and she would have loved to 

dig into people’s questions more. She wanted to see more about the map and case 

studies. She said that she is glad we’re pulling ideas outside the region, but is hoping case 

studies have factors that are relatable. Chattanooga has different considerations from 

Charleston, WV, for example. 

• Another attendee agreed with the infrastructure piece and the community health 

column, but suggested that we also look at multimodal transportation 

infrastructure. Also Best Practice #7 highlights a possible case study in Cleveland, VA 

vs OH and TN? We might want to measure out-migrations and be ready to integrate 

with 2020 Census. 

• A participant commented that people don’t want to talk about funding for public 

education (in many places, it has dropped off the map), but we have no alternative. If we 

decide to do more meetings, she would be happy to host a meeting in a central location. 

Christine asks if it would be best for in-person format, with the response being that 

people will pour their heart out if they know it’s being used for good. 

• Another attendee agreed that people will travel and suggested including a lunch. 

After the discussion concluded, the Focus Group meeting ended and the group was invited to 

send additional feedback by email or phone, with additional opportunities for engagement 

forthcoming. 
55 |	Page

Focus	Group	Participants	

Jimmie Adkins, LENOWISCO PDC 

Philippa Belsches, Just Transition Fund 

Shannon Blevins, UVa-Wise Office of Economic Development

Dee Davis, Center for Rural Strategies

Eric Dixon, Appalachian Citizens Law Center

Peter Hille, MACED

Ginny Kidwell, Tennessee Institute of Public Health

Jennifer Simon, Ohio University

Ada Smith, Appalshop 

Kent Spellman, Rails to Trails Conservancy

Project	Team	Participants	

Christine Gyovai, facilitator, Dialogue + Design Associates 

Emily Carlson, Dialogue + Design Associates

Evan Fedorko, Downstream Strategies

Fritz Boettner, Downstream Strategies

Stephan Goetz, PSU Regional Center for Rural Development

Allan Collins, WVU Economics
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APPENDIX	B:	INTERVIEW	SUMMARY

Interview	Summary

Strengthening	Economic	Resilience	in	Appalachian	Communities

June	20,	2017	(revised	from	April	7th version)

Overview	

Initial	interviews	for	the	Strengthening	Economic	Resilience	in	Appalachian	Communities

project,	sponsored	by	the	Appalachian	Regional	Commission,	were	held	between	January	–

March	2017	and	conducted	by	Christine	Gyovai	of	Dialogue	+	Design	Associates. A Focus	

Group	meeting	was	held	in	April	of	2017	to	solicit	feedback	on	the	draft	best	practices	and	

strategies,	and	then	a	second	round	of	interviews	were	held	in	April	and	May	of	2017	to	

gather	additional	feedback	and	to	continue	to	refine	the	initial	best practices	and	strategies	

based	on	Focus	Group	feedback. Finally,	a	survey	was	shared	in	late	May	– early	June	of	

2017	with	practitioners	across	Appalachia	to	prioritize	the	draft	best	practices	and	

strategies	and	solicit	new	ideas,	with	151	responses	across	Appalachia.	

The	ideas	from	the	interviews	are	included	below	under	the	themes:	Best	Practices	and	

Related	Strategies,	Tools,	Resources,	Points	of	Inspiration,	Challenges,	Guidebook	Format	

Ideas,	ideas	of	others	to	reach	out	to,	and	potential	case	study	locations.	Interviewees	and	

interview	questions	are	listed	at	the	end	of	the	document.	The	Interview	Summary	

document	served	as	a	foundation	for	refining	the	Best	Practices	and	Strategies	with	Project	

Team,	Advisory	Team,	and	Focus	Group	participant	feedback	over	the	course	of	the	

Economic	Resilience	project.

Best	Practices	for	growing	economic	resilience	in	Appalachia

1. Invest	in	education,	technology,	infrastructure	and	broadband

2. Engage	the	community	over	the	long-term

3. Create	communities	where	people	want	to	live	

4. Grow	youth	engagement	and	next	generation	leadership	

5. Identify	and	grow	the	assets	in	the	community	and	region

6. Build	networks	and	foster	collaboration

7. Move	multiple	sectors	forward	for	economic	development	and	grow	value	

chains

8. Cultivate	entrepreneurs	and	develop	resources	for	business	start-ups
67 |	Page

Best	practices	and	strategies	for	growing	economic	resilience	in	

Appalachia	

- Prioritized	strategies	are	listed	under	each	best	practice

1. Invest	in	education,	technology,	infrastructure	and	broadband	

A. Develop	broadband	accessibility	throughout	Appalachia.	

o Develop	training	opportunities	for	communities	and	organizations	on	

communications,	community	outreach,	and	how	to	effectively	use	the	

internet	and	social	media.	

o Foster	broadband	development	as	a	key	component	for	building	remote	

work	(telecommuting)	possibilities,	existing	business	expansion,	and	new	

business	location.	

B. Invest	in	infrastructure	including	public	water,	public	sewer,	energy accessibility,	

and	transportation	-- including	multi-modal	transportation.	

o Create	renewable	energy	projects	throughout	Appalachia	as	many	businesses	

have	a	renewable	energy	requirement	to	locate	in	an	area.	

o Develop	public	and	private	partnerships	to	create	needed	infrastructure.	

o Consider	proximity	to	highways,	markets,	transportation	options,	and	

airports	which	are	all	strong	factors	in	determining	the	competitive	

advantage	of	an	area	for	new	build	location	or	existing	business	expansion.

o Expand	public sewer	throughout	Appalachia	using	innovative	technologies	

where	needed.	

C. Foster	educational	opportunities	from	preschool	through	post-secondary	for	a	wide	

range	of	community	members.

o Grow	educational	attainment	to	grow	community	quality	of	life,	

entrepreneurship	opportunities	and	attract	high-level	employment	

possibilities.

o Develop free	public	vocational	and	higher	education	classes	to	prepare	and	

train	workers	for	employment	and	for	entrepreneurship	possibilities.	

o Develop	both	general	and	specialized	educational	offerings	as	a	way	to	grow	

community	and	economic	resilience,	to	grow	needed	skillsets	for	specific	

sectors,	and	for	developing	new	business	opportunities	and	entrepreneurs.	

D. Develop	“learn	and	earn”	models	of	workforce	training	where	workforce	training	

and	academic	classes	are	both	offered	around	a	specific	topic	such	as	local	food	or	

renewable	energy.

E. Grow	opportunities	around	technology	in	multiple	sectors.	
68 |	Page

2. Engage	the	community	over	the	long-term	

A. Engage	under-engaged	community	members	-- those	affected	by	decisions	need	to	

be	part	of	the	solution.	

B. Create	ongoing	engaging,	safe	spaces	for	the	community	to	gather	across	sectors	

and	for	all	community	members	to	feel	valued,	welcomed,	and	heard.

o Welcome	newcomers	to	communities,	or	returning	community	members,	by	

opening	up	leadership	circles	and	creating	communities	that	will	attract	

them.	

C. Share	ideas,	challenges	and	successes	with	consistent	communication	with	the	

broader	community.

o Tell	stories	about	other	communities	that	have	built	their	resilience.

• Develop	opportunities	for	people	within communities	to	identify	both	the	problem	

and	solution	to	community	challenges	and	opportunities. Ideas	that	emerge	from	

within	the	community	should	be	prioritized	for	growing	economic	resilience.

D. Cultivate	leadership	and	trust	for	long-term	efforts	and	success.	Create	the	space	

where	leadership	can	come	forward	and	flourish	to	create	change	together	as	a	

community.

o Connect	with	community	“sparkplugs	and	bridgemakers”	in	the	community	

to	sustain	leadership,	growth,	and	momentum.

o Foster	creative	long-range	planning,	strategic	planning	and	opportunities	to	

engage	with	elected	officials	and	in	policy	creation.	

E. Develop	a	consistent	forum	for	community	engagement	around	a	specific	focus	with	

a	long-term	facilitator	or	convener.

o Use	innovative	ways	to	engage	the	community,	including	through	media,	

outreach,	knocking	on	doors,	sharing	ideas,	recognizing	participants	for	their	

contributions,	and	make	meetings	fun.	

ADDITIONAL	IDEAS/QUOTES:

a. Share	ideas,	challenges	and	success	with	consistent	communication	with	the	

broader	community,	and	provide	regular	forums	for	engagement.

o “Celebrate	what	is	positive!	There	are	so	many	reasons	for	celebration.”

o “We	need	to	consider	individual	and	collective	identity. If folks	

understand	where	they	come	from	and	what	their	community	is	and	what	

their	role	is	with	the	community,	the	assets,	and	their	uniqueness	– this	is	

very	important	in	creating	sustainable	economic	growth.”

o “We	need	to	tell	a	compelling	narrative	succinctly,	repeatedly,	through	

direct	stories	and	facts,	and	tell	it	again	and	again	to	change	that	narrative	

and	implement	better	policies.”

b. Develop	an	ongoing	place	for	having	community	conversations	around	a	focus.	
69 |	Page

o “As	a	community	– we	must	create	the	habit	of	dialogue	and	deliberation	

and	then	structures	for	action.”

o “Stuff	only	works	when	citizens	come	together	to	make	change	– include	

everyone.”

c. Listen	and	learn	from	community	members	where	there	are	opportunities	and	

challenges,	and	grow	ideas	that	can	be	developed	by	the	community.	

o Develop	a	strong	sense	of	who a	community	is	to	create	an	economic	plan	

that	will	work	over	the	long-term.	

o “Ideas	need	to	come	out	of	the	people	themselves	or	they	won’t	be	

sustained.”

o “We	need	to	listen	and	to	hear	what	is possible	from	folks	in	communities	

about	what	they	see	and	experience.”

o Community	energy	and	excitement	needs	to	be	present	to	start	to	make	

change,	and	to	stay	positive	and	optimistic	for	the	long	road	of	

community	transformation.

o Practitioners	need	to	“get	on	the	balcony”	to	see	the	big	picture	view	to	

make	connections	with	other	people	and	partners,	as	well	as	across	ideas	

and	projects	to	advance	the	work.

3. Create	communities	where	people	want	to	live	

A. Grow	community	health	and	wellness.	Focus	on	creating	a	high	quality	of	life	with	

strong	community	health	and	wellness	which	are	strong	drivers	for	new	business	

location	and	for	new	residents	to	move	to	communities.

o Create	amenities,	programs,	and	opportunities	for	all	community	members,	

including	childcare,	elder	care,	women,	single-parent	households,	and	

vulnerable	community	members	to	ensure	that	basic	needs	are	met.

o Determine	barriers	and	opportunities	for	community	members	to	access	

social	services,	including	ways	to	address	addiction.

o Address	systemic	forms	of	poverty	by	reaching	out	to	people	where	they	are	

and	use	a	grassroots	approach	to	grow	economic	resilience	in	communities.	

B. Cultivate	a	strong	sense	of	place	and	pride	in	place	through	storytelling,	arts,	and	

cultural	heritage	opportunities,	and	value	them	as	building	blocks	for	a	new	economy	in	

Appalachia.	

o Tell stories,	share	stories,	including	new,	positive	stories.	

o Share	information	for	peer	learning	among	community	members	and	

leaders,	including	through	in-person	gatherings,	using	audio	and	video	to	

share	stories,	and	to	identify	points	of	contact to	learn	more	about	specific	

stories,	people	and	places.	
70 |	Page

C. Welcome	new	and	diverse	people	into	communities.	Encourage	new	residents,	

including	telecommuters,	young	families,	and	immigrants.

o Use an	intentional	approach	to	address	population	decline	in	communities.

D. Grow	anchor	institutions,	businesses,	and	opportunities	that	grow	thriving	

communities.

o Develop	and	grow	colleges,	universities,	hospitals,	as	well	as	elder	and	youth	

co-care	opportunities.

o Develop	“third	spaces”	– neutral	spaces	where	community	members	can	

come	together	outside	of	home	and	work	(such	as	coffee	shops,	co-working	

spaces,	and	churches.)

o Examine	land	ownership,	including	absentee	landowners,	and	how	it	impacts	

communities	and	the	broader	region.

o Promote local	investing	via	community	foundations	and	seed	funding	for	

local	ideas.	

ADDITIONAL	IDEAS/QUOTES:

a. Create	high	quality	communities	with	amenities	for	all	community	members.

o This	should	include	“creating	communities	with	broadband	and	coffee	shops”	

and	“basic	infrastructure,	like	sewer	lines,	broadband,	decent	housing,	and	a	

community	center.”

o “We	need	honest	communication	systems	that	work	and	connect	people	to	

the	world.”

o “We	need	to	focus	on	the	creative	economy	– we	need	to	create	places	where	

people	want	to	raise	their	kids	and	retire.	Rural	communities	like	

Appalachian	can	get	a	fresh	look,	and	what	is	important	is	that	we	have	

attractive	communities	with	high	amenities	that	people	make	choices	to	

come	live	here	-- folks	who	are	job	creators.	That	gives	us	a	chip	in	the	game.“

b. Value	arts,	culture,	and	heritage	and	use	them	as	building	blocks	for	a	new	economy	

in	Appalachia.	

o “Culture,	environment,	our	history	– all	of	a	sudden,	those	things	we	thought	

were	holding	back	the	economy	turned	out	to	be	what	is	left	to	build	a	future	

from.”

4. Grow	youth	engagement	and	next	generation	leadership

A. Identify,	mentor,	and	grow	young	leaders	for	the	next	generation.	

o Develop	mentoring,	learning	opportunities,	idea	exchanges	and	programs	to	

grow	skills	and	to	learn	about	entrepreneurship	(like	Appalachian	

Fellowship	Program,	with	elected	leaders,	and	across	generations).
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o Encourage	youth	and	provide	opportunities	for	them	to	be	in	positions	of	

responsibility.

o Connect	to	schools	and	work	with	teachers,	students,	and	parents	in	multiple	

ways.	

B. Keep	smart,	young	people	in	communities,	and	bring	smart,	young	people	from	

outside	of	communities	in.

C. Engage	youth	in	arts,	culture	and	community-building	opportunities.

D. Welcome	new	ideas,	bold	visions,	and	unlikely	partnerships.	

E. Develop	programs	by	youth,	for	youth.

F. Build	the	capacity	of	local	leaders	to	build	the	economy,	and	grow	and	engage	local,	

diverse	leadership	across	a	community.	

o Grow	peer-to-peer	learning	networks,	coaching	opportunities	and	leadership	

development	opportunities	to	build	capacity	of	local	leaders.	

o Foster	opportunities	for	young	leaders	to	engage	with	elected	officials	in	

policy	creation,	to	become	elected	officials,	as	well	as	to	engage	with	

community-based	initiatives.	

G. Welcome	young	people	in	communities	– ensure	that	youth	feel	welcome.
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ADDITIONAL	IDEAS/QUOTES:

a. Develop	programs	by	youth,	for	youth.

b. Advance	programs	such	as	the	Appalachian	Fellowship	program	for	young	

leaders	to	grow	skills,	see	what	is	happening	regionally,	and	to	learn	about	best	

practices	and	innovation	throughout	the	region.	

c. Engage	youth	in	arts,	culture	and	community	building	opportunities.

o Create	community,	economic,	and	cultural	events,	projects,	and	venues	that	

welcome	and	facilitate	participation	from	all	ages

d. Identify,	mentor,	and	grow	young	leaders	for	the	next	generation.

o “When	look	around	the	state	[of	WV]	at	the	leadership	in	different	NGOs,	

there	are	incredible	young	people	doing	amazing	work	around	

community	development.	We	need	to	support	them.	We	need	to	be	

passing	the	baton	to	remarkable	young	leaders	and	be	around	to	mentor	

them.”	(http://generationwv.org/)”

e. Welcome	young	people	in	communities	– ensure	that	youth	feel	welcome.

f. Keep	smart,	young	people	in	communities,	and	bring	smart,	young	people	from	

outside	of	communities	into	them.	

g. Welcome	new	ideas,	bold	visions,	and	unlikely	partnerships.	

o Cultivate	out-of-the-box	thinking	and	program	opportunities	such	as	a	

“Why	Not	Appalachia”	social	media	campaign	to	generate	new,	bold,	and	

crazy	ideas.

o Welcome	new	thinking	and	ideas	from	new	people;	be	willing	to	

experiment.	

h. Develop	mentoring	and	learning	opportunity	to	cross-pollinate	ideas	and	grow	

skills.	

i. Create	role	models	and	mentoring	opportunities	for	youth	to	learn	about	

entrepreneurship.

j. Connect	to	schools	and	work	with	teachers,	students,	parents	in	a	variety	of	

programs	and	partnerships.	

5. Identify	and	grow	the	assets	in	the	community	and	region

A. Tell	the	stories	of	the	community,	especially	positive	stories,	to	change	the	

narrative.

o Create	intergenerational	learning	exchanges	between	elders	and	youth,	

including	gathering	oral	histories,	as	well	as	learning	opportunities	between	

towns	and	between	practitioners.	

B. Foster	interconnection	between	rural	and	urban	areas,	in	broader	regions	around	

resources,	as	well	as	food,	recreation,	and	energy	supply	connections.	

C. Identify	and	promote	assets	within	the	region	and	community	through	partnerships.	
73 |	Page

o Focus	on	integrating	arts	and	creative	placemaking,	into	community	and	

economic	development	efforts	across	the	community.	

D. Share	resources	across	the	region	such	as	marketing	resources	or	research	into	

regional	trends.

E. Link	multiple	destinations	and	activities	to	create	more	local	business	opportunities	

and	encourage	longer	visits.

o Promote	living	wage	jobs,	especially	those	that	are	related	to	the	tourism	

industry.

o Retain	the	vibrancy	and	uniqueness	of	communities	– don’t	allow	the	effects	

of	tourism	to	significantly	alter	local	community	character	or	landscapes.	

F. Build	community-based	philanthropy	by	tapping	community	wealth	such	as	a	

community	foundation	so	that	communities	can	capture	and	invest	in	economic	

development	locally.

ADDITIONAL	IDEAS/QUOTES:

a. “Positive	things	happen	when	communities	get	the	message	that	no	one	is	going	to	

come	in	and	create	a	stronger	economy	for	them	-- change	is	going	to	happen	from	

within	the	community.	When	communities	say,	“We	need	to	figure	that	out.	We	need	

to	invest	in	ourselves	and	leadership,	then	we	are	going	to	figure	out	what	drives	

this	economy”,	and	then	when	people	will	go	out	and	see	who	the	entrepreneurs	are	

in	their	communities	and	they	have	honest	conversations	about	what	would	it	take	

to	grow	businesses,	recognizing	that	this	is	their	job	and	responsibility	–- that	

creates	the	BIGGEST	CHANGE.”

b. Identify	and	promote	assets	within	the	region	and	community	through	collaborative	

partnerships.	

o As	a	community,	identify	and	understand	the	assets	that	exist,	what	is	

missing,	and	then	find	ways	to	build	on	the	assets	that	exist	and	build	the	

ones	that	don’t	yet	exist.

o Identify	cultural	associations	across	the	region,	as	both	the	audience	and	the	

market.	

c. Identify	and	develop	regional	promotion	around	regional	topics	and	assets.	

o “Appalachian	heritage	is	important	and	should	be	leveraged.	“

d. Tell	the	stories	of	the	community,	especially	positive	stories,	to	change	the	

narrative.

o “Go	to	communities	that	are	succeeding	– we	need	a	culture	of	being	

imaginative.	We	need	sparkplugs	that	will	keep	trying	new	ideas.”	

o ‘Test	the	remedies	that	other	people	offer	to	see	if	makes	sense	to	them.	

People	know	their	own	communities	and	what	will	work.	We	need	to	be	

using	a	host	of	strategies.	We	need	to	be	willing	to	take	everything	we	learn,	

test	assumptions,	take	diverse	approaches,	and	create	a	vision	that	others	
74 |	Page

can	see.	We	need	to	paint	a	picture	that	glows	in	the	dark	so	that	people	can	

see	it.	People	will	rally	to	an	idea	that	is	culturally	appropriate;	if	it	makes	

sense,	if	it	inspires	them,	they	will	help.	You	have	to	have	a	community	

shaman.”	

e. Determine	how	rural	and	urban	areas,	and	broader	regions,	are	interconnected	

especially	around resources	and	supply	connections	around	food,	recreation,	and	

energy.

6. Build	networks	and	foster	collaboration

A. Use	a	systems	approach	to	develop	a	shared	understanding,	community	

opportunities,	and	needs	to	grow	sectors,	develop	assets,	and	foster	regional	

collaboration	with	long-term	vision	and	effort.

o Engage	economists	at	the	regional	scale	to	share	knowledge	and	best	

practices.	

B. Cultivate	grassroots	efforts	and	community	leadership,	and	effective	communication	

using	different	tools.	

o Determine	the	right	level	of	partnership	needed	for	effective	engagement.

C. Develop	networks	of	communities,	both	local	and	regional,	working	towards	a	

shared	understanding	of	the	opportunities,	gaps,	strengths,	and	unique	elements	of	

each	community.

o Foster	opportunities	for	generational	leadership	(both	in	terms	of	staff	and	

organizations)	to	grow	the	work	in	the	future	and	adapt	to	changing	needs	

and	opportunities	of	an	initiative.	

o Develop	structured	MOU’s	between	partner	organizations,	which	may	result	

in	opportunities	for	greater	funding.

D. Identify	and	grow	a	dedicated	long-term	entity	or	anchor	organization(s)	to:

o Build	capacity,	move	ideas	forward,	provide	a	consistent	forum	for	

collaboration,	develop	multiple	funding	streams,	and	help	implement	ideas.	

o Identify	ways	for	funders	and	practitioners	to	work	together	as	partners	to	

shift	the	power	structure.

E. Work	across	silos	and	sectors;	encourage	entrepreneurial	spirit	and	mindsets	for	

project	growth	and	development.

F. Develop	long-term	staffing	capacity	to	move	ideas	forward	and	foster	economic	

transformation.

o Create	a	training	program	and	a	cohort	of	practitioners	on	how	to	accelerate	

the	building	of	an	economic	resilience	model	from	the	bottom–up.
75 |	Page

ADDITIONAL	IDEAS/QUOTES:

Ask:	What	is	your	network?

Ask:	What	are	the	anchor	institutions	in	your	community	and	region?

a. Identify	and	grow	a	dedicated	entity	or	anchor	organization	to:	build	capacity;	move	

ideas	forward;	provide	a	consistent	forum	for	collaboration;	develop	multiple	

funding	streams;	and	help	implement	ideas.	This	might	include	an	academic	

institution,	a	non-profit	organization,	a	consensus-building	group,	or	another	entity.	

o “Anchor	institutions	allow	voices	to	come	together	to	speak	for	something	

greater	than	themselves.”

o “There	is	more	knowledge,	less	duplication,	more	efficiency	when	we	work	

together	with	a	more	effective	use	of	resources	– we	get	deeper	impact	and	at	

a	broader	

b. Develop	funding	opportunities	and	revenue	sources	collaboratively	based	on	

regional	opportunities	and	needs	from	a	variety	of	sources.	

c. Develop	networks	of	communities	working	together.	

o “The	future	will	be	in	partnerships.”

d. Foster	effective	communication	using	different	tools.

o Develop	intergovernmental	cooperative	agreements	for	counties	and	

agencies	to	collaborate	together	in	ongoing	forums	to	foster	greater	

collaboration.	

e. Use	a	systems	approach	to	grow	sectors,	develop	assets,	and	foster	regional	

collaboration	noting,	that	long-term	vision	and	effort	are	needed	to	grow	resilience	

and	sustainability.

7. Move	multiple	sectors	forward	for	economic	development	and	grow	

value	chains

A. Connect	multiple	opportunities	to	grow	a	value	chain	around	what	is	being	created	

locally.

o Use	a	systems	approach	to	develop	value	chains;	look	across	a	region	to	

determine	what	levers	can	grow	economic	resilience.	

o Develop	a	broader	recognition	of	the	interdependence	between	urban	and	

rural	areas.	Create	a	deeper	recognition	of	the	value	of	rural.

B. Build	grassroots	support	for	economic	diversification,	and	collaborate	with	public	

and	private	sectors.	

o Engage	both	non-profit	organization	involvement	as	well	as	marketconnected	enterprises.
76 |	Page

o Develop	models	that	engage	more	than	one	discipline	that	can	yield	multiple	

results

C. Promising	sectors	include:

o Advanced	manufacturing,	health	care,	renewable	energy,	outdoor	recreation,	

local	food	systems	and	agriculture,	technology	and	others

o Find	ways	to	focus	on	ideas	that	come	from	within	the	community	around	

sector-based	economic	growth	(not	ideas	that	are	externally	imposed	on	

communities).

o Develop	means	to	not	create	only	service-sector	work	through	tourism.

D. Connect	to	regional	markets	for	sales	of	products	and	services.	

o Grow	and	connect	with	the	broader	region's	economy	and	to	market	

opportunities	in	regional	urban	centers.	

o Examine	what	financial	and	tax	incentives	can	be	made	available	to	

encourage	businesses	to	locate	in	a	particular	area,	expand	programs	and	

resources	(such	as	loans	and	grants)	to	expand	existing	businesses,	and	ways	

to	connect	with	regional	marketing	opportunities	to	promote	localities	and	

regions.	

E. Develop	a	coordinated	strategy	for	economic	growth.

F. Learn	how	other	communities	and	regions	built	economic	growth	and	share	ideas.	

ADDITIONAL	IDEAS/QUOTES:

a. Build	grassroots	support	for	economic	diversification	and	collaborate	with	

public	and	private	sectors.	

o “Build	a	foundation	on	identifying	assets,	focusing	on	certain	sectors,	and	

then	make	investments	in	certain	areas... Having	government	agencies	at	

the	table	is	important	to	codify	collaborative	processes.	Getting	political	

and	policy	support	for	investments	has	been	crucial	[in	our	success]. “

b. Learn	how	other	communities	and	regions	built	economic	growth	and	share	

ideas.	

c. Connect	multiple	opportunities	to	grow	a	value	chain	around	what	is	being	

created	locally.

d. Grow	and	connect	with	the	broader	region's	economy.

e. Use	a	systems	approach	to	develop	value	chains;	look	across	a	region	to	

determine	what	the	levers	are	to	grow	economic	resilience.	

a. “We	need	to	use	systems	thinking	to	generate	multiple	forms	of	wealth	

and	wealth	creation.”

f. Connect	to	market	opportunities	in	regional	urban	centers.	

g. Develop	a	broader	recognition	of	the	interdependence	between	urban	and	rural	

areas.	Create	a	deeper	recognition	of	the	value	of	rural.	
77 |	Page

h. “Storytelling,	which	many	Appalachian	people	do	well,	needs	to	be	part	of	our	

products	and	services. Storytelling	needs	to	infiltrate	all	small	business	

messaging.“	

8. Cultivate	entrepreneurs	and	develop resources	for	business	start-ups

A. Create	entrepreneurial	ecosystems	with	locally–centered	markets.	

o Conduct	organizing within	a	community	to	think	about	economic	

development	and	what	opportunities	are	there;	talk	to	entrepreneurs	around	

what	opportunities	are	emerging.

B. Develop	entrepreneur	resources,	business	plan	competitions,	and	opportunities	to	

grow	local	businesses	and	access	capital.	

o Identify	CDFIs	(Community	Development	Financial	Institutions)	within	

Appalachia	that	could	be	strong	resources	for	entrepreneurs	or	community	

groups.	

o Develop	statewide	programs	to	promote	the	growth	of	new	industries	within	

states.	

o Build	the	capacity	of	communities	for	entrepreneur-focused	economic	

development	through	developing	multi-year	leadership	teams,	fostering	

leadership	development,	training,	and	coaching	mentors,	building	on	ideas	

and	assets	that	are	identified	that	arise	from	within	the	community.

o Connect	to	the	assets	of	a	community	around	to	how	to	transition	businesses	

to	a	new	generation.

C. Connect	with	schools,	colleges	and	universities	for	training,	mentoring,	and	research	

opportunities.

D. Advance	opportunities	for	workforce	training	in	specific	sectors	and	industries	

including	for	regional	context	and	displaced	workers.

E. Use	social	enterprise	resources	and	tools	to	develop	community-based	businesses.

o Cultivate	successful	business	accelerators,	co-working	spaces,	and	maker	

spaces.	

F. Develop	opportunities	for	remote	work	and	telecommuting.	

ADDITIONAL	IDEAS/QUOTES:

Ask:	Who	are	the	entrepreneurs	in	your	area?	

Ask:	What	resources	are	available	for	entrepreneurs	and	business	start-ups?

a. Advance	opportunities	for	workforce	training	in	a variety	of	specific	sectors	and	

industries.	Connect	to	regional	opportunities	and	needs,	and	develop	programs	and	

possibilities	specific	to	displaced	workers’	skillsets	and	opportunities.	
78 |	Page

o Re-envision	the	structure	of	farmer’s	markets	to	allow	for	more	diversity	of	

goods	to	be	sold. Create	opportunities	to	enable	low-income	participation.

b. Note	that	growing	entrepreneurs	and	small	businesses	takes	time	in	Appalachia	– it	

is	a	different	mindset	than	what	many	community	members	are	used	to.	

o “Tell	stories	and	change	the	narrative.”	

c. Develop	opportunities	for	small	business	owners	to	teach	and	mentor	students	in	

high	schools	and	colleges	to	develop	skills	and	leadership.	

o For	example,	in	the	PA	Wilds	effort,	they	are	creating	opportunities	for	

outfitters	to	teach	in	high	schools	during	the	off-season.	

d. Develop	multiple	ways	for	small	businesses	to	access	capital.

GENERAL	TOOLS	AND	RESOURCES:

• Orton	Family	Foundation	– Heart	and	Soul	communities

• Main	Street	Communities	and	Main	Street	resources	

• Connect	with	non-profit	organization	leadership	and	initiatives	

• Tupelo	– 5	level	Pyramid	from	Vaughn	Grisham	around	community	selfactualization

• Water	barrel	model	from	Brushy	Fork	around	a	community’s	economy	

• Just	Transition	network	and	model	

• MACED	ETEK	Model	(graphic)	

• Policies	are	important	including	the	RECLAIM	Act

• Funding	is	important	– POWER	funding	has	been	key	to	foster	local	and	regional	

collaboration

• Wealthworks.org	(mentioned	by	several	people;	also	under	resources)

• VISTA	program	“can	be	transformative”

• VEDP	guidebook:	

http://www.yesvirginia.org/Content/pdf/guides/Guidebook%20for%20Elected%2

0Officials%202017-2018.pdf

• Appalachian	Fellowship	program

Resources

• CDBG	(Community	Development	Block	Grant)	and	EDA	(Economic	Development	

Administration)	funding	

• Center	for	Rural	Entrepreneurship	

• Appalachian	Funders	Network	and	investment	potential	in	Appalachia	

• Energizing	Entrepreneurs is	a	great	book

• Heartland	Center	in	Nebraska

• BALLE

• Open	Space	Technology	
79 |	Page

• Democracy	Collaboratives	http://democracycollaborative.org/

• Ron	Eller,	John	Stephenson,	Vaughn	Grisham	have	all	done	great	work	around	

building	community	resilience.

o Ron	Eller	- https://history.as.uky.edu/users/eller

o John	Stephenson	- https://en.wikipedia.org/wiki/John_B._Stephenson

o Vaughn	Grisham	- https://www.kettering.org/catalog/product/tupeloevolution-community and	

https://assets.aspeninstitute.org/content/uploads/files/content/docs/pubs

/Tupelo_0.pdf

• Resilience	Institutes	from	the	Rockefeller	Foundation

• Just	Transition	Fund	

• Project	for	Public	Spaces

• ASPEN	Institute

• Association	for	Enterprise	Opportunity

• Central	Appalachian	Network	

• Alliance	for	Appalachia,	including	their	new	toolkit	for	economic	transition	

opportunities	around	post-mined	lands

• Community	Economic	Development	Network	of	eastern	TN

• Barbara	Wycoff	shared	three	possible	case	study	profiles	that	we	are	welcome	to	

use

• Wealthworks.org	

o https://www.wealthworks.org/economic-developmentresources/wealthworks-videos

• POWER	funding	and	the	resulting	partnerships	that	have	been	developed	(even	in	

unsuccessful	grant	applications)

• Kellogg’s	Rural	People	and	Rural	Policy	work	

• EPA	Competitive	Advantage	brief	(similar	to	this	work):	

https://www.epa.gov/sites/production/files/2015-

05/documents/competitive_advantage_051215_508_final.pdf

• Brushy	Fork	case	studies	

o Video	from	the	Brushy	Fork	community	leadership	curriculum	that	includes	

a	few	of	the	spark	plugs: https://www.youtube.com/watch?v=pJ1B7HvTT0E

• AceNet	resources:

o http://acenetworks.org/community/

o http://acenetworks.org/acenet-consulting/case-studies/

• Dawn	of	Systems	Leadership –

https://ssir.org/articles/entry/the_dawn_of_system_leadership

• https://tgkvf.org/presidents-message-dr-michelle-foster-2016/
80 |	Page

• Temali, M. (2004).The community economic development handbook: Strategies 

and tools to revitalize your neighborhood. St. Paul, MN: Amherst H. Wilder 

Foundation

• TOOL:	http://whatsnextwv.org/

• http://www.yesmagazine.org/commonomics/these-young-people-are-pioneeringa-post-coal-economy-in-appalachia

• WV	Hub	model	for	building	capacity	in	communities	

o WV	Community	Building	Toolkit	from	the	WV	Hub

o Hub	NewsFeed	of	successful	stories	from	the	region	(possible	case	study	

resource)

o Hubapalooza	– face-to-face,	place-based	learning	exchange	

• The	Center	for	Rural	Entrepreneurship	is	a	national	NGO.	

https://www.energizingentrepreneurs.org/ There	are	many	tools,	videos	and	

examples	on	their	website.

Points	of	inspiration

• Benham	school	in	and	Lynch	in	Kentucky	and	its	creation	as	a	community	space

• FARMACY	- giving	prescriptions	for	food	in	partnership	with	farmer’s	market.	

http://cfaky.org/what-we-do/appal-tree/farmacy-program/

• Connect	KY	– successful	example	of	regional	broadband	development

• NorrisBuilt	Fabrication	in	Norton,	Virginia	

• Western	Front	and	the	Inn	at	Wise	in	SWVA	

• Northrop	Grumman	and	CGI	businesses	in	SWVA	

• Berea	Tourism	Business	Accelerator

• Learning	what the	younger	generation	is	interested	in	is	a	point	of	inspiration	of	

what	kinds	of	businesses	to	recruit	and	develop.

• Success	stories	of	entrepreneurs	in	the	area.

• Bren	Smith	

• Clearfork	Community	Institute	

• The	Next	in	SWVA

• Foundation	for	Appalachian	Kentucky	– Community	Foundation	

• Higher	Ground	Theatre	in	Eastern	Kentucky

• West	Virginia	Hive	Accelerator	(http://nrgrda.org/our-team/joseph-brouse/)

• Opportunity	SWVA	and	the	Clinch	River	Valley	Initiative	

• Crooked	Road

• Mountain	Tech	Media

• Lost	Creek	Farm	in	WV

• Next	Generation	and	Art	of	the	Rural	

• Dilapidated	housing	project	in	WV	(WV	Hub	is	working	on	this)	
81 |	Page

• The	town	of	Gilbert	in	Mingo	County,	WV	has done	an	excellent	job	of	building	small	

business	opportunities	around	trails	tourism,	and	those	opportunities	were	made	

possible	by	the	local	town	government’s	embracing	the	positive	changes	brought	

about	by	the	Trails

• Appalachian	Conference	on	Social	Enterprise	with	forthcoming	facilitator’s	guide	

and	video.

• Appalachian	Harvest	as	a	model	of	an	NGO	(Appalachian	Sustainable	Development	

project)	that	is	taking	advantage	of	market	forces	and	the	positive	elements	of	being	

an	NGO. They	have	been	working	for	15	years	and	they	drew	$2.5M	in	the	last	year,	

most	of	which	has	gone	back	to	farmers.	

• Williamson	WV	Health	and	Wellness	Center	– successfully	fostered	small	business	

development	that	has	been	an	outgrowth	around	growing	the	local	food	

involvement,	recreational	opportunities,	garnering	more	community	engagement.

• Refresh	Appalachia	– growing	farmers	to	become	producers	that	develop	small	

business	ventures.	

• Coalfield	Development	Corporation	workforce	training	model	which	includes	in	the	

example	of	local	food	involves	crew	members	working	33	hours	a	week	in	an	

incubator	farm	center	– growing	vegetables	and	livestock	on	a	post	mined	land	with	

hydroponic	growing.	Crew	members	also	have	6	hours	of	academic	classes	a	week	

that	leads	to	an	Associates	degree,	and	3 hours	of	life	skills	training	as	well.	

o Coalfield	employs	a	family	of	social	enterprises	with	same	model	across	

several	different	sectors	to	recreate	communities,	in	construction,	solar	

development,	agriculture,	post-mined	land	use	and	other	sectors.	

• “KY	Valley	Educational	Cooperative	-Amazing	things	are	happening	in	the	schools	

with	creativity	and	action.	The	Gates	Foundation	came	in	to	the	area	to	see	how	

evaluation	is	happening.	Kids	are	building	wind	tunnels,	prosthetics,	Kids	are	taking	

up	the	challenge	of	recreating	place,	and	they	are	doing	it.”	

• Social	Enterprise	gathering	in	Morgantown	in	May	

Challenges	to	grow	economic	resilience	in	Appalachia	

• A	lot	of	information,	“know	how”	and	knowledge	that	is	associated	the	older	

generation	is	being	lost	as	those	generations	are	passing	away.	

• Access	to	capital	for	small	businesses	and	risk	aversion.

• A	lack	of	infrastructure	has	been	one	of	the	biggest	factors	holding	regions	back	in	

Appalachia	from	economic	growth.	

• There	is	a	rich	civil	rights	history	but	also	a	troubled	history	of	white	supremacy	and	

segregation	in	many	areas	of	Appalachia.	

• Difficulty	in	doing	pro-active	planning:	“Communities	bumble	along	unless	have	a	

great	opportunity	or	a	great	crisis.	It	is	horrible	to	see	what	is	happening	with	the	

coal	sector,	but	it	does	sparks	activity	and	that	is	a	silver	lining.	It	should	have	
82 |	Page

happened	20	years	ago.	Due	to	generational	change	and	economic	pressure,	many	

coal	communities	are	re-thinking	their	foundations	of	local	economies.”

• There	is	a	need	to	look	at	what	the	barriers	are	to	growing	community	health	and	

wellness,	including	how	to	access	social	services	and	address	addiction.	

• Lack	of	innovating thinking,	bold	planning	and	fresh	ideas	and	planning	for	

economic	development.	“Many	of	the	generation	of	economic	developers	are	from	

the	60’s-70’s	– lots	of	good	old	boys	who	are	locked	in	old	ways	of	doing	things.	

They	are	becoming	stayed.	It	can	be	hard	for	new	ideas	and	for	new	ideas	and	

people	to	break	in.	Old	ways	of	thinking	about	economic	development- you	see	it	in	

action	too.”

• Entrenched	power	structures,	including	federal	agencies,	and	state	politicians	who	

aren’t	morally	just.

• Limited	acceptance	of	newcomers	– many	people	feel	threatened	by	new	ideas	and	

by	newcomers.	

• Many	people	feel	marginalized	and	discounted,	like	their	ideas	don’t	matter.	Many	

haven’t	been	engaged	or	including	in	opportunities	for	new	or	innovative	thinking	

of	planning,	but	would	participate	if	they	were	asked	or	if	there	was	a	consistent	

forum	for	their	ideas	to	be	heard	and	valued.	

o “How	do	you	have	the	naysayer	see	the	long-term,	and	bring	them	to	the	

table	and	see	that	they	are	important	part	to	processes?”

• Extreme	poverty	and	the	inability	to	meet	basic	needs,	including	long-term	

generational	poverty.

o “People	are	leaving,	there	is	no	work	and	growing	despair	and	addiction.	It	is	

not	having	a	clear	path	forward	that	is	a	big	part	of	the	problem.”

o “Folks	who	were	former miners	have	beaten	bodies	– they	have	homes	they	

can	no	longer	pay	for	and	a	skill	set	that	is	not	transferrable	outside	of	

manufacturing.”	

o “Coal	severance	taxes	supported	schools	and	infrastructure,	and	was	a	

godsend	for	so	long.	Now	that	it	has	taken	this	turn,	people	are	really	

struggling. The	devastating	human	impact	of	this	shift	is	indescribable	on	the	

livelihoods	on	homes	and	families.”	

• People	are	leaving	and	the	resource	(coal)	won’t	return.	

o “People	are	staying	– some	– but	with	the	hope	that	the	new	administration	

will	restore	coal	mining	opportunities	– the	market	shift,	the	price	of	natural	

gas	and	the	investment	in	natural	gas	producing	facilities	mean	that	(coal	

opportunities)	will	never	be	what	it	was.	“

o “Many	people	are	leaving.	There	has	been	a	mass	exodus	for	quite	some	time,	

but	many	are	coming	back.	There	are	a	lack	of	opportunities,	but	a	strong	

sense	of	belonging	and	sense	of	place	here	with	family	bonds	that	run	so	

deep. There	is	strong	grounding	in	a	community.	“

o “The	reality	that coal	is	not	coming	back	to	the	level	it	was,	and	the	difficulty	
83 |	Page

and	sadness	in	that	reality,	is	very	hard	for	people	to	look	at	or	

acknowledge.”

o “As	a	community	(in	eastern	Kentucky)	we	are	down	to	around	3,000	coal	

miners	now.	But	there	are	30,000	health care	workers,	but	we	don’t	pay	

attention	to	them.”

• The	boom	and	bust	cycles	of	an	extraction-based	economy	have	created	a	nondurable	monoeconomy	create	multiple	challenges.

• Many	undocumented	immigrants	and	Latino	families	especially	are	in	vulnerable	

positions	in	communities	and	need	opportunities	to	grow	community	and	safety.	

• “Economic	resilience	will	come	back	to	monoculture	based	on	gas,	not	coal.	

Everything	else	will	be	driven	out.”

Practitioner	Guidebook	Format	ideas	and	sharing	best	practices	

• Use “drumbeat	storytelling”	as	a	way	to	change	things	via	making	connections.

• Develop	checklists	and	menus	that	communities	can	use	with	real	practical	

examples.

• Develop	a	printed	guidebook	with	digital	resources.

o Easy	to	access	with	scaffolding	ideas.

o Connect	to	videos,	reports,	links	to	resources.

• Use	simple,	common	language.	

• Share	stories	from	one	community	to	another	– this	is	important	– lifting	up	stories	

of	real	people	in	real	places.	

• Create	different	elements	of	the	report/	product	so	that	“folks	can consume	what	is	

relevant	to	them.”

• Videos	to	highlight	examples.

• Share	principles	and	practical	examples.	

• Focus	on	sector-based	work	to	divide	content/	subjects	up	by	a	specific	topic	or	idea	

for	specific	audiences.	

o Look	for	specific	points	of	entry	by	topic	(such	as	around	dilapidated	

buildings	or	renewable	energy).	

o Share	information	about	different	types	of	capital	and	social	enterprises.

o Include	information	about	systems	thinking	(thinking	long-term,	

interconnection	of	ideas	and	resources—“when	you	push	something	else	is	

pulled”).

• Long-term	ideas	for	growing	economic	resilience:

o Create	a	long-term	regional	focus/advisory	group	comprised	of	individuals	

who’ve	had	success	in	their	respective	communities	of	building	economic	

resilience	through	collaborative	partnerships	and	creative	thinking.

o Create	face-to-face	inter-region	exchanges	for	community	members	to	learn	
84 |	Page

from	one	another.	

o Develop	a	road	show	of	what	has	worked,	and	smaller	meetings,	and	take	it	

to	different	communities.	

• Guidebook	audience	considerations:	

o Ask:	“What	is	it	in	the	world	do	you	want	to	change?	Who	needs	the	info	to	

make	change	happen? How	do	those	people	get	their	information	(so	that	

you	aren’t	using	channels	that	don’t	work)?	How	do	you	motivate	

communities?”	Create	living	documents	with	photos	and	video	links.

o Create	different	formats	for	different	audiences

• “Have	a	mix	of	experience,	analysis	and	implications.	Create	2-4	good	examples	that	

are	different	enough	to	appeal	to	a	broad	base	of	practitioners,	but	that	common	

learnings	and	broader	lessons	can	be	distilled	from.	Have	practical	experience,	and	

also	hard,	solid	facts	and	regular	thinking.	We	need	real	examples	– grounding	in	the	

real	world	and	examples	that	are	mixed	enough	– not	singularly	unique	– not	in	a	

place	or	in	a	stage	that	is	too	far	from	the	audience’s	experience.	Not	Asheville	or	

Portland	– but	lessons	learned	from	places.	Stating	things	as	simply	as	possible.”

• Peer	learning	opportunities	are	key.

o This	needs	to	be	in	person	to	build	trust	and	legitimacy.	

o Site	visits	and	place-based	learning	are	key.	

o Holding	convenings	around	sectors.

o Use	social	media	more	fluently.

o Hold	hybrid	webinars	and	conference	calls	where	people	can	be	peer	

learners	together.

• In	the	digital	engagement,	ask	respondents	about	how	they	do	implementation–with	

questions	such	as	“What	is	your	revenue	generating	process.	How	did	you	set	it	up?	

What	do	you	do	with	the	revenues?”

o Gather	ideas	for	practitioners	to	proactively	plan	how	to	fund	initiatives	by	

understanding	other	revenue	generating	programs.

• In	the	case	studies,	ask	specific	questions	about	how	revenue	streams	are	set	up,	

how	organizations	functions,	how	it	was	established	and	how	revenue	is	generated.	

Who	else	to	engage	in	the	effort	moving	forward

• USDA	Forest	Service	

• Main	Street	

• Business	owners	

• Williamson	Health	&	Wellness	Center	- www.williamsonhealthwellness.com. Darrin	

McCormick	is	the	outreach	coordinator,	and	he	can	be	reached	at	(304)	235-3400.

• Foundations	such as	Benedum,	Babcock,	and	ARC

o Ray	Daffner	of	ARC
85 |	Page

• Todd	Christensen

• Stacy	Mitchell	of	the	Institute	for	Local	Self	Reliance

• Kimber	Lanning	– founder	and	executive	director	– Local	First	Arizona

• Kate	Green	– Mainstreet	Fairmont	

• June	Holley	– founder	at	ACEnet.	http://www.networkweaver.com/

• Deb	Markley

http://www.energizingentrepreneurs.org/about/team.html/title/deborah-markley

• Charleston	WV	Main	Street	– Rick	Cavender

• Williamson,	WV	- Health	and	Wellness	Center	– Maria	Arnott	or	Jenny	Hudson	form	

the	Mingo	Co.	Diabetes	coalition

• Buckhannon,	WV	- CJ	Ryland

• Contact	for	small	economic	development	projects	beside	tourism	economies	-

Deborah	Bahr,	Cosby,	Tennessee

• Using	historic	African	American	history	infrastructure	as	economic	development	

components:	Carlene	Robinson,	Tanner	Preservation	Alliance	&	Tennessee	Picnic,	

Newport,	Tennessee

• Stella	Gudger,	Price	Public	Community	Center	&	Swift	Museum,	Rogersville,	

Tennessee

• Beth	Vanlandingham,	Carson	Newman	University's	African-American	Heritage	

Alliance	Mapping	project,	Jefferson	City,	Tennessee

• African	American	data:William	Turner

• Marie	Cirillo,	Clearfork	Valley	of	TN	

• Marie	Webster	– the	Director	of	the	Clearfork	Community	Institute

http://www.clearforkcommunityinstitute.com/

• Tonia	Brookman	– Director	of	the	Woodland	Community	Land	Trust

• Michelle	Mockbee	who	worked	on	the	Clearfork	Community	Institute	for	many	

years	and	helped	develop	the	Volunteers	in	Partnership	(VIP)	program there	and	

ran	a	participatory	action	research	practicum	in	the	summers	with	VIPs.	

• Lyndsay	Taurus - The	Economic	Transition	Coordinator	for	The	Alliance	for	

Appalachia

The	Alliance	for	Appalachia	is	an	alliance	of	16	member	groups	across	Appalachia	

working	on	issues	around coal	mining	and	economic	diversification.

https://theallianceforappalachia.org/

• Emma	Cosigua.	She	is	from	Guatemala	and	lives	in	Knoxville.	She	is	very	involved	in	

community	work with	immigrants,	education	reform,	over-policing,	etc.	and	would	

be	great	to	talk	to.	The	2015	Appalachian	Media	Institute	cohort,	Appalshop's	youth	

media	program,	traveled	to	Knoxville	to	interview	Emma	and	other	community	

members	about	their	experiences	living	as	Latinx	in	Appalachia	and	produced	this	

documentary:	Dos	Patrias.

Potential	case	studies	suggested	from	interviewees

• Look	outside	of	Appalachia,	particularly	to	communities	that	have	bounced	back	
86 |	Page

from	natural	disasters	or	that	have	lost	entire	industries	(such	as	in	VA	where	VEE	

was	created).8

• “The	best	places	are	still	developing	their	ability	to	be	resilient.	We	need	actual	data	

though.	Many	communities	are	coming	to	grips	with	the	need	for	greater	reliance	

and	tools	for	building	local	capital	and	increasing	self-reliance,	and	with	that	higher

recognition	and	more	time	will	be	dedicated	toward	getting	more	resilient,	and	they	

will	become	more	resilient.	But,	there	are	way	more	that	are	vulnerable	than	

resilient.”	

• Several	locations	were	noted	by	multiple	interviewees.

																																								 																				 8 In February 1977, the United States District Court for the Eastern District of Virginia fined Allied 

Chemical Corporation $13.2 million for polluting the James River with the insecticide 

Kepone. www.vee.org/about/history/
1. Elkhart,	IN

2. Jacksonville,	FL

3. British	Columbia	

4. East	Central	Vermont:	Addison,	

Orange,	Rutland	(particularly),	

Windsor	Counties

5. Southwest	VA	(counties	of	Wise,	

Russell,	Scott	and	Tazewell;	towns	

of	Wise	and	Norton)

6. Athens	County,	OH

7. Chattanooga,	TN

8. Maine	Fisheries:	(counties	of	York,	

Cumberland,	Sagadahoc,	Lincoln,	

Knox,	Waldo,	Hancock,	Washington	

Counties)

9. PA	WILDS	(counties	of	Cameron,	

Centre,	Clarion,	Clearfield,	Clinton,	

Elk Forest,	Jefferson,	Lycoming,	

McKean,	Potter,	Tioga,	and	

Warren)

10. Rutland,	VT

11. Beckley,	WV

12. Morganton,	NC

13. Mingo	County	WV

14. Gilbert,	WV

15. Asheville,	NC

16. Greenbrier	County	

17. Eastern	KY	Counties:	(Especially	

the	town	of	Whitesburg)	

18. Fayetteville,	WV

19. Charleston,	WV

20. Huntington,	WV

21. Tri-Cities:	Johnson	City,	TN, Bristol	

and	Kingsport,	VA

22. Danville,	PA

23. Bloomsburg, PA

24. Columbia,	PA

25. Ohiopyle,	PA

26. Fayette	County,	PA

27. Tupelo,	MS

28. Oxford,	MS

29. Florence,	MS

30. Mussell	Shoals,	MS

31. Bucksport,	ME

32. Dickinson	County,	IA

33. Cleveland,	OH

34. Sparksville,	MO

35. Columbus,	MO

36. Roanoke,	VA

37. Blacksburg,	VA

38. Louisville,	KY

39. Marion,	VA

40. Moorehead,	MN

41. Belfast, ME

42. Damariscotta,	ME

43. Pittsburgh,	PA

44. Rock	Castle	County,	KY

45. Lawrence,	KS

46. Beckwith	County,	KS

47. Buckhannon,	WV

48. Grafton,	WV

49. Wheeling,	WV

50. Williamson,	WV

51. Princeton,	WV

52. Fairmont,	WV

53. Bellingham,	Washington
88 |	Page

Interviewees	include:

1. Jonathan	Belcher,	Executive	Director,	Virginia	Coalfield	Economic	Development	

Authority	

2. Dee	Davis,	President,	Center	for	Rural	Strategies

3. Ta	Enos,	Executive	Director,	PA	Wilds	Center	

4. Anthony	Flaccavento,	President,	Rural	Scale

5. Michelle	Foster,	President	and	CEO,	The	Greater	Kanawha	Valley	Foundation

6. Peter	Hille,	President,	MACED

7. William	Isom,	Sustainable	and	Equitable	Agricultural	Development	Task	Force	

8. Leasha	Johnson,	Executive	Director,	Mingo	County	RDA

9. Jake	Lynch,	Director	of	Network	Communications,	WV	Community	Development	Hub

10. Deborah	Markley,	Managing	Director,	Center	for	Rural	Entrepreneurship

11. Erik	Pages,	President,	EntreWorks	Consulting

12. Leslie	Schaller,	Director	of	Programs,	Appalachian	Center	for	Economic	Networks

13. Ada	Smith,	Institutional	Development	Director,	Appalshop

14. Kent	Spellman,	Consultant,	Rails-To-Trails	Conservancy

15. Tom	Torres,	Vice	Chair	of	the	Appalshop	Board	Executive	Committee	

16. Stephanie	Tyree,	Executive	Director,	WV	Community	Development	Hub

17. Bonnie	Swinford,	Tennessee	Chapter	of	Sierra	Club	

18. Thomas	Watson,	Executive	Director,	Rural	Support	Partners

19. Barbara	Wycoff,	Executive	Director,	One	Foundation

Interview	questions	included:

1. Tell	me	a	little	about	yourself	and	your	background	- how	are	you working	with	

community	building	and economic	resilience	in	your	local	area?

2. How would	you	rate	own	community’s	level	of	resiliency	on	a	five-point	scale	(1 being	

the	least	resilient,	5	being	the	most	resilient)?

3. What	has	created	the	greatest	positive	change	in	your	community	or region	over	time?	

What	have	been	the	greatest	challenges	and	untapped opportunities?

4. What	have	been	the	biggest	points	of inspiration	(people,	places,	or	ideas)	in	your	

community	-- or	beyond	your community	-- that	you	have drawn	from?

5. What	tools	or	resources	have	been	the	most important	leverage	points	for	creating	

resilience	in	your	community	(these might	include	a funding	or	agency	resource,	

decision-making	process,	or	best practice)?

6. What have	been	your	greatest	lessons	learned	over	time?

7. What	advice	or	ideas	would you	offer	to other	practitioners	or	community	leaders	as	

they	work	to	building	thriving communities	and regions?

8. What	tools	or	resources	do	you	think	would	be	the	most	helpful	for community	leaders	

and	practitioners	to	build	economic	resilience	in	their communities? What	specific	

formats	would	be	the	most	helpful	in	creating	a	practitioner	guidebook?

9. Who	else	do	you	think	should	be	involved	or	consulted	in	this	effort?

10. Are there	any	other	ideas,	best	practices,	or	examples	of	resilient	communities that	you

would	like	to	share?
89 |	Page

APPENDIX	C:	ADVISORY	TEAM	CALL	SUMMARIES

Advisory	Team	call	summary

February	6,	2017

For	Strengthening	Economic	Resilience	in	Appalachian	Communities	

The	first	call	of	the	Advisory	Team	for	the	Strengthening	Economic	Resilience	in	Appalachian	

Communities	project	was	held	on	Monday,	February	6,	2017. Advisory	Team	call	

participants	are	listed	at	the	end	of	the	summary. Christine	Gyovai	of	Dialogue	+	Design	

Associates	welcomed	participants	to	the	call	and	gave	an	overview	of	the	Advisory	Team	

focus.	Fritz	Boettner	of	Downstream	Strategies	then	gave	an	overview	of	the	project	and	

project	deliverables	(see	the	project	description	below).	Participants	then	introduced	

themselves,	and	Christine	gave	an	overview	of	the	initial	strategies	and	best	practices	for	

the	effort.	Finally,	Stephan	Goetz	of	the	Northeast	Regional	Center	for	Rural	Development

at	Penn	State	provided	an	overview	of	the	initial	economic	analysis	findings,	and	

participants	shared	their	feedback	around	both	the	initial	qualitative	and	quantitative	

findings.	The	next	Advisory	Team	call	will	be	in	mid-spring	2017,	and	the	third	and	final	

Advisory	Team	call	will	be	in	early	summer	2017. Below	are	highlights	from	the	Advisory	

Team	call.	

Project	overview	and	Introductions	

In	addition	to	sharing	names	and	affiliations,	Advisory	Team	members	shared	their	favorite	

Appalachian	town	to	visit	at	the	beginning	of	the	call	which	included:	Fayetteville,	WV;	

Thomas	and	Davis,	WV;	Johnstown,	PA;	St.	Paul,	VA;	Harpers	Ferry,	WV;	Slade,	KY;	Elkhorn	

City	and	Breaks	Interstate	Park,	KY;	Waterford,	OH;	High	Knob	area	at	Bark	Camp	Lake,	VA;	

Whitesburg,	KY;	the	Wild	Ramp	in	Huntington,	WV;	and	Pittsburg,	PA.

Fritz	then	provided	an	overview	of	the	project and	project	approach	(which	may	be	found	

in	the	attached	project	highlights	document,	which	was	shared	with	Advisory	Team	

members	in	advance	of	the	call). One	Advisory	Team	member	noted	that	it	is	a	challenge	to	

delineate	what	we	mean	by	resilience	and	how	we	define	it,	noting	that	resilience	could	

refer	to	the	ability	of	a	community	to	use	a	shock	as	an	opportunity	to	“bounce	forward,”

not	just	“come	back.”

Draft	strategies	and	best	practices	

Christine	then	provided	an	overview	of	the	draft	strategies	and	best	practices	for	the	

project	(included	in	the	project	highlights	document	below). Advisory	Team	(AT)	member	

questions	and	feedback	included:	
90 |	Page

! One	of	the	greatest	challenges	in	this	work	is	in	how	we	can	change	mindsets	in	the	

community,	visioning	how	communities	can	be	reimagined	for	growth. Learning	

how	other	communities	have	done	this	would	be	helpful.	

! For	another	AT	member,	regarding	changing	mindsets,	one	of	the	one	of	the	biggest	

take-aways	from	a	recent	ARC	meeting	for	POWER	grantees	with	very impressive	

“younger	generation”	folks	from	KY,	WV	(including	from	the	WV	Hub)	and	others	

who	are	spreading	amazing	personal	stories	and	saying	that	we	need	to	get	away	

from	“poverty	porn”.	We	need	to	share	the	very	positive	real	faces	and	real	people	

and	positive	stories.”

! Another	participant	noted	that	it	is	difficult	for	community	members	to	change	their	

mindsets	– it	is	hard	to	get	people	to	change	what	they	are	used	to	doing.	

Several	AT	members	noted	the	importance	of	“changing	the	narrative”.	One	participant	

noted	that	in	Appalachia	there	is	a	strong	legacy	and	inherent	nature	of	storytellers	that	

can	be	built	on	with	“networks	of	networks	of	storytellers	coming	together.” Some	

resources	and	examples	of	this	include	the	Central	Appalachian	Network	(CAN),	which	

created	a	video	a	few	years	ago	around	local	food	value	chains,	grounded	in	WealthWorks	

metrics	(which	were	made	by	Curtis	Creative).	“We	need	to	use	media	in	new	ways	to	get	

stories	out	and	shift	the	narrative	at	a	grassroots	level.”	Additional	AT	member	feedback	

included:	

! Some	communities	in	Mingo	County	are	doing	a	good	job	in	changing	the	narrative	

by	rebranding	and	recreating	who	we	are	through	a	grassroots	effort	with	

community	buy-in.	There	has	been	resistance	of	community	members	as	well;	there	

is	a	strong	recognition	of	the	important	heritage	and	backbone	of	coal	in	the	region,	

but	also	a	need	to	look	to	the	future. At	the	Williamson	Health	and	Wellness	Center,	

community	members	are	working	to	build	a	culture	of	health	by	addressing	longterm	health	issues.	Several	small	business	start-ups	have	resulted	from	Wellness	

Center	initiatives	and	from	building	out	networks,	as	well	as	from	implementing	

broadband	strategies	and	finding	ways	to	change	the	narrative	and	offer	long	termterm	ideas	for	sustainable	growth.	

! In	eastern	Kentucky,	the	way	state-led	economic	development	infrastructure	is	

conducted	can	be	problematic.	Economic	development	efforts	in	southwest	Virginia	

have	had	more	support	from	the	state	level.	

! At	the	state	policy	level,	state	Economic	Development	cabinets	for	economically	

distressed	communities	could	be	a	supportive	driver	for	growing	economic	

resilience.	

! State	Economic	Development	Authority,	including	commerce	and	infrastructure	

departments,	could	developed	as	a	potential	case	study. Recently	in	WV,	there	has	

been	new	collaboration	with	the	Dept.	of	Commerce	and	the	work	of	the	WV	

Community	Development	Hub	to	share	ideas	around	transition	work	and	economic	

development	work	for	possible	policy	recommendations.	This	took	the	organization	

of	the	networks	of	people	(not	just	one	entity	and	organization,	but	a	series	of	

entities	and	towns	that	are	working	together).
91 |	Page

! We	need	to	focus	on	the	assets	that	we	have	in	the	community,	and	how	to	

encourage	state-level	economic	development	entities	to	reflect	both	short-term	and	

long-term	innovative	solutions	that	arise	from	within	communities. There	is	a	sense	

of	urgency	to	create	real	economic	growth	and	resilience	in	many	communities	that	

have	been	devastated	by	coal-related	job	loss,	as	well	as	to	build	long-term	solutions	

that	consider	the	heritage	of	the	region.	

! There	may	be	a	time	and	place	to	consider	that,	for	some	communities,	coming	back	

isn’t	the	right	solution.	There	may	be	a	need	for	some	community	members	to	move	

on	as	growing	resilience	really	isn’t	an	option.	We	need	to	support	people	in	

changing	their	communities,	but	that	also	might	include	moving	away	– not	just	

focusing	on	ways	a	community	can	bounce	back.	

! One	element	that	is	woven	throughout	supporting	the	economic	development	

capacity	of	communities	is	to	have	a	good,	paid,	skilled	practitioner	doing	the	work	

who	can	provide	key	training,	grow	support,	and	build	capacity	in	communities.

Project	team	members	then	noted	that	the	team	is	developing	case	studies	for	the	effort	as	

well.	Finally,	the	primary	product	of	the	effort	is	a	Practitioner	Guidebook	for	economic	

development	practitioners,	which	could	include	web,	infographic,	print	or	possibly	video	

components.	The	Project	Team	welcome AT	suggestions	for	most	useful	formats	and	

outreach	methods	for	guidebook.

Quantitative	Analysis	initial	approach	and	findings	

Stephan	then	gave	an	overview	of	the	Quantitative	Analysis	initial	approach	and	findings,	

which	is	included	on	the	project	highlights	document	below. Advisory	Team	member	

questions	and	feedback	included:

! The	WealthWorks	metrics	might	be	helpful	in	measuring	social	capital	

(Wealthworks.org).	There	has	been	a	significant	information	gathering	in	southeast	

Ohio	around	leverage	what	interactions	build	strong	social	capital.	One	of	the	

experiences	at	ACE-NET	that	might	be	valuable	is	around	the	long-term	

relationships	with	clients	and	tenants,	and	the	ability	to	measuring	more	specific	

impacts	of	WealthWorks	forms	of	capital	-- which has	strongly	informed	their	work	

-- particularly	around	the	food	sector.	Measuring	social	capital	has	been	important,	

and	the	challenge	has	been	how	to	scale	it	up	to	think	and	act	across	the	region.

! Having	more	baseline	economic	data	and	information	around	economic	resilience	

would	be	very	helpful.	Poverty	statistics	are	so	prominent,	and	elected	officials	are	

making	promises	but	we	don’t	have	a	baseline	to	measure	policy	or	legislative	

impacts	against. Some	of	the	data	around	national	rural	economic	development	and	

future	projections	are	now	being	scrutinized	in	more	detail.	For	example,	national	

retail	density	assumptions	were	made	and	used	extensively	in	rural	areas	to	create	

economic	development	plans	around	reviving	the	retail	sector,	but	we	need	to	look	

at	that	strategy	more	closely	to	see	if	it	is	really	viable.	If	the	retail	sector	is	

declining,	we	need	to	develop	alternative	economic	growth	plans	such	as	around	

health	care.	
92 |	Page
93 |	Page

Next	steps	and	follow	up	

At	the	end	of	the	Advisory	Team	call,	Project	Team	members	shared	next	steps	for	the	

project	outreach	which	included	conducting	interviews,	a	digital	engagement,	and	a	Focus	

Group.	Future	Advisory	Team	calls	will	be	in	mid-spring	and	in	late	June	2017.	

Participants	from	the	Feb.	6	Advisory	Team	call	

Youth

Eric	Dixon,	Appalachian	Citizen	Law	Center

Representing	Philanthropic	Foundations

Justin	Maxson,	Mary	Reynolds	Babcock	Foundation

Barbara	Wycoff,	One	Foundation

Representing	Community	Groups

Dee	Davis,	Center	for	Rural	Strategies	

Peter	Hille,	Mountain	Association	for	Community	Economic	Development

Debra	Horne,	Mayor,	Town	of	Dungannon,	VA

Leslie	Schaller,	Appalachian	Center	for	Economic	Networks

Universities

Shannon	Blevins,	University	of	Virginia’s	College	at	Wise

Jennifer	Simon,	Ohio	University

Local	Development

Leasha	Johnson,	Mingo	County	Redevelopment	Authority

Project	Team	members

Fritz	Boettner,	Downstream	Strategies

Stephan	Goetz,	Penn	State

Christine	Gyovai,	Dialogue	+	Design	Associates	

Advisory	Team	and	Project	Overview	for	the	Strengthening	Economic	Resilience	in	

Appalachian	Communities	project	overview

The	Advisory	Team	for	the	Economic	Resilience	project	will	be	comprised	of	a	dozen	key	

advisors	with	experience	in	economic	resilience	and	building	thriving	communities.	This	

Economic	Resilience	project,	sponsored	by	the	Appalachian	Regional	Commission,	will	

explore	and	document	strategies	and	policies	local	leaders	can	use	to	enhance	the	future	

economic	prospects	of	coal-impacted communities	throughout	the	Appalachian	Region.	

FRITZ There	are	four	key	components	to	this	research	project:	1)	develop	a	

comprehensive,	quantitative	framework	to	explore	economic	resilience;	2)	identify	a	series	

of	best-practice	strategies	for	strengthening	local	economic	resilience;	3)	conduct	up	to	10	

in-depth	case	studies;	and	4)	produce	a	concise	guidebook	that	interprets	and	integrates	

findings	of	the	research,	written	specifically	for	local	economic	development	practitioners.	
94 |	Page

Advisory	Team	members will	give	input	at	key	points	in	the	project	around	the	approach	of	

the	quantitative	and	qualitative	analyses,	web-based	survey,	Focus	Group development,	

and	final	Guidebook	and	Final	Report	development,	and	other	potential	areas.	Three	

conference	calls	will	be	held	with	the	Advisory	Team	during	each	of	the	three	overarching	

phases	of	the	project,	with	conference	calls	likely	held	in	late	January	or	early	February	

2017,	early	April	2017,	and	late	June	2017.
95 |	Page

Advisory	Team	call	summary

June	8,	2017

For	the Strengthening	Economic	Resilience	in	Appalachian	Communities	project

The	third	call	of	the	Advisory	Team	for	the	Strengthening	Economic	Resilience	in	Appalachian	

Communities	project,	sponsored	by	the	Appalachian	Regional	Commission,	was	held	on	June	8,	

2017. The	first	Advisory	Team	call	was	held	in	February	of	2017,	and	the	second	call	was	held	as	

part	of	a	broader	Focus	Group	webinar	in	April	of	2017.	The	June	8th call	was	moderated	by	

Christine	Gyovai	of	Dialogue	+	Design	Associates.	A	complete	list	of	participants	is	available	at	

the	end	of	this	document.	

Christine	opened	the	call	by	summarizing	the	state	of	the	project	thus	far.	She	then	reviewed	

that	agenda	and	purpose	for	the	call	which	was	to:	1)	updated	the	Advisory	Team	about	the	

draft	best	practices	and	strategies	which	were	being	shared	in	a	survey	across	Appalachia,	and	

2)	to	solicit	feedback	from	the	Advisory	Team	about	how	best	to	integrate	the	quantitative	

analysis	with	the	qualitative	analysis	for	the	practitioner	guidebook	(one	of	the	outputs	of	the	

project).	

Christine	then	presented	a	summary	of	the	quantitative	analysis	to	date.	Following	this,	

Stephan	Goetz	and	Alan	Collins	presented	the	final	results	of	their	respective	quantitative	

modeling	of	resilience	in	a	PowerPoint	presentation.	

Christine	then	welcomed	feedback	from	Advisory	Team	members	on	the	economic	analysis	

findings	and	around	the	questions:	

• How	to	best	communicate	the	economic	analysis	findings	in	the	practitioner	

guidebook?

• What	information	do	economic	development	practitioners	most	need? In	what	

formats?	

• How	should	information	be	shared	in	the	practitioner	guidebook?

• Additional	suggestions	and	ideas	

Stephan	noted	that	it	is	interesting	that	self-employment	doesn’t	rise	to	the	top	of	important	

factors,	but	business density	is	broadly	positive;	business	density	values	should	require	selfemployment	to	grow.	Christine	then	commented	that	community	and	economic	development	

practitioners	have	found	that	access	to	rationalizing	approaches	to	build	economic	resilience	

from	economists	is	valuable.
96 |	Page

Following	the	presentation,	Advisory	Team	(AT)	members	had	the	following	questions	and	

comments:	

• An	AT	member	asked:	“What	is	employment	– how	is	it	characterized?”	Stephan	

answered	that	this	is	BLS	monthly	employment	data.	A	follow-up	questions	was	

asked	if	part-time	employment	is	included	in	these	numbers?	Stephan	was	unsure,	

and	offered	to	follow	up.	

• A	question	was	raised	if	there	is	a	step	beyond	these	analyses,	noting	that	the	

quantitative	models	raise	more	questions	than	answers,	and	that	many	of	these	

things	are	confusing	because	statistical	truths	don’t	translate	into	knowledge	and	he	

recommended	creating	a	clear	distinction	of	observable	correlations	that	promote	

resilience	and	illustrate	viable	strategies.	

• An	AT	member	commented	that	the	team	should	be	careful	in	the	narrative	to	

explain	variables	and	analysis	and	ensure	the	message	is	not	“these	things	[that	

aren’t	significant	or	have	negative	signs	in	the	equations]	aren’t	viable	strategies	–

it’s	much	more	complicated	than	that.”

• Stephan	noted	that	the	quantitative	team	tried	to	introduce	time	lag	to	demonstrate	

more	clear	cut	cause	and	effect	relationships,	but	that	it	will	need	to	be	more	clearly	

explained	in	the	narrative.	

• A	comment	was	noted	that	from	the	point	of	view	of	traditional	economic	

development	practitioners,	these	findings	are	important	and	some	of	them	

emphasize	the	positivity	of	existing	efforts.	He	also	noted	that	perhaps	selfemployments	lag	effect	is	very	large	and	not	measured	here.	

• A	question	arose:	“What	is	innovation	potential?” Stephan	responded	by	noting	that	

this	is	the	number	of	firms	in	a	county	engaged	in	“process	innovation.”	This	is	a	

summation	of	firms	that	buy	from	many	other	firms	and	those	that	sell	to	many	

other	firms.	It	is	developed	with	an	input-output	table	of	buying	and	selling.	It	is	a	

very	new	statistic	and	is	under	peer	review	currently,	but	it	seems	to	show	new	

information	about	the	capacity	for	innovation	in	a	county.	

• Christine	commented	that	we	need	to	ensure	that	we	define	things	very	clearly	and	

asked	for	input	on	methods	of	sharing	results.	

• Another	AT	member	responded	by	stating	that	it	is hard	to	know	how	this	

information	will	translated	into	actionable	intelligence	and	that	these	presentations	

weren’t	a	sufficient	platform	for	response	to	the	question	about	how	to	share	the	

information.	

• A	question	was	asked:	“Why	are	none	of	these	variables	about	politics?”	Stephan	

answered	that	we	used	voting	competitiveness	and	that	we	see	tighter	margins	of	

this	variable	as	indicative	of	more	willingness	among	legislators	to	produce	tangible	

results.	This	has	been	demonstrated	an	important	variable	elsewhere,	but	didn’t	

show	up	here.	

• A	participant	responded	by	asking	about	other	visible	political	phenomenon	such	as	

corruption.	They	continued	by	citing	a	book	about	rural	development	in	which	the	

author	discusses	corruption	as	an	under-reported	phenomenon	that	really	hurts	
97 |	Page

development	projects.	The	author	notes	that	poor	communities	outside	of	

Appalachia	have	done	better	with	the	same	amount	of	money.	Christine	noted	that	

similar	questions	were	raised	in	the	qualitative	work.	

• Stephan	noted	that	there	is	a	literature	on	distance	from	state	capitals	and	how	it	

relates	to	corruption,	but	the	group	hadn’t	considered	that	variable.	

At	the	end	of	the	call,	the	Project	Team	thanked	the	Advisory	Team	for	their	time,	and	noted	

that	they	would	be	following	up	with	the	draft	Guidebook	for	early	review	in	the	near	future.	

Call	Participants	

Eric	Dixon,	Appalachian	Citizen	Law	Center

Justin	Maxson,	Mary	Reynolds	Babcock	Foundation

Dee	Davis,	Center	for	Rural	Strategies	

Peter	Hille,	Mountain	Association	for	Community	Economic	Development

Debra	Horne,	Mayor,	Town	of	Dungannon,	VA

Leslie	Schaller,	Appalachian	Center	for	Economic	Networks

Shannon	Blevins,	University	of	Virginia’s	College	at	Wise

Jennifer	Simon,	Ohio	University

Jim	Baldwin,	Cumberland	Plateau	Planning	District	Commission

Evan	Fedorko,	Downstream	Strategies

Evan	Hansen,	Downstream	Strategies

Christine	Gyovai,	Dialogue	+	Design	Associates

Stephan	Goetz,	Penn	State	University

Allan	Collins,	West	Virginia	University
98 |	Page

APPENDIX	D:	PRACTITIONER	SURVEY
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APPENDIX	E:	REGRESSION	RESULTS WITH	QUADRATIC	TERMS

Table	15:	Regression	results	with	quadratic	terms,	Models	1	through	6

Basic Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Demographic/mobility

Age	25	to	44 0.099** 0.095** 0.1** 0.099** 0.1** 0.1** 0.106**

Age	65	and	up -0.033 -0.039 -0.032 -0.033 -0.033 -0.034 -0.029

Ethnic	Diversity 0.086*** 0.089*** 0.086*** 0.087*** 0.082*** 0.086*** 0.084***

Mobility	In-Migration 0.119*** 0.121*** 0.117*** 0.118*** 0.113*** 0.119*** 0.114***

Mobility	Out-Migration -0.089*** -0.088*** -0.089*** -0.089*** -0.09*** -0.09*** -0.088***

Mobility	In-Commuting 0.058** 0.064** 0.059** 0.058** 0.061** 0.057** 0.06**

Mobility	Out-Commuting 0.014 0.003 0.013 0.013 0.015 0.014 0.016

Residence	Born 0.004 0.005 0.003 0.004 0.001 0.003 0.003

Population 0.279*** 0.287*** 0.281*** 0.278*** 0.285*** 0.282*** 0.274***

Land	Area -0.032 -0.034 -0.033 -0.032 -0.034 -0.032 -0.034

College	 0.107*** 0.104*** 0.109*** 0.109*** 0.111*** 0.106*** 0.114***

Economic/industry

Distance	to	City 0.147*** 0.145*** 0.146*** 0.147*** 0.147*** 0.147*** 0.143***

Business	Density 0.036 0.031 0.036 0.035 0.043 0.036 0.044

Self-Employment 0.007 0.158** 0.007 0.007 0.01 0.007 0.01

Self-Employment Squared -0.145***

Poverty -0.111*** -0.112*** -0.111*** -0.111*** -0.118*** -0.11*** -0.104***

Participation 0.085** 0.087** 0.086** 0.085** 0.079* 0.084** 0.089**

Innovation	Potential 0.06** 0.062** 0.06** 0.061** 0.06** 0.06** 0.059**

Industry Diversity 0.092* 0.082* 0.093* 0.092* 0.083* 0.091* 0.077

Knowledge Diversity -0.051** -0.051** -0.051** -0.051** -0.05** -0.051** -0.053**

Farming 0.059 0.054 0.059 0.059 0.061 0.059 0.059

Oil	&	Gas 0.044 0.043 0.045 0.044 0.044 0.044 0.043

Coal -0.063*** -0.062*** -0.063*** -0.063*** -0.063*** -0.063*** -0.063***

Manufacturing -0.03 -0.029 -0.03 -0.03 -0.03 -0.029 -0.031

Community/health

Primary	and	Secondary -0.007 -0.006 -0.007 -0.007 -0.007 -0.007 -0.006

Community	College -0.026* -0.025* -0.026* -0.026* -0.027* -0.026* -0.024

Broadband -0.035 -0.035 0.001 -0.035 -0.037 -0.035 -0.04

Broadband Squared -0.040

Child/Elder	Care 0.004 0.004 0.003 0.021 0.003 0.004 0.003

Child/Elder	Care Squared -0.018

Health	Practitioner -0.095*** -0.094*** -0.095*** -0.095*** -0.097*** -0.096*** -0.097***

Recreational Opportunity -0.022 -0.021 -0.022 -0.022 -0.023 -0.022 0.082

Recreational Opportunity Squared -0.474*

Natural	Amenity 0.006 0.004 0.006 0.006 0.005 0.006 0.008

Social	Capital 0.004 0.006 0.004 0.004 0.003 0.024 -0.003

Social	Capital Squared -0.016

Federal	Funds -0.008 -0.008 -0.007 -0.008 0.46* -0.008 -0.014

Federal	Funds Squared -0.104**

Voting	Competitiveness -0.007 -0.007 -0.006 -0.006 -0.004 -0.007 -0.006

Agritourism 0.016 0.016 0.016 0.016 0.015 0.016 0.016

Direct	Farm	Sales 0.027* 0.027* 0.028* 0.027* 0.026 0.028* 0.027*

Constant *** ** *** *** *** *** ***

N 3,049 3,049 3,049 3,049 3,049 3,049 3,049

Adjusted R2 0.2577 0.2586 0.2574 0.2574 0.2584 0.2574 0.2585

Note: Table shows robust standardized coefficients with state fixed effect. Significance levels: different from zero at *10%, **5%, and ***1% or lower. No linear 

effects detected for broadband, child/elder care, etc.
133 |	Page

APPENDIX	F:	REGRESSION	RESULTS WITH	INTERACTION	TERMS

Table	16: Regression	results	with	interaction	terms,	Models	1	through	4

Basic Model 1 Model 2 Model 3 Model 4

Demographic/mobility

Age	25	to	44 0.099** 0.091** 0.097** 0.096** 0.1**

Age	65	and	up -0.033 -0.029 -0.034 -0.031 -0.024

Ethnic	Diversity 0.086*** 0.084*** 0.083*** 0.086*** 0.091***

Mobility	In-Migration 0.119*** 0.115*** 0.11*** 0.115*** 0.118***

Mobility	Out-Migration -0.089*** -0.089*** -0.089*** -0.09*** -0.09***

Mobility	In-Commuting 0.058** 0.057** 0.052* 0.058** 0.055*

Mobility	Out-Commuting 0.014 0.015 0.016 0.014 0.012

Residence	Born 0.004 0.004 0.001 0.008 0.006

Population 0.279*** 0.253*** 0.278*** 0.24*** 0.231***

Land	Area -0.032 -0.033 -0.032 -0.032 -0.025

College	 0.107*** 0.118*** 0.113*** 0.124*** 0.127***

Economic/industry

Distance	to	City 0.147*** 0.146*** 0.145*** 0.141*** 0.107**

Business	Density 0.036 0.017 0.025 0.019 0.017

Self-Employment 0.007 0.002 0.006 0.001 0.004

Poverty -0.111*** -0.109*** -0.107*** -0.115*** -0.107***

Participation 0.085** 0.097** 0.09** 0.085** 0.09**

Innovation	Potential 0.06** 0.062** 0.063** 0.059** 0.06**

Industry Diversity 0.092* 0.106** 0.096** 0.104** 0.097**

Knowledge Diversity -0.051** -0.052** -0.061*** -0.047** -0.052**

Farming 0.059 0.05 0.062 0.047 0.04

Oil	&	Gas 0.044 0.045 0.049 0.044 0.046

Coal -0.063*** -0.062*** -0.061*** -0.064*** -0.059***

Manufacturing -0.03 -0.028 -0.028 -0.029 -0.027

Community/health

Primary	and	Secondary -0.007 -0.002 -0.007 -0.002 -0.001

Community	College -0.026* -0.024 -0.026* -0.024* -0.022

Broadband -0.035 -0.039 -0.036 -0.037 -0.034

Child/Elder	Care 0.004 0.005 0.004 0.006 0.005

Health	Practitioner -0.095*** -0.09*** -0.089*** -0.089*** -0.085***

Recreational Opportunity -0.022 -0.02 -0.022 -0.021 -0.023

Natural	Amenity 0.006 0.011 0.011 0.006 0.008

Social	Capital 0.004 0.27** 0.088* 0.274* -0.098**

Federal	Funds -0.008 -0.011 -0.01 -0.011 -0.01

Voting	Competitiveness -0.007 -0.008 -0.007 -0.007 -0.005

Agritourism 0.016 0.017 0.015 0.016 0.014

Direct	Farm	Sales 0.027* 0.027* 0.028* 0.027* 0.027*

social	capital	x	%age_25-44 -0.271**

social	capital	x	%mobility_in-com -0.087**

social	capital	x	population -0.283*

social	capital	x	distance_0.25M 0.139***

Constant *** *** *** *** ***

N 3,049 3,049 3,049 3,049 3,049

Adjusted R2 0.2577 0.2591 0.2589 0.2586 0.2613

Note: Table shows robust standardized coefficients with state fixed effect. Significance levels: different from zero at *10%, **5%, and ***1% or lower.
134 |	Page

Table	17:	Regression	results	with	interaction	terms,	Models	5	through	10

Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Demographic/mobility

Age	25	to	44 0.094** 0.097** 0.099** 0.099** 0.103** 0.1**

Age	65	and	up -0.037 -0.035 -0.035 -0.033 -0.042 -0.032

Ethnic	Diversity 0.092*** 0.087*** 0.084*** 0.083*** 0.091*** 0.085***

Mobility	In-Migration 0.272*** 0.117*** 0.115*** 0.116*** 0.117*** 0.116***

Mobility	Out-Migration -0.109*** -0.089*** -0.083*** -0.085*** -0.085*** -0.083***

Mobility	In-Commuting 0.065** 0.061** 0.062** 0.062** 0.055* 0.066**

Mobility	Out-Commuting 0.01 0.104** 0.003 0.012 0.018 -0.002

Residence	Born 0.002 0.006 0.002 0.002 0.005 0.005

Population 0.259*** 0.286*** 0.16* 0.281*** 0.285*** 0.29***

Land	Area -0.032 -0.034 -0.033 -0.033 -0.034 -0.034

College	 0.102*** 0.106*** 0.11*** 0.109*** 0.106*** 0.107***

Economic/industry

Distance	to	City 0.146*** 0.148*** 0.145*** 0.283*** 0.149*** 0.148***

Business	Density 0.037 0.021 0.033 0.045 0.047 0.024

Self-Employment 0.191*** 0.087** -0.291** 0.055* 0.289* 0.066*

Poverty -0.111*** -0.111*** -0.105*** -0.109*** -0.116*** -0.11***

Participation 0.09** 0.092** 0.086** 0.081** 0.17*** 0.089**

Innovation	Potential 0.067** 0.061** 0.067** 0.064** 0.056** 0.062**

Industry Diversity 0.107** 0.087* 0.099** 0.099** 0.086* 0.088*

Knowledge Diversity -0.055** -0.052** -0.048** -0.049** -0.05** -0.046**

Farming 0.048 0.058 0.073* 0.072* 0.059 0.219**

Oil	&	Gas 0.044 0.044 0.045 0.045 0.044 0.044

Coal -0.059*** -0.063*** -0.063*** -0.066*** -0.06*** -0.061***

Manufacturing -0.029 -0.032 -0.034 -0.03 -0.029 -0.034

Community/health

Primary	and	Secondary -0.005 -0.006 -0.008 -0.01 -0.008 -0.008

Community	College -0.021 -0.024 -0.023 -0.026* -0.027* -0.026*

Broadband -0.035 -0.036 -0.035 -0.034 -0.034 -0.034

Child/Elder	Care 0.004 0.004 0.002 0.003 0.004 0.002

Health	Practitioner -0.096*** -0.087*** -0.096*** -0.098*** -0.097*** -0.087***

Recreational Opportunity -0.02 -0.022 -0.021 -0.021 -0.023 -0.022

Natural	Amenity 0.011 0.002 0.009 0.009 0.007 0.006

Social	Capital 0.003 0.004 0.008 0.004 0.004 0.006

Federal	Funds -0.006 -0.007 -0.008 -0.006 -0.007 -0.009

Voting	Competitiveness -0.004 -0.008 -0.006 -0.006 -0.007 -0.006

Agritourism 0.02 0.016 0.014 0.015 0.016 0.016

Direct	Farm	Sales 0.025 0.027* 0.029* 0.027* 0.027* 0.028*

%self-emp	x	%mobility._in-mig -0.27***

%self-emp	x	%mobility._out-com -0.147**

%self-emp	x	population 0.291**

%self-emp	x	distance_0.25M -0.155*

%self-emp	x	%participation -0.293*

%self-emp	x	%industry_Farming -0.174**

Constant   ** * ** * **

N 3,049 3,049 3,049 3,049 3,049 3,049

Adjusted R2 0.2610 0.2584 0.2587 0.2591 0.2584 0.2592

Note: Table shows robust standardized coefficients with state fixed effect. Significance levels: different from zero at *10%, **5%, and ***1% or lower.
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Table	18:	Regression	results	with	interaction	terms,	Models	11 through	14

Model 11 Model 12 Model 13 Model 14

Demographic/mobility

Age	25	to	44 0.091** 0.102** 0.108*** 0.106***

Age	65	and	up -0.04 -0.032 -0.026 -0.057

Ethnic	Diversity 0.082*** 0.088*** 0.075*** 0.081***

Mobility	In-Migration 0.111*** 0.115*** 0.207*** 0.109***

Mobility	Out-Migration -0.087*** -0.084*** -0.097*** -0.089***

Mobility	In-Commuting 0.453*** 0.062** 0.062** 0.059**

Mobility	Out-Commuting 0.014 0.006 0.013 0.031

Residence	Born -0.001 0.005 0.006 0.001

Population 0.283*** 0.283*** 0.268*** 0.273***

Land	Area -0.034 -0.035 -0.029 -0.031

College	 0.108*** 0.113*** 0.104*** 0.12***

Economic/industry

Distance	to	City 0.146*** 0.147*** 0.153*** 0.142***

Business	Density 0.036 0.027 0.031 0.175***

Self-Employment 0.008 0.008 0.006 0.016

Poverty -0.112*** -0.113*** -0.111*** -0.1***

Participation 0.085** 0.087** 0.09** 0.072*

Innovation	Potential 0.067** 0.064** 0.063** 0.062**

Industry Diversity 0.082* 0.177*** 0.1** 0.074

Knowledge Diversity -0.052** -0.058*** -0.051** -0.053**

Farming 0.058 0.063 0.062 0.061

Oil	&	Gas 0.043 0.042 0.044 0.041

Coal -0.064*** -0.063*** -0.061*** -0.063***

Manufacturing -0.028 -0.034 -0.03 -0.023

Community/health

Primary	and	Secondary -0.006 -0.007 -0.006 -0.004

Community	College -0.028* -0.023 -0.024 -0.024

Broadband -0.038 -0.038 -0.038 -0.045*

Child/Elder	Care 0.001 0.216* 0.003 0.002

Health	Practitioner -0.089*** -0.089*** -0.089*** -0.092***

Recreational Opportunity -0.022 -0.022 -0.021 0.112**

Natural	Amenity 0.014 0.01 0.006 0.011

Social	Capital -0.007 0.007 0.011 -0.006

Federal	Funds 0.092** -0.011 -0.008 -0.018

Voting	Competitiveness -0.004 -0.005 0.166*** -0.009

Agritourism 0.016 0.016 0.014 0.018

Direct	Farm	Sales 0.026 0.028* 0.03* 0.026

federal	fund	x	%mobility_in-com -0.415***

child/elder	care	x diversity_industry -0.267*

%voting_comp.	x	%mobility_in-mig -0.207***

recreational	opp.	x	business	density -0.211***

Constant   **  ***

N 3,049 3,049 3,049 3,049

Adjusted R2 0.2600 0.2583 0.2607 0.2609

Note: Table shows robust standardized coefficients with state fixed effect. Significance levels: different from zero at *10%, **5%, and ***1% or lower.
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APPENDIX	G:	EMPLOYMENT FOR	COUNTIES	WITH	HIGH RESIDUALS IN	

THE	OLS	MODEL
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APPENDIX	H:	EMPLOYMENT	FOR	TOP	COUNTIES	BASED	ON	

RESILIENCE IN	THE	OLS	MODEL
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APPENDIX	I:	TOP	COUNTIES	BASED	ON	RESIDUALS	IN	THE	OLS	MODEL	

THAT	ARE	SELF-EMPLOYMENT	DOMINANT

Table	19:	Top	counties	based	on	residuals	and	self-employment	dominant

County

Population

(2015) Resilience Residuals

Net

growth

Dominant	sector

in	recovery

Sector

growth

Percent	of	job	

growth	

accounted	for	

by	this	sector

Taylor,	WV 16,318 0.83 0.25 641 Self-employment 199 31.0%

Choctaw,	MS 9,026 0.71 0.20 483 Self-employment 261 54.0%

Buncombe,	NC 221,642 0.75 0.12 21,785 Self-employment 7,161 32.9%

Oktibbeha,	MS 43,985 0.75 0.11 3749 Self-employment 1036 27.6%

Fannin,	GA 22,174 0.68 0.11 1309 Self-employment 445 34.0%

Alcorn,	MS 35,688 0.67 0.10 2583 Self-employment 1053 40.8%

Source: USDL (2015). 
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