FEBRUARY 2015

Appalachia Examining Changes to the Appalachian Region Since 1965

THEN AND NOW

Prepared by the Center for Regional Economic Competitiveness 

and West Virginia University for the Appalachian Regional Commission
EXECUTIVE SUMMARY

FEBRUARY 2015

Appalachia Examining Changes to the Appalachian Region Since 1965

THEN AND NOW

Prepared by the Center for Regional Economic Competitiveness 

and West Virginia University for the Appalachian Regional Commission

Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 1

Table of Contents

Assessing Appalachia’s Progress: An Overview.......................................................2

Background .................................................................................................................2

About the Appalachian Region..............................................................................2

About the Appalachian Regional Commission..................................................3

A Region Apart: Measuring Appalachia’s Progress toward Parity ...................5

Key Measures ..............................................................................................................5

Income Disparity.........................................................................................................5

Unemployment...........................................................................................................6

Employment Change.................................................................................................7

Population Change ................................................................................................... 8

Economic and Industry Mix.................................................................................... 8

Educational Attainment...........................................................................................9

Housing Quality ........................................................................................................10

Health...........................................................................................................................10

Isolation: Transportation Access............................................................................11

Isolation: Telecommunications Capacity........................................................... 12

Impacts from Appalachian Regional Commission Investments....................... 13

Investments in Appalachia .................................................................................... 13

Economic Impacts from ARC Non-Highway Grant Investments................14

Economic Impacts from ADHS Investments....................................................15

Assessing ARC’s Role in Achieving Growth in the Region........................... 17

Input from Stakeholders ...................................................................................................19

Key Report Findings ..........................................................................................................22

Appalachia Moving Forward............................................................................................24
2 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

Assessing Appalachia’s Progress: 

An Overview 

Background

I

n the late 1950s, intense poverty and 

economic struggle characterized 

the existence of many of the 19 

million people living in the Appalachian 

Region. The magnitude and vastness of 

Appalachia’s challenges led the Region’s 

governors in 1960 to form the Conference 

of Appalachian Governors, in order to 

develop a regional approach to addressing 

Appalachia’s needs. In 1961, the governors 

took their case to newly elected President 

John F. Kennedy, who had been deeply 

moved by the poverty he saw during 

campaign trips to West Virginia. 

In 1963, President Kennedy formed 

a federal-state commission, the 

President’s Appalachian Regional 

Commission (PARC), and directed it 

to draw up a comprehensive program 

for the economic development of the 

Appalachian Region. The commission’s 

report, released in April 1964, called 

on the federal government to make 

substantial investments in the Region 

to provide infrastructure and other 

programs essential to help the Region 

grow and provide opportunity for its 

people. The report also recommended 

establishing a regional organization, 

the Appalachian Regional Commission 

(ARC), to leverage the use of existing 

resources and maximize new ones in an 

ongoing development effort. 

The PARC report’s recommendations 

were endorsed by the Conference of 

Appalachian Governors and cabinet-level 

officials, and were subsequently used as 

the basis for the Appalachian Regional 

Development Act of 1965 (ARDA), ARC’s 

governing legislation. A broad bipartisan 

coalition in Congress passed the ARDA 

early in 1965, and President Lyndon B. 

Johnson signed the legislation into law on 

March 9, 1965. 

It has now been 50 years since the 

passage of the ARDA. Appalachia 

has experienced significant progress 

since then, but still faces persistent 

challenges. This research report was 

undertaken to quantify and document 

changes to Appalachia over the past 50 

years, to evaluate ARC’s contribution 

to the Region’s economic development 

during this period, and to assess the 

extent to which Appalachia remains 

“a region apart” from the rest of the 

United States. 

The research summarized in this report 

used an array of evaluation techniques 

developed to evaluate the impact and 

legacy of ARC’s investments in the 

Appalachian Region. In particular, 

this report (1) documents 50 years of 

socioeconomic and structural changes 

in the Region; (2) analyzes the economic 

impacts of ARC’s investments, using 

two techniques: regional input-output 

analysis and a quasi-experimental 

method that compares ARC-assisted 

counties with a control group of counties 

outside the Region; and (3) examines 

stakeholder perceptions about past 

performance and future priorities.

About the Appalachian Region 

The current boundary of the 

Appalachian Region (see Figure 1) 

includes all of West Virginia and 

parts of 12 other states: Alabama, 

Georgia, Kentucky, Maryland, 

Mississippi, New York, North Carolina, 

Ohio, Pennsylvania, South Carolina, 

Tennessee, and Virginia. It covers 

205,000 square miles and 420 counties, 

and is home to more than 25 million 

Americans. Forty-two percent of the 

Region’s population is rural, compared 

with 20 percent of the nation’s.

Credit: Ken Murray
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 3

In 1965, the ARDA defined the 

Appalachian Region as 360 counties 

in 11 states (Alabama, Georgia, 

Kentucky, Maryland, North Carolina, 

Ohio, Pennsylvania, South Carolina, 

Tennessee, Virginia, and West Virginia). 

The official boundary has expanded 

several times since then: New York 

joined the Commission in late 1965, 

adding 13 counties to the Region, and 

Mississippi joined in 1967, adding 20. 

Also in 1967, two Alabama counties, one 

New York county, and one Tennessee 

county were added to the Region. In 

1990, one Ohio county was added; in 

1991 one Mississippi county was added; 

in 1998 two Alabama counties, two 

Georgia counties, one Mississippi county, 

and two Virginia counties were added; 

in 2002 two Kentucky counties and two 

Mississippi counties were added, and in 

2008, three Kentucky counties, three 

Ohio counties, two Tennessee counties, 

and two Virginia counties were added to 

the Region.

About the Appalachian 

Regional Commission 

The Appalachian Regional Commission 

represents a partnership of federal, 

state, and local governments. 

Established by the Appalachian Regional 

Development Act of 1965, ARC’s 

mission is to help the Region achieve 

socioeconomic parity with the rest 

of the nation by partnering with the 

Region and advocating for sustainable 

community and economic development 

in Appalachia. 

The 1964 report by the President’s 

Appalachian Regional Commission 

sketched out the major priorities for 

ARC-funded activities: improving 

the Region’s physical accessibility, 

developing the Region’s economy while 

reducing dependence on the extraction 

of natural resources, and enhancing 

the capability of the Region’s people to 

achieve economic prosperity. 

Figure 1: The Appalachian Region

Source: Appalachian Regional Commission

HART

G EORGIA

A LABAMA

T ENNESSEE

K ENTUCKY

O HIO

V IRGINIA

N ORTH C AROLINA

S OUTH C AROLINA

N EW Y ORK

M A RY LA N D

P ENNSYLVANIA

MISSISSIPPI

WEST VIRGINIA

October 8, 2008

Appalachia is a 205,000-square-mile region that follows the spine of the Appalachian Mountains 

from southern New York to northern Mississippi. It includes all of West Virginia and portions of 

12 other states: Alabama, Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, 

Ohio, Pennsylvania, South Carolina, Tennessee, and Virginia. Forty-two percent of the Region’s 

population is rural, compared with 20 percent of the nation’s.

Credit: Ken Murray
4 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

ARC’s structure was designed to ensure 

an active federal-state-local partnership. 

There are 14 Commission members: 

the governors of the 13 Appalachian 

states and a federal co-chair, who 

is appointed by the president and 

confirmed by the Senate. Each year one 

of the 13 governors is elected by the 

others to serve as states’ co-chair of the 

Commission. Each governor appoints 

an alternate, who oversees the state’s 

ARC program and serves as the statelevel contact for those seeking ARC 

assistance. The states’ interests at ARC/

Washington are handled by the states’ 

Washington representative, who is hired 

by the states.

Commission members appoint an 

executive director to serve as the chief 

executive, administrative, and fiscal 

officer. Commission staff serve both 

the federal and the state members 

in carrying out ARC programs 

and activities. The Commission’s 

administrative costs are shared equally 

by the federal and state governments. 

All program strategies, allocations, and 

other policy matters must be approved 

by both a majority of the governors and 

the federal co-chair. This consensus 

model ensures close collaboration 

between the federal and state partners 

in carrying out ARC’s mission. Local 

participation is provided through multicounty local development districts, with 

boards made up of elected officials, 

businesspeople, and other local leaders.

Unlike economic development agencies 

that are primarily grant makers, the 

Commission also performs advocacy, 

regional planning, and research 

activities in combination with its 

grant programs. These planning and 

technical assistance activities multiply 

the influence and shape of ARC’s grant 

programs. No other entity has this 

regional mandate for Appalachia.

Credit: Ken Murray
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 5

A Region Apart: Measuring Appalachia’s 

Progress toward Parity

Key Measures

The 1964 PARC report called 

Appalachia “a region apart—

geographically and statistically,” 

adding that the Region had many 

natural advantages that had benefitted 

too few of its people. At that time, 

incomes in the Region were much lower 

than the U.S. average, unemployment 

was much higher, and the Region 

suffered from many other disadvantages 

that reflected what the PARC report 

called “the realities of deprivation.” 

These and other measures helped make 

the case for creating a focused regional 

development program.

This research uses the same ten key 

measures that led to the creation of 

the Appalachian Regional Commission 

to determine whether there has been 

measurable improvement across the 

Region over the last 50 years. While 

these ten indicators in some ways reflect 

the concerns of earlier decades, most are 

just as relevant today. 

1. Large income disparity between 

Appalachia and the rest of the nation.

2. Relatively high unemployment.

3. Decline in employment.

4. Outmigration of people (and 

workers) from the Region.

5. Over-concentration of employment 

in extractive industries. 

6. Relatively low educational 

attainment. 

7. Relatively poor quality of available 

housing.

8. Health of the Region’s population.

9. Relative isolation due to lack of 

transportation access.

10. Relative isolation due to limited 

communications.

INCOME DISPARITY

Improving, but still Problematic in 

some Parts of the Region

Appalachia’s poverty rate has been 

cut in half over the past five and a half 

decades, from nearly 31 percent in 1960 

to about 16.6 percent today. In 1960, 

per capita income in Appalachia was 

measured at about 74 percent of U.S. 

per capita income. A big challenge at 

that time was the rural character of the 

Region and the lack of a formal economy 

in much of it. 

Family incomes in the Region were 84 

percent of the U.S. level in 1960. This 

number reflects the facts that the 

Region’s families were larger and more 

family members earned income (many 

in informal ways, including barter and 

exchange) than was typical for the rest 

of the nation.

Appalachia’s poverty rate has

been cut in half over the past

five and a half decades, from

nearly 31 percent in 1960 to

about 16.6 percent today.

Credit: Ken Murray
6 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

Currently, government transfer 

payments account for nearly 24 percent 

of personal income in the Appalachian 

Region. By comparison, those payments 

account for 17.5 percent of personal 

income nationally.

Figure 2 shows that per capita income in 

many counties across Appalachia in 1969 

was less than half the national average, 

and that significant progress had been 

made by 2012, with only five counties 

still having such a low income. By 

2012, many counties were approaching 

socioeconomic parity with the rest of the 

nation, although a number of counties 

were still struggling, with per capita 

income levels below 75 percent of the 

U.S. average.

UNEMPLOYMENT

Much Improved Compared with the 

Nation as a Whole

In 1960, the Appalachian Region’s 

unemployment rate stood at 7.1 percent, 

more than 2 points higher than the 

national rate of 5.0 percent. The lack 

of job opportunities in the Region 

was due to declines in key sectors, 

including mining and agriculture. As the 

Region’s economic structure evolved, its 

unemployment rate converged with that 

of the nation as a whole, as illustrated in 

Figure 3. 

The Region’s unemployment rate tracked 

slightly higher than the rest of the nation’s 

in the late 1970s, 1980s, and 1990s, but 

never quite matched the U.S. rate until the 

recession of the early 2000s. However, 

since the 2008–09 recession, the Region’s 

unemployment rate has tracked the U.S. 

rate closely, with a few persistent pockets 

of joblessness.

Despite the improvement in the 

unemployment rate, labor force 

participation rates in Appalachia have 

remained well below the national 

average throughout the past 30 years. 

Figure 2: Per Capita Income in Appalachia, Relative to the U.S. (Percent of the U.S. Average)

NEW YORK

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

OHIO PENNSYLVANIA

MARYLAND

WEST

VIRGINIA

VIRGINIA KENTUCKY

NEW YORK

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

OHIO PENNSYLVANIA

MARYLAND

WEST

VIRGINIA

VIRGINIA KENTUCKY

1969 2012

0 50 100

Miles

0 50 100

Miles

Percent of the U.S. Average

31.7% - 49.9%

50.0% - 74.9%

75.0% - 99.9%

100.0% - 104.6%

Appalachian average = $15,447

U.S. average = $19,623

Percent of the U.S. Average

46.0% - 49.9%

50.0% - 74.9%

75.0% - 99.9%

100.0% - 117.2%

Appalachian average = $35,849

U.S. average = $44,194

Source: U.S. Bureau of Economic Analysis

Notes: Per capita income is total personal income divided by total population. The percent of U.S. average is computed by dividing the county 

personal income by the U.S. average and multiplying by 100. Income data are adjusted for inflation, expressed in 2012 dollars.

By 2012, many counties were approaching socioeconomic

parity with the rest of the nation, although some were still

struggling, with per capita income levels below 75 percent of

the U.S. average.
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 7

The most recently available data found 

national labor force participation at 64.2 

percent of working-age adults (age 16 

and above), compared with 59.5 percent 

of Appalachian working-age adults. 

Part of the difference can be explained 

by greater participation in the informal 

barter economy in the Region, and a 

larger proportion of the population 

receiving government transfer payments, 

as well as the limited job opportunities in 

the Region’s rural communities. 

EMPLOYMENT CHANGE 

Lagging, with a Widening Gap

In the 1950s, Appalachian businesses 

were shedding jobs, while the U.S. 

economy grew. While the nation 

increased its total employment by 

17 percent during the 1950s, the 

Appalachian Region lost 1.5 percent of 

its total employment. Among the goals 

of ARC was to help stabilize this job loss. 

By the 1970s, Appalachian employment 

was growing, but more slowly than 

employment in the nation as a whole. 

National recessions have been 

particularly harsh to the Appalachian 

economy. The recession in the early 

1980s began a period of much slower 

employment growth in the Region—

slower than the growth in the rest of 

the nation (see Figure 4). The 1981–82 

recession caused rapid employment 

decline in Appalachia, while employment 

in the rest of the nation simply 

stagnated. The Region’s reliance on steel 

and coal employment drove much of that 

decline. The recessions of 1990–91 and 

2001 also adversely affected the Region.

As the U.S. economy transformed over 

the past 30 years, regional economic 

growth kept pace with it, but the Region 

started from a much smaller base. 

While the rest of the nation experienced 

economic expansion, driven in part 

by the real estate bubble, Appalachia 

missed out on that growth period. But 

Appalachia was not affected as seriously 

as the rest of the nation was by the real 

estate market crash in 2008. In 2012, 

employment in Appalachia stood at 

nearly 50 percent above its 1975 level, 

while the national figure was nearly 83 

percent above its 1975 level. 

3%

5%

7%

9%

11%

13%

15%

Percent of Labor Force 

Source: U.S. Bureau of Labor Statistics’ 1990 to 2012 published rates and 1976 to 1989 unofficial rates. 

Data prior to 1976 were not available.

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Appalachian Region U.S. 

Figure 3: Unemployment, Appalachia and the U.S.

Source: U.S. Bureau of Labor Statistics

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

U.S. Appalachian Region

Figure 4: Total Private Employment, Appalachia and the U.S.

100

110

120

130

140

150

160

170

180

190

200

Index, 1975 = 100

In the 1950s, Appalachian

businesses were shedding jobs,

while the U.S. economy grew:

the nation increased its total

employment by 17 percent

during the decade, while the

Appalachian Region lost 1.5

percent of its total employment.
8 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

POPULATION CHANGE 

Much Slower Growth, 

Older Population

During the past generation, the U.S. 

population has grown at a pace nearly 

twice that of the Appalachian Region’s 

(see Figure 5). This is due in part 

to the fact that the vast majority of 

Appalachian counties have a smaller 

share of young people (those below 

age 20) and a much greater (and more 

rapidly growing) share of seniors (those 

aged 65 and older) than does the nation. 

Today, the area remains much more 

rural than the rest of the nation. Fortytwo percent of the people in Appalachia 

still live in rural areas, compared with 

20 percent nationally; and Appalachians 

who live in cities are much more likely 

to live in smaller urban centers than are 

residents of other parts of the country. 

Throughout the country, young adults 

have flocked to major cities for access to 

jobs and other opportunities. Since there 

are few major cities in the Appalachian 

Region, it is not surprising that the 

Region continues to witness a steady 

outmigration of adults between the ages 

of 18 and 35. This trend contributes to 

the more rapidly aging population in the 

Appalachian Region. 

Recessionary periods exacerbate 

outmigration. During the economic 

stagnation of the 1980s, population 

growth in Appalachia languished. Even 

when the Region’s population began to 

grow again in the early 1990s, its growth 

rate significantly trailed the U.S. rate. 

ECONOMIC AND INDUSTRY MIX 

Converging toward the U.S. Norm

The Appalachia of 1964 was heavily 

dependent on large-scale logging and 

coal mining, but employment in both 

sectors was rapidly declining. The 

job losses in these industries were 

particularly hard on the Region, because 

they were often in the highest-wage 

sectors and served as a primary source 

of family-sustaining wages. The declines 

limited economic opportunities for area 

residents and contributed to stagnated 

regional economic growth. 

At the same time, the 1950s and 

1960s were a period in which U.S. 

manufacturing reigned supreme. 

Building on a very small base, growth in 

the Region’s manufacturing sector was 

much lower than in the nation as a whole. 

The raw materials extracted from the 

hills of Appalachia were used modestly 

in the Region’s growing manufacturing 

sector; most were shipped to cities in the 

industrial heartland to supply factories 

there. When there was a shock to the 

business cycle, it was Appalachia that felt 

the impacts first, as manufacturers cut 

back on their orders in response to drops 

in demand.

Over time, manufacturing employment 

adjusted to consumer demand, shifts 

in energy sources, advances in product 

materials, and access to global supply 

chains, as well as to improvements 

in productivity and relative loss of 

U.S. manufacturing competitiveness. 

As a result, the Region’s extractive 

industries required less labor, and 

employment in those sectors has 

continued to decline. Manufacturing 

employment has also declined—not only 

in Appalachia, but nationally as well. The 

Region is becoming less dependent on 

employment from resource-extractive 

Figure 5: Average Annual Population Growth in Appalachia, 1969–2012

Source: U.S. Bureau of Economic Analysis

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

KENTUCKY

OHIO PENNSYLVANIA

MARYLAND

VIRGINIA

NEW YORK

WEST

VIRGINIA

Average Annual Growth

-2.0% - -0.1%

0.0% - 0.9%

1.0% - 1.9%

2.0% - 6.0%

0 50 100

Miles

Appalachian average = 0.6%

U.S. average = 1.0%
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 9

and goods-producing sectors, and 

more dependent on the service sector 

for employment. 

As Figure 6 demonstrates, the Region’s 

economy trails the U.S. economy as 

a whole in its transition to becoming 

more service-oriented. The serviceproducing sector now accounts for 

more than 75 percent of the Region’s 

employment. According to data from the 

U.S. Bureau of Labor Statistics, servicesector employment increased nationally 

from 3 million jobs in 1979 to nearly 6 

million jobs in 2012 (even after declines 

attributed to the recession of 2008–09). 

Still, one-quarter of the economy relies 

on highly cyclical manufacturing and 

extractive industries or the government 

sector. Manufacturing employment in 

Appalachia declined from its peak of 

2.2 million jobs in 1979 to 1.2 million 

jobs in 2012, while mining and natural 

resources–related employment peaked at 

241,000 workers in 1979 and stabilized at 

about 131,000 workers in 2012. 

EDUCATIONAL ATTAINMENT 

The Gap is Wide, and Widening

In 1960, the Appalachian Region 

struggled with a poorly educated 

populace. Ninety-two percent of the 

U.S. population over the age of 25 had 

completed the 5th grade, compared 

with 89 percent of the Appalachian 

population. Thirty-two percent of 

the Appalachian population had 

completed high school (10th grade or 

higher), compared with 42 percent of 

the U.S. population. Only 5 percent of 

Appalachian residents had earned a 

college degree; compared with nearly 8 

percent of all Americans. 

As it is today, education 50 years ago 

was seen as a pathway to prosperity. 

Concern over the Region’s educational 

attainment rates at the time was acute, 

because primary and secondary school 

systems were funded locally, by tax 

bases often limited by low income levels 

in the community. Breaking this cycle 

required investments from outside 

the Region. 

Today the Appalachian Region has 

achieved near-parity with the nation 

in high school graduation rates, an 

important accomplishment since 

the 1960s; but the Region remains 

behind in post-secondary educational 

attainment. In today’s economy, the 

best-paying jobs in the service sector 

(the sector that dominates the economy 

now) are highly knowledge-intensive. 

A high school education is seen as a 

Source: U.S. Bureau of Labor Statistics

U.S. Appalachian Region

Figure 6: Private Service Sector Employment as a Share of Total 

Private Employment, Appalachia and the U.S.

45%

50%

55%

60%

65%

70%

75%

80%

85%

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

Percent of Total Employment

The Region is becoming less

dependent on employment

from resource-extractive and

goods-producing sectors, and

more dependent on the service

sector for employment.

Source: U.S. Census Bureau, 1970, 1980, 1990, and 2000 Censuses, and 2008–2012 American 

Community Survey

Appalachian Region U.S. 

Figure 7: Share of Population 25 Years+ with a Bachelor’s Degree 

or Higher, Appalachia and the U.S.

1970

1980

1990

2000

2008–12

0%

5%

10%

15%

20%

25%

30%

7.4

10.7 11.1

16.2

20.3

14.2

17.6

24.4

21.3

28.5
10 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

prerequisite, but not sufficient, for 

economic success. 

Increasingly, businesses demand postsecondary education from workers 

receiving middle-class wages. Using 

“college graduates” as a proxy measure 

for the phenomenon reveals steady 

improvements in the Region, but the 

improvement is not as rapid as it is in 

the rest of the nation.

Figure 7 shows that the share of the 

Region’s population over age 25 with 

at least a bachelor’s degree has tripled 

since 1970 (to 21.3 percent). However, at 

28.5 percent, the share of the nation’s 

population over 25 with at least a 

bachelor’s degree has nearly tripled, and 

the gap between the share of Appalachian 

adults and the share of the nation’s 

adults with a college degree has widened 

steadily over the past two generations.

HOUSING QUALITY 

Significantly Improved, but 

Continues to Lag

Not long ago, many homes in the Region 

lacked adequate plumbing and relied on 

water from local streams. Many houses 

were dilapidated, with 7.5 percent in 

such poor shape that they were deemed 

a danger to the health and safety of the 

families living in them. The PARC report 

found that the median value of homes 

in the Region was 27 percent below the 

median value of homes in the rest of 

the nation, and one in four homes in the 

Region was valued at less than $5,000 

in 1960, nearly double the national rate. 

ARC investments were targeted to 

help address these concerns, and 

the Region has made great strides in 

moving toward parity with the nation 

on housing quality. Today, 3.2 percent 

of Appalachian houses lack complete 

plumbing, compared with 2 percent 

nationally, as illustrated in Figure 8; a 

stark improvement from the 13.6 percent 

of houses in Appalachia that lacked 

complete plumbing in 1970. 

HEALTH 

Losing Ground

Combined with other factors, individual 

health is a prime indicator of quality 

of life and impacts economic vitality. 

In the early 1960s, living standards in 

Appalachia, as measured by the health 

and well-being of the population, were 

well below those of the rest of the 

nation, and they remain so today. 

One of the most basic measures of a 

region’s health is the mortality rate of its 

population. The Region’s mortality rate 

declined rapidly during the 1960s and 

1970s, as did the nation’s, due to improved 

medical care and preventative care. Infant 

mortality rates, in particular, dropped 

by more than two-thirds in Appalachia, 

moving much closer toward parity with the 

rest of the nation. Today, however, overall 

mortality rates are up in the Region, even 

as they continue to fall in the rest of the 

country. One reason for Appalachia’s 

increased mortality rates could be 

rising rates of obesity; other possible 

explanations include lack of access to 

care and the Region’s aging population. 

As shown in Figure 9, the problem has 

become particularly acute in areas of 

Central and Southern Appalachia. These 

same regions have also seen a rapid rise 

in the prevalence of diabetes, suggesting 

that health issues may be increasingly 

contributing to a lower quality of life 

there. This is a particularly important 

problem for government; in 2012 more 

than one third of the Region’s population 

was covered under government 

health insurance. The Region’s rate of 

population covered by government health 

insurance has been consistently higher 

than the rest of the nation’s for the past 

Source: U.S. Census Bureau, 1970, 1990, and 2000 Censuses, and 2008–2012 American 

Community Survey

Appalachian Region U.S. 

Figure 8: Percent of Homes Lacking Complete Plumbing Facilities,

Appalachia and the U.S.

1970

1990

2000

2008–12

0%

2%

4%

6%

8%

10%

12%

14%

16%

6.9

2.0

1.1

13.6

1.7 2.0

1.2

3.2

The share of the Region’s population over age 25 with at least a

bachelor’s degree has tripled since 1970.
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 11

25 years. (These data are from years 

prior to the implementation of the Patient 

Protection and Affordable Care Act.)

ISOLATION: 

TRANSPORTATION ACCESS 

Lagging, Fallen Behind

One of the most critical challenges 

facing the Appalachian Region in 1964 

was the isolation caused by its limited 

transportation network. The Interstate 

Highway System, in the midst of a 

tremendous building boom in the 1950s, 

largely bypassed Appalachia, going 

through or around the Region’s rugged 

terrain as cost-effectively as possible. 

This resulted in limited access to the 

rest of the nation for large swaths of 

Appalachia, and constituted barriers 

to trade with the rest of the nation and 

with global markets. Construction of 

the Appalachian Development Highway 

System (ADHS), authorized by the ARDA, 

was given priority as a key to economic 

development. The system was designed 

to generate development in previously 

isolated areas; to connect Appalachia 

federal-aid to, and to supplement, the 

interstate system; and to provide access 

to areas within the Region as well as to 

markets in the rest of the nation.

For many years, Appalachian states 

built federal-aid highway miles at a rate 

comparable to that of the rest of nation 

(see Figure 10). A major increase in the 

rate of miles built in both the Region 

and the rest of the nation occurred 

Figure 9: Mortality Rates in Appalachia (Deaths per 100,000 People), Relative to the U.S. (Percent of 

the U.S. Average)

Source: U.S. Centers for Disease Control and Prevention

Notes: A mortality rate is computed by dividing the number of deaths by total population and multiplying it by 100,000. These rates are not adjusted to differences 

in mortality rates by age. The percent of U.S. average is computed by dividing the county share by the U.S. average and multiplying by 100.

NEW YORK

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

OHIO PENNSYLVANIA

MARYLAND

WEST

VIRGINIA

VIRGINIA KENTUCKY

NEW YORK

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

OHIO PENNSYLVANIA

MARYLAND

WEST

VIRGINIA

VIRGINIA KENTUCKY

0 50 100

Miles

0 50 100

Miles

Percent of the U.S. Average

150.0% - 166.8%

100.1% - 149.9%

75.0% - 100.0%

60.4% - 74.9%

Appalachian average = 1,083

U.S. average = 967

Percent of the U.S. Average

150.0% - 268.6%

100.1% - 149.9%

75.0% - 100.0%

50.2% - 74.9%

Appalachian average = 1,018

U.S. average = 800

1968 2010

Source: Federal Highway Administration, Highway Statistics

Figure 10: Federal-Aid Highway Miles (Indexed to 1980 Mileage Levels)

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Index, 1980 = 100

100

105

110

115

120

125

130

U.S. Balance of States States in the Appalachian Region
12 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

in 1992; another increase occurred in 

2005 as part of a steady increase in 

funds available with the passage of 

Safe, Accountable, Flexible, Efficient 

Transportation Equity Act: A Legacy for 

Users (SAFETEA-LU). Resources made 

available through the 2009 American 

Recovery and Reinvestment Act also 

helped to increase the number of 

highway miles built, both regionally and 

nationally. However, recent data suggest 

that declines in federal funding have 

impacted the Appalachian Region more 

than the rest of country, partly because 

the relative per-mile cost of building 

major highways through mountainous 

terrain and the sparse population 

in these areas make it difficult for 

Appalachian states to compete for 

limited federal funds.

ISOLATION: 

TELECOMMUNICATIONS CAPACITY 

Improving, but Lagging in the Form 

of Broadband

Another key aspect of the Region’s 

isolation in the 1960s was its relative 

inability to communicate with the 

outside world. This limited not only the 

inflow of new ideas and technologies, 

but also the ability of area residents and 

leaders to imagine a different future. In 

the 1960s, a key public policy goal was 

universal access to telephone service. 

Many areas of Appalachia lacked access 

to phone service, and, often, where it 

was available, multiple families shared 

party lines. As late as 1970, 16.2 percent 

of area homes did not have access to 

phone service, compared with 13 percent 

of households nationally. By 2012, the 

proportion of households without phone 

service in the Appalachian Region was 

about 2.8 percent, very nearly the same 

as the national average of 2.5 percent.

Today, the gap in use of the Internet—

the communications engine driving the 

information and knowledge economy—is 

more relevant. As Figure 11 illustrates, 

some Appalachian states are improving 

as rapidly as the rest of the U.S. in this 

area, but the Region began the Internet 

era of the late 1990s lagging other parts 

of the country in Internet use. 

What these data mask, however, is 

the fact that Internet access alone is 

not enough: it is important to have 

access to high-speed Internet services. 

Anecdotal evidence from interviews with 

local leaders suggests that Appalachia 

trails badly in the affordability of 

Internet service, as well as in access to 

high-speed broadband, especially at 

the household or business-unit level. 

High-speed Internet represents the 

superhighway for transporting services 

to customers in today’s global economy. 

Many local leaders understand that 

building this infrastructure is as vital 

as building major highways for helping 

Appalachian companies compete and 

win in the high-growth technology 

industries that offer the best-paying jobs 

for area citizens.

Percent of Total Households

Note: Select Appalachian States include Alabama, Pennsylvania, and West Virginia

Source: U.S. Census Bureau, Current Population Surveys

1997

1998

2000

2001

2003

2007

2009

2010

2011

2012

Select Appalachian States U.S. 

Figure 11: Percent of Households Using the Internet

0%

10%

20%

30%

40%

50%

60%

70%

80%

14.2 17.8

23.226.4

38.441.6 45.3

51.854.7 50.7

56.5

61.8 64.0

68.7 67.3 67.6 71.0 71.7 70.9

74.7

Building telecommunications

infrastructure is as vital as

building major highways for

helping Appalachian companies

compete and win in the highgrowth technology industries

that offer the best-paying jobs

for area citizens.
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 13

Impacts from Appalachian 

Regional Commission Investments 

Investments in Appalachia

More than $25 billion in public 

investments1

 (through FY 

2013) have been made in the 

Appalachian Region by ARC and other 

federal, state, and local agencies in 

both highway- and non-highway-related 

activities. Since 1965, ARC has made 

investments in nearly 25,000 strategic 

non-highway activities in the Region. 

These investments were funded by $3.8 

billion in appropriated ARC funds and 

$9 billion in matching funds from other 

federal, state, and local funding sources. 

Since 1978, these matching funds have 

averaged $2.50 for each $1.00 in funds 

invested by ARC. This ratio has been 

relatively steady; in the most recent fiveyear period (2007–2012), it was 2.74 to 1.

ARC investments in Appalachia have 

attracted nearly $16 billion in leveraged 

private investment (LPI), the dollar 

amount of private-sector financial 

commitments (non-project funds), that 

result from an ARC investment. Since 

1978, when ARC began tracking this 

data, for each $1 in funds invested by 

ARC in non-highway projects, an average 

of $6.40 in private-sector funding has 

been leveraged (see Figure 12). This 

figure was nearly 10 to 1 in the most 

recent five-year period (2007–2012) and 

nearly 15 to 1 in 2013. See Figure 12 for 

1 This figure includes $3.8 billion in ARC Area 

Development Program funds, $9 billion in other 

federal, state, or local public match dollars 

connected to these investments, $9.1 billion 

obligated to the Appalachian Development 

Highway System, including funds from TEA-21, 

SAFETEA-LU and their extension acts, and $3.5 in 

state and local funds, assumed to be a 20 percent 

ADHS match. This total does not include 100 

percent state- and local-funded highway projects.

Figure 12: Ratio of ARC Investments to Non-ARC Matching Project 

Funds and to LPI (Non-Highway Investments)

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

$14.00

$16.00

$2.41

$0.02

$3.93

$8.30

$2.21

$5.92

$2.60 $2.74 $2.74 $2.10 $2.50

$9.95

$14.92

$5.59 $6.40

$2.34 $2.52

$13.40

1978–1981

1982–1986

1987–1991

1992–1996

1997–2001

2002–2006

2007–2012

2013

Cumulative

Non-ARC Public Funds Ratio LPI Ratio 

Since 1965, ARC has made

investments in nearly

25,000 strategic nonhighway activities in

the Region.

Credit: Ken Murray
14 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

data on non-ARC matching funds and LPI 

for ARC non-highway investments.

Through FY 2013, more than $9 billion 

has been obligated to the states for 

the Appalachian Development Highway 

System (ADHS). An additional $3.5 

billion has been provided in state and 

local match funds. As of September 30, 

2014, a total of 2,762.9 miles, or 89.4 

percent of the 3,090 miles authorized 

for the ADHS, were completed or under 

construction (see Figure 13). Another 96 

miles were in the final design or right-ofway acquisition phase, and 231.2 miles 

were in the location studies phase. 

Economic Impacts from 

ARC Non-Highway 

Grant Investments

This section quantifies the employment 

and income impacts attributable to the 

ARC non-highway grant investments 

made in the Region over the years. 

ARC’s investments have helped 

stimulate economic activity that has 

fueled the hiring of workers and the 

purchase of material goods and services 

needed to put new developments in 

place. Income from all of these activities 

fueled additional demand, further 

multiplying the total economic impacts 

of these investments. 

ARC investments have been made over a 

50-year time span, presenting a unique 

challenge in measuring these multiplier 

impacts. The economic structures of 

the Appalachian Region and the nation 

have changed significantly, so impact 

models must be recalibrated over time 

to reflect those structural changes. For 

this study, a customized input-output 

(I-O) model for the Appalachian Region 

was constructed, with adjustments to 

the modeling made after every five-year 

period to help account for measured 

economic changes. Detailed investment 

data were provided by the Appalachian 

Regional Commission and were assigned 

to one of 39 final demand categories, 

and then aggregated to one of 11 

industry sectors.2

The results from the economic modeling 

show that the $3.8 billion in ARC nonhighway investments (Area Development 

Program) were responsible for creating 

nearly 312,000 jobs and $10 billion 

in added earnings in the Region. On 

average, annually, these ARC funds 

supported an estimated 6,364 jobs and 

$204 million (in constant 2013 dollars) 

in earnings. 

Figure 14 shows the detailed results 

from the I-O analysis. Many of the new 

jobs created were in professional and 

technical services, manufacturing, trade, 

and construction industries. These 

figures include the direct, indirect, and 

induced job growth. Direct effects are 

the employment directly attributable 

to the spending of ARC funds within 

a particular industry. Indirect effects 

are the spending and employment of 

suppliers and contractors to produce 

inputs for the industry. Induced effects 

Location

Design/Right-of-Way

Construction

Open—Stage Construction Work Remaining

Open—All Eligible Work Complete

Figure 13: Appalachian Development Highway System

Status of Completion as of 9/30/2014

3,090 Eligible Miles

Source: Appalachian Regional Commission

82.5%

7.5%

3.1%

2.2%

4.7%

2 The investment data covered ARC Area 

Development Program investments made from 

1965 to 2013.

Many of the new jobs

created over the past five

decades were in professional

and technical services,

manufacturing, trade, and

construction industries.

Credit: Ann Hawthorne
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 15

include household spending on goods 

and services by both industry employees 

and the employees of contractors and 

suppliers (both direct and indirect 

employees). Total economic impact is 

the sum of direct, indirect, and 

induced effects. 

Only the ARC portion of funds for nonhighway investments was included in 

the economic modeling produced for 

this study. In every ARC-funded activity 

there are also public monies from 

other federal, state, and local sources, 

as well as the likelihood of leveraged 

private investment. Therefore, the total 

employment and income impacts to the 

Region would be substantially higher if 

all funding sources were included in the 

modeling. However, in this report, only 

the ARC portion of funds was modeled in 

order to prevent attributing the overall 

economic effect from a combined total 

investment to only the ARC portion 

of that investment. Therefore, the 

figures reported here should be read as 

conservative estimates of the ultimate 

employment and income effects from 

various ARC-supported non-highway 

investments. Given the large amount of 

non-ARC dollars that were directed to 

many of these investments, the actual 

resulting employment and income 

effects may be many times greater.

Economic Impacts from 

ADHS Investments

In 1964, the President’s Appalachian 

Regional Commission reported to 

Congress that economic growth in 

Appalachia would not be possible 

until the Region’s isolation had been 

overcome. Because the nation’s 

Interstate Highway System sought 

out the most cost-effective routes, 

it largely bypassed the rugged 

terrain of the Appalachian Region. 

Congress responded by creating the 

Appalachian Development Highway 

System (ADHS) expressly to provide 

growth opportunities for the residents 

of Appalachia—the same benefits 

afforded the rest of the nation through 

the construction of the Interstate 

Highway System. The ADHS, a system 

of modern highway corridors, would 

Figure 14: Employment Impacts—Appalachian Region Totals (Non-Highway Investments) Period Farming Agricultural Services, Forestry, and Fishing Mining Construction Manufacturing Transportation and Public Utilities Wholesale and Retail Trade Finance, Insurance, and Real Estate

ServicesFederal Civilian GovernmentState and Local GovernmentTotal

1965–1971 1,989 2,057 962 3,932 11,852 3,282 10,609 2,885 41,130 1,545 5,308 85,551

1972–1976 2,396 2,533 642 5,132 14,387 3,963 12,645 3,378 47,448 1,810 6,289 100,622

1977–1981 1,149 1,225 639 4,622 7,630 2,041 7,499 2,028 21,571 844 3,527 52,775

1982–1986 424 553 247 1,906 2,664 684 2,884 755 6,369 291 1,224 18,002

1987–1991 255 333 111 1,315 1,546 415 1,862 474 4,282 176 695 11,463

1992–1996 309 498 123 1,914 1,975 598 2,661 537 5,662 231 916 15,426

1997–2001 169 308 61 2,098 1,203 395 1,658 335 3,576 18 134 9,954

2002–2006 134 147 53 1,548 811 259 948 173 4,300 13 106 8,491

2007–2013 122 162 57 1,952 760 241 971 195 4,970 10 113 9,552

All Years 6,946 7,816 2,895 24,420 42,828 11,877 41,736 10,760 139,307 4,937 18,312 311,835

Credit: Ken Murray
16 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

replace a network of worn, winding twolane roads, with the aim of generating 

economic development in previously 

isolated areas, supplementing the 

interstate system, and providing access 

to areas within the Region as well as to 

markets in the rest of the nation and 

overseas (see Figure 15). 

Additionally, as part of the ADHS program 

and its federal funding authority, 

Congress authorized use of a portion of 

the funds for local access roads, at the 

discretion of ARC member states. This 

approach provides the flexibility to meet 

local needs with a financing mechanism 

to support a variety of economic 

development opportunities throughout 

the Region. In addition to using ADHS 

funds for local access road projects, a 

state may also use ARC non-highway 

funds and have its state department of 

transportation and the Federal Highway 

Administration administer the project. 

The ARDA authorized construction of 

2,350 miles of the ADHS; revisions to 

the ARDA over the years increased the 

authorized mileage to 3,090.

A recent study of the ADHS measured 

the economic development and safety 

benefits to the Region from sustained 

highway-system investments. The June 

2008 study, conducted by Cambridge 

Systematics, Inc., with Economic 

Development Research Group, Inc., 

assessed the travel performance, trade, 

and economic development impacts that 

would result from completing the ADHS.3

The study also assessed connectivity, 

accessibility, and how well the ADHS 

corridor improvements would connect 

Appalachian people and businesses to 

other highway facilities, multimodal 

transportation, and economic markets 

after completion of the ADHS. 

3 Cambridge Systematics, Inc., Economic 

Development Research Group, and HDR Decision 

Economics. Economic Impact Study of Completing 

the Appalachian Development Highway System.

Appalachian Regional Commission, June, 2008.

Figure 15: Appalachian Development Highway System 

Source: Appalachian Regional Commission

HART

D H

N

O

O

M M

P

P-1

U-1 U

T T

B

F

S

F

I

I

B

B R

G B

D

L

E

X

V

J

A

A

W

V

K

J

J-1

J

K

B

B

C D

M

Q

V

Q

O-1

C-1

B-1

A-1

C

X-1

G EORGIA

A LABAMA

T ENNESSEE

D E L A W A R E

K ENTUCKY

O HIO

W ISCONSI N

NEW

JERSEY

V IRGINIA

N ORTH C AROLINA

S OUTH C AROLINA

I LLINOIS I NDIANA

M ICHIGAN

N EW Y ORK

M A RY LA N D

P ENNSYLVANIA

MISSISSIPPI

WEST VIRGINIA

Interstate Highway System

ADHS Miles Not Open

to Traffic

ADHS Miles Open to Traffic–

September 30, 2014

Recent studies of the ADHS

measured the economic

development and safety

benefits to the Region from

sustained highway-system

investments and found

significant positive results.

Credit: Ken Murray
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 17

The study found that total economic 

impacts on the Region of completing the 

ADHS would include the direct effects of 

reduced travel time and costs, along with 

increased regional competitiveness via 

market accessibility gains and multiplier 

effects. These effects would gradually 

increase over time and by 2035 would 

result in an estimated 80,500 jobs, $5.0 

billion in increased value-added production, 

and $3.2 billion in increased wages for 

workers in the Region. At the national level, 

the estimated return on investment would 

yield $3.00 for every $1.00 invested.

Assessing ARC’s Role in 

Achieving Growth in the Region 

Reviewing ARC’s role in helping 

Appalachia develop its economy and 

the funds it has invested in the Region 

to do so raises the question as to 

whether this experiment in regional 

economic development has paid off 

for U.S. taxpayers. The research aimed 

to answer this question by using a 

quasi-experimental methodology that 

compares counties in the Appalachian 

Region to similar counties outside the 

Region. The results of this research 

indicate that employment and per capita 

income grew at a faster rate in the 

Appalachian counties than in the nonAppalachian counties, as a result of the 

ARC investments. 

Prior to this report, assessing the impact 

of Appalachian Regional Commission 

investments in the Region’s counties 

was the topic of the seminal Isserman 

and Rephann study (1995) of the 

Region, in which the authors sought to 

assess whether actual changes in the 

Appalachian Region could be attributed 

to ARC programs.4 They determined that 

answering the question of the effectiveness 

of ARC’s investments requires positing an 

alternative scenario: if the ARC investments 

had not taken place, would the Region have 

grown less rapidly? Although this seems 

like a daunting task, the methodology 

outlined in Isserman and Rephann (1995) 

can be used to assess the effectiveness 

of these investments and answer the 

counterfactual question posed above.

The Quasi-Experimental Method (QEM) 

is an empirical technique designed to 

answer such counterfactual claims. 

Somewhat similar to a traditional 

scientific experiment, the QEM 

methodology uses matching techniques to 

assign each “treated” entity (or counties 

in this instance) with one (or more) 

“control” entities (or counties). Then, 

QEM uses statistical methods to see if 

the difference in the outcome variable in 

the two entities is statistically significant. 

If the matching is done accurately and 

the differences between treatments and 

controls are statistically different, it can 

confidently be stated that the divergence 

is due to the policy implementation.

Isserman and Rephann (1995) conducted 

a study in which they constructed a 

counterfactual by matching counties 

in Appalachia that received ARC 

funding (i.e., the “treated” group) to 

counties outside of Appalachia that 

did not receive any ARC funding (i.e., 

the “control” group). The matching of 

treated counties and control counties is 

a fundamental step in this process and 

“closer” matches tend to provide more 

convincing evidence.

The results presented here use a 

methodology similar to the one used 

by Isserman and Rephann (1995), with 

two differences designed to improve the 

matching process. First, while the 1995 

Isserman and Rephann study used 24 

variables to match the treated and control 

counties, this study used five additional 

variables (a total of 29 variables) to 

assist in the matching. The use of these 

additional variables in the matching 

algorithm ensured a match as good as, 

or better than, the match obtained by 

Isserman and Rephann. Second, this study 

used a different econometric technique 

to implement the matching, based on 

developments that have occurred in the 

field since the 1995 study. 

4 Isserman, Andrew and Terrance Rephann. “The 

Economic Effects of the Appalachian Regional 

Commission: An Empirical Assessment of 26 Years 

of Regional Development Planning.” Journal of 

the American Planning Association, vol. 61, no. 3, 

summer 1995, pp. 345-364.

Credit: Ken Murray
18 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

The empirical examples undertaken in 

this study concentrate on two important 

metrics: the growth in employment and 

the growth in per capita income. Job 

growth is one economic indicator that 

is important to many stakeholders, as 

it measures how economically viable a 

region is in terms of employment. The 

same may be said for the importance 

of per capita income. The results of the 

analysis using these two metrics follow.

The first set of results appears in 

Figure 16, “Employment Growth Rates 

between Treated and Control Counties.” 

The results indicate that for virtually 

every year used in the analysis (i.e., 

from 1970 to 2012), the counties in the 

Appalachian Region (treatment counties) 

had higher employment growth than the 

matched counties that did not receive 

ARC funding. The average difference in 

growth rates between the counties that 

obtained ARC investments and those 

that did not receive ARC investments was 

approximately 4.2 percent. This statistical 

analysis provides evidence that ARC 

investments led to higher employment 

growth over the time period 1970–2012.

The second set of results, illustrated in 

Figure 17, “Per Capita Income Growth 

Rates between Treated and Control 

Counties,” indicates that the per capita 

income growth rate in counties that 

received ARC investments grew an 

average of 5.5 percent more over the 

time period than the counties that did 

not receive ARC investments, providing 

evidence that ARC investments led to 

higher growth in per capita income over 

the time period 1970–2012.

In summary, using a well-established 

QEM technique, the research shows 

that employment growth and per 

capita income growth over the period 

1970–2012 were higher in Appalachian 

counties than in counties that did not 

receive ARC investments. On average, 

counties that received ARC investments 

experienced 4.2 percent higher 

employment growth and 5.5 percent 

higher per capita income growth than 

the counties that did not receive ARC 

funding. These results indicate the 

effectiveness of ARC investments for the 

Appalachian counties in the study.

Treated Controls

Figure 16: Employment Growth Rates between Treated and Control 

Counties, Base Year 1969

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

0%

10%

20%

30%

40%

50%

60%

70%

Treated Controls

Figure 17: Per Capita Income Growth Rates between Treated and 

Control Counties, Base Year 1969

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

0%

50%

100%

150%

200%

250%

The research shows that

employment growth and per

capita income growth over the

period 1970–2012 were higher

in counties that received ARC

investments than in counties

that did not.
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 19

Input from Stakeholders 

As part of this research, meetings 

were held in all 13 Appalachian 

states to give key stakeholders the 

opportunity to provide insights about their 

region’s ARC-related activities. More than 

220 stakeholders participated in these 

facilitated sessions, each of which involved 

roughly 10 to 20 participants. This section 

provides a brief synopsis of the comments 

and broad themes that arose during those 

sessions. They speak to ARC’s legacy, the 

strength of its partnership model, and how 

ARC investments can be used to generate 

significant regional impact. 

The ARC Model

■■ Stakeholders were quick to mention 

ARC’s catalyzing role in spurring 

regional development. Regional 

stakeholders shared many examples 

of relatively small ARC investments 

that planted the seed for significantly 

more investment and growth. In 

many of these examples, later 

investments would not have occurred 

without the initial ARC investment. 

This was especially true for many 

investments in the Region’s most 

distressed communities, which have 

few accessible funding sources 

because of the difficulty they have in 

raising even a modest amount of local 

matching funds. 

■■ ARC does more than fund projects—it 

also provides leadership, advocacy, 

planning, research, and timely seedinvestments to advance these efforts, 

and has done so with countless 

federal, state, nonprofit, and private 

partners. This model has proven 

effective over the past 50 years and 

may become even more important for 

achieving the Region’s development 

goals in the future.

■■ Stakeholders noted that over the 

past 50 years, ARC’s federal-statelocal partnership model has proven 

effective in helping Appalachian 

communities advance efforts with 

great local support and impact. 

■■ ARC allows states and regions to set 

their own priorities and make their 

own decisions about how ARC funding 

is used. This model has allowed states 

to shift their focus in response to 

changing economic, political, or fiscal 

conditions. Diminished funds have 

led a number of states to refocus 

their ARC investments from relatively 

expensive physical infrastructure 

projects to business enterprise and 

tourism development, workforce 

training, and health promotion 

activities. Many stakeholders see this 

new generation of investments as 

having greater regional impact with 

the available funds. 

Flexibility of ARC Funding

Local and regional stakeholders 

universally praise ARC’s flexibility. 

Unlike other funding programs, ARC 

funding allows regions to think creatively 

about how best to address pressing 

regional challenges. 

ARC allows states and regions

to set their own priorities

and make their own decisions

about how ARC funding is

used. This allows states to

shift their focus in response to

changing economic, political,

or fiscal conditions.

Credit: Ken Murray
20 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

Many stakeholders spoke of ARC’s role 

in supporting the expansion of healthcare and education facilities and the 

importance of those investments to the 

well-being of the Region’s population. In 

many ways, these efforts helped address 

serious market failures that would have 

significantly diminished the health and 

welfare of the Region’s residents. 

Several focus group participants 

said that economic transformation in 

Appalachia is less about diversification, 

and more about forging entirely new 

economies. To help facilitate this 

process, ARC has made investments 

that support these transformations. This 

includes investing in the preparation 

of industrial sites, but also providing 

support for entrepreneurship, 

tourism destination development 

and promotion, export expansion, 

and business development programs, 

helping companies access programs 

that support technology acceleration or 

advanced manufacturing processes. 

State and Local Partnerships

ARC’s approach to development 

prioritizes partnerships with other 

federal, state, private, and nonprofit 

partners. Therefore ARC funding aligns 

with and complements state development 

initiatives, such as New York State’s 

Regional Economic Development Councils 

and Kentucky’s Shaping Our Appalachian 

Region (SOAR) initiative. 

Stakeholders from every state noted 

the importance of ARC’s support for the 

Region’s 73 local development districts 

(LDDs). Several focus group participants 

cited the critical ability of LDDs to serve 

as an interagency connection between 

different service providers and local 

jurisdictions within the Region; and to 

connect local residents and businesses 

with resources at the state, regional, 

and federal levels, as well as with private 

resources. ARC funding also allows the 

LDDs to assist communities with project 

development, including assistance in grant 

writing for common funding sources. 

Without this support, stakeholders 

maintained, many of the most successful 

ARC projects would never have started. 

ARC Investments Complement

One Another

■■ While the challenges facing Appalachia 

shift over time, ARC continues 

to lay the groundwork for future 

development by making investments 

that are designed to advance the 

Commission’s strategic goals. Few 

investments embody this more than 

those that improve the Region’s 

broadband infrastructure. Efforts 

to increase broadband access and 

speed have proven to be a vital 

foundation in addressing many other 

issues, including entrepreneurship 

development, tourism, telemedicine, 

distance learning, and even in-person 

education. Stakeholders throughout 

the Region noted that this was one 

of the most prominent issues that 

required ARC attention and investment. 

■■ ARC investments in basic physical 

infrastructure, such as highways; 

water and wastewater; and, more 

recently, broadband, have allowed 

Appalachian communities to lay 

the basic foundation for additional 

development. Without this assistance, 

these communities would be at a 

significant disadvantage in taking 

advantage of future economic 

development opportunities.

■■ ARC has made investments to advance 

education and training programs to 

prepare the Region’s workers for the 

jobs of tomorrow. These investments 

are not only for curricula and 

educational programs, but also for the 

construction of new facilities and the 

purchase of training equipment, with an 

emphasis on assisting the Region’s 

most economically distressed or 

underserved communities.

ARC’s approach to development

prioritizes partnerships with

other federal, state, private, and

nonprofit partners.

Credit: Ken Murray

Credit: Ken Murray
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 21

■■ Place-making projects are another 

area where ARC investments not only 

create new economic activity, but also 

preserve Appalachian culture and 

improve the Region’s overall quality 

of life. Focus group participants noted 

several ARC-supported projects that 

helped preserve historic buildings or 

revitalize downtowns. Similarly, efforts 

to develop local food systems are 

beneficial in that they can serve as an 

attraction to visitors while creating new 

economic opportunities and healthier 

food options for residents. Focus group 

participants noted that these kinds of 

ARC-supported projects contribute to 

the Region’s tourism infrastructure. 

■■ ARC has invested in efforts to link 

individual projects, such as Virginia’s 

Crooked Road, which connects 

heritage music venues and events; 

and the Great Allegheny Passage 

trail, which provides bike access from 

Pittsburgh to Cumberland, Maryland. 

These projects leverage many 

individual attractions to make the 

Region a more compelling destination 

for visitors since they can take 

advantage of many attractions rather 

than just one.

Accomplishments and Challenges

■■ Many communities said they are 

continuously trying to do more 

with less. In order to do this, they 

must find ways to partner and 

leverage other public and private 

funding opportunities. For example, 

ARC launched multi-year Global 

Appalachia grants to foster member 

states’ rural trade development 

efforts, building on the State Trade 

and Export Promotion Program of the 

U.S. Small Business Administration. 

Another example is ARC’s 

participation in the Rural Jobs and 

Innovation Accelerator Challenge, in 

partnership with the U.S. Economic 

Development Administration and the 

U.S. Department of Agriculture. 

■■ Communities can find projects 

that fit into other ARC or regional 

investments. Tourism and cultural 

projects provide a structure that 

allows regions to connect individual 

attractions into a bigger idea that 

makes the region itself a more 

attractive destination for visitors. By 

leveraging other investments, the 

sum of the parts becomes greater 

than the whole and is an effective 

way of doing more with less. 

■■ Stakeholders recognize the 

importance of prioritizing ARC 

funding based on need. This does not 

mean, however, that the classification 

of counties by distress levels is 

free from challenges. For instance, 

persistent pockets of poverty can 

remain even in counties that overall 

are performing well relative to other 

counties. Additionally, while not a 

common occurrence, fluctuations in 

county economic status from year 

to year can also pose challenges for 

communities. Given that a change in 

status results in a change in matching 

funds requirements, this can 

introduce some long-term planning 

difficulties for some counties. 

■■ Finally, stakeholders also suggested 

that ARC recognize that its impact has 

been well beyond what might be easily 

measured. As noted in several states 

and in similar ways, it is impossible 

to drink water, flush a toilet, or drive 

down a highway without seeing firsthand the result of an ARC investment. 

Given this significant impact on the 

Region, many stakeholders thought 

that ARC should more actively 

promote its accomplishments in ways 

similar to what other agencies do (e.g., 

signs that read “This road was paid for 

in part through ARC funds”). 

Credit: Kathryn Whiteman

Credit: Courtesy of Jackson County Commissioners
22 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

Based on the analysis in this 

report, key findings emerge.

1. ARC represents a highly valued and 

active player in the Appalachian Region’s 

economic development, supporting state 

and local partners in their efforts to 

transform their communities.

Since 1965, ARC has made nearly 

25,000 non-highway strategic 

investments in the Region. Working 

with federal, state, and local partners, 

ARC has invested more than $3.8 

billion in these projects. These 

investments supported a variety of 

community and economic development 

initiatives, including basic infrastructure 

improvements, job creation initiatives, 

and leadership development. For every 

dollar in ARC funds, state and local 

partners were able to leverage an 

average of $2.50 from other federal, 

state and local funds, as well as $6.40 in 

private-sector investments. 

In addition, more than $9 billion has 

been obligated since 1965 for the 

Appalachian Development Highway 

System, which is now 89 percent 

complete or under construction. When 

finished, the 3,090-mile ADHS will 

connect almost every part of the Region 

to an interstate-quality highway and to 

the national Interstate Highway System.

2. ARC has had a significant and 

important impact on the Region’s 

economic vitality.

Over the past 50 years (with much 

higher appropriations in the earlier years 

of the program), ARC’s $3.8 billion in 

non-highway investments have resulted 

in nearly 312,000 direct, indirect, and 

induced jobs for the Region and $10.5 

billion (in constant 2013 dollars) in 

additional earnings. Between 2007 and 

2013, ARC non-highway investments 

accounted for nearly 10,000 jobs and 

$400 million in regional earnings. These 

impacts do not include the benefits 

accruing as a result of the investments 

ARC has leveraged over the years.

Furthermore, our research demonstrates 

that counties receiving ARC investments 

grew at a slightly faster pace than 

similar counties that did not receive ARC 

investments. Using a rigorous quasiexperimental research method, our 

analysis suggests that ARC investments 

helped counties add employment at a 

4.2 percent faster pace, and per capita 

income at a 5.5 percent faster pace, 

than similar counties that did not receive 

ARC investments. 

Moreover, leaders in the Region 

expressed consensus about the value 

of ARC’s role as a catalyst in helping 

to make projects happen that might 

not otherwise have gone forward. In 

interviews with over 220 local, state, 

and federal stakeholders in every 

Appalachian state, ARC was praised for 

helping leaders respond to uniquely local 

problems and for its ability to leverage 

other resources (by seeding new 

projects or providing the “last dollars” 

in) when projects did not fit neatly into 

other funding program models and 

might not have otherwise happened.

Key Report Findings

Credit: Ken Murray
Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965 23

3. ARC has made progress toward 

helping the Appalachian Region reach 

socioeconomic parity with the nation, 

but much work remains in moving 

some key indicators.

ARC’s strategic plan has four major 

goals: (1) increasing job opportunities 

and per capita income in the Region; 

(2) strengthening the capacity of the 

people of Appalachia to compete in the 

global economy; (3) developing and 

improving the Region’s infrastructure; 

and (4) building the ADHS to reduce 

Appalachia’s isolation. On measures 

related to poverty, income disparity, 

unemployment, the industrial mix, 

and housing quality, the Region has 

improved significantly. For instance, 

as Figure 18 shows, there has been a 

dramatic reduction in the number of 

Appalachian counties where the share of 

the population living in poverty exceeds 

150 percent of the national average. The 

number of high-poverty counties in the 

Region (those with poverty rates above 

150 percent of the U.S. average) declined 

from 295 in 1960 to 107 for the period 

2008–2012. The overall poverty rate for 

Appalachia is almost half that of 1960, 

dropping from over 30 percent to just 

under 17 percent.

Figure 18: Poverty Rates in Appalachia, Relative to the U.S. (Percent of the U.S. Average)

Source: U.S. Census Bureau, 1960 Census and 2008–2012 American Community Survey

Notes: A poverty rate is the ratio of persons whose income falls below the poverty level, to the total number of persons for whom poverty status is determined. 

The percent of U.S. average is computed by dividing the county rate by the U.S. average and multiplying by 100.

1960 2008–2012

NEW YORK

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

OHIO PENNSYLVANIA

MARYLAND

WEST

VIRGINIA

VIRGINIA KENTUCKY

NEW YORK

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

OHIO PENNSYLVANIA

MARYLAND

WEST

VIRGINIA

VIRGINIA KENTUCKY

0 50 100

Miles

0 50 100

Miles

Percent of the U.S. Average

150.0% - 367.0%

100.1% - 149.9%

75.0 - 100.0%

44.9% - 74.9%

Appalachian average = 30.9%

U.S. average = 22.1%

Percent of the U.S. Average

150.0% - 278.2%

100.1% - 149.9%

75.0% - 100.0%

44.9% - 74.9%

Appalachian average = 16.6%

U.S. average = 14.9%

ARC investments have

supported a variety of

community and economic

development initiatives,

including basic infrastructure

improvements, job creation

initiatives, and leadership

development.

Credit: Ken Murray
24 Appalachia Then and Now: Examining Changes to the Appalachian Region Since 1965

4. ARC has not yet fully accomplished 

its mission of bringing the Region to 

parity with the rest of the nation on 

key socioeconomic indicators.

The Region still lags in many key 

areas. The Region’s population growth 

is relatively stagnant, reflecting 

an economy that lags in terms of 

employment growth and educational 

attainment, so that there are not enough 

high quality jobs to support its citizens. 

Government transfer payments account 

for one-quarter of all personal income, 

a 41 percent higher rate than the rest of 

the nation. By comparison, the ratio was 

17 percent after the 1981-82 recession, 

about 35 percent higher than the rest of 

the nation. Some, but certainly not all, 

of this increase can be explained by the 

rise in citizens aged 65 and over now 

eligible for Social Security. 

Furthermore, the Region’s citizens have 

relatively poorer health outcomes (in 

terms of mortality rates as well as the 

prevalence of obesity and diabetes), 

reflecting changes in modern-day life 

that have reduced the quality of life for 

many residents. In addition, the Region 

remains relatively more isolated from 

the rest of the nation because the ADHS 

is not yet completed and, increasingly, 

because the Region lags the rest of the 

nation in access to affordable highspeed broadband service. 

Credit: Ken Murray

Appalachia Moving Forward

The data from this research suggest that ARC has been a vital partner in the 

economic and community development progress achieved in the Appalachian 

Region. However, there is a continued need for investment to help Appalachia 

reach socioeconomic parity with the rest of the nation. Many questions remain 

about how best to address the challenges Appalachia faces today. How can 

leaders create a climate of entrepreneurship and opportunity so that local 

citizens can remain and contribute to Appalachia’s future success? How can 

the Region compete and succeed in the global economy when it cannot always 

reach new markets? Few states or localities have the capacity to address these 

significant challenges without outside help. Addressing the Region’s disparities 

will require continued local-state-federal partnerships and strategic investments 

that build opportunity for growth.
TECHNICAL REPORT

FEBRUARY 2015

Appalachia Examining Changes to the Appalachian Region Since 1965

THEN AND NOW

Prepared by the Center for Regional Economic Competitiveness 

and West Virginia University for the Appalachian Regional Commission
1

Contributing Authors

Center for Regional Economic Competitiveness

Ken Poole, Ph.D.

Marty Romitti, Ph.D.

Mark White, Ph.D.

Ed Feser, Ph.D.

Troy Mix

Sarah Gutschow

Brendan Buff

Randall Arthur

Wen Sun

Regional Research Institute at West Virginia University

Randy Jackson, Ph.D.

Don Lacombe, Ph.D.

Gianfranco Piras, Ph.D.

Juan Tomas Sayago Gomez

Caleb Stair

Bureau of Business and Economic Research at West Virginia University

John Deskins, Ph.D.

Jane Ruseski, Ph.D.

Christiadi, Ph.D.

Eric Bowen

Brian Lego

Patrick Manzi
2

Contents

Technical Report Introduction ......................................................................................................................5

Assessing ARC’s Impacts—A Review of Past Program Evaluations ......................................................6

Evaluating ARC’s Impacts by Priority Area.............................................................................................9

Assessing ARC’s Impacts—Present Program Evaluation Approach ..................................................... 15

Chapter I: Fifty Years of Socioeconomic and Structural Change in the Appalachian Region ............. 16

Section Summary................................................................................................................................... 16

Employment, Unemployment, and Labor Force Participation in Appalachia .................................. 17

Poverty in Appalachia ........................................................................................................................... 38

Population in Appalachia...................................................................................................................... 42

Aging in Appalachia............................................................................................................................... 47

Educational Attainment in Appalachia ................................................................................................ 51

Health in Appalachia ............................................................................................................................. 53

Chapter II: Assessing Economic Impacts of Non-Highway Investments.................................................. 70

Section Summary .................................................................................................................................... 70

Appalachian Regional Commission Investments.................................................................................... 70

IO Concept and Method.......................................................................................................................... 74

Data......................................................................................................................................................... 79

Results..................................................................................................................................................... 82

Summary................................................................................................................................................. 84

Chapter III: “But For” the Appalachian Regional Commission Investments: Answering the 

Counterfactual with Quasi-Experimental Methods (QEM)........................................................................ 85

Section Summary .................................................................................................................................... 85

Control Group Selection ......................................................................................................................... 86

Data......................................................................................................................................................... 90

Results..................................................................................................................................................... 93

Summary............................................................................................................................................... 101

Key Report Findings................................................................................................................................. 102

Appalachia Moving Forward .................................................................................................................... 104

References................................................................................................................................................. 105

Appendices................................................................................................................................................ 110
3

List of Figures

Figure 1: Major Findings and Methods of Analysis from Existing Evaluations ........................................ 12

Figure 2: Total Private Employment (1975 – 2012)................................................................................... 17

Figure 3: Total Private Employment – Non-metro Comparison (1975 – 2012)......................................... 18

Figure 4: Average Annual Growth Total Private Employment (1975 – 2012)........................................... 19

Figure 5: Unemployment Rate (1976 – 2012) ............................................................................................ 20

Figure 6: Labor Force Participation Rate (1970 – 2012)............................................................................ 21

Figure 7: Natural Resources & Mining Employment (1975 – 2012).......................................................... 22

Figure 8: Natural Resources & Mining Share of Total Private Employment (1975 – 2012)...................... 23

Figure 9: Manufacturing Employment (1975 – 2012)................................................................................ 24

Figure 10: Manufacturing Share of Total Private Employment (1975 – 2012).......................................... 25

Figure 11: Private Service-Providing Employment (1975 – 2012) ............................................................ 26

Figure 12: Private Service-Providing Share of Total Private Employment (1975 – 2012) ........................ 27

Figure 13: Average Annual Growth Service-Providing Employment (1975-2012)................................... 28

Figure 14: Per Capita Personal Income (1969 – 2012)............................................................................... 29

Figure 15: Per Capita Personal Income – Non-metro Comparison (1969 – 2012)..................................... 30

Figure 16: Earnings Per Capita (1969 – 2012) ........................................................................................... 31

Figure 17: Earnings Per Capita – Non-metro Comparison (1969 – 2012).................................................. 32

Figure 18: Government Transfer Payments as a Share of Personal Income (1969 – 2012) ....................... 33

Figure 19: Per Capita Income Relative to the U.S. (Percent of the U.S. Average)..................................... 34

Figure 20: Average Annual Per Capita Income Growth (1969-2012)........................................................ 35

Figure 21: Per Capita Personal Income from Assets (1969 – 2012)........................................................... 36

Figure 22: Proprietors' Income as a Share of Total Earnings (1969-2012)................................................. 37

Figure 23: Share of Population Below Poverty Line (1960 – 2012)........................................................... 38

Figure 24: Share of Population Below Poverty Line – Non-metro comparison (1960 – 2012).................. 39

Figure 25: Poverty Rates Relative to the U.S. (Percent of the U.S. Average)............................................ 40

Figure 26: Share of Population 65 or Older Below Poverty Line (1970 – 2012) ....................................... 41

Figure 27: Poverty Rates of Population 65 or Older relative to the U.S. (Percent of the U.S. Average) ... 41

Figure 28: Total Resident Population (1969 – 2012).................................................................................. 42

Figure 29: Appalachian Region’s Population Relative to the U.S. (1969 – 2012) ..................................... 43

Figure 30: Average Annual Population Growth, 1969-2012...................................................................... 44

Figure 31: Share of Population Who Moved in from Different County in Last Five Years (1970-2000).. 45

Figure 32: Share of Population Who Moved in from Different State in Last Five Years (1970-2000)...... 46

Figure 33: Share of Population 0 to 19 Years (1970-2012)........................................................................ 47

Figure 34: Share of Population 65 Years or Older (1970-2012)................................................................. 48

Figure 35: Share of Population 0 to 19 Years Old Relative to the U.S. (Percent of the U.S. Average) ..... 49

Figure 36: Share of Population 65 Years or Older Relative to the U.S. (Percent of the U.S. Average)..... 50

Figure 37: Share of Population 25 Years and Older with a Bachelor's Degree or Higher (1970-2012)..... 51

Figure 38: Share of Population with a Bachelor's Degree or Higher (Percent of the U.S. Average).......... 52

Figure 39: Mortality Rates (Deaths per 100,000 People), (1968-2010)...................................................... 53

Figure 40: Mortality Rates (Deaths per 100,000 people) (Percent of the U.S. Average) ........................... 54

Figure 41: Diabetes Prevalence (2004-2011).............................................................................................. 55
4

Figure 42: Diabetes Prevalence Rates Relative to the U.S. (Percent of the U.S. Average)........................ 56

Figure 43: Obesity Prevalence (2004-2011)............................................................................................... 57

Figure 44: Obesity Prevalence Rates Relative to the U.S. (Percent of the U.S. Average).......................... 58

Figure 45: Percent of People Covered by Health Insurance (1987-2012) .................................................. 59

Figure 46: Percent of People Covered by Government Health Insurance (1987-2012) ............................. 60

Figure 47: Percent of Homes without Phone Services (1970-2012)........................................................... 61

Figure 48: Percent of Households Using the Internet (1997-2012)............................................................ 62

Figure 49: Percent of Homes Lacking Complete Plumbing Facilities (1970-2012)................................... 63

Figure 50: Share of Housing Units Built 30 Years Ago or Before (1970-2000) ........................................ 64

Figure 51: Share of Housing Units Whose Source of Water comes from Public System or Private 

Company (1970-1990)................................................................................................................................ 65

Figure 52: Federal-Aid Highway Miles Indexed to 1980 Mileage Levels (1980-2012)............................. 66

Figure 53: ARC Appropriations and Employment Growth (1965-2012) ................................................... 67

Figure 54: ARC Appropriations and Per Capita Earnings (1965-2012)..................................................... 68

Figure 55: ARC Appropriations and Poverty (1960-2012)......................................................................... 69

Figure 56: Ratio of Non-ARC Matching Project Funds and LPI for Non-Highway Investments.............. 71

Figure 57: Appalachian Development Highway System............................................................................ 72

Figure 58: ARC Funds Expended by Investment Classification (Cumulative Total) ................................ 73

Figure 59: ARC Funds Expended for Area Development Program ........................................................... 73

Figure 60: Year of U.S. Input-Out Benchmark Table Applied to Each ARC Investment Period .............. 75

Figure 61: Characterization of Interindustry Transactions as Reported in the U.S. Benchmark InputOutput Table ............................................................................................................................................... 76

Figure 62: Algorithm for Analyzing ARC Impacts .................................................................................... 78

Figure 63: North American Industrial Classification System Industry Sectors......................................... 79

Figure 64: ARC Appropriated Non-Highway Investment by Period and State (millions of dollars)......... 81

Figure 65: Employment Impacts-Appalachian Region Totals.................................................................... 82

Figure 66: Summary of All Impacts ........................................................................................................... 83

Figure 67: ARC Coefficient of Specialization............................................................................................ 84

Figure 68: Number of Counties by State that Received their First Investment from the ARC................... 90

Figure 69: Variables Used in the QEM Matching Algorithm..................................................................... 92

Figure 70: Comparison of averages for variables used for estimating the matching procedure between 

treated and control and significance levels for full period matching .......................................................... 94

Figure 71: Tests of fitting between matching procedures for full period matching.................................... 95

Figure 72: Comparison of objective variables before treatment between treatment and control counties 

and significance level for full period matching. Rates of growth of per capita income. ............................ 95

Figure 73: Matched counties for full period matching from data for 1959 (Full Period Matching)........... 96

Figure 74: Per-Capita Income Growth Rate Results and significance levels for full period matching. ..... 97

Figure 75: Per Capita Income Growth Rates between Treated and Control Counties, Base Year 1969 .... 98

Figure 76: Employment Growth Rate Results and significance levels for full period matching................ 99

Figure 77: Employment Growth Rates between Treated and Control Counties, Base Year 1969............100
5

Technical Report Introduction

The Appalachian Regional Commission (ARC) represents a truly unique federal-state-local 

partnership reflecting a shared commitment toward eradicating regional poverty through 

grassroots efforts to create long-term sustained investments in public works, economic 

development, and planning. In its early years, these investments focused on to some success on 

developing growth centers to serve as the foundation for development, but as the program 

evolved, ARC placed greater emphasis in more recent years on making investments in distressed 

and rural areas. This report explores the 50-year history of ARC and assesses the actual impacts 

that ARC investments have had on the Region.

Evaluating the impact of the ARC has always been uniquely challenging. As ARC’s first director 

Ralph Widner noted, the agency’s objectives were not clearly articulated in the statute 

intentionally. The Region is so complex and the Congressional alliance required at that time to 

gain support for the agency was so vague and contradictory in their intentions that ARC had to 

navigate a very fine line between Federal investment and state/local control (Widner 1973). 

Consequently, the ARC has developed as an amalgamation of programs—managed largely at the 

state and regional level—to achieve a broad array of goals and objectives based on a variety of 

models of growth and change. Not surprisingly, measuring impact in this environment is 

necessarily a complex task.

This Technical Report companion to Appalachia Then and Now: Examining Changes to the 

Appalachian Region since 1965: Executive Summary, provides a more detailed description of the 

methodology and analysis that helped inform the findings found in the Executive Summary. 

The project team contributing to the report represents a collaboration between the Center for 

Regional Economic Competitiveness (CREC), a nonprofit research organization based in 

Arlington, Virginia and two West Virginia University research units: the Regional Research 

Institute (RRI) and the Bureau of Business and Economic Research (BBER). The team brings 

together nationally recognized expertise in regional quantitative and economic impact analysis, 

economic development policy design and implementation, economic development program 

evaluation, and local and regional economic development strategic planning. The team also has 

extensive experience working in Appalachia as well as in rural and distressed communities 

elsewhere. 

The project team also benefitted from the input, insights, and review of hundreds of others over 

the course of completing this report. This included fellow academics and regional development 

practitioners, ARC staff, and residents and stakeholders throughout the Appalachian Region. A 

complete list of those who contributed to this report is found in Appalachia Then and Now: 

Examining Changes to the Appalachian Region since 1965: State Meetings Report. 
6

Before launching into a more detailed discussion of the present program evaluation approach 

used for this report and what we found, it is necessary to review the many past program 

evaluations that have already been conducted and to provide a discussion of the findings from 

these assessments of ARC’s impact in the Region over the past 50 years.

Assessing ARC’s Impacts—A Review of Past Program Evaluations

Signed into law on March 9, 1965, the Appalachian Regional Development Act (ARDA), ARC’s 

governing legislation, seeks to advance the Region’s economic development and achieve parity 

with the relative prosperity enjoyed by the rest of the nation (Appalachian Regional Commission, 

2014; Bradshaw, 1992). In diagnosing the symptoms of pre-ARC Appalachia, the President’s 

Appalachian Regional Commission (1964) also sketched out the major priorities that would form 

the thrust of ARC-funded activities—improving the Region’s physical accessibility, developing 

the Region’s economy while reducing dependence on natural resources extraction, and 

enhancing the capability of the Region’s human resources to achieve economic prosperity.

What impacts have been made by this experiment in regional development? As the 50th

anniversary of the Appalachian Regional Commission approaches, this document addresses this 

question by presenting an assessment of ARC-commissioned and independent, scholarly 

evaluations of ARC’s programs and individual projects. Addressing the priorities laid out by the 

President’s Appalachian Regional Commission, findings of these evaluations are reviewed for 

three areas—physical accessibility, economic development, and human resource development. 

Additionally, this document reviews select findings regarding ARC’s overall impact as an entity

for leveraging funds and catalyzing regional development. This discussion serves as a synthesis

of ARC’s impacts and the challenges inherent in evaluations of regional public policy. To assess 

the validity of these reported impacts, a review of the methods used to conduct these evaluations 

precedes this topically-organized assessment of impacts. 

A summary of the evaluations described in this section can be found in Figure 1.

Approaches to Evaluation

In an assessment of impact studies conducted for federal economic development agencies, the 

U.S. General Accounting Office [GAO] (1996) identified three characteristics of studies that 

would make a persuasive case for program impact:

First, it would have to document that there had been some improvement in the targeted 

area. Second, it would have to link specific elements in the program to the economic 

changes. Finally, it would have to measure the growth stemming from other influences on 

the region's economy in order to isolate the impact that could be attributed to the 

economic development program (p 4).

Evaluations that satisfy even more than one of these elements are rare, and the accomplishment 

of all three in a public policy context is exceedingly difficult (U.S. General Accounting Office, 
7

1996). Most evaluations document project success and suggest progress in the achievement of 

program goals, but studies that identify specific pathways leading from program elements to 

regional improvements, while also controlling for other potential influences, are much rarer. 

Recognized as one evaluation that goes further than most in controlling for factors other than the 

ARC program, Isserman and Rephann (1995) concurred with the GAO study that existing ARC 

program evaluations—with their reliance on “interviews, on-site observation, and comparative 

statistics”—have fallen short of actually measuring economic impacts (p. 351). Essentially, 

documentation of program investments, development activity, and changes in economic and 

demographic conditions have been forthcoming, but few evaluations link program investments 

with impacts in a rigorous cause-and-effect manner.

For the purposes of this assessment, evaluation methods were grouped into five categories—

surveys, before-and-after comparison, case studies, economic modeling, and quasi-experimental 

designs.1The remainder of this section defines each grouping, identifies frequency of use for 

evaluating ARC, and discusses the typical characteristics and advantages and disadvantages of 

these techniques as applied in ARC evaluations.2

Surveys 

Employed most frequently, this general category relies upon standardized data collection 

methods, such as surveys, questionnaires, interviews of program participants, and reviews of 

project databases, to report the outputs and outcomes of program activities. In most instances, 

these methods allow for an audit of program investments that ensures project conformance with 

planned activities and results. They tend to be carried out within the timeframe of a few years 

after project completion. Most commonly, this method of evaluation involves a sample of ARC 

funding recipients reporting outputs such as jobs created or households or clients served. 

Respondents may be asked more general evaluation questions such as their satisfaction with 

ARC assistance or the degree to which ARC funds allowed projects to move forward at all. 

While this method documents whether projects were implemented as planned, it does not often 

provide evidence that can establish a clear cause-and-effect relationship between projects and 

desired program outcomes. Additionally, at least four factors can affect the reliability and 

usefulness of data collected. First, self-reported data from those implementing projects may bias 

results in favor of more successful project outcomes. Second, respondents may lack the resources 

necessary to document all but the most direct, short-run impacts of projects, and even accurately 

reporting the common metric, “jobs created,” can prove difficult or misleading. For example, 

 

1 As part of their evaluation of ARC’s infrastructure and public works projects, HDR Decision Economics, Cambridge 

Systematics, Economic Development Research Group, and Mt. Auburn Associates (2013) identified and assessed the merits of 

twelve methods used in program evaluations (pp. 16-30). 

2

Figure 1 lists the evaluations reviewed for this study, identifies the use of the five methods, and provides brief, illustrative 

summaries of the evaluation findings relative to the noted four focus areas.
8

training programs aimed at enhancing entrepreneurship may not create many direct jobs, 

particularly in the short run. Third, addressing the likelihood for a project to move forward in the 

absence of ARC funds may be difficult, and responses may only roughly proxy for ARC’s ability 

to spur otherwise dormant activity. Fourth, survey data only reflect overall program results to the 

degree that respondents represent a random sample of all projects. Deviations from a random 

sample, such as the tendency to target only closed—at least nominally successful—projects 

presents a distorted picture of results.

Before-and-after comparison

Used for six of the reviewed evaluations, these methods identify a policy treatment of interest 

(e.g., the entire ARC program or select elements of it) and compare quantitative or qualitative 

conditions before-and-after this treatment to identify potential impacts. For example, the 

Appalachian Regional Commission (2010) and Wood and Bischak (2000) tracked changes in 

measures such as income levels and poverty rates since the initiation of the ARC program. 

Consistent with a logical approach to establishing causation, this method requires a treatment to 

precede the outcomes that it may cause. Further, statistical tests may be used to assess the 

significance of any changes uncovered in a before-and-after comparison. However, these 

methods do not systematically control for other potential causes of outcomes.

Case studies

The second most frequently used method provides evidence of program implementation and 

impact that cannot be captured through quantitative metrics such as jobs created. Typically, case 

studies offer a narrative description of projects and programs based on details gathered from 

project records, interviews with participants, and on-site observations. This context may provide 

insight on particular characteristics that make for successful or unsuccessful projects. In concert 

with before-and-after comparisons, case studies may be useful for specifying causal pathways 

linking programs and outcomes. By design, case studies focus on only one or a few projects at a 

time. If case studies focus on only successful projects or only projects in certain settings, then 

they may offer limited insights for understanding impacts in many contexts.

Economic modeling

This group includes tests for statistical relationships between independent (i.e., causes) and 

dependent variables (i.e., effects); statistical assessments of observed differences in metrics; or 

simulations of project impacts based on economic assumptions. Regression-based models that 

test relationships among variables may be used to evaluate ARC’s programmatic focus. For 

example, evidence that increased educational attainment is associated with reduced economic 

distress may result in the prioritization of education programming. Statistical tests of the 

differences between two data samples (e.g., a comparison of county-based per-capita incomes in 

1965 and 2000) can identify the significance of changes in before-and-after circumstances. 

Input-output models were the simulation methods applied most frequently. A typical application 

inputs job creation data from project files into a commercially-produced input-output model 
9

(e.g., IMPLAN) that estimates the trade relationships between industries. Based on these 

estimated relationships, the number of jobs directly created in one industry sector can be used to 

estimate the indirect employment generated in other trade-linked sectors. Further, the wages 

earned from new jobs results in new household spending that induces additional demand for 

employment to satisfy consumer wants. Transportation simulations were also used to estimate 

the impacts highway improvements make on travel-times and economic accessibility in the 

Region. As with all models, these simulations simplify reality and, short of painstaking data 

collection efforts for each study, are likely to miss important differences, such as those between 

firms within the same industry or those across regions within Appalachia.

Quasi-experimental methods

Isserman and Rephann (1995) implemented the only full scale (entire Region) ARC-focused 

quasi-experimental evaluation conducted to date. Quasi-experimental methods match the sample 

of interest (i.e., ARC counties) with control group twins based on similarity in factors that might 

produce changes of interest (e.g., income growth). Control group counties have not been 

subjected to the policy treatment of interest, in this case participation in ARC programs. After 

matching, statistical tests allow for the assessment of differences resulting from the policy 

treatment.

Evaluating ARC’s Impacts by Priority Area

Major findings and methods of analysis from existing evaluations are summarized in the figure

below. The following narrative summarizes results, limitations, and caveats by ARC priority 

area.

Improving Physical Accessibility

ARC infrastructure investments include the Appalachian Development Highway System

(ADHS), local access road improvements, extended water and wastewater service, and enhanced 

telecommunications. In many cases these projects serve sites or facilities that have been 

identified for future development. Consequently, estimating the number of direct jobs impacted

under these circumstances can be relatively less complicated than for projects that may not be 

targeted to support a specific project. ADHS improvements aimed to more broadly improve the 

Region’s accessibility by increasing and speeding traffic flows within Appalachia and from 

Appalachia to regional centers. The ARDA authorized construction of 2,350 miles of the ADHS; 

revisions to the ARDA over the years increased the authorized mileage to 3,090. Job creation 

was also attributed to these improvements, though there is some evidence that these 

improvements led to improved accessibility conditions for counties at the periphery of ARC, but 

not necessarily to increased traffic flows to the more isolated, distressed counties of Central 

Appalachia (Hale & Walters, 1974; Moore, 1994; Widner, 1990). Many of the evaluated 

telecommunications projects resulted in few direct jobs or short-term outcomes, as they tended to 

focus on providing new training or educational opportunities to the Region’s residents (Westat, 

2003).
10

More recent studies of the ADHS have measured the economic development and safety benefits 

to the Region from sustained highway-system investments. Those studies have found significant 

positive results in the 13-state Region. A June 2008 economic impact study on the ADHS 

conducted by Cambridge Systematics, Inc., with Economic Development Research Group, Inc., 

assessed the travel performance, trade, and economic development impacts that would result 

from completing the ADHS.3The study also assessed connectivity, accessibility, and how well 

the ADHS corridor improvements would connect Appalachian people and businesses to other 

highway facilities, multimodal transportation, and economic markets after completion of the 

ADHS. 

The study found that total economic impacts of completion of the ADHS for the Appalachian 

Region would include the direct effects of reduced travel time and costs, along with increased 

regional competitiveness via market accessibility gains and multiplier effects. These effects 

would gradually increase over time and by 2035 would result in an estimated 80,500 jobs, $5.0 

billion in increased value added production, and $3.2 billion in increased wages for workers in 

the Region. At the national level, the estimated return on investment would yield $3.00 for every 

dollar invested.

Developing Appalachian Economies

ARC’s projects in this category seek to diversify regional economies and build capacity for new 

development opportunities by investing in activities such as strategic planning, entrepreneurship 

programming, and tourism infrastructure. Easily measured, short-term outcomes are not 

available for many of these projects. For example, general, longer run, and hard-to-quantify 

outcomes such as greater awareness of and commitment to addressing regional issues are cited 

frequently as outcomes of community capacity building projects (Westat, 2004). Existing studies 

find that entrepreneurship programs helped to foster new startup activity and encourage business 

to adopt new business methods (Regional Technology Strategies Inc., 2001; Rural Policy 

Research Institute: Center for Rural Entrepreneurship, 2008). Tourism-related investments are 

more site-specific in nature, and the creation of direct jobs is reported, but evaluations completed 

soon after project close may not capture important long-term impacts (Regional Technology 

Strategies Inc., Mt. Auburn Associates, & Appalachian State University, 2010).

Developing Human Resource Capabilities

Educational attainment rates in Appalachia continue to lag behind national averages 

(Appalachian Regional Commission, 2010). That said, ARC investments in education and 

workforce training programs have enabled a considerable number of Appalachians to receive 

training and career counseling and earn credentials (Westat, 2001, 2002, 2012). As with the 

 

3 Cambridge Systematics, Inc., Economic Development Research Group, and HDR Decision Economics, “Economic Impact 

Study of Completing the Appalachian Development Highway System” Appalachian Regional Commission, June, 2008.
11

capacity building projects noted previously, direct impacts can be difficult to evaluate in the 

short-term. Further complicating the evaluation of these programs from a regional perspective is 

that the benefits of these programs accrue mainly to mobile individuals that may choose to take 

their improved capabilities elsewhere within the Region or entirely outside Appalachia.

Catalyzing Regional Development

Existing before-and-after evaluations of ARC investments find mixed results for the Region as a 

whole, and significant variation within the Region, in improving education, income, or poverty 

conditions (Appalachian Regional Commission, 2010; Widner, 1990). In a more systematic 

evaluation of impacts, Isserman and Rephann (1995) found that growth of income, population, 

and per capita income in Appalachian counties significantly outpaced growth in control-group 

counties between 1969 and 1991. 

Another way to assess performance might also be in analyzing the ability for ARC to leverage 

other funding. By design, ARC grant investments leverage funds by requiring that significant 

funds be contributed by other sources (U.S. General Accounting Office, 1996). Consistent with 

this requirement, program evaluations typically report significant amounts of leveraged private 

investment acquired to implement ARC projects (Brandow Company & Economic Development 

Research Group, 2000; Regional Technology Strategies Inc., 2001; Regional Technology 

Strategies Inc. et al., 2010). Perhaps the more important question is whether ARC designation 

affords Appalachian counties greater capacity to conduct development activity than they would 

otherwise have. That question remains largely unanswered, though Hall (2008) provides 

evidence that Kentucky’s ARC counties were able to access more and larger total amounts of 

federal grants than their non-ARC counterparts, potentially pointing to capacity advantages 

granted by ARC programming.

Program evaluation surveys conducted tend to reveal that ARC-funded grant investments would 

not have been implemented without the benefits of ARC funds (Brandow Company & Economic 

Development Research Group, 2007; Westat, 2001, 2003). Many of these evaluations report on 

projects that resulted in significant job creation. There is also some evidence of job creation 

resulting in more diversified economies, and that more economically diverse counties were more 

likely to be lifted out of economic distress (Brandow Company & Economic Development 

Research Group, 2007; Wood & Bischak, 2000). Moving away from a strict focus on job 

creation, Partridge, Lobao, Jeanty, Beaulieu, and Goetz (2008) indicate that forward-looking 

measures such as evidence of entrepreneurship and educational attainment are better predictors 

of future economic well-being than are current incomes and poverty rates. To the degree that 

ARC programs create opportunities for regions to advance on those fronts above and beyond 

what would have been otherwise possible, ARC may play a significant role in moving regions 

out of distress. 
12

Figure 1: Major Findings and Methods of Analysis from Existing Evaluations

Methods Evaluation results by priority area

Evaluation

Survey Before-and-after Case studies Economic modelingQuasi-experimental

Improving physical 

accessibility

Developing Appalachian 

economies

Developing human 

resource capabilities

Catalyzing regional 

development

HDR Decis io n Eco no mics , Cambridge Sys tematics 

Eco no mic Develo pment Res earch Gro up, & Mt. 

Auburn As s o ciates . (2013). P ro gram Evaluatio n o f the 

Appalachian Regio nal Co mmis s io n’s Infras tructure & 

P ublic Wo rks P ro jects .

S

C

S

34 co mpleted infras tructure 

pro jects res ulted in creatio n o r 

maintenance o f >8,000 jo bs ; 

Repo rted impacts exceeded 

recipient pro jectio ns

ARC funded 20% o f to tal 

pro ject co s t, with 35% o f 

recipients indicating pro ject 

wo uld no t have advanced 

witho ut ARC funding

Wes tat. (2012). Evaluatio n o f the Appalachian 

Regio nal Co mmis s io n’s Educatio n and Wo rkfo rce 

Develo pment P ro jects : 2000–2008.

S

C

S

Educatio n pro jects s erved 

mo re s tudents than pro jected; 

Impro ved s cho o l readines s 

and credential attainment rates 

amo ng cited impacts

Regio nal Techno lo gy Strategies Inc., Mt. Auburn 

As s o ciates , & Appalachian State Univers ity. (2010). 

P ro gram Evaluatio n o f ARC’s To uris m, Cultural 

Heritage and Natural As s et-Related P ro jects .

S

C

S

Recipients repo rted creating 

fewer direct jo bs than 

pro jected, tho ugh time 

ho rizo n, pro ject type, and data 

availability may limit accuracy

$ 0.40 ARC inves tment 

leveraged $ 1.00 private 

inves tment, o n average

Appalachian Regio nal Co mmis s io n. (2010). Eco no mic 

As s es s ment o f Appalachia: An Appalachian Regio nal 

Develo pment Initiative Repo rt.

B

&

A

Regio n fares wo rs e than 

natio nal average o n variety o f 

develo pment meas ures s uch 

as emplo yment/earning levels , 

po verty rates , and educatio n

Rural P o licy Res earch Ins titute: Center fo r Rural 

Entrepreneurs hip. (2008). Creating an Entrepreneurial 

Appalachian Regio n: Findings and Les s o ns fro m an 

Evaluatio n o f the Appalachian Regio nal 

Co mmis s io n’s Entrepreneurs hip Initiative 1997 – 2005.

S

B

&

A

C

S

Increas ed entrepreneurial 

activity, with inco mes lagging; 

J o b creatio n metrics may no t 

meas ure pro gres s o n lo ngterm s trategy

P ro gram rais ed pro file o f 

entrepreneurs hip in regio n, 

s etting s tage fo r inno vative 

appro aches ; Leveraged $ 1.20 

fo r every ARC do llar inves ted

P artridge, M., Lo bao , L., J eanty, P . W., Beaulieu, L. J ., & 

Go etz, S. (2008). An As s es s ment o f Alternative 

Meas ures fo r Determining Eco no mically Dis tres s ed 

Co unties and Areas in the Appalachian Regio n.

E

M

Co ncludes that po verty 

do minates current dis tres s 

indicato r and reco mmends 

meas ures better-aligned with 

future develo pment pro s pects

Hall, J . L. (2008). The Fo rgo tten Regio nal 

Organizatio ns : Creating Capacity fo r Eco no mic 

Develo pment. P ublic Adminis tratio n Review, 68(1), 110-

125.

S

E

M

ARC co unties in Kentucky 

receive mo re federal grants 

and higher average grant 

amo unts than do their no nARC co unterparts

Cambridge Sys tematics , Eco no mic Develo pment 

Res earch Gro up, & HDR Decis io n Eco no mics . (2008). 

Eco no mic Impact Study o f Co mpleting the 

Appalachian Develo pment Highway Sys tem.

E

M

Co mpleting ADHS pro jected 

to s ignificantly reduce travel 

times ; increas e develo pment 

o ppo rtunities due to 

acces s ibility impro vements

Significant indirect and induced 

gro wth expected fro m new, 

acces s ibility-enabled 

eco no mic develo pment
13 Evaluation Survey Before-and-after Case studies Economic modeling Quasi-experimental

Improving physical 

accessibility

Developing Appalachian 

economies

Developing human 

resource capabilities

Catalyzing regional 

development

Brando w Co mpany, & Eco no mic Develo pment 

Res earch Gro up. (2007). P ro gram Evaluatio n o f the 

Appalachian Regio nal Co mmis s io n’s Infras tructure 

and P ublic Wo rks P ro jects .

S

C

S

E

M

Sample o f 104 pro jects 

(including 33 indus trial parks 

and 51 water and s ewer 

pro jects ) created >17k direct 

new jo bs and retained >9k jo bs

Evidence that new jo bs 

created res ulted in mo re 

divers e eco no mies

Eco no mic develo pment 

pro jects leveraged $ 75 in 

private funds fo r every $ 1 o f 

ARC funds ; 73% s aid ARC 

funds were critical fo r pro ject

Wes tat. (2004). Evaluatio n o f the Appalachian 

Regio nal Co mmis s io n’s Co mmunity Capacity 

Building P ro jects .

S

C

S

Amo ng 100 pro jects s urveyed, 

35% co nducted s trategic 

planning; 26% o btained 

technical as s is tance; few 

meas urable o utco mes

51% o f pro jects s urveyed 

co nducted a wo rks ho p o r 

co urs e; 28% held fo rums

Co mmo nly cited o utco mes 

include greater awarenes s o f 

regio nal is s ues and s tro nger 

buy-in fo r adddres s ing 

co mmunity is s ues

Wes tat. (2003). Evaluatio n o f the Appalachian 

Regio nal Co mmis s io n’s Teleco mmunicatio ns 

P ro jects : 1994–2000.

S

C

S

84% o f 70 pro jects s urveyed 

pro vided o ns ite educatio n; 

92% indicated pro ject s ucces s 

met o r exceeded expectatio ns ; 

Limited meas urable o utco mes

Survey indicated 63% o f 

pro jects wo uld no t have been 

implemented witho ut ARC 

funds ; >75% o f pro jects 

o perating after grant perio d

Wes tat. (2002). Evaluatio n o f the Appalachian 

Regio nal Co mmis s io n’s Vo catio nal Educatio n and 

Wo rkfo rce Training P ro jects .

S

C

S

Median o utputs fro m 67 

pro jects incuded 195 pers o ns 

receiving academic/vo catio nal 

training; 51 co uns eled o n 

career; 50 earning credentials

Mo s t pro jects co ntinued to 

o perate after grant perio d

Wes tat. (2001). Evaluatio n o f The Appalachian 

Regio nal Co mmis s io n’s Educatio nal P ro jects : Final 

Repo rt.

S

C

S

Amo ng 84 pro jects s urveyed, 

almo s t 75% indicated meeting 

o r exceeding educatio n o r 

training go als ; do cumentatio n 

s uppo rted many claims

67% o f res po ndents indicated 

pro jects wo uld no t have 

advanced witho ut ARC funds

Regio nal Techno lo gy Strategies Inc. (2001). Evaluatio n 

o f the Early Stages o f the Appalachian Regio nal 

Co mmis s io n’s Entrepreneurs hip Initiative.

S

C

S

Amo ng 23 s ampled pro jects , 

75% indicated develo pment o f 

new pro ducts ; 55% s aid firms 

upgraded techno lo gies ; 50% 

identified bus ines s s tartups

ARC funds acco unted fo r half 

o f to tal s ampled pro ject 

budgets ; 70% o f pro gram 

clients were highly s atis fied 

with s ervices received

Wo o d, L. E., & Bis chak, G. A. (2000). P ro gres s and 

Challenges in Reducing Eco no mic Dis tres s in 

Appalachia An Analys is o f Natio nal and Regio nal 

Trends Since 1960.

B

&

A

Co unties mo ving o ut o f 

dis tres s had mo re divers ified 

eco no mies , larger s hares o f 

manufacturing emplo yment, 

and mo re res ident retirees

Higher rates o f educatio nal 

attainment as s o ciated with 

co unties that mo ved o ut o f 

dis tres s

Number o f dis tres s ed 

co unties decreas ed by mo re 

than half s ince 1960, tho ugh 

appro ximately 25% o f tho s e 

dis tres s ed in 1960 remained s o

Brando w Co mpany, & Eco no mic Develo pment 

Res earch Gro up. (2000). Evaluatio n o f the 

Appalachian Regio nal Co mmis s io n’s Infras tructure 

and P ublic Wo rks P ro gram P ro jects .

S

C

S

E

M

Sample o f 99 pro jects directly 

res ulted in >23,000 new jo bs 

and an es timated to tal o f 

>44,000 indirect jo bs ; Created 

>$ 575 millio n in new wages

ARC funds us ed to leverage 

s ignificantly larger s ums o f 

o ther public and private funds 

Methods Evaluation results by priority area
14

Methods Evaluation results by priority area

Evaluation

Survey Before-and-after Case studies Economic modelingQuasi-experimental

Improving physical 

accessibility

Developing Appalachian 

economies

Developing human 

resource capabilities

Catalyzing regional 

development

U.S. General Acco unting Office. (1996). Limited 

Info rmatio n Exis ts o n the Impact o f As s is tance 

P ro vided by Three Agencies . Was hingto n, D.C.

S

3-to -1 ratio in to tal funds vs . 

ARC funds devo ted to 

pro jects ; Few s tudies as s es s 

caus e-effect relatio ns hips 

between ARC and o utco mes

Is s erman, A., & Rephann, T. (1995). The eco no mic 

effects o f the Appalachian Regio nal Co mmis s io n: An 

empirical as s es s ment o f 26 years o f regio nal 

develo pment planning. J o urnal o f the American 

P lanning As s o ciatio n, 61(3), 345-364.

E

M

Q

E

Co ntro l-gro up analys is , 

indicates ARC co unties grew 

fas ter than their co ntro l-gro up 

twins in terms o f po pulatio n 

and per capita inco me

Mo o re, T. G. (1994). Co re-periphery mo dels , regio nal 

planning theo ry, and Appalachian develo pment. 

P ro fes s io nal Geo grapher, 46(3), 316.

B

&

A

E

M

Co unty highway impro vements 

co rrelated with increas ed 

inco mes ; weakes t/leas t 

s ignificant relatio ns hips in 

Central Appalachia

Widner, R. R. (1990). Appalachian develo pment after 25 

years : An as s es s ment. Eco no mic develo pment 

quarterly, 4(4), 291-312.

B

&

A

C

S

Evidence o f impro ved acces s 

to centers at periphery o f 

regio n, with little evidence o f 

diverted traffic increas ing 

demand to lo cate in regio n

Significant educatio n gaps 

remain, with pro mis ing res ults 

achieved fro m inves tments in 

vo catio nal educatio n

Gaps between ARC and natio n 

narro wed o n s everal fro nts ; 

Federal-lo cal partners hip can 

effect inno vatio n, with 

s ignificant regio nal variatio n

Hale, C. W., & Walters , J . (1974). Appalachian regio nal 

develo pment and the dis tributio n o f highway benefits . 

Gro wth and Change, 5(1), 3-11.

B

&

A

E

M

Areas lo cated within and jus t 

o uts ide ARC's periphery 

benefitted mo s t fro m highway 

impro vements , bas ed o n 

changes in driving times

Widner, R. R. (1973). Evaluating the Adminis tratio n o f 

the Appalachian Regio nal Develo pment P ro gram. 

Gro wth and Change, 4(1), 25-29.

C

S

Federal co o rdinatio n has been 

challenging; State and dis trict 

ro les s ho w pro mis e o f 

effecting inno vative s o lutio ns 

thro ugh experimentatio n
15

Assessing ARC’s Impacts—Present Program Evaluation Approach

As with most public policy evaluations, ARC’s impact assessments fall short of the experimental 

ideal that would unequivocally assign credit to specific program investments. The default 

approach is to document project implementation and capture short-term outcomes. While 

valuable, this short-term focus should be supplemented by multi-dimensional evaluation 

approaches that use diverse sources to tie investments to immediate outputs and the prospects for 

long-term outcomes. This is particularly critical in the case of longer-term initiatives, with the 

recent assessment of entrepreneurial programs exemplifying how a comprehensive evaluation 

approach can measure progress on a concept not easily summed up by simple job creation 

metrics (Rural Policy Research Institute: Center for Rural Entrepreneurship, 2008). 

Given the inherent limitations in reaching an experimental ideal in most all public policy 

evaluations, and also given the long time span of 50 years to assess the progress being made in 

the Appalachian Region, the project team chose to utilize a multidimensional, mixed-methods 

approach to this present program evaluation. In this way, we could build on an array of 

evaluation techniques to better understand the impact and legacy of the ARC. In particular, this 

report: (1) documents the Appalachian Region’s 50 years of socioeconomic and structural 

changes; 2) analyzes the economic impacts of ARC investments through regional input-output 

analysis and 3) uses a quasi-experimental method designed to compare ARC-assisted counties 

with a control group. An additional technique employed assesses stakeholder perceptions about 

past performance and future priorities, and is discussed in Appalachia Then and Now: Examining 

Changes to the Appalachian Region since 1965: State Meetings Report.

The following chapters detail these methods and analysis; the findings are discussed in 

Appalachia Then and Now: Examining Changes to the Appalachian Region since 1965:

Executive Summary. 

Chapter I details major socioeconomic trends that have occurred in the Appalachian Region over 

roughly the past half-century using available data sets which span all or at least most of the 

period. 

Chapter II lays out the conceptual and methodological basis for the customized input-output (IO) 

modeling approach used to assess the economic impacts of non-highway investments made in 

the Region by the Appalachian Regional Commission. 

Chapter III discusses our use of an empirical technique designed to answer the counterfactual, 

namely, what would have happened in Appalachia without the ARC? This Quasi-Experimental 

Methods (QEM) approach uses matching techniques to assign to each “treated” entity one (or 

more) “control”. Statistical methodology is then employed to see if the difference in the 

outcome variable of interest is statistically significant. In the case of Appalachia, we concentrate 

on two important metrics: the growth in employment and the growth in per-capita income. 
16

Chapter I: Fifty Years of Socioeconomic and Structural Change in the 

Appalachian Region

Section Summary

This chapter details major socioeconomic trends that have occurred in the Appalachian Region 

over roughly the past half-century. While Appalachia has enjoyed significant economic progress 

over this period along many dimensions, the Region still lags the nation significantly in several 

aspects. Highlights of this research are as follows:

 Private-sector employment in Appalachia has grown by nearly 50 percent since 1975, but 

this growth rate falls well short of the national employment growth rate over the same 

period. 

 The Appalachian unemployment rate has been roughly on par with the national figure for 

around 14 years, after having surpassed the national rate for much of the period of 

analysis.

 Labor force participation in Appalachia has lagged the national figure for the entire 

period of analysis. 

 Per capita personal income in Appalachia was 81.1 percent of the national average in 

2012, an improvement from 78.7 percent in 1969. 

 Earnings per capita in Appalachia have fallen relative to the national figure over the 

period of analysis. This drop in earnings has been more than offset by a more-thanproportional increase in federal transfers per capita to Appalachia relative to the national 

average.

 Poverty rates in Appalachia have consistently surpassed national figures over the period 

of analysis, although the degree to which Appalachia lags the nation has lessened 

considerably.

 Overall population in Appalachia has grown by over 30 percent over the period of 

analysis, but this falls short of national population growth.

 The degree to which the population has aged in Appalachia has exceeded the national 

level.

 The share of the Region’s population under age 19 declined substantially, especially 

since the mid-1980s.

 Although educational attainment has increased substantially in Appalachia over the 

period of analysis, the Region has consistently lagged the nation. 

 The overall mortality rate in Appalachia has only slightly improved over the period of 

analysis and has consistently lagged the nation. 

 The share of homes in Appalachia that lack telephones or complete plumbing facilities 

has fallen dramatically over the period of analysis and the current figures are roughly on 

par with the nation. 
17

In this chapter we examine the myriad ways in which the Appalachian Region has evolved 

socioeconomically since the creation of the Appalachian Regional Commission (ARC) in 1965. 

Our analysis is organized based on the four general goals outlined in ARC’s current strategic 

plan, which are as follows:

 Goal 1: Increase job opportunities and per capita income in Appalachia to reach parity with the nation. 

 Goal 2: Strengthen the capacity of the people in Appalachia to compete in the global economy.

 Goal 3: Develop and improve Appalachia’s infrastructure to make the Region economically competitive. 

 Goal 4: Build the Appalachian Development Highway System to reduce Appalachia’s isolation. 

Employment, Unemployment, and Labor Force Participation in Appalachia

We begin with an analysis of employment outcomes in Appalachia.4Figure 2 illustrates total 

private sector employment growth in the Appalachian Region and in the U.S. overall since 

1975.5 Both series are indexed to their 1975 level. As the figure shows, employment growth in 

 

4 Throughout this chapter of the report we define the Appalachian Region to consist of the 420 counties that are defined as such 

by the Appalachian Regional Commission currently. See www.arc.gov for a complete list of these counties.

5

Ideally we would examine trends to 1965, the year of the founding of the Appalachian Regional Commission. However, in 

many cases county-level data do not exist for the early years of this time frame. In any such instance, we begin our analysis of 

each data series with the earliest year available. 

Figure 2: Total Private Employment (1975 – 2012)
18

Appalachia has lagged national employment growth overall during the period. By 2012, 

employment in Appalachia stood at nearly 50 percent above its 1975 level, whereas the figure 

was nearly 83 percent nationally. Employment growth was significantly faster for the nation 

compared to Appalachia during the years 1978 – 2000; however, overall employment has been 

mostly flat for both the Region and the nation since around the year 2000. 

Since the Appalachian Region tends to be less urban than the nation as a whole, in Figure 3 we 

illustrate the growth in employment for only the non-metropolitan counties in Appalachia versus 

those in the nation as a whole.6The general pattern does not vary significantly from what was 

presented in the previous figure. By this measure, employment in the non-metropolitan 

Appalachian Region has grown by 45 percent over the period of analysis, whereas growth has 

been just over 90 percent for the non-metropolitan areas of the U.S. 

 

6

For this division we use the definitions of rural-urban continuum codes constructed by the United States Department of 

Agriculture (USDA). The USDA provides 9 classifications to describe US counties, which are as follows: 1-Counties in metro 

areas of 1 million population or more; 2-Counties in metro areas of 250,000 to 1 million population; 3 – Counties in metro areas 

of fewer than 250,000 population; 4-Urban population of 20,000 or more, adjacent to a metro area; 5-Urban population of 20,000 

or more, not-adjacent to a metro area; 6-Urban population of 2,500-19,999, adjacent to a metro area; 7-Urban population of 

2,500-19,999, not adjacent to a metro area; 8-Completely rural or less than 2,500 urban population, adjacent to a metro area; 9-

Completely rural or less than 2,500 urban population, not adjacent to a metro area. We consider all counties that are classified as 

3 through 9 on this scale as non-metro. 

Figure 3: Total Private Employment – Non-metro Comparison (1975 – 2012)

100

120

140

160

180

200

220 Index, 1975 = 100

Figure 2. Total Private Employment – Non-metro Comparison

Source: US Bureau of Labor Statistics

Notes: Non-metro area consists of areas within the categories of 3 to 9 of the 1974 USDA urban and non-urban codes.

U.S. non-metro

Appalachian non-metro
19

Figure 4 illustrates the rate of employment growth among counties within Appalachia from 1975 

through 2012. While a great deal of variation exists among the 400-plus counties in the Region, 

many of the faster growing counties tend to be concentrated in the southern part of the Region, 

such as in Alabama, Georgia, Kentucky, North Carolina, and Tennessee. The relatively small 

number of counties that have lost jobs over the period are scattered throughout every state in the 

Region except New York. 

Figure 4: Average Annual Growth Total Private Employment (1975 – 2012)
20

Unemployment in Appalachia and the nation are illustrated in Figure 5. Appalachian 

unemployment tracked slightly higher than the rest of the nation in the late 1970s, 1980s, and 

1990s, but never quite matched the U.S. rate until the recession of the early 2000s. However, 

since the 2008-09 recession, the Region’s unemployment rate has tracked the U.S. rate closely, 

with a few persistent pockets of joblessness. 

Figure 5: Unemployment Rate (1976 – 2012)
21

Related to the unemployment rate is the labor force participation rate, which illustrates the share 

of the adult population (age 16 and above) that is available to work, whether they are employed 

or not. As illustrated in Figure 6, the labor force participation rate nationally and in Appalachia 

grew significantly between 1970 and 1990 - primarily as more women entered the workforce -

but the figure has remained mostly steady since 1990. 

However, labor force participation rates in Appalachia have remained well below the national 

average throughout the past 30 years. The most recently available data found national labor force

participation at 64.2 percent of working age adults (age 16 and above), compared with 59.5 

percent of Appalachian adults. Part of the difference can be explained by greater participation in 

the informal barter economy in the Region, and a larger proportion of the population receiving 

government transfer payments, as well as the limited job opportunities in the Region’s rural 

communities.

Figure 6: Labor Force Participation Rate (1970 – 2012)
22

Now we examine employment for a few key industrial sectors for counties in the Appalachian 

Region. Figure 7 illustrates the evolution of natural resources and mining employment growth 

for the nation and for Appalachia since 1975, again indexed to 1975 values. As illustrated, 

employment in this sector underwent a steady decline from the early-1980s through the late 

1990s, falling by approximately one-half since its peak. 

However, the figure has grown by a noticeable margin in Appalachia since 2003. Nationally, 

natural resource and mining employment expanded rapidly from around 1976 through 1981 due 

primarily to rapid growth in agriculture in the Midwestern U.S., remained steady from the early1980s through the early 2000s, and has then grown since around 2003. Overall employment in 

this sector nationally stands at more than double its 1975 level. 

Figure 7: Natural Resources & Mining Employment (1975 – 2012)
23

Figure 8 illustrates the total share of private employment in natural resource and mining 

(representing agriculture, mining, and related extractive activities). As illustrated, the figure fell 

rapidly for Appalachia from the early-1980s through the early 2000s, to rebound slightly over the 

past decade or so. The figure has roughly remained steady for the nation at approximately two 

percent of private employment since the mid-1980s. 

Overall, while natural resources and mining employment as a share of total employment was 

nearly three times higher for Appalachia compared to the nation in 1975, the figure has been 

roughly the same for the Region and for the nation since the early 1990s.

Figure 8: Natural Resources & Mining Share of Total Private Employment (1975 – 2012)
24

Turning to manufacturing, total employment in the sector has fallen substantially in both the 

nation and in Appalachia overall since 1975, with the decline being noticeably larger in 

Appalachia. As reported in Figure 9, manufacturing employment in the U.S. stands at 

approximately 70 percent of its 1975 level, while the figure for Appalachia is approximately 59 

percent. Since the 2008-2009 recession, manufacturing employment has rebounded slightly in 

Appalachia as well as the U.S. as a whole.

Figure 9: Manufacturing Employment (1975 – 2012)
25

Figure 10 reports the manufacturing share of total private employment for the Region and the 

nation. As illustrated, manufacturing’s employment share has fallen to approximately 16 percent 

of total employment in Appalachia from nearly 40 percent in 1975. However, despite this 

decline, manufacturing accounts for a significantly larger share of the workforce in the Region 

compared to the nation, where a similar decline occurred. 

It should be noted that, while manufacturing employment has fallen, manufacturing output has 

increased for both the nation and the Region over the period of analysis, reflecting technological 

improvements and increasing capital intensity in manufacturing.

Figure 10: Manufacturing Share of Total Private Employment (1975 – 2012)
26

Service-providing employment has been the driving force behind the overall employment growth 

regionally and nationally as suggested by the strong employment growth illustrated in Figure 

11.

7 As illustrated, service-sector employment for the Region and the nation has grown virtually 

in lockstep over the period of analysis, resulting in total service sector employment growth of 

approximately 136 percent. 

 

7

Service-providing industries include the following: Trade, Transportation, and Utilities; Information; Financial Activities; 

Profession-Business Services; Education-Health Services; Other Services.

Figure 11: Private Service-Providing Employment (1975 – 2012)
27

In a similar fashion, service sector employment as a share of total employment has grown 

substantially over the period of analysis, as shown in Figure 12. As illustrated, by the end of the 

period of analysis, service sector employment stood at approximately 78 and 82 percent of total 

employment for the Region and the nation, respectively. 

Figure 12: Private Service-Providing Share of Total Private Employment (1975 – 2012)

45%

50%

55%

60%

65%

70%

75%

80%

85%

Figure 11: Private Service-Providing Share of Total Private Employment

Percent of Total Employment

Source: U.S. Bureau of Labor Statistics

Appalachian Region

U.S.
28

As illustrated in Figure 13, growth in service-providing employment exhibits a great deal of 

variation among Appalachian counties. However, this figure does correlate to a large extent with 

overall employment growth with many of the high growth counties concentrated in the southern 

part of the Region. Only two counties in the Region have lost service-providing jobs overall 

throughout the period of analysis. 

Figure 13: Average Annual Growth Service-Providing Employment (1975-2012)
29

Income in Appalachia

Turning to income, as reported in Figure 14, per capita personal income has grown substantially 

in Appalachia over the past half-century, rising by around 136 percent over the entire period in 

inflation-adjusted terms. Growth has mostly been steady, with a few relatively brief periods of 

decline over the long-run. Per capita income in Appalachia has remained below the national 

average for the entire period of analysis. However, the gap is decreasing in relative terms: per 

capita personal income in Appalachia was 81.1 percent of the national average in 2012, an 

improvement from 78.7 percent in 1969. Despite this relative improvement, the absolute size of 

the per capita personal income gap has widened, expanding to $8,344 in 2012 from $4,175 in 

1969, again in inflation-adjusted terms. 

Figure 14: Per Capita Personal Income (1969 – 2012)
30

It is also valuable to compare income growth in the non-metro portion of the Region with growth 

in the non-metro U.S. As reported in Figure 15, the pattern of how per capita personal income in 

non-metro Appalachia tracks income in the non-metro U.S. looks the same as that in the whole 

region illustrated in the previous figure. Looking at more detail shows that per capita income in 

both non-metro Appalachian and the non-metro U.S. is lower than that in the whole region. 

In addition, in relative terms, income in non-metro Appalachian is closer to that of the non-metro 

U.S. and the gap remains the same. In this context, per capita personal income in non-metro 

Appalachia stands at 88.2 percent of the national average in 1969 and at 88.4 percent by 2012. 

Figure 15: Per Capita Personal Income – Non-metro Comparison (1969 – 2012)
31

To investigate income growth more fully, we turn to earnings per capita in Figure 16. Earnings 

per capita differs from per capita personal income in that the former excludes transfer payments 

from government, such as social security payments or unemployment insurance compensation 

and it takes taxes paid to the federal government into consideration. Here the relative gap 

between earnings per capita in the Region has widened substantially since 1969 rather than 

narrowed. In particular, earnings per capita in Appalachia fell from 74.6 percent of the national 

average in 1969 to 69.3 percent by 2012. 

Figure 16: Earnings Per Capita (1969 – 2012)
32

We also report earnings per capita for the non-metro parts of Appalachia and the U.S. in Figure 

17. Consistent with the pattern seen with per capita personal income, earnings per capita are 

much closer between Appalachia and the U.S. when considering only non-metro counties. 

However, the pattern remains in this context; Appalachia has fallen in comparison to the nation 

in terms of earnings per capita over the period of analysis. As reported in the figure, earnings per 

capita in non-metro Appalachia fell from 90.2 percent of the national average in 1969 to 84.8 

percent by 2012. Also note the general lack of significant growth in the figure in both 

Appalachia and the U.S. over the period of analysis. 

Figure 17: Earnings Per Capita – Non-metro Comparison (1969 – 2012)
33

The divergence between per capita personal income versus earnings per capita can be explained 

by federal transfers. As reported in Figure 18, federal transfers as a share of personal income 

have increased substantially in Appalachia over the period and by 2012 accounted for around 24 

percent of personal income, significantly higher than the national figure of 17 percent. In 1969, 

federal transfers as a share of personal income was around 10 percent in the Region, compared to 

around 8 percent nationally.

Figure 18: Government Transfer Payments as a Share of Personal Income (1969 – 2012)
34

Figure 19 illustrates cross sectional variation in per capita personal income within Appalachia for 

1969 (left panel) and for 2012 (right panel). The figure shows that per capita income in many 

counties across Appalachia in 1969 was less than half the national average. The map 

demonstrates significant progress by 2012, with only five counties still having such a low 

relative income. By 2012, development in many counties was approaching parity with the rest of 

the nation, although a number of counties were still struggling, with per capita income levels 

below 75 percent of the U.S. average.

Figure 19: Per Capita Income Relative to the U.S. (Percent of the U.S. Average)
35

Figure 20 illustrates average annual growth in per capita income by county over the period 1969-

2012. While a significant amount of variation exists, to some extent a “catch-up” effect is 

illustrated in that counties that were initially lower income grew at a faster rate over the period 

compared to counties that were initially higher income. For instance, numerous counties that 

were initially lower income in Kentucky, Mississippi, and West Virginia exhibited the highest 

relative growth rates. These counties compare to several counties in Ohio, Pennsylvania, and 

New York that were initially higher income but exhibited slower growth rates over the period.

Figure 20: Average Annual Per Capita Income Growth (1969-2012)
36

In Figure 21 we provide some insight into wealth in Appalachia versus the nation. Since we do 

not have direct data on wealth, we use per capita personal income derived from assets (e.g., 

dividend, interest, and rent income) as a proxy measure. As illustrated, since around 1980 

Appalachia has grown faster than the nation in terms of asset income, suggesting that the wealth 

gap between the Region and the nation has lessened somewhat. This income results from rent, 

dividends, and interest. It is unclear why this gap has lessened. 

The proportion of the Region’s assets found in the 10 wealthiest counties (in terms of assets) 

might be used as a proxy for the Region’s money centers. In 1969 and 2006, these counties 

accounted for 30 percent of all regional assets, but for most other years, their share of 

Appalachia’s total assets has been between 26 and 30 percent. Thus, one might conclude that the 

overall increased value in regional assets is tied as much to the increased value of rural lands 

(with their natural gas and other mineral rights) as it does to any other possible explanation.

Figure 21: Per Capita Personal Income from Assets (1969 – 2012)
37

In Figure 22 we examine a measure of entrepreneurship in Appalachia and in the nation. While it 

is difficult to measure entrepreneurship, typical metrics are often related to self-employment 

activity and/or small business activity. Here we consider sole-proprietors’ income as a share of 

total earnings, the idea being that a larger metric might be indicative of a more entrepreneurial 

economy. As illustrated, sole-proprietors’ income in Appalachia has been very similar to that in 

the nation as a whole throughout most of the period of analysis, although there is a slight gap 

between Appalachia and the rest of the nation during the past few years.

Figure 22: Proprietors’ Income as a Share of Total Earnings (1969 – 2012)
38

Poverty in Appalachia

Turning to poverty, Figure 23 illustrates poverty status over the period of analysis. The 

Appalachian Region has consistently reported a higher poverty rate compared to the U.S. overall. 

However, the degree to which poverty in Appalachia exceeds the national figure fell 

considerably during the 1970s and has remained fairly constant since. Appalachia’s poverty rate 

has been cut in half over the past five and a half decades, from nearly 31 percent in 1960 to about 

16.6 percent today.

Figure 23: Share of Population Below Poverty Line (1960 – 2012)

30.9%

18.0%

14.0%

15.3%

13.6%

16.6%

22.1%

13.7%

12.4%

13.1%

12.4%

14.9%

10%

15%

20%

25%

30%

35%

Figure 21: Share of Population Below Poverty Line

Appalachian Region U.S.

Sources: U.S. Census Bureau, 1960, 1970, 1980, 1990, and 2000 Censuses, and 2008-2012 American Community Survey

Percent 
39

Figure 24 reports poverty rates for the non-metro counties of the Appalachian Region and the 

U.S. While poverty rates are higher for non-metro regions, a similar pattern emerges. 

Figure 24: Share of Population Below Poverty Line – Non-metro comparison (1960 – 2012)

36.7%

21.3%

15.9%

17.4%

15.3%

18.3%

31.9%

18.9%

14.6%

15.7%

13.7%

16.5%

10%

15%

20%

25%

30%

35%

40%

Appalachian Non-metro U.S. Non-metro

Percent 

Figure 22: Share of Population Below Poverty Line - Non-metro Comparison

Sources: 1960, 1970, 1980, 1990, and 2000 Censuses, and 2008-2012 American Community Survey, US Census Bureau

Notes: Non-metro area consists of areas within the categories of 3 to 9 of the 1974 USDA urban and non-urban codes.
40

Figure 25 depicts poverty rates within the Appalachian Region for 1960 (left panel) as well as 

the average over 2008-2012 (right panel). As the figure below shows, there has been a dramatic 

reduction in the number of Appalachian counties where the share of the population living in 

poverty exceeds 150 percent of the national average. The number of high-poverty counties in the 

Region (those with poverty rates above 150 percent of the U.S. average) declined from 295 in 

1960 to 107 for the period 2008–2012.

Figure 25: Poverty Rates Relative to the U.S. (Percent of the U.S. Average)
41

In Figure 26 we turn to poverty among the elderly population in Appalachia versus the U.S. As 

was the case with overall poverty, as discussed above, there is a large reduction in elderly 

poverty rates from 1970 to 1980, as well as a reduction in the degree to which Appalachian 

poverty rates exceed those of the nation. By the most recent year illustrated, elderly poverty in 

Appalachia was only 0.8 percentage points higher than the national figure. 

Figurer 27 shows the pattern for poverty rates for the elderly population among Appalachian 

counties for 1970 (left panel) and over the years 2008-2012 (right panel) is similar to the one for

overall poverty rates in the Region.

Figure 26: Share of Population 65 or Older Below Poverty Line (1970 – 2012)

36.4%

19.4%

16.7%

11.9%

13.8%

27.3%

14.8%

12.8%

9.9%

13.0%

5%

10%

15%

20%

25%

30%

35%

40%

Figure 24: Share of Population 65 or Older Below Poverty Level

Appalachian Region U.S.

Sources: U.S. Census Bureau, 1970, 1980, 1990, and 2000 Censuses, and 2008-2012 American Community Survey

Percent 

Figure 27: Poverty Rates of Population 65 or Older relative to the U.S. (Percent of the U.S. Average)
42

Population in Appalachia

Figure 28 reports population growth in Appalachia and the nation since 1969, indexed to initial 

values. As illustrated, U.S. population grew more rapidly and more consistently. Altogether 

Appalachian population stands at nearly 132 percent of its 1969 level whereas the U.S. stands at 

155 percent of its 1969 level. 

During the past generation, the U.S. population has grown at a pace more than twice that of the 

Appalachian Region. This is due in part to the fact that the vast majority of Appalachian counties 

have a lower share of young people (those below age 20) and a much higher (and more rapidly 

growing) share of seniors (those aged 65 and older) than the nation. 

Today, the area remains much more rural than the rest of the nation. Forty-two percent of the 

people in Appalachia still live in rural areas, compared with 20 percent nationally; and 

Appalachians who live in cities are much more likely to live in smaller urban centers than are 

residents of other parts of the country. 

Recessionary periods also exacerbate outmigration. During the economic stagnation of the 

1980s, population growth in Appalachia languished. Even when the Region’s population began 

to grow again in the early 1990s, the growth rate significantly trailed the U.S. rate. 

Figure 28: Total Resident Population (1969 – 2012)
43

As depicted in Figure 29, population in the Appalachian Region has fallen from 9.5 percent of 

the U.S. population in 1969 to 8.1 percent by 2012. 

Figure 29: Appalachian Region’s Population Relative to the U.S. (1969 – 2012)
44

Figure 30 illustrates variation in population growth rates over the period 1969 through 2012 

among Appalachian counties. As illustrated, the fastest growing counties tend to be in the 

southern part of the Region, whereas the counties that have lost population tend to be located in 

the northern and central parts of the Region. 

Figure 30: Average Annual Population Growth, 1969-2012
45

Next we turn to population mobility. Figure 31 depicts the share of the population that has 

moved from another county within a given state in the last five years. In terms of movement 

between counties (and within a given state), Appalachia has lagged the nation over the entire 

period of analysis, but that gap has declined considerably over the period. 

Figure 31: Share of Population Who Moved in from Different County in Last Five Years (1970 – 2000)

12.0%

15.0%

16.1%

16.6%

17.1%

19.5%

19.0%

18.1%

10%

12%

14%

16%

18%

20%

1970 1980 1990 2000

Figure 46: Share of Population Who Moved in from Different County in the 

Last Five Years

Appalachian Region

U.S.

Sources: U.S. Census Bureau, 1970, 1980, 1990, and 2000 Censuses

Percent of Population 5-Years and Older
46

The same general trend is apparent in terms of movement from another state. Figure 32 depicts 

the share of the population that has moved from another state in the past five years. In this case

the degree to which Appalachia lags the nation became very small by the end of the period of 

analysis.

Figure 32: Share of Population Who Moved in from Different State in Last Five Years (1970 – 2000)
47

Aging in Appalachia

Next we consider the aging of Appalachia’s population that has occurred over the past several 

decades. As illustrated in Figure 33, the share of Appalachia’s population that is under 20 years 

old has fallen substantially since 1970 – falling from just under 38 percent in 1970 to around 25 

percent by 2012. This decline occurred in tandem with the U.S. from 1970 through the early 

1980s, but since then has been more pronounced in Appalachia. 

Figure 33: Share of Population 0 to 19 Years (1970 – 2012)

24%

26%

28%

30%

32%

34%

36%

38%

40%

Figure 29: Share of Population 0 to 19 Years Old

Source: U.S. Census Bureau, Intercensal Estimates

U.S.

Appalachian Region

(Share of Total Population)
48

In a parallel fashion, Figure 34 depicts the rise of the over age 65 share of the population in 

Appalachia, which rose from just over 10 percent of the population in 1970 to around 16 percent 

in 2012. This aging pattern also occurred at the national level, although to a lesser extent.

Figure 34: Share of Population 65 Years or Older (1970 – 2012)

8%

9%

10%

11%

12%

13%

14%

15%

16%

17%

Figure 30: Share of Population 65 Years or Older

Source: U.S. Census Bureau, Intercensal Estimates

U.S.

Appalachian Region

(Share of Total Population)
49

Figure 35 depicts variation in the under-20 population share across Appalachian counties for 

1970 (left panel) and 2012 (right panel). While a strong pattern is hard to discern in 1970, the 

counties stretching from Northeast Tennessee, through Virginia, West Virginia, and into 

Pennsylvania tend to exhibit low population shares in the under age 20 category. 

By 2012, a significant reduction in the population share under age 20 is apparent, especially in 

the counties of Tennessee, North Carolina, Virginia, West Virginia, and Pennsylvania. The 

counties with higher under age 20 population shares tend to be located in the southernmost 

portion of the Region.

Figure 35: Share of Population 0 to 19 Years Old Relative to the U.S. (Percent of the U.S. Average)
50

Figure 36 depicts variation in the share of the 65-and-over population among Appalachian 

counties for 1970 (left panel) and for 2012 (right panel). Little consistency is apparent in the 

cross-county variation in the over age 65 population share, although the overall aging of the 

population can be observed.

Figure 36: Share of Population 65 Years or Older Relative to the U.S. (Percent of the U.S. Average)
51

Educational Attainment in Appalachia

Now we turn to educational attainment in Appalachia. In 1960, the Appalachian Region 

struggled with a poorly educated populace. Ninety-two percent of the U.S. population over the 

age of 25 had completed the 5th grade, compared with 89 percent of the Appalachian population.

Thirty-two percent of the Appalachian population had completed high school (10th grade or 

higher), compared with 42 percent of the U.S. population. Only 5 percent of Appalachian 

residents had earned a college degree; compared with nearly 8 percent of all Americans.

Today, the Appalachian Region has achieved near parity with the nation in high school 

graduation rates, an important accomplishment since the 1960s; but the Region remains behind 

in post-secondary educational attainment. 

Figure 37 shows that the share of the Region’s population over age 25 with at least a bachelor’s 

degree has tripled since 1970 (to 21.3 percent). However, the share of the nation’s population 

over 25 with at least a bachelor’s degree has nearly tripled (to 28.5 percent), and the gap between 

the share of Appalachian adults and the share of the nation’s adults with a college degree has 

widened steadily over the past two generations.

Figure 37: Share of Population 25 Years and Older with a Bachelor’s Degree or Higher (1970 – 2012)
52

In Figure 38 we depict the variation in educational attainment across Appalachian counties for 

1970 (left panel) and for the average over the years 2008-2012 (right panel). The figures 

illustrate a great deal of randomness in terms of where the higher- and lower-educational 

attainment counties are located. Perhaps the only weak pattern across the counties that is 

discernable is that more of the higher educational attainment counties tend to be located in the 

northern part of the Region in Pennsylvania and New York. 

In today’s increasingly knowledge-driven and global economy, having educational attainment 

beyond a high school degree is considered an important prerequisite for individual success in the 

job market. It is also viewed as an important measure for the economic growth potential of an 

area. Therefore, the share of population with a Bachelor’s Degree or higher may be seen as a 

proxy measure for having a highly skilled workforce. However, it is also the case that there are 

many good job opportunities for individuals, and equally important to an area’s economic 

development prospects, for those with or who are seeking 2 year/vocational/and certificate 

education. The share of the Region’s population over age 25 with some post-secondary education 

or higher has more than tripled since 1970 (14 percent to 48.0 percent). This rate of growth is 

faster than that of the nation (21 percent to 57 percent), but the overall gap has widened.

Figure 38: Share of Population with a Bachelor’s Degree or Higher (Percent of the U.S. Average)
53

Health in Appalachia

Next we turn to health outcomes in Appalachia. Combined with other factors, individual health is 

a prime indicator of quality of life and impacts economic vitality. In the early 1960s, living 

standards in Appalachia, as measured by the health and well-being of the population, were well 

below those of the rest of the nation, and they remain so today.

We begin with the overall mortality rate shown in Figure 39. As illustrated, mortality in the 

Appalachian Region fell from the late-1960s through the early-1980s, but then rose through 

2004. Since 2004, the overall number of deaths per 100,000 residents has fallen slightly. 

Throughout the entire period, mortality in Appalachia has been higher than that at the national 

level. 

Figure 39: Mortality Rates (Deaths per 100,000 People), (1968 – 2010)

750

800

850

900

950

1,000

1,050

1,100

Figure 35: Mortality Rates (Deaths per 100,000 People)

Source: U.S. Centers for Disease Control and Prevention, National Center for Health Statistics

Note: Data are not adjusted for differences in mortality by age group.

Deaths per 100,000 residents

Appalachian Region

U.S.
54

In Figure 40 we depict mortality across counties within Appalachia for 1968 (left panel) and for 

2010 (right panel). In 1968, there tended to be concentrations of higher mortality rates in Central 

Appalachia, in states such as West Virginia, while lower mortality rates tended to be 

concentrated in the southern part of the Region. By 2010, the higher mortality rate counties 

tended to be located in the central part of the Region, particularly in various parts of Kentucky 

and West Virginia, along with a few other heavy pockets, such as in Alabama. 

Figure 40: Mortality Rates (Deaths per 100,000 people) (Percent of the U.S. Average)
55

In Figure 41 we illustrate the prevalence of diabetes in Appalachia and in the U.S. Data are only 

available since 2004. As illustrated, diabetes has increased for both Appalachia and the nation, 

and Appalachia has consistently exhibited higher diabetes rates compared to the nation as a 

whole over the period. 

Figure 41: Diabetes Prevalence (2004 – 2011)

4

6

8

10

12

14

Figure 37: Diabetes Prevalence

Percent

Appalachian Region

U.S.

Source: U.S. Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System (BRFSS)

Note: Diabetes Prevalence Rate represents the proportion of adult people 20 years and over suffering from diabetes.
56

In Figure 42 we illustrate how diabetes varies across counties in the Region for 2004 (left panel) 

and 2011 (right panel). Counties with higher rates of diabetes tend to be located in Kentucky and 

West Virginia in Central Appalachia, as well as in Mississippi and Alabama in Southern 

Appalachia.

Figure 42: Diabetes Prevalence Rates Relative to the U.S. (Percent of the U.S. Average)
57

In Figure 43 we illustrate the prevalence of obesity in Appalachia and in the U.S. for 2004 

through 2011. The pattern is very similar to that of diabetes, as discussed above: Obesity has 

increased for both Appalachia and the nation, and Appalachia has consistently exhibited higher 

obesity rates compared to the nation as a whole over the period. 

Figure 43: Obesity Prevalence (2004 – 2011)

20

22

24

26

28

30

32

Figure 39: Obesity Prevalence

Percent

Appalachian Region

U.S.

Source: U.S. Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System (BRFSS)

Note: Obesity is a condition in which body mass index or BMI (a ratio of body mass, in kg, to height, in squared m, 

is 30 or higher. Obesity prevalence rate represents the proportion of adults 20 years and over classified as obese.
58

In Figure 44 we illustrate how obesity varies across counties in the Region for 2004 (left panel) 

and 2011 (right panel). A similar pattern to that of diabetes emerges in this context as well.

Figure 44: Obesity Prevalence Rates Relative to the U.S. (Percent of the U.S. Average)
59

Next we consider health insurance coverage since it can be an important driver of overall health 

outcomes. Here, we select three states that are predominately part of Appalachia to use as 

representative of the Region as a whole. In particular, we use Alabama, Pennsylvania, and West 

Virginia.8

As illustrated in Figure 45, a larger portion of residents in the Appalachian Region (as 

represented by these three states) have health insurance compared to the nation as a whole. This 

trend has been the case since 1987. By 2012, nearly 87 percent of the population in Appalachia 

was covered by health insurance, compared to nearly 85 percent nationally. 

 

8

These states were determined as representative of the Appalachian Region because the population share and the share of their 

counties that fall within the Region was the largest among the 13 states that form the Region. The U.S. Census Bureau also 

produces estimates of health insurance coverage through its Small Area Health Insurance Estimates (SAHIE) program. The 

SAHIE program produces single-year estimates of health insurance coverage which are model-based and consistent with the 

American Community Survey (ACS).

Figure 45: Percent of People Covered by Health Insurance (1987 – 2012)
60

In Figure 46 we investigate health insurance coverage further by examining the population share 

that has access to government health insurance. As illustrated, here the Appalachian Region also 

has a slightly higher rate of public health insurance coverage, and that has been the case for most 

of the time since 1987. By 2012, however, around 33 percent of the population in both the 

Appalachian Region and the U.S. was covered by government health insurance. 

Figure 46: Percent of People Covered by Government Health Insurance (1987 – 2012)
61

Infrastructure in Appalachia

Now we turn to infrastructure in Appalachia. Another key aspect of the Region’s isolation in the 

1960s was its relative inability to communicate with the outside world. This limited not only the 

in-flow of new ideas and technologies and the ability of area residents to learn, but also the 

ability of area residents and leaders to imagine a different future.

In the 1960s, a key public policy goal was universal access to telephone service. Many areas of 

Appalachia lacked access to phone service, and, where it was available, multiple families shared

party lines. As Figure 47 shows as late as 1970, 16.2 percent of area homes did not have access 

to phone service, compared with 13 percent of households nationally. By 2012, the proportion of 

households without phone service in the Appalachian Region was about 2.8 percent, very nearly 

the same as the national average of 2.5 percent.

Figure 47: Percent of Homes without Phone Services (1970 – 2012)

16.2%

9.3%

7.1%

3.3%

2.8%

13.0%

7.1%

5.2%

2.4% 2.5%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Figure 43: Percent of Homes without Phone Services

Appalachian Region U.S.

Source: U.S. Census Bureau, 1970, 1980, 1990, and 2000 Censuses, and 2008-2012 American Community Survey
62

In a similar fashion we explore Internet access in Figure 48. As depicted, the three states that we 

have chosen to represent Appalachia exhibit an internet usage rate that has grown tremendously 

since 1997, but has lagged the U.S. by a small margin. 

Figure 48: Percent of Households Using the Internet (1997 – 2012)
63

In Figure 49 we depict the share of homes that lack complete plumbing facilities in Appalachia 

and in the nation. Not very long ago, many homes in the Appalachian Region lacked adequate 

plumbing and relied on water from local streams. Many houses were dilapidated, with 7.5 

percent in such poor shape that they were deemed a danger to the health and safety of the 

families living in them.

Today, 3.2 percent of Appalachian houses lack complete plumbing, compared with 2 percent 

nationally. That is a stark improvement from the 13.6 percent of houses in Appalachia that 

lacked complete plumbing in 1970. 

Figure 49: Percent of Homes Lacking Complete Plumbing Facilities (1970 – 2012)
64

In Figure 50 we examine the age of the housing stock in Appalachia versus in the nation. Here 

we depict the share of the housing stock that was built more than 30 years earlier. As illustrated, 

by this measure the housing stock in Appalachia was considerably older compared to the nation 

in 1970, but that gap had virtually disappeared by 2000. 

Figure 50: Share of Housing Units Built 30 Years Ago or Before (1970 – 2000)

49.0%

43.9%

48.8%

40.6%

41.4%

48.7%

20%

30%

40%

50%

60%

1970 1990 2000

Figure 48: Share of Housing Units Built 30 Years Ago or Before

Appalachian Region U.S.

Sources: U.S. Census Bureau, 1970, 1990, and 2000 Censuses

Percent of All Housing Units
65

In Figure 51 we consider household access to a public water system, a data point that is no 

longer collected as part of the U.S. Census but one that was considered very important in the 

early days of ARC. Here we examine the share of housing in Appalachia and in the nation that 

have access to water from a public system or from a private company (i.e., households that do 

not have to rely on a private well). By this metric, both the Region and the nation have improved 

over the period of analysis. Further, the degree to which Appalachia has lagged the nation has 

diminished over the period, although Appalachia continues to lag the nation in this metric.

Figure 51: Share of Housing Units Whose Source of Water comes from Public System or Private Company

(1970 – 1990)

68.2%

73.5%

81.7%

84.2%

50%

60%

70%

80%

90%

1970 1990

Figure 49: Share of Housing Units whose Source of Water Comes from 

Public System or Private Company

Appalachian Region U.S.

Sources: U.S. Census Bureau, 1970 and 1990 Censuses

Percent of All Housing Units
66

One of the most critical challenges facing the Appalachian Region in 1964 was its isolation due 

to a limited transportation network. The Interstate Highway System, in the midst of a tremendous 

building boom in the 1950s, largely by-passed Appalachia, going through or around the Region's 

rugged terrain as cost-effectively as possible. This resulted in limited access to the rest of the 

nation for large swaths of Appalachia, and constituted barriers to trade with the rest of the nation 

and to global markets.

For many years, ARC states built highway miles at a rate comparable to that of the rest of nation 

(see Figure 52). A major increase in the rate of miles built in both the Region and the nation as a 

whole in 1992, and again in 2005 as part of a steady increase in funds available with the passage 

of Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA:LU). Additional resources made available through the 2009 American Recovery and 

Reinvestment Act also helped to increase the number of highway miles built, both regionally and 

nationally. However, recent data suggest that declines in federal funding have impacted the 

Appalachian Region more than the rest of country, partly because the relative per-mile cost of 

building major highways through mountainous terrain and the sparse population in these areas 

make it difficult for Appalachian states to compete for limited federal funds.

Figure 52: Federal-Aid Highway Miles Indexed to 1980 Mileage Levels (1980 – 2012)

100.0

105.0

110.0

115.0

120.0

125.0

130.0

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Index, 1980 = 100

U.S., Balance of States

States in the Appalachian Region

Source: Federal Highway Administration, Highway Statistics
67

Appalachian Regional Commission Investment and Economic Outcomes 

In this section we provide a brief examination of investment by ARC in the Region since 1965 

and loosely relate it to employment, income, and poverty outcomes. Here any findings are only 

suggestive of a relationship between ARC investment and the various outcomes of interest; we 

do not provide an in-depth statistical analysis nor do we provide evidence of a causal relationship 

between ARC appropriations and the outcomes we examine. 

In Figure 53 we depict total ARC non-highway appropriations, by year, since the inception of the 

ARC in 1965. We exclude appropriations made for the Appalachian Development Highway 

System in our analysis. As illustrated, after adjusting for inflation, ARC spending stood at 

around $300 million during the early years of the ARC. This figure rose rapidly to nearly $600 

million by 1971. After four years of spending in excess of $500 million, spending began to fall 

after 1975, reaching the $300 million-range by the late-1970s, and then more significantly to less 

than $100 million by the early-1980s. Appropriations have remained fairly constant since the 

early-1980s.

Also in Figure Figure53, we overlay the five-year rate of employment growth in the Appalachian 

Region. Unfortunately the necessary employment data do not begin until 1975, preventing a full 

analysis. While it is difficult to discern a strong relationship between the two variables, it is 

interesting to note that the highest recorded rate of job growth (over the 1975-1979 period; 

Figure 53: ARC Appropriations and Employment Growth (1965 – 2012)

-5

0

5

0

100

200

300

400

500

600

700

Notes: Appropriations for 1965 and 1966 are the average of the 1965-1966 appropriation.

Sources: ARC and US Bureau of Economic Analysis

Appalachian 5-Year Avg. Employment Growth

ARC Non-Highway Appropriation

ARC Non-Highway Appropriations - Inflation Adjusted ($’000) 5-Year Average Employment Growth

Figure 51: ARC Appropriations and Employment Growth
68

marked 1979 in the figure) began just after ARC investment stood at its peak. It is perhaps 

interesting that the sharp decline that occurred in employment growth from the late-1970s 

through the early-1980s does seem to correspond to the decline in ARC investment. However, 

the national economy was in recession over much of this period of the early-1980s as well, 

blurring the comparison.

In Figure 54 we compare ARC appropriations to wage and salary earnings. In particular, we 

report earnings per capita in Appalachia relative to the national figure. Here it is interesting to 

note that earnings per capita in Appalachia did rise relative to the national figure during the 

1970s, when ARC investment was relatively high. Then the figure fell significantly from the 

late-1970s through the mid-1980s, occurring simultaneously with the drop in ARC investment. 

Earnings in Appalachia did bounce back to some degree relative to the national figure from the 

mid-1980s through the mid-1990s, which does not seem relate to a change in ARC 

appropriations. 

Figure 54: ARC Appropriations and Per Capita Earnings (1965 – 2012)
69

In Figure 55 we depict ARC appropriations as it compares to poverty. In this context we depict 

the poverty rate in the Appalachian Region relative to that of the nation. As illustrated, poverty 

rates in Appalachia did improve relative to national rates to a significant degree between 1960 

and 1970, and then further from 1970 to 1980, improving from around 140 percent of the 

national average in 1960 to 113 percent of the national average in 1980. This drop largely 

occurred during the years when ARC investment was at its highest level.

Figure 55: ARC Appropriations and Poverty (1960 – 2012)
70

Chapter II: Assessing Economic Impacts of Non-Highway Investments

Section Summary

This chapter quantifies the employment and income impacts attributable to the ARC nonhighway grant investments made in the Region over the years. ARC’s investments have helped 

stimulate economic activity that has fueled the hiring of workers and the purchase of material 

goods and services needed to put new developments in place. Income from all of these activities 

fueled additional demand, further multiplying the total economic impacts of these investments.

ARC investments have been made over a 50-year time span, presenting a unique challenge in 

measuring these multiplier impacts. The economic structures of the Appalachian Region and the 

nation have changed significantly, so impact models must be recalibrated over time to reflect 

those structural changes. For this study, a customized input-output (IO) model for the 

Appalachian Region was constructed, with adjustments to the modeling made after every five 

year period to help account for measured economic changes. Detailed investment data were 

provided by the Appalachian Regional Commission and were assigned to one of 39 final demand 

categories, and then aggregated to one of eleven industry sectors.

9

The results from the economic modeling show that the $3.8 billion in ARC non-highway 

investments (Area Development Program) were responsible for creating nearly 312,000 jobs and 

$10 billion in added earnings in the Region. On average, annually, these ARC funds supported

an estimated 6,364 jobs and $204 million (in constant 2013 dollars) in earnings. 

Appalachian Regional Commission Investments

More than $25 billion in public investments10 (through FY 2013) have been made in the 

Appalachian Region by ARC and other federal, state and local agencies in both highway- and 

non-highway-related activities. Since 1965, ARC has made investments in nearly 25,000 

strategic non-highway activities in the Region, which were funded by $3.8 billion in 

appropriated ARC funds and $9 billion in matching funds from other federal, state and local 

funding sources. Since 1978, these matching funds have averaged $2.50 for each $1 in funds 

invested by the ARC (see Figure 56). This ratio has been relatively steady; in the most recent 

five year period (2007-2012), it was 2.74 to 1.

 

9 The investment data covered ARC Area Development Program investments made from 1965 to 2013.

10 This figure includes $3.8 billion in ARC Area Development Program funds, $9 billion in other federal, state or local public 

match dollars connected to the ADP investments, $9.1 billion obligated to the Appalachian Development Highway System, 

including funds from TEA-21, SAFETEA-LU and their extension acts, and $3.5 in state and local funds assumed to be a 20 

percent ADHS match. This total does not include 100 percent state and local funded highway projects.
71

ARC investments in Appalachia have also attracted nearly $16 billion in leveraged private 

investment (LPI), the dollar amount of private-sector financial commitments (non-project funds), 

that result from an ARC investment. Since 1978, when ARC began tracking this data, for each 

$1 in funds invested by ARC in non-highway projects, an average of $6.40 in private-sector 

funding has been leveraged. This figure was nearly 10 to 1 in the most recent five-year period 

(2007-2012) and nearly 15 to 1 in 2013. 

Through FY 2013, more than $9 billion has been obligated to the states for the Appalachian 

Development Highway System (ADHS). An additional $3.5 billion has been provided in state 

and local match funds. As of September 30, 2014, a total of 2,762.9 miles, or 89.4 percent of the 

3,090 miles authorized for the ADHS, were completed or under construction (see Figure 57).

Another 96 miles were in the final design or right-of-way acquisition phase, and 231.2 miles 

were in the location studies phase.

Figure 56: Ratio of Non-ARC Matching Project Funds and LPI for Non-Highway Investments

$0.02

$3.93

$13.40

$8.30

$5.92

$5.59

$9.95

$14.92

$6.40

$2.41 $2.34 $2.52

$2.21

$2.60 $2.74 $2.74

$2.10

$2.50

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

$14.00

$16.00

1978-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2012 2013 Cumulative Ratio of Other Federal/State/Local/Private Dollars to ARC Dollars

Ratio of Other Federal/State/Local and Leveraged Private Investment (LPI) Dollars per ARC 

Dollar, by Year

LPI Ratio

Non-ARC Public Funds Ratio
72

Considering the totality of 

the ARC investments over 

the past 50 years directed 

toward improving the 

Region’s physical 

accessibility, economic 

development, human 

resource capabilities, and 

catalyzing regional 

development, a review of 

the investment data shows 

that a great majority of the 

funds went toward the 

development of basic 

infrastructure. This is not 

surprising, since having 

basic road, housing, and 

water and sewer services 

are essential precursors to 

being able to more fully 

develop an area’s 

economy. 

Figure 58 shows that 83 percent of ARC funds went to this particular category of investment. 11 

The next largest category for cumulative ARC investments since 1965 were directed to 

developing human resource capabilities in Appalachia at 10 percent.12 This category includes 

funding for child development, education, workforce training, and health investments. 

Developing Appalachian economies received 4 percent of the funding, while catalyzing regional 

development activities had 3 percent.13 

 

11 The category of improving physical accessibility includes the ARC investment classifications: Appalachian Development 

Highway System, community development, and housing. 

12 The category of developing human resource capabilities includes the ARC investment classifications: child development, 

education, education & workforce development, education & job training, and health.

13 The category of developing Appalachian economies includes the ARC investment classifications: asset-based development, 

business development, and leadership & civic capacity. The category of catalyzing regional development includes the ARC 

investment classifications: civic entrepreneurship, local development district planning & administration, research & evaluation, 

research & technical assistance, and state & LDD administration.

Figure 57: Appalachian Development Highway System

Source : Appa lach ian Reg iona l Commiss ion
73

In this chapter of the report, however, we are only considering the economic impact related to 

ARC’s non-highway investments stemming from its Area Development Program. Given this 

focus, and thereby removing highway funds from the calculation, the ARC investment totals 

since 1965 for non-highway funds have been much more balanced. In fact, as Figure 59 shows 

the largest share of ARC non-highway funds have gone to developing human resource 

capabilities. 

Figure 59: ARC Funds Expended for Area Development Program 

by Investment Classification (Cumulative Total)

Improving Physical 

Accessibility

35%

Developing 

Human Resource 

Capabilities

39%

Developing 

Appalachian 

Economies

14%

Catalyzing Regional 

Development

12%

ARC Funds Expended by Investment Classification (Cumulative Total)

Improving Physical 

Accessibility

35%

ARC Funds Expended by Investment Classification (Cumulative Total)

Figure 58: ARC Funds Expended by Investment Classification (Cumulative Total)
74

IO Concept and Method

There are many economic models from which to choose when estimating the regional 

employment and income impacts of public sector investments. Such choices are conditioned and 

often constrained by data availability, time, effort, and budget, and of course each method is 

subject to assumptions about the ways in which the impacts of economic shocks of various sorts 

– including public investments – work their ways through regional economies. There are 

sometimes also very unique challenges that accompany specific problem contexts. 

The unique challenge in the present context is presented by the fact that the ARC investments 

have been made over a 50-year time span. As a consequence, impacts models, which rely on 

representations of regional economic structure, would ideally be recalibrated over time to reflect 

economic structural changes. This applies whether the model is a simple economic base type 

model, where the multiplier relies on the ratio of basic economic activity to total; an econometric 

model where multipliers are calibrated to time series that lead up to the dates of investment, an 

input-output (IO) model whose multipliers are derived from relationships among regional 

industries, or even complex computable general equilibrium (CGE) models whose parameters 

are calibrated to input-output structures.

The economic time series data that would be necessary for calibrating econometric models for 

multiple time periods – particularly at the regional level extending back to the beginnings of 

ARC and its investment activities – are not available. Calibrating regional CGE models can be a 

data-intensive process even for a single region in a recent period, so even were the necessary 

data available, such analyses would be well beyond the scope and available resources of this 

project. There are data that could support an economic base analysis, but because those same 

data also can support the more sophisticated IO modeling framework, an IO approach was 

chosen.

This section lays out the conceptual and methodological foundations of the impacts assessments.

Concept

The impacts of investments in a region are a function of its economic structure. Industries buy 

and sell from one another, and workers spend portions of their incomes on goods and services, 

some of which are produced and provided locally. Impacts of investments in more self-sufficient 

regions, in terms of producing and providing industries with their production requirements and 

consumers with their goods and services, tend to be larger than impacts of those same 

investments in regions that depend on other regions to satisfy their needs. Good impact 

assessment models account for these levels of self-sufficiency and dependence on other regions. 

Regions that are more dependent on other regions are described as being more open than their 

self-sufficient counterparts. More open regions will generally experience smaller beneficial 

impacts of goods production and service provision activity that accompanies regional 

investments than more closed regions. Good impact models capture this kind of variation, and 
75

IO models are particularly well suited to modeling intra-regional and inter-regional sales and 

purchases relationships – indeed often referred to as the input-output structure of a region. 

Good models also can reflect temporal changes in economic structure.

It would be impractical to generate regional impacts models for multiple regions for every year 

of the ARC’s 50-year existence, so it is fortunate that IO structures change gradually rather than 

rapidly. Nevertheless, IO structures do change as regions grow and develop, so the approach 

taken here is to analyze ARC’s regional investments for multi-year periods. The benchmark year 

for each multi-year period was selected subject to constraints of national counterpart IO data, 

which are used as the foundation for 

calibrating the region-specific IO models. 

Specifically, the benchmark years used 

here for regional IO models are for years 

ending in 2 and 7 (1972, 1977, 1982, etc.) 

from 1972 through 2007. As described in 

Figure 60, the 1972 U.S. IO structure, the 

first that used the commodity-by-industry 

accounting framework – was used as a 

benchmark for the 1965 and 1972 

regional models. Industry-specific 

investments during the 1965-1971 period 

were deflated to benchmark year dollars 

and summed and used to generate their 

regional impacts. For subsequent 

periods, investments between benchmark 

years (i.e., between 1972 and 1976) were deflated to the benchmark year and summed to 

generate their impacts. Impacts for investments from 2007 through the present were used to 

generate their impacts. Impacts in benchmark year dollars were then inflated to 2013 dollars to 

generate the cumulative impacts of ARC investments.

Figure 60: Year of U.S. Input-Out Benchmark 

Table Applied to Each ARC Investment Period
76

Method

Mathematically, IO models are relatively straightforward. Most presentations of IO begin by 

acknowledging that the data foundation on which they are built is essentially a double-entry 

accounting system, as shown in the simplified diagram below. In Figure 61, the box labeled 

Interindustry Transactions characterizes interindustry sales and purchases structure, and contains 

a row and a column for every industry in the national (or regional) economy. Tracing along a 

row will identify all of the sales of the row industry to other industries, to households, and other 

final demand sectors, including investment, government purchasers, and the world outside the 

region. The sum of these values is the value of total output of the row industry, which will be the 

same as the sum of the entries in that industry’s corresponding column – hence the double-entry 

nature of the accounting framework.

In its annotation version, X corresponds to industry output (or input), and Y represents final 

demand. For those interested in the math underlying the model, we designate interindustry 

transactions by variable z, and posit a relationship a between industry inputs and industry output, 

or 

z

ij

= aijX j

. We can then write out a set of equations that describes the disposition of outputs 

of each industry, as shown below. The subscripts indicate industry of origin and or destination.

Figure 61: Characterization of Interindustry Transactions as Reported in the U.S. Benchmark 

Input-Output Table
We then 

following

Subject t

model all

Investme

X is an 

in the reg

and outpu

intermed

double-en

call on matr

g: 

o certain ma

lows us to so

ent). 

array of val

gion. If we a

ut, we can ea

diate step tow

ntry account

rix notation t

athematical c

olve for the c

lues that corr

also know th

asily compu

ward setting u

ting framew

to simplify th

X

X

I

conditions (t

change ( ) 

I  A



and I 

respond to im

he relationsh

ute the corres

up a solution

orks – build

77 

he mathema

 AX  Y

 AX  Y

 A X  Y

that essential

in output gi

1

Y  X

 A

1

Y 

mpacts of th

hip between e

sponding imp

n equation fo

ing the acco

atical express

lly prevent u

iven a chang

X

he final dema

employment

pacts on the

for each regio

ounts. 

sions, and w

us from divid

ge in final de

and changes 

t and output,

se variables

on is develop

which gives t

ding by zero

emand (e.g., 

on each ind

, and earning

. Therefore,

ping their 

the 

o) the 

dustry 

gs 

, the 
78

Building the regional accounts is accomplished by using available region-specific data to adapt 

the U.S. national accounts data to a close approximation of the region in a process known as IO 

regionalization. The specific regionalization procedure we follow is described in detail in a 

paper by Jackson (1998), and used regional earnings by industry as the primary driving variable 

in the regionalization process. The U.S. Benchmark IO accounts and the employment and 

earnings data are described more fully in the data section. In general, the regionalization process 

follows that in the schematic diagram in Figure 62.

To this point there has been little mention of the way in which the geographical differentiation 

within ARC enters into the analytical process. Impact assessments are generally more accurate 

when the entire study region has the potential to respond to the specific investment whose 

impacts are of interest. One would typically not assume that the economic structure of 

Pennsylvania, for example, would be particularly relevant to the determination of impacts of 

investments made in Alabama. Without going to the extreme of attempting to match every 

investment to a model of the county or multi-county region in which it was targeted, we have 

moved in this direction by generating, in addition to an overall Appalachian Region model

comprising all counties, additional state models comprising all of the counties in each of the 13 

ARC states in each benchmark year, for a total of 14 regional models for each of nine analysis 

periods. The investments were similarly partitioned by region by period to generate the impact 

estimates.

Figure 62: Algorithm for Analyzing ARC Impacts
79

When conducting analyses of this type, it is most common and intuitive to observe that the 

impacts on the larger Appalachian Region will be greater than or equal to the sum of the impacts 

on the sub-regions, and indeed this is the case in the vast majority of results. The reason that this 

is usually the case is that imports to one sub-region from another sub-region contribute the 

openness of the sub-region, but they are internal to and therefore contribute to the selfsufficiency of the larger region.

However, precisely because these model results are partly a function of regional openness, it is 

not uncommon to observe a very small number of instances where the sum of the sub-regional 

impacts is greater than the impact on the entire region. This counterintuitive result can happen 

when an industry-specific investment is targeted to a sub-region that is substantially more selfsufficient in a subset of activities than is the larger region. A sub-region, for example, could 

produce enough steel for its own use, but a larger region of which it is a part might actually need 

to import steel for production. This phenomenon explains why the sum of impacts from an 

investment on specific states within the ARC Region, for instance, might not add precisely to 

overall impacts within the entire Appalachian Region. 

Data

IO Data

Earnings data come from the Bureau of Economic Analysis (BEA) in their CA05 and CA05N 

data series. The CA05 files use SIC classifications and include a breakdown of income by county 

for multiple sectors from 1969 to 2000 (CA05, 

2013). The CA05N files use NAICS 

classifications and include a breakdown of 

income by county for multiple sectors from 

2001 on (CA05N, 2013). The data were 

aggregated to 11 industry sectors (see Figure 

63), which was the highest level of detail at 

which reliable employment and earnings 

estimates could be compiled for all counties in 

the ARC Region. The eleven industry sectors are 

shown below.

Employment data also come from the BEA in 

their CA25 and CA25N data series. The CA25 

files use SIC classifications and include a 

breakdown of employment by county for 

multiple sectors from 1969 to 2000 (CA25, 2013). The CA25N files use NAICS classifications 

include a breakdown of employment by county for multiple sectors from 2001 on (CA25N). The 

CA25N tables were aggregated to the 11-sector level.

Figure 63: North American Industrial 

Classification System Industry Sectors
80

Both the income and employment data files have unreported values in the tables. These missing 

values are given placeholder (D) and (L) when they are unreported for confidentiality reasons, or 

(NA) when the information was not available for that reporting year. The (L) placeholder 

indicates that the value is less than $50,000 in the income files and less than 10 jobs in the 

employment files. Income files typically contain more unreported values than the employment 

files. Years 2001 to 2011 have higher proportions of reporting issues for both income and 

employment files than years prior. Missing values were imputed using an algorithm that begins 

with feasible initial estimates of the missing values and alternating between a procedure that 

applies a moving average algorithm within sectors across years and a procedure that reconciles 

with known totals (summing up constraints). The result of applying this alternating procedure a 

large number of times is a set of estimates that minimizes inter-year variation while ensuring that 

the results are consistent with all published values. 

Employment and earnings data are available for each base year (1965, 1972, 1977, 1982, 1987, 

1992, 1997, 2002, and 2007). Although coefficient change does occur gradually, IO structure 

was assumed to be stable throughout each 5-year period of analysis. The BEA’s income and 

employment files span years 1969-2011. For this reason, and because the commodity-byindustry accounts were initiated in 1972, the 1972 structure was used for the periods beginning 

and ending in that year. Any county included in the ARC during any year within the 5-year 

period is included for the entire 5-year period for purposes of defining the respective multicounty regions. ARC investment data were deflated and summed to correspond to each of the 5-

year periods, for each of the 13 ARC sub-regions and in total for the larger ARC Region.

Personal consumption expenditure (PCE) data come from the BEA at the state level from 1997-

2012. The personal consumption expenditure data use the NAICS classification (PCE, 2014). 

The PCE data were compiled to conform to the model sectoring scheme using a bridge that is 

also provided by the BEA (PCE, 2014). Where necessary, aggregate totals for PCE sectors were 

disaggregated using corresponding national PCE distributions. In the absence of state-specific 

PCE data for earlier years, the PCE distribution for 1997 was applied to corresponding 

consumption totals to generate PCE expenditures for the regionalization process for earlier years. 

State and Local Expenditure data come from the U.S. Census Bureau in their Annual Surveys of 

State and Local Government Finances. Data are available for years from 1992 to 2011 at the 

state level (U.S. Census Bureau, 2014). These data were processed using an approach that 

parallels that described above for PCE data. These tables are based on information from public 

records and contain no missing values or confidential data (U.S. Census Bureau, 2014). In the 

absence of state-specific state and local expenditures data for earlier years, the PCE distribution 

for 1997 was applied to corresponding consumption totals to generate PCE expenditures for the 

regionalization process for earlier years. 

Input-output data come from BEA, which publishes benchmark commodity-by-industry accounts 

every five years for years ending in 2 and 7, beginning in 1972 and ending in 2007. The tables 
81

for years 1972-1992 use SIC classifications and 1997-2012 tables use NAICS classifications 

(Benchmark Input-Output, 2014). National Federal Expenditures, State and Local Expenditures, 

Investments, Exports, and Personal Consumption Expenditures are included in the Final Demand 

Sections of the BEA Use tables (Benchmark Input-Output, 2014). There are no missing values in 

the BEA’s Make and Use tables.

ARC Non-Highway Investment Data 

The investment data were provided by the Appalachian Regional Commission and processed by 

assigning each of the nearly 25,000 project records initially to one of 39 final demand categories, 

which were subsequently used to aggregate further to one of the eleven industry sectors 

identified above. Each investment was further assigned to its appropriate analysis period and 

region for the impacts assessment. The following Figure 64 reports these data in millions of

dollars.

Note that these data are reported in current dollars. For analytical purposes, each investment was 

deflated to correspond to the appropriate analytical benchmark year. Also, some of the projects 

were “Commission” investments, labeled as ARC in the figure above. These broader regional 

investments were not allocated to any specific states, but were a part of the direct impacts for the 

overall regional impacts assessments.

Figure 64: ARC Appropriated Non-Highway Investment by Period and State (millions of dollars)

Alabama Georgia Kentucky Maryland Mississippi New York North Carolina

1965-1971 $43.5 $30.5 $47.6 $14.9 $12.8 $20.0 $32.2

1972-1976 $62.4 $47.9 $59.5 $28.0 $38.3 $38.5 $52.7

1977-1981 $52.0 $36.2 $53.5 $24.8 $37.1 $42.7 $40.7

1982-1986 $20.4 $18.6 $29.6 $7.8 $17.0 $15.1 $17.3

1987-1991 $19.0 $11.1 $28.5 $6.6 $12.8 $11.7 $13.7

1992-1996 $26.3 $18.2 $38.1 $12.1 $15.7 $14.3 $18.8

1997-2001 $23.2 $20.0 $48.4 $11.0 $22.8 $16.9 $19.3

2002-2006 $26.6 $15.9 $46.6 $11.2 $25.9 $13.6 $18.7

2007-Present $35.6 $26.7 $76.2 $14.9 $43.6 $19.6 $26.5

Summary $309.0 $225.1 $427.9 $131.3 $226.0 $192.4 $239.8

Ohio Pennsylvania South Carolina Tennessee Virginia West Virginia ARC 

1965-1971 $35.0 $81.7 $32.2 $34.3 $27.8 $44.1 $6.6

1972-1976 $49.6 $108.2 $47.4 $59.5 $39.1 $75.2 $9.2

1977-1981 $38.2 $77.9 $40.1 $59.8 $34.2 $53.3 $23.8

1982-1986 $15.5 $31.5 $14.2 $26.1 $11.7 $26.9 $33.6

1987-1991 $13.9 $27.2 $11.6 $22.3 $12.3 $20.4 $16.4

1992-1996 $23.1 $35.1 $16.6 $26.7 $18.2 $37.7 $53.6

1997-2001 $26.1 $35.2 $16.4 $29.4 $18.5 $44.9 $24.1

2002-2006 $24.3 $28.6 $12.8 $23.3 $18.3 $36.9 $36.7

2007-Present $33.2 $40.2 $20.2 $45.1 $22.3 $48.3 $31.1

Summary $258.9 $465.4 $211.6 $326.5 $202.5 $387.6 $235.0
82

Results

The results from the economic modeling show that the $3.8 billion in ARC non-highway 

investments (Area Development Program) were responsible for leveraging nearly 312,000 jobs 

and $10 billion in added earnings in the Region. On average annually, these ARC funds 

supported an estimated 6,364 jobs and $204 million (in constant 2013 dollars) in earnings. (See 

Appendix A for details on Appalachian Region state specific estimated impacts). 

Figure 65 shows the detailed results from the IO analysis. Many of the new jobs created were in 

professional and technical services, manufacturing, trade, and construction industries. These 

figures include the direct, indirect and induced job growth. Direct effects are the employment 

directly attributable to the spending of ARC funds within a particular industry. Indirect effects

are the spending and employment of suppliers and contractors to produce inputs for the industry.

Induced effects include household spending on goods and services by both industry employees 

and the employees of contractors and suppliers (both direct and indirect employees). Total 

economic impact is the sum of direct, indirect, and induced effects. 

Figure 65: Employment Impacts - Appalachian Region Totals (Non-Highway Investments) 

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 1,989 2,057 962 3,932 11,852 3,282 10,609 2,885 41,130 1,545 5,308 85,551

1972-1976 2,396 2,533 642 5,132 14,387 3,963 12,645 3,378 47,448 1,810 6,289 100,622

1977-1981 1,149 1,225 639 4,622 7,630 2,041 7,499 2,028 21,571 844 3,527 52,775

1982-1986 424 553 247 1,906 2,664 684 2,884 755 6,369 291 1,224 18,002

1987-1991 255 333 111 1,315 1,546 415 1,862 474 4,282 176 695 11,463

1992-1996 309 498 123 1,914 1,975 598 2,661 537 5,662 231 916 15,426

1997-2001 169 308 61 2,098 1,203 395 1,658 335 3,576 18 134 9,954

2002-2006 134 147 53 1,548 811 259 948 173 4,300 13 106 8,491

2007-2013 122 162 57 1,952 760 241 971 195 4,970 10 113 9,552

All Years 6,946 7,816 2,895 24,420 42,828 11,877 41,736 10,760 139,307 4,937 18,312 311,835
83

Only the ARC portion of funds for non-highway investments was included in the economic 

modeling produced for this study. In every ARC-funded activity there are also public monies 

from other federal, state and local sources, as well as the likelihood of leveraged private 

investment. This was noted in the previous discussion of leverage. Therefore, the total 

employment and income impacts to the Region would be substantially higher if all funding 

sources were included in the modeling. However, in this report, only the ARC portion of funds 

were modeled in order to prevent attributing the overall economic effect from a combined total 

investment to only the ARC portion of that investment. Therefore, the figures reported in Figure 

66 should be read as conservative estimates of the ultimate employment and income effects from 

various ARC-supported non-highway investments. Given the large amount of non-ARC dollars 

that are directed to many of these investments, the actual resulting employment and income 

effects may be many times greater.

Figure 66: Summary of All Impacts

Period

Employment 

Impacts

Earnings Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 85,551 $ 2,570,534 $ 30.0 $ 2,646.8 $ 463.2 

1972-1976 100,622 $ 3,026,601 $ 30.1 $ 3,057.2 $ 715.4 

1977-1981 52,775 $ 1,730,431 $ 32.8 $ 1,866.7 $ 614.2 

1982-1986 18,002 $ 588,186 $ 32.7 $ 590.9 $ 285.4 

1987-1991 11,463 $ 394,647 $ 34.4 $ 405.7 $ 227.6 

1992-1996 15,426 $ 567,140 $ 36.8 $ 536.3 $ 354.5 

1997-2001 9,954 $ 377,341 $ 37.9 $ 487.0 $ 356.0 

2002-2006 8,491 $ 351,580 $ 41.4 $ 425.6 $ 339.2 

2007-2013 9,552 $ 395,247 $ 41.4 $ 530.2 $ 483.5 

Summary 311,835 $ 10,001,707 $ 32.1 $ 10,546.4 $ 3,839.0 

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $27.3 $56.6 $52.4 $164.9 $468.7 $162.0 $289.9 $67.5 $1,031.6 $84.3 $165.4 $2,570.5

1972-1976 $32.9 $69.7 $35.0 $215.3 $569.0 $195.5 $345.5 $79.0 $1,190.1 $98.7 $195.9 $3,026.6

1977-1981 $13.2 $26.6 $48.2 $197.4 $339.2 $110.3 $203.5 $45.6 $577.7 $53.1 $115.7 $1,730.4

1982-1986 $3.9 $11.7 $17.8 $74.1 $121.9 $35.6 $71.6 $16.5 $175.0 $18.9 $41.0 $588.2

1987-1991 $2.1 $6.3 $5.9 $53.1 $74.9 $22.0 $48.3 $12.3 $130.7 $12.0 $27.1 $394.6

1992-1996 $4.6 $10.6 $9.0 $76.7 $99.9 $32.4 $71.2 $16.8 $189.1 $18.1 $38.5 $567.1

1997-2001 $1.9 $5.9 $4.2 $87.7 $62.9 $21.2 $47.5 $12.3 $126.3 $1.5 $6.0 $377.3

2002-2006 $1.3 $4.3 $3.0 $72.0 $47.5 $15.3 $33.0 $7.9 $161.1 $1.2 $5.0 $351.6

2007-2013 $1.9 $4.7 $3.7 $85.6 $46.7 $14.3 $34.3 $7.6 $189.7 $1.0 $5.7 $395.2

All Years $89.0 $196.4 $179.3 $1,026.8 $1,830.7 $608.6 $1,144.9 $265.6 $3,771.2 $288.8 $600.4 $10,001.7
84

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of 0 to a 

maximum of 100. Figure 67 shows that the ARC’s COS values ranged from 9.51 in the first 

period to 8.89 in the latest period. This indicates the Region is becoming more diversified.

Summary

This chapter quantified the employment and income impacts attributable to the sizable amount of 

ARC grant investments made in the Region over the years. While the primary objectives of 

ARC investments are to improve the quality of life and socioeconomic status of the Region, 

ARC’s investments help to achieve economic gains that fuel the hiring of workers and purchase 

of material goods and services needed to put new developments in place. Incomes from all of 

these activities also fuel additional demand, further multiplying the total economic impacts of 

these investments.

Figure 67: ARC Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

ARC COS
85

Chapter III: “But For” the Appalachian Regional Commission 

Investments: Answering the Counterfactual with Quasi-Experimental 

Methods (QEM)

Section Summary

Reviewing ARC’s role in helping Appalachia develop its economy and the funds it has invested 

in the Region to do so raises the question as to whether this experiment in regional economic 

development has paid off for U.S. taxpayers. The research aimed to answer this question by 

using a quasi-experimental methodology that compares counties in the Appalachian Region to 

similar counties outside the Region. The results of this research indicate that employment and 

per-capita income grew at a faster rate in the Appalachian counties than in the non-Appalachian 

counties, as a result of the ARC investments. 

Assessing the impact of ARC investments in the counties of Appalachia can be a challenge 

because in order to determine the effectiveness of, we have to posit an alternative scenario as if 

the investments did not take place. Although this seems like a daunting task, the methodology 

outlined in Isserman and Rephann (1995) can be used to assess the effectiveness of these 

investments and answer the counterfactual question posed above.

These kinds of counterfactual claims can be evaluated through the use of Quasi-Experimental 

methods (hereafter QEMs). QEMs attempt to replicate the case-control group framework most 

commonly associated with clinical trials and other forms of scientific experiments (Rosenbaum, 

2010; Guo and Fraser, 2010). They use matching techniques to assign to each “treated” entity 

one or more “controls” and then use statistical tests to see if outcomes significantly differ 

between the cases and the controls. If the matching is done accurately and the differences 

between treatments and controls are significant, we can be confident that the divergence is due to 

the experimental intervention.

Among the pioneering studies using QEM methods to evaluate regional development programs 

is Isserman and Rephann’s 1995 study of the economic impacts of the ARC. In this study, the 

researchers constructed a counterfactual by matching counties in Appalachia that received ARC 

funding (i.e. the “treated” group) to counties outside of Appalachia that did not receive any ARC 

funding (i.e. the “control” group) but were otherwise similar to funded counties in terms of 

demographic, socioeconomic, and other characteristics. The matching of treated counties and 

control counties is a fundamental step in this process and “closer” matches tend to provide more 

convincing evidence than those based on pure correlation methods.

We use a generally similar methodology as Isserman and Rephann to evaluate the employment 

and income impacts of the ARC over its first fifty years. There are, however, several differences. 

First, while the original Isserman and Rephann (1995) study used 24 variables to match the 

treated and control counties, this study uses 29. The added variables include important starting 
86

conditions such as poverty rates, racial composition, and farming employment, whose omission 

was questioned by critics of Isserman and Rephann’s findings (Feser, 2013). These additional 

variables in the matching algorithm helps to ensure that we are obtaining a match that is as good 

as or better than that of Isserman and Rephann. Second, building on recent advances in the field 

(Caliendo and Kopeinig, 2005), we use a more sophisticated econometric technique to 

implement the matching. Third, we study a longer period of analysis for which we have 

developed smaller sub-periods to observe the empirical effects of ARC policy in these subperiods. 

The remainder of this technical report will outline several details and results from the QEM 

methodology.

Control Group Selection

QEM analyses generally combine two s t e p s in selecting control groups: eligibility criteria

and matching. In both cases, the objective is to select a control group with close similarity to 

the treatment group (before the treatment has occurred). In the eligibility criteria, the counties 

selected for the control group have similar characteristics (e.g. income and education) as the 

treated counties. For example, consider a government program to improve development in 

counties, such as funding for highways. In order to compare the success of this policy, we need 

to compare to a group of similar counties that are not part of the program. However to make this 

comparison reasonable, we need to be sure that the only difference between the counties is the 

fact that certain counties received the funds and others did not receive funds. The counties should 

be similar in every respect possible with the exception being who received the highway funding. 

Regression discontinuity methods (Bhutta, 2009) use a similar methodology, where regions are 

assigned to control and treatment group based on a threshold value (i.e. a cutoff point) of the 

selected assignment variable (Lee and Lemieux, 2009). Cases and controls closer to the threshold 

are assumed to be more similar, allowing the estimation of local treatment effects. 

Matching is the second step involved in selecting the control groups. There are many varieties of 

methods that can be used in selecting control groups that match the treatment group. These 

include difference of means tests (Isserman and Merrifield 1982), Mahalanobis distance 

(Aleseyed, Rephann, and Isserman, 1998; Stenberg et.al 2009), and propensity score methods 

(Artz, Orazem, and Otto, 2007), as well as combinations of the above (i.e. Mahalanobis distance 

with propensity score). Propensity score matching is the most common methodology employed 

(Rosenbaum 2010a). A propensity score is the conditional probability of being selected for 

treatment based on observable characteristics (Artz, et al., 2007, Wenz, 2007, and Johnson, 

2009). 
87

Smith and Todd (2005) argue that, while the propensity score is a valuable econometric tool, it 

should not be taken as a general solution to policy evaluation problems. They show that the 

results are very sensitive to the choice of the variables included in the estimation of the scores as 

well as to the sample used in the estimation.

Even though all three methods provide similar results for the treated region in the periods before 

the policy, there is no consensus as to what and how many variables should be used to select the 

control group, even in studies that assess the same public policy. In evaluating the impact of 

highways on economic development, Broder et al (1992) used only five county socioeconomic 

characteristics (i.e. population, per capita income, manufacturing employment, service 

employment, relationship to interstate highways, and proximity to metropolitan statistical areas) 

compared to Rephann and Isserman (1994) who use 31 socioeconomic characteristics to match 

the treatment and control groups.

We now turn our attention to the matching algorithms utilized in the empirical analysis.

Matching Algorithms

We begin our description of matching techniques by assigning the counties into two groups: the 

“treated group” that received ARC investments and the rest of the counties that did not receive 

ARC investments. In order to test the effect of the policy, we need to compare the effect of the 

investment in the treated group with a comparable group of counties that did not receive the 

investment which will be called the “control group.” This control group should be similar to the 

treated group before the treatment (pre-test) and this similarity is measured by how closely 

counties match in terms of selected variables. We define D=1 for the counties that received 

treatment and D=0 for control counties, where the treated counties are defined as the counties 

where investment by the ARC occurred in the period of 1965 to 1970; the counties that received 

these ARC investments include a group similar to the one used by Isserman and Rephann (1995) 

who included counties that are members of the ARC. For the sub-periods the treated group is 

defined as the group of counties that received investments from ARC projects in the beginning of 

the analyzed sub-period. For example, for the sub-period from 1966 to 1974 we included in the 

treated group the counties that received investments from 1966 to 1968. The effects of the policy 

(post-test) are measured by evaluating the behavior of target variables. Note that creating subperiods for all the different years when ARC investments occurred would be intractable because 

ARC investments occurred every year.

There are several matching methods available: propensity score matching using kernel matching, 

nearest neighbor matching, radius matching, stratification, and the Mahalanobis distance metric 

(Caliendo and Kopeinig, 2005; Isserman and Repphan, 1995; Rosenbaum, 1989). In this study 

we examined matches based on three criteria: propensity score matching using nearest neighbors, 

kernel matching and Mahalanobis distance. Both nearest neighbor matching and kernel 

matching are considered propensity score methods, because both use fitted values of the probit 

model to determine similar cases and controls. Propensity score matching and Mahalanobis 
88

distance methodologies have been used to more accurately measure the impact of policies in 

regional science (Isserman and Rephann, 1995; Pender and Reeder, 2010). These methods 

perform better than standard regression techniques when the policy adoption is endogenous 

(Reed and Rogers, 2003); this is appropriate in this analysis since the counties that are selected 

for ARC investments have to be within the Appalachian Region and not randomly chosen.

Even when using the same matching criteria variables, different matching methods can produce 

different control groups. No method is universally accepted as superior to any other method. In 

this study we test three different matching algorithms: propensity score using nearest neighbor 

matching, propensity score using kernel matching, and Mahalanobis distance.

The propensity score matching algorithm is based on matching the result of a probit model with 

D as the dependent variable and x as a matrix of explanatory variables:

The probit model provides the probability that a county is a control county and this probability 

will be used as the propensity score to match treated counties with untreated or control counties. 

There are various methods to match counties based on their propensity scores, which comes from 

the fitted value of the probit model. 

The nearest neighbor approach matches each treated observation i with one of the control 

observations j that has the closest propensity score using the formula . The 

procedure can be employed either with or without replacement. For our estimation purposes we 

use without replacement since we have a large number of candidate counties to be selected for 

the control group, and to avoid the situation where a single county was selected to match a large 

group of treated counties. 

The kernel matching estimator does not find a county to be the closest match to each treated 

observation but rather calculates a combination of control counties that provide a closer 

comparison group, while the nearest neighbor procedure finds the control county with the closest 

value of the propensity score. The controls are weighted by their degree of similarity to the 

treated observation. The weights used in the matching algorithm are defined as follows:

p x prob D x E D x       1   

min p p i j 

 

0

1

,

j i

n

j i

j

p p

K

h

w i j

p p

K

 h

  

   



  

    
89

Where p measures the propensity score of each i (treated) and j (not treated) counties, K is the 

kernel function, h is the bandwidth in the kernel density function, and the kernel function used in 

the matching is the Epanechnikov kernel. The function creates weights for each of the j counties. 

The Mahalanobis Distance metric is not a propensity score method per se, although it follows the 

same general principles. This model measures the distance between the treated county and other 

counties and the inverse of the variance-covariance matrix of the variables and can be 

mathematically represented as follows:

Where T is the treated county, C is a possible control county, d is the distance between the two 

vectors, and is the variance-covariance matrix.

To determine the most appropriate matching method, we consider the standardized bias for each 

variable, which measures the differences in the X variables between the treated and control

groups (Rosenbaum and Rubin, 1985). Such result compares the overall match between cases 

and controls variables “as the difference of sample means in the treated and matched control 

subsamples as a percentage of the square root of the average of sample variances in both groups” 

(Caliendo and Kopeinig, 2005 pp. 15). The best matching algorithm (between Mahalanobis 

distance, nearest neighbors, and Kernel) is the one with the lowest pseudo R2and likelihood ratio 

test statistic values (estimated after treated and control groups were selected), the least number of 

variables with significant biases, and lowest bias values (Caliendo and Kopeinig, 2005). First we 

identify variables used in the model that are significantly different between treated and matched 

controls, and focus on the percentage difference between treated and control (bias), and how 

many variables in the matched counties have large and significant differences. We then look for 

low values among pseudo R2and the likelihood ratio test statistics, because low values on these 

tests indicate that the explanatory variables will predict a lower difference in the propensity score 

between treated and untreated matched counties. 

The decision of which matching algorithm to choose is not clear, because the pseudo R2and 

likelihood ratio test statistics can point towards one model and the average bias, max bias and 

number of significant variables point to another model as the best fitting algorithm. Therefore, 

choosing the best matching algorithm involves looking at all of the above criteria, not just a 

single one.

The next step in the analysis is to use a difference-in-difference model (DID). DID is applied 

when panel data on outcomes are available before (b) and after (a) the experiment the 

experiment occurs. It measures the difference-in-differences average treatment effect on the 

treated (ATET) and is specified as: 

     

2 1

, d X X X X X X T C T C T C

 

    1 
90

where the first term in the equation refers to the differences in outcomes before and after the 

treatment for the treated group. The before and after differences alone may be biased if there are 

time trends. The second term in the equation measures the before and after change in the control

groups. Together they are used to eliminate this bias under the assumption that both groups 

experience the same time trend.

Data

Figure 68 shows by year and state how many counties began their first investments from the 

Appalachian Region. We measure the timing of the "treatment" according to the ARC 

investments in each county. 

      

   

1 0 1 0

1 1 0 0

1 , 1

, 1 , 1

a b a a b b

a b a b

ATET E D E y y y y x D

E y y x D E y y x D

         

     

Figure 68: Number of Counties by State that Received their First Investment from the ARC

State 1965-

1969

1970-

1979

1980-

1989

1990-

1999

2000-present

Alabama 22 13 2

Georgia 25 10 2

Kentucky 34 15 5

Maryland 3

Mississippi 15 5 2 2

New York 12 2

North Carolina 27 2

Ohio 18 10 1 3

Pennsylvania 39 12 1

South Carolina 6

Tennessee 45 5 2

Virginia 16 3 4 2

West Virginia 39 16

NEW ARC Counties 301 93 1 11 14
91

There are a few considerations worth making before describing the investment data. The original 

data were separated by investment type. However, we decided not to have the analysis by type of 

investment but rather having a set of outcomes variables (described below) to evaluate the 

investments. The rationale behind this is that it would have been very difficult to isolate the 

effect of each type of investment (even with a small number of categories) since different types 

of investments were done during the same year. On the other hand, aggregating different types of 

investments generated a situation in which once a county received an investment, the ARC 

generally kept investing for at least a few years.14 Also to avoid the issues from including 

counties with small investments we consider only investments above a yearly average or median. 

Finally, we also evaluated measuring the timing of the treatment according to the year of the first 

investment in each county. By 1980 almost 400 counties had already received funding for 

different projects. At the same time we wanted to be able to say more about recent years’ 

investments and therefore we decided not to pursue this route. 

Ideally, one would like to estimate the effect of only one program in one particular year (i.e. only 

one investment in each county in a specific year). This is to avoid any sorts of spillover effects, 

either spatial as well as coming from some sort of combination of different investments. 

The ARC investments were made over a large period of time, therefore we decided to break up 

the overall period into sub-periods. Of course, this implied finding controls at the beginning of 

each time interval. 

The variables used for the analysis include the economic structure of the county, the level of 

economic development, other socioeconomic factors and demographic characteristics. These 

variables are collected for periods before the investment in the ARC counties took place, 

allowing us to perform a pre-treatment matching of counties. The variables included for each 

classification are listed in Figure 69. The original variables used by Isserman and Rephann 

(1995) totaled 24 and in the analysis presented here, five additional variables were used to bring 

the number of variables used in the matching exercise to 29.We excluded counties located within 

60 miles of the ARC counties to avoid issues related to spatial spillovers. The 60 miles distance 

is important because as Plane and Rogerson (1994) explain, this distance accounts for a local 

labor market that could obtain benefits from the jobs created in the ARC counties (Isserman and 

Rephann, 1995). 

 

14 We have created a dataset containing a single variable that is equal to 1 if the county received any form of investment from the 

ARC in a particular year and 0 otherwise. Note that in many instances, the ARC investment is part of a larger investment made

by the state or even private party institutions. However, with QEMs the only matters is whether the investment happened or not 

in a specific county. 
92

Figure 69: Variables Used in the QEM Matching Algorithm

Isserman and Rephann (1995) Original Variables

freeway Binary (or dummy) variable for presence of highway

lpop log of population 1959

spc state and local per capita earnings

rtot rate of growth of total income 1950-1959

rpop population growth rate for period 1950-1959

dens population density per square mile for 1959

city25 distance of county to the closest city with 25,000 population

city100 distance of county to closest city with 100,000

city250 distance of county to closest city with 250,000

city500 distance of county to closest city with 500,000

city1000 distance of county to closest city with 1,000,000 population

psvc share of services earnings in 1959

prtl share of retail trade earnings

ptpu share of transportation and public utilities earnings

pmfg share of manufacturing earnings

pcon share of construction earnings

pfar share of farming and agriculture earnings

ptrf share of transfer income earnings

pdir share of earnings from dividends, interest, and rents

pres residential adjustment share

pmil share of military earnings

pfed share of federal earnings

pstl share of state and local earnings

pwhl share of wholesale trade earnings

New Variables Added

pov percent of population in poverty 1959

pc17 percent of population under 17 years of age

pc65 percent of population over 65 years of age

black percent of black population 1959

nfarms percent of population living on farms 1959
93

The variables included in Figure 69 are designed to capture various aspects of the counties under 

study. For example, the economic characteristics of counties such as the variables measuring 

earnings and income measure the overall economic strength of the counties. The variables that 

measure the various industry shares are designed to measure the industrial mix of the various 

counties, while the demographic variables measure the population characteristics. Although 

categorizing these into distinct groups is somewhat arbitrary, these variable groupings are the 

ones that are most important in this study.

For the various sub-period analyses we use the same variable selection methods for the county 

matching procedure, but for each sub-period we will define a matching pre-test sample of 

counties, a specific dummy treatment variable, and a specific objective variable. The periods of 

study are: (1) from 1965 to 1974; (2) from 1975 to 1984; (3) from 1985 to 1994; (4) from 1995 

to 2002; and (5) from 2003 to 2012. The sub-period analysis is included to determine if the 

effects of ARC investments differs over time. (See Appendix B for details on the sub-period 

analysis). 

In the empirical examples undertaken in this study, we concentrate on two important metrics: the 

growth in per-capita income and the growth in employment. Income growth is one economic 

indicator that is important to many stake holders as it measures the overall well-being of people 

living in ARC counties over the past fifty years; the same may be said for the importance of 

employment growth. (See Appendix C for regression model results related to these variables). 

Results

The matching was performed using data from 1959 and 1960 for all counties, excluding counties 

located within 60 miles of the Appalachian Region to avoid spatial spillovers issues. The 

matching from the three procedures generated control counties that are very similar to the treated 

ones as can be seen in Figure 70, where we compare the variables used in the matching 

procedures and estimates from differences in their averages. Also highlighted are the statistically 

significant differences between treated and control, which are the variables that show a 

significant difference that can explain the difference between treated and control on average. As 

noted earlier the better model is the one that finds a control group that is more homogeneous. 

Therefore from the results in Figure 70 the models that fit better would be those using the 

Mahalanobis distance metric and those using nearest neighbors matching. 
94

Figure 70: Comparison of averages for variables used for estimating the matching procedure between 

treated and control and significance levels for full period matching. * = 10% ** = 5% and *** = 1%

Variable Mean Treated

Mahalanobis Distance Nearest Neighbors without 

replacement Kernel Matching

Mean Control Bias t-test Mean Control Bias t-test Mean Control Bias t-test

freeway 0.435 0.408 5.6 0.79 0.473 -7.7 -1.1 0.423 2.5 0.4

lpop59 10.149 10.179 -2.7 -0.44 10.217 -6.2 -0.9 9.698 41.3 5.5***

spc59 0.085 0.098 -3.9 -3.93*** 0.098 -3.8 -4.6*** 0.108 -7.1 -3.8***

rtot59 59.617 56.212 6.7 1.43 56.885 5.4 0.8 129.04 -137.2 -5.4***

rpop59 -2.041 1.097 -13.2 -2.97*** 2.818 -20.4 -4.3*** 1.115 -13.2 -2.5**

dens59 90.048 86.179 0.7 0.36 114.62 -4.6 -1.8* 85.442 0.9 0.3

pov59 19.006 16.625 26.2 2.98*** 14.696 47.5 6*** 21.14 -23.5 -2.3**

pc1760 37.926 36.570 34.1 5.67*** 37.204 18.1 2.8 37.353 14.4 1.8*

pc6560 9.796 11.193 -48.3 -9.06*** 10.553 -26.2 -4.4*** 9.179 21.3 3.6***

black60 6.171 6.332 -1.2 -0.21 9.214 -23.1 -3.5*** 9.033 -21.7 -3.7***

city2560 32.319 33.484 -3.8 -0.75 34.261 -6.4 -1.1 32.489 -0.6 -0.1

city10060 67.6 67.311 0.4 0.12 64.063 5.4 1.3 59.85 11.8 2.6***

city25060 105.72 105.470 0.3 0.07 100.27 6.5 1.4 94.889 12.9 2.5**

city50060 185.85 172.240 8.8 1.97** 172.02 8.9 1.7* 210.19 -15.7 -2.3**

city100060 355.9 366.060 -4.5 -0.77 327.34 12.5 2.1** 385.37 -12.9 -1.7*

psvc59 0.064 0.067 -6.3 -0.94 0.067 -5.5 -0.8 0.056 19.2 2.7**

prtl59 0.088 0.097 -32.5 -5.49*** 0.092 -16 -2.5** 0.081 24.9 3.7***

ptpu59 0.045 0.045 1.3 0.19 0.043 6.5 1 0.028 43.8 6.8***

pmfg59 0.217 0.215 1.6 0.2 0.226 -5.6 -0.7 0.188 19.7 2.4

pcon59 0.034 0.037 -10 -2.02** 0.036 -7.4 -1.4 0.035 -3.8 -0.7

pfar59 0.104 0.108 -3.4 -0.61 0.109 -4.4 -0.7 0.182 -66.1 -7.4***

ptrf59 0.121 0.117 10.5 1.37 0.109 28.3 3.5*** 0.101 48.3 6.4***

pdir59 0.077 0.092 -38.1 -7.44*** 0.089 -32.4 -6.5*** 0.076 1.5 0.3

pres59 0.064 0.058 4.3 0.6 0.062 1.7 0.2 0.061 2.4 0.3

pmil59 0.007 0.007 0 -0.01 0.009 -5.5 -1.6 0.007 1.3 0.5

pfed59 0.022 0.020 5.1 1.1 0.021 2.7 0.5 0.032 -25.1 -3.6***

pstl59 0.07 0.065 14.6 2.68** 0.067 8.2 1.3 0.084 -42.4 -4***

pwhl59 0.021 0.023 -11.3 -1.74* 0.023 -13.8 -2.1** 0.016 23.2 3.5***
95

As noted before a lower pseudo R2and likelihood ratio test statistic will point towards the better 

fitted model, and the results in Figure 71 point towards the nearest neighbors matching and the 

kernel matching as the best fitting models. 

Furthermore from Figure 72 we can see the target measure compared before treatment15 and their 

averages are not significantly different from zero for the matched counties using the nearest 

neighbors matching procedure. These results indicate that the nearest neighbor matching 

procedure is the better algorithm for matching.

 

15 The analysis before treatment can only be applied to per capita income, because we do not have employment available for 

periods before 1969. 

Figure 71: Tests of fitting between matching procedures for full period matching

Procedure Pseudo-R

2 LR Chi2 Average Bias

Variables

Significantly

Different

Mahalanobis 

Distance 0.393 430.31 -2.107 10

Nearest Neighbors 

without replacement 0.229 251.22 -1.332 11

Kernel

Matching 0.184 201.54 -2.854 19

Figure 72: Comparison of objective variables before treatment between treatment and control 

counties and significance level for full period matching. Rates of growth of per capita income 

between 1962-1959 and 1965-1962. * = 10% ** = 5% and *** = 1%

Procedure Variable Treated Control Difference T-stat

Mahalanobis 

distance

PCI 1962-59 0.181 0.183 -1.400 -0.220

PCI 1965-62 0.114 0.107 8.000 1.780*

Nearest Neighbors

PCI 1962-59 0.181 0.182 -0.800 -0.120

PCI 1965-62 0.114 0.119 -5.000 -1.030

Kernel Matching

PCI 1962-59 0.181 0.149 37.200 4.380***

PCI 1965-62 0.114 0.117 -3.700 -0.610
96

Figure 73 plots the various counties that were deemed to be a good match (i.e. the control 

counties) for the counties that were contained in the ARC investment areas. 

Figure 74 contains information regarding the results of the QEM analysis on per-capita income 

growth. The first column is the year, where the estimates correspond to each year reported (i.e. 

the growth rate from the original year to the year in question), followed by the growth rate in 

per-capita income for the treated counties, i.e. those that received ARC investment funds. The 

third column is the growth rate for the matched control counties, i.e. those that did not receive 

ARC investment funds, while the fourth column is the difference in the growth rate in per-capita 

income between the treated and control counties. The final column is the t-statistic which is a 

metric used to determine if the difference in the growth rate in per-capita income between the 

treated and control counties is statistically significant. 

Figure 73: Matched counties for full period matching from data for 1959 (Full Period Matching)
97

Figure 74: Per-Capita Income Growth Rate Results and significance levels for full period matching. 

* = 10% ** = 5% and *** = 1%

year

Per capita income Growth rate

Treated Controls Difference T-stat

1970 7.9% 5.8% 2.0% 4.65***

1971 15.0% 13.4% 1.6% 3.36***

1972 24.7% 22.8% 1.9% 3.68***

1973 36.5% 36.4% 0.1% 0.09

1974 46.0% 43.8% 2.1% 3.22***

1975 54.6% 51.8% 2.9% 3.82***

1976 65.0% 61.4% 3.6% 4.93***

1977 74.1% 69.8% 4.4% 5.78***

1978 85.2% 80.6% 4.6% 5.78***

1979 95.8% 91.1% 4.7% 5.55***

1980 105.1% 99.0% 6.1% 7.21***

1981 114.8% 109.8% 5.0% 5.70***

1982 120.6% 115.0% 5.6% 6.35***

1983 125.4% 120.4% 5.0% 5.97***

1984 135.7% 131.3% 4.4% 5.29***

1985 140.9% 136.3% 4.5% 5.30***

1986 145.4% 140.6% 4.8% 5.58***

1987 150.1% 144.9% 5.2% 6.33***

1988 156.2% 150.4% 5.8% 6.71***

1989 163.4% 157.8% 5.6% 6.40***

1990 168.8% 161.6% 7.2% 8.09***

1991 172.7% 165.1% 7.7% 8.12***

1992 178.9% 170.9% 8.0% 8.35***

1993 182.1% 174.3% 7.8% 8.03***

1994 186.3% 179.1% 7.1% 7.24***

1995 189.6% 182.2% 7.3% 7.29***

1996 193.8% 186.9% 6.9% 6.73***

1997 198.7% 191.6% 7.1% 6.96***

1998 203.3% 195.9% 7.4% 7.25***

1999 206.5% 199.1% 7.4% 7.20***

2000 211.6% 204.2% 7.3% 7.07***

2001 218.1% 211.7% 6.4% 5.91***

2002 219.5% 212.7% 6.9% 6.42***

2003 222.1% 216.3% 5.9% 5.43***

2004 227.2% 221.4% 5.8% 5.15***

2005 230.7% 224.6% 6.2% 5.29***

2006 235.0% 228.6% 6.5% 5.47***

2007 239.4% 233.3% 6.2% 5.2***

2008 243.5% 237.8% 5.7% 4.65***

2009 243.3% 236.7% 6.7% 5.28***

2010 245.5% 238.7% 6.7% 5.36***

2011 250.1% 243.9% 6.2% 5.00***

2012 253.8% 247.5% 6.3% 5.00***

Average 5.5%
98

These results are also illustrated in Figure 75 entitled “Per-Capita Income Growth Rates between 

Treated and Control Counties”.

Over most of the study period, counties that received ARC funding had higher per-capita income 

growth compared to the control counties that did not. Per-capita income growth rate in ARC 

counties grew an average of 5.5 percent over the entire study time period compared to the control 

counties. The differences in per-capita income growth between the treated and control counties 

are positive and statistically significant for nearly every year, meaning that it is unlikely that the 

growth in ARC counties are simply due to random chance. The only exception is in 1973, where 

there was no difference in per-capita income growth between the ARC counties and the 

comparison group. Historically speaking, 1973 was a year that was plagued by various economic

evidence that these investments undertaken by the ARC led to higher growth in per-capita 

income over the time period 1970-2012.

Overall, these results paint a very positive picture for the counties that are located in Appalachia 

and provide evidence that these investments undertaken by the ARC led to higher growth in percapita income over the time period 1970-2012.

Employment growth is another important metric that can be used to measure the economic 

vitality of a region. Figure 76 contains information regarding the results of the QEM analysis 

regarding employment growth. 

Figure 75: Per Capita Income Growth Rates between Treated and Control Counties, Base Year 1969
99

Figure 76: Employment Growth Rate Results and significance levels for full period matching. 

* = 10% ** = 5% and *** = 1%

year

Employment Growth Rate

Treated Controls Difference T-stat

1970 0.4% 0.3% 0.1% 0.36

1971 2.2% 1.4% 0.5% 1.62

1972 6.0% 4.7% 1.3% 2.13**

1973 10.4% 8.7% 1.7% 2.36**

1974 11.8% 10.0% 1.8% 2.19**

1975 11.1% 8.3% 2.8% 3.03***

1976 15.2% 11.9% 3.3% 3.32***

1977 18.7% 14.8% 3.9% 3.58***

1978 22.5% 17.8% 4.6% 3.95***

1979 24.3% 19.5% 4.8% 3.88***

1980 23.8% 18.5% 5.3% 4.10***

1981 23.6% 18.8% 4.8% 3.50***

1982 21.8% 16.9% 4.9% 3.28***

1983 22.7% 18.5% 4.2% 2.67***

1984 26.1% 21.7% 4.3% 2.64***

1985 27.2% 22.7% 4.5% 2.62***

1986 28.8% 24.0% 4.8% 2.64***

1987 31.5% 26.6% 4.8% 2.54**

1988 33.5% 28.4% 5.1% 2.65***

1989 35.4% 30.2% 5.2% 2.61***

1990 37.5% 32.0% 5.5% 2.71***

1991 37.1% 32.0% 5.1% 2.45**

1992 38.9% 33.3% 5.6% 2.59***

1993 41.3% 35.7% 5.6% 2.53**

1994 43.5% 38.6% 4.9% 2.13**

1995 46.0% 41.3% 4.6% 1.96**

1996 46.9% 42.5% 4.4% 1.79*

1997 48.9% 44.5% 4.4% 1.75*

1998 50.5% 46.0% 4.5% 1.73*

1999 51.6% 47.3% 4.3% 1.61

2000 52.9% 48.8% 4.1% 1.48

2001 52.2% 48.0% 4.1% 1.45

2002 51.6% 47.3% 4.2% 1.46

2003 51.9% 47.6% 4.2% 1.43

2004 53.3% 48.7% 4.6% 1.52

2005 55.1% 50.0% 5.1% 1.62

2006 56.5% 51.1% 5.3% 1.68*

2007 57.7% 52.5% 5.3% 1.62

2008 56.7% 51.8% 4.9% 1.52

2009 53.1% 48.6% 4.5% 1.37

2010 52.7% 48.3% 4.4% 1.38

2011 54.4% 49.8% 4.6% 1.41

2012 55.4% 51.0% 4.4% 1.36

Average 4.2%
100

Employment grew significantly faster in ARC counties compared to the control counties for 

most of the study period. The average difference in growth rates between the counties that 

obtained ARC investments and those matched counties that did not receive ARC investments 

was approximately 4.2 percent. This is shown in Figure 77, which shows that ARC counties had 

higher employment growth than the matched counties for nearly every year. 

This gap narrowed after 1995, but the difference remained statistically significant at the 90

percent confidence threshold. The difference in employment growth was rather small and 

insignificant at first (1970 to about 1972), but the groups began to quickly diverge throughout 

most of the seventies and eighties. As mentioned earlier, the early seventies was an atypical 

period for the United States economy, and it is reasonable to expect that ARC investments would 

take some time to manifest themselves especially when it comes to employment growth. 

Again, these findings strongly suggest that ARC investments had a positive influence on the 

employment prospects for residents of the region.

Figure 77: Employment Growth Rates between Treated and Control Counties, Base Year 1969

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

1970197219741976197819801982198419861988199019921994199619982000200220042006200820102012

Graph 2: Employment growth rate - Base year 1969

Treated Controls
101

Summary

In summary, using a well-established QEM technique, the research shows that employment 

growth and per-capita income growth over the period 1970-2012 were higher in Appalachian 

counties than in counties that did not receive ARC investments. On average, counties that 

received ARC investments experienced 4.2 percent higher employment growth and 5.5 percent 

higher per-capita income growth than the counties that did not receive ARC funding. These 

results indicate the effectiveness of ARC investments for the Appalachian counties in the study.
102

Key Report Findings

Based on the analysis in this report, key findings emerge. 

1. ARC represents a highly valued and active player in the Appalachian Region’s 

economic development, supporting state and local partners in their efforts to 

transform their communities.

Since 1965, ARC has made nearly 25,000 non-highway strategic investments in the Region. 

Working with federal, state, and local partners ARC has invested more than $3.8 billion in these 

projects. These investments supported a variety of community and economic development 

initiatives, including basic infrastructure improvements, job creation initiatives, and leadership 

development. For every dollar in ARC funds, state and local partners were able to leverage an 

average of $2.50 from other federal, state and local funds as well as $6.40 in private sector 

investments. 

In addition, more than $9 billion has been obligated since 1965 for the Appalachian 

Development Highway System, which is now 89 percent complete or under construction. When 

finished, the 3,090-mile ADHS will connect almost every part of the Region to an interstatequality highway and to the national Interstate Highway System.

2. ARC has had a significant and important impact on the Region’s economic vitality.

Over the past 50 years (with much higher appropriations in the earlier years of the program), 

ARC’s $3.8 billion in non-highway investments have resulted in nearly 312,000 direct, indirect, 

and induced jobs for the Region and $10.5 billion (in constant 2013 dollars) in additional 

earnings. Between 2007 and 2013, ARC non-highway investments accounted for nearly 10,000 

jobs and $400 million in regional earnings. These impacts do not include the benefits accruing as 

a result of the investments that ARC has leveraged over the years.

Furthermore, our research demonstrates that counties receiving ARC investments grew at a 

slightly faster pace than similar counties that did not receive ARC investments. Using a rigorous 

quasi-experimental research method, our analysis suggests that ARC investments helped 

counties add employment at a 4.2 percent faster pace, and per-capita income at a 5.5 percent 

faster pace, than similar counties that did not receive ARC investments. 

Moreover, leaders in the Region expressed consensus about the value of ARC’s role as a catalyst 

in helping to make projects happen that might not otherwise have gone forward. In interviews 

with over 220 local, state, and federal stakeholders in every Appalachian state, ARC was praised 

for helping leaders respond to uniquely local problems and for its ability to leverage other 
103

resources (by seeding new projects or providing the “last dollars” in) when projects did not fit 

neatly into other funding program models and might not have otherwise happened.

16

3. ARC has made progress toward helping the Appalachian Region reach 

socioeconomic parity with the nation, but much work remains in moving some 

key indicators. 

ARC’s strategic plan has four major goals: (1) increasing job opportunities and per capita income 

in the Region; (2) strengthening the capacity of the people of Appalachia to compete in the 

global economy; (3) developing and improving the Region’s infrastructure; and (4) building the

ADHS to reduce Appalachia’s isolation. On measures related to poverty, income disparity, 

unemployment, the industrial mix, and housing quality, the Region has improved significantly. 

For instance, as Figure 25 shows, there has been a dramatic reduction in the number of 

Appalachian counties where the share of the population living in poverty exceeds 150 percent of 

the national average. The number of high-poverty counties in the Region (those with poverty 

rates above 150 percent of the U.S. average) declined from 295 in 1960 to 107 for the period 

2008–2012. The overall poverty rate for Appalachia is almost half that of 1960, dropping from

over 30 percent to just under 17 percent.

4. ARC has not yet fully accomplished its mission of bringing the Region to parity 

with the rest of the nation on key socioeconomic indicators.

The Region still lags in many key areas. The Region’s population growth is relatively stagnant, 

reflecting an economy that lags in terms of employment growth and educational attainment so 

that there are not enough high quality jobs to support its citizens. 

Government transfer payments account for one-quarter of all personal income, a 41 percent 

higher rate than the rest of the nation. By comparison, the ratio was 17 percent after the 1981-82 

recession, about 35 percent higher than the rest of the nation. Some, but certainly not all of this 

increase can be explained by the rise in citizens aged 65 and over now eligible for Social 

Security. 

Furthermore, the Region’s citizens have relatively poorer health outcomes (in terms of mortality 

rates as well the prevalence of obesity and diabetes), reflecting changes in modern-day life that 

have reduced the quality of life for many residents. In addition, the Region remains relatively 

more isolated from the rest of the nation because the ADHS is not yet completed and, 

increasingly, because the Region lags the rest of the nation in access to affordable high-speed 

broadband service. 

 

16 See Appalachia Then and Now: Examining Changes to the Appalachian Region since 1965: State Meetings Report for more 

details leading to these specific report findings.
104

Appalachia Moving Forward

The data from this research suggest that ARC has been a vital partner in the economic and 

community development progress achieved in the Appalachian Region. However, there is a 

continued need for investment to help Appalachia reach socioeconomic parity with the rest of the 

nation. Many questions remain about how best to address the challenges Appalachia faces today. 

How can leaders create a climate of entrepreneurship and opportunity so that local citizens can 

remain and contribute to Appalachia’s future success? How can the Region compete and succeed 

in the global economy when it cannot always reach new markets? Few states or localities have 

the capacity to address these significant challenges without outside help. Addressing the 

Region’s disparities will require continued local-state-federal partnerships and strategic 

investments that build opportunity for growth. 
105

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110

Appendices
111

Appendix A: Appalachian Region State Specific: Total Estimated Impacts
112

Alabama: Total Estimated Impacts 

The production impacts for Alabama naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 531 jobs and 

$16.5 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

Alabama: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)
113

Alabama: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 7,888 $ 227,288 $ 28.8 $ 248.5 $ 43.5

1972-1976 8,727 $ 250,166 $ 28.7 $ 266.6 $ 62.4

1977-1981 4,361 $ 138,107 $ 31.7 $ 158.0 $ 52.0

1982-1986 1,200 $ 39,103 $ 32.6 $ 42.2 $ 20.4

1987-1991 899 $ 31,993 $ 35.6 $ 33.9 $ 19.0

1992-1996 1,045 $ 39,912 $ 38.2 $ 39.8 $ 26.3

1997-2001 624 $ 24,373 $ 39.1 $ 31.7 $ 23.2

2002-2006 598 $ 26,377 $ 44.1 $ 33.3 $ 26.6

2007-2013 669 $ 29,659 $ 44.3 $ 39.1 $ 35.6

Summary 26,012 $ 806,978 $ 31.0 $ 893.2 $ 309.0

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 219 226 34 464 1,137 267 1,014 257 3,653 160 459 7,888

1972-1976 242 247 38 422 1,260 294 1,098 286 4,159 178 504 8,727

1977-1981 106 112 33 372 660 149 622 141 1,805 89 273 4,361

1982-1986 27 37 10 148 172 43 193 52 417 23 78 1,200

1987-1991 19 30 5 149 126 31 158 29 286 15 51 899

1992-1996 18 39 6 171 131 43 186 33 344 17 58 1,045

1997-2001 8 23 2 117 65 27 100 20 250 1 9 624

2002-2006 9 11 3 96 63 26 68 13 302 1 8 598

2007-2013 8 11 2 94 60 16 65 14 390 1 8 669

All Years 655 736 134 2,033 3,674 895 3,503 845 11,606 484 1,448 26,012

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 3.3 $ 6.2 $ 2.0 $ 17.1 $ 42.2 $ 12.6 $ 27.9 $ 7.2 $ 86.0 $ 9.9 $ 13.1 $ 227.3

1972-1976 $ 3.7 $ 6.8 $ 2.2 $ 15.5 $ 46.7 $ 13.9 $ 30.2 $ 8.0 $ 97.9 $ 11.0 $ 14.3 $ 250.2

1977-1981 $ 1.4 $ 2.4 $ 2.4 $ 14.7 $ 27.3 $ 7.8 $ 17.4 $ 3.7 $ 46.2 $ 6.0 $ 8.7 $ 138.1

1982-1986 $ 0.3 $ 0.8 $ 0.8 $ 5.5 $ 7.2 $ 2.4 $ 5.0 $ 1.3 $ 11.7 $ 1.6 $ 2.6 $ 39.1

1987-1991 $ 0.2 $ 0.6 $ 0.3 $ 5.4 $ 5.8 $ 1.7 $ 4.4 $ 0.9 $ 9.6 $ 1.1 $ 1.9 $ 32.0

1992-1996 $ 0.4 $ 0.9 $ 0.4 $ 6.5 $ 6.2 $ 2.5 $ 5.1 $ 1.2 $ 12.7 $ 1.4 $ 2.4 $ 39.9

1997-2001 $ 0.2 $ 0.4 $ 0.2 $ 4.9 $ 3.2 $ 1.6 $ 2.9 $ 0.8 $ 9.6 $ 0.1 $ 0.4 $ 24.4

2002-2006 $ 0.2 $ 0.3 $ 0.2 $ 4.5 $ 3.6 $ 1.5 $ 2.6 $ 0.6 $ 12.4 $ 0.1 $ 0.4 $ 26.4

2007-2013 $ 0.1 $ 0.3 $ 0.2 $ 4.2 $ 3.7 $ 1.0 $ 2.6 $ 0.6 $ 16.4 $ 0.1 $ 0.4 $ 29.7

All Years $ 9.7 $ 18.7 $ 8.8 $ 78.4 $ 146.0 $ 45.1 $ 98.1 $ 24.2 $ 302.5 $ 31.3 $ 44.1 $ 807.0
114

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of 0 to a 

maximum of 100. Alabama’s COS values ranged from 7.61in the first period to 10.60 in the 

latest period. This indicates the region is becoming more specialized.

 

Alabama Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Alabama COS
115

Georgia: Total Estimated Impacts 

The production impacts for Georgia naturally follow the investment trend, which results in larger 

impacts in earlier years. On average annually, the ARC funds supported 386 jobs and $10.7 

million (in 2013 dollars) in earnings. The economic model examined only ARC investments so 

these estimate job and earnings impacts reflect only the results from the ARC portion of larger 

projects. Given that the average leverage ratio may be several times the ARC investment, the 

actual project impacts may be several times higher than these estimates.

Georgia: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)
116

Georgia: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 5,424 $ 139,154 $ 25.7 $ 174.5 $ 30.5

1972-1976 6,605 $ 168,013 $ 25.4 $ 204.5 $ 47.9

1977-1981 3,090 $ 84,128 $ 27.2 $ 110.1 $ 36.2

1982-1986 1,080 $ 29,271 $ 27.1 $ 38.5 $ 18.6

1987-1991 523 $ 16,496 $ 31.6 $ 19.9 $ 11.1

1992-1996 755 $ 26,536 $ 35.1 $ 27.5 $ 18.2

1997-2001 557 $ 21,897 $ 39.3 $ 27.4 $ 20.0

2002-2006 393 $ 17,412 $ 44.3 $ 19.9 $ 15.9

2007-2013 512 $ 22,115 $ 43.2 $ 29.3 $ 26.7

Summary 18,937 $ 525,022 $ 27.7 $ 651.5 $ 225.1

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 107 115 13 298 771 173 709 175 2,625 50 387 5,424

1972-1976 130 185 16 263 926 209 832 213 3,304 62 465 6,605

1977-1981 57 55 5 165 411 98 414 98 1,519 22 245 3,090

1982-1986 23 25 2 131 161 37 182 55 371 7 87 1,080

1987-1991 10 14 1 78 83 18 94 18 169 3 35 523

1992-1996 12 21 1 113 107 30 141 22 251 6 50 755

1997-2001 7 16 2 108 77 24 96 18 200 1 7 557

2002-2006 4 6 1 77 41 11 47 8 193 1 5 393

2007-2013 4 8 1 145 42 11 59 10 226 0 6 512

All Years 355 446 41 1,378 2,618 611 2,575 616 8,858 153 1,287 18,937

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $1.4 $ 3.1 $ 0.5 $10.5 $23.9 $ 7.4 $19.2 $ 3.2 $ 57.2 $ 2.4 $ 10.4 $ 139.2

1972-1976 $1.7 $ 5.0 $ 0.5 $ 9.2 $28.7 $ 8.9 $22.5 $ 3.9 $ 72.0 $ 2.9 $ 12.5 $ 168.0

1977-1981 $1.0 $ 1.2 $ 0.3 $ 5.9 $13.7 $ 4.6 $11.3 $ 1.6 $ 36.7 $ 1.2 $ 6.7 $ 84.1

1982-1986 $0.3 $ 0.5 $ 0.1 $ 3.9 $ 5.5 $ 1.6 $ 4.4 $ 0.8 $ 9.2 $ 0.4 $ 2.6 $ 29.3

1987-1991 $0.1 $ 0.3 $ 0.0 $ 2.7 $ 3.3 $ 0.9 $ 2.8 $ 0.4 $ 4.5 $ 0.2 $ 1.3 $ 16.5

1992-1996 $0.4 $ 0.5 $ 0.0 $ 4.1 $ 4.8 $ 1.4 $ 4.5 $ 0.7 $ 7.7 $ 0.4 $ 2.0 $ 26.5

1997-2001 $0.3 $ 0.3 $ 0.1 $ 4.8 $ 3.7 $ 1.2 $ 3.4 $ 0.6 $ 7.0 $ 0.1 $ 0.3 $ 21.9

2002-2006 $0.1 $ 0.2 $ 0.0 $ 3.7 $ 2.3 $ 0.5 $ 2.1 $ 0.3 $ 7.7 $ 0.0 $ 0.2 $ 17.4

2007-2013 $0.1 $ 0.3 $ 0.0 $ 6.5 $ 2.4 $ 0.5 $ 2.7 $ 0.3 $ 8.8 $ 0.0 $ 0.3 $ 22.1

All Years $5.6 $ 11.2 $ 1.5 $51.3 $88.3 $27.2 $72.9 $12.0 $210.8 $ 7.8 $ 36.3 $ 525.0
117

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Georgia’s COS values ranged from 21.29 in the first period to 14.10 in the latest 

period. This indicates the region is becoming more diversified.

Georgia Coefficient of Specialization

0.00

5.00

10.00

15.00

20.00

25.00

Georgia COS
118

Kentucky: Total Estimated Impacts 

The production impacts for Kentucky naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 613 jobs and 

$17.2 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

Kentucky: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)
119

Kentucky: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 8,838 $ 227,833 $ 25.8 $ 271.8 $ 47.6

1972-1976 8,293 $ 216,991 $ 26.2 $ 254.3 $ 59.5

1977-1981 4,268 $ 127,015 $ 29.8 $ 162.6 $ 53.5

1982-1986 1,676 $ 50,961 $ 30.4 $ 61.3 $ 29.6

1987-1991 1,400 $ 43,611 $ 31.2 $ 50.8 $ 28.5

1992-1996 1,628 $ 49,796 $ 30.6 $ 57.6 $ 38.1

1997-2001 1,344 $ 41,545 $ 30.9 $ 66.1 $ 48.4

2002-2006 1,108 $ 38,498 $ 34.8 $ 58.4 $ 46.6

2007-2013 1,477 $ 46,039 $ 31.2 $ 83.6 $ 76.2

Summary 30,032 $ 842,289 $ 28.0 $ 1,066.6 $ 427.9

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 262 247 79 154 1,182 346 997 298 4,607 166 502 8,838

1972-1976 250 244 66 348 1,126 320 989 286 4,033 152 478 8,293

1977-1981 114 117 86 530 612 162 640 158 1,537 59 253 4,268

1982-1986 43 53 30 240 234 63 279 53 559 23 99 1,676

1987-1991 35 48 20 201 188 48 232 51 475 19 82 1,400

1992-1996 35 61 17 234 207 65 280 53 548 22 106 1,628

1997-2001 25 52 11 383 162 48 234 44 362 2 21 1,344

2002-2006 20 29 12 317 110 37 136 22 408 2 17 1,108

2007-2013 20 33 14 360 120 37 153 29 688 1 21 1,477

All Years 802 884 335 2,767 3,938 1,126 3,940 996 13,217 447 1,581 30,032

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 4.4 $ 7.8 $ 4.0 $ 5.6 $ 39.1 $ 15.6 $ 25.3 $ 5.7 $ 98.8 $ 7.5 $ 14.1 $ 227.8

1972-1976 $ 4.2 $ 7.7 $ 3.3 $ 12.7 $ 37.2 $ 14.5 $ 25.1 $ 5.5 $ 86.5 $ 6.9 $ 13.4 $ 217.0

1977-1981 $ 1.2 $ 2.6 $ 6.6 $ 19.0 $ 23.6 $ 7.8 $ 16.5 $ 3.6 $ 35.2 $ 3.1 $ 7.7 $ 127.0

1982-1986 $ 0.5 $ 1.1 $ 2.1 $ 9.3 $ 9.7 $ 3.0 $ 6.8 $ 1.1 $ 13.1 $ 1.2 $ 3.1 $ 51.0

1987-1991 $ 0.2 $ 0.7 $ 1.1 $ 8.9 $ 8.3 $ 2.4 $ 5.5 $ 1.1 $ 11.6 $ 1.1 $ 2.8 $ 43.6

1992-1996 $ 0.3 $ 0.9 $ 1.2 $ 7.5 $ 8.6 $ 3.2 $ 6.3 $ 1.3 $ 14.8 $ 1.5 $ 4.2 $ 49.8

1997-2001 $ 0.2 $ 1.2 $ 0.8 $ 12.2 $ 6.5 $ 2.2 $ 5.5 $ 1.2 $ 10.8 $ 0.2 $ 0.8 $ 41.5

2002-2006 $ 0.0 $ 0.4 $ 0.6 $ 12.0 $ 5.5 $ 1.6 $ 4.0 $ 0.9 $ 12.7 $ 0.1 $ 0.7 $ 38.5

2007-2013 $ 0.0 $ 0.6 $ 0.8 $ 11.7 $ 5.8 $ 1.7 $ 4.3 $ 0.9 $ 19.1 $ 0.1 $ 0.9 $ 46.0

All Years $ 11.0 $ 23.0 $ 20.6 $ 98.9 $ 144.2 $ 51.9 $ 99.4 $ 21.4 $ 302.7 $ 21.6 $ 47.7 $ 842.3
120

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Kentucky’s COS was 17.55 in 1965, increased over the subsequent 3 periods, 

then declined steadily for three periods, and finally rose to a value of 17.83 in the most recent 

period.

Kentucky Coefficient of Specialization

0.00

5.00

10.00

15.00

20.00

25.00

Kentucky COS
121

Maryland: Total Estimated Impacts 

The production impacts for Maryland naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 218 jobs and 

$7.04 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

Maryland: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)

Improving Physical 

Accessibility

39%

Developing Human 

Resource Capabilities

36%

Developing 

Appalachian 

Economies

15%

Catalyzing Regional 

Development

10%

Maryland ARC Funds Expended by Investment Classification (Cumulative Total)
122

Maryland: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 2,697 $ 82,382 $ 30.5 $ 85.3 $ 14.9

1972-1976 3,702 $ 115,754 $ 31.3 $ 119.5 $ 28.0

1977-1981 2,149 $ 70,655 $ 32.9 $ 75.3 $ 24.8

1982-1986 456 $ 15,314 $ 33.6 $ 16.2 $ 7.8

1987-1991 295 $ 10,270 $ 34.8 $ 11.7 $ 6.6

1992-1996 531 $ 18,265 $ 34.4 $ 18.3 $ 12.1

1997-2001 289 $ 10,181 $ 35.3 $ 15.0 $ 11.0

2002-2006 266 $ 10,863 $ 40.8 $ 14.0 $ 11.2

2007-2013 275 $ 11,391 $ 41.4 $ 16.4 $ 14.9

Summary 10,660 $ 345,074 $ 32.4 $ 371.8 $ 131.3

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 47 76 9 169 385 119 371 89 1,239 39 155 2,697

1972-1976 68 107 13 305 565 171 535 121 1,546 52 218 3,702

1977-1981 36 53 13 167 286 89 313 76 946 33 138 2,149

1982-1986 11 14 5 67 77 21 85 24 115 6 30 456

1987-1991 7 10 2 42 42 12 53 22 83 5 18 295

1992-1996 11 20 3 65 69 26 97 16 186 7 31 531

1997-2001 4 11 2 53 33 14 49 15 104 1 4 289

2002-2006 4 7 1 61 25 12 33 6 116 0 3 266

2007-2013 4 6 1 65 23 9 32 6 125 0 3 275

All Years 191 303 48 995 1,506 473 1,567 374 4,459 143 600 10,660

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 1.2 $ 1.9 $ 0.4 $ 7.1 $ 18.2 $ 6.2 $ 10.1 $ 1.8 $ 28.5 $ 2.2 $ 4.9 $ 82.4

1972-1976 $ 1.7 $ 2.6 $ 0.6 $ 12.8 $ 26.7 $ 8.9 $ 14.6 $ 2.4 $ 35.6 $ 2.9 $ 6.9 $ 115.8

1977-1981 $ 0.5 $ 1.1 $ 1.4 $ 6.6 $ 15.1 $ 5.1 $ 8.1 $ 1.7 $ 24.1 $ 2.2 $ 4.9 $ 70.7

1982-1986 $ 0.1 $ 0.3 $ 0.4 $ 2.5 $ 4.1 $ 1.1 $ 2.1 $ 0.5 $ 2.9 $ 0.4 $ 1.1 $ 15.3

1987-1991 $ 0.1 $ 0.2 $ 0.1 $ 1.7 $ 2.3 $ 0.7 $ 1.3 $ 0.4 $ 2.2 $ 0.3 $ 0.8 $ 10.3

1992-1996 $ 0.2 $ 0.1 $ 0.3 $ 2.5 $ 3.5 $ 1.2 $ 2.4 $ 0.4 $ 5.7 $ 0.5 $ 1.5 $ 18.3

1997-2001 $ 0.0 $ 0.2 $ 0.1 $ 2.2 $ 1.8 $ 0.7 $ 1.2 $ 0.5 $ 3.3 $ 0.0 $ 0.2 $ 10.2

2002-2006 $ 0.0 $ 0.3 $ 0.1 $ 2.8 $ 1.4 $ 0.6 $ 1.0 $ 0.2 $ 4.1 $ 0.0 $ 0.2 $ 10.9

2007-2003 $ 0.1 $ 0.2 $ 0.1 $ 3.0 $ 1.5 $ 0.5 $ 1.0 $ 0.2 $ 4.7 $ 0.0 $ 0.2 $ 11.4

All Years $ 3.9 $ 6.9 $ 3.3 $ 41.2 $ 74.6 $ 25.0 $ 41.8 $ 8.2 $ 111.2 $ 8.5 $ 20.6 $ 345.1
123

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Maryland’s COS was 9.99 in 1965, decreased over the subsequent 6 periods, 

then increased to a value of 9.64 in the most recent period.

Maryland Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Maryland COS
124

Mississippi: Total Estimated Impacts

The production impacts for Mississippi naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 308 jobs and $7.8

million (in 2013 dollars) in earnings. The economic model examined only ARC investments so 

these estimate job and earnings impacts reflect only the results from the ARC portion of larger 

projects. Given that the average leverage ratio may be several times the ARC investment, the 

actual project impacts may be several times higher than these estimates.

Mississippi: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)
125

Mississippi: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 2,209 $ 50,001 $ 22.6 $ 73.3 $ 12.8

1972-1976 5,325 $ 116,500 $ 21.9 $ 163.7 $ 38.3

1977-1981 3,150 $ 78,626 $ 25.0 $ 112.8 $ 37.1

1982-1986 1,051 $ 26,791 $ 25.5 $ 35.1 $ 17.0

1987-1991 611 $ 16,875 $ 27.6 $ 22.8 $ 12.8

1992-1996 641 $ 19,366 $ 30.2 $ 23.8 $ 15.7

1997-2001 636 $ 20,656 $ 32.5 $ 31.1 $ 22.8

2002-2006 624 $ 22,112 $ 35.4 $ 32.4 $ 25.9

2007-2013 834 $ 29,784 $ 35.7 $ 47.8 $ 43.6

Summary 15,081 $ 380,712 $ 25.2 $ 542.9 $ 226.0

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 66 33 3 174 335 67 293 72 968 34 163 2,209

1972-1976 148 96 6 215 726 155 641 175 2,702 86 375 5,325

1977-1981 78 83 3 245 455 90 432 102 1,380 41 241 3,150

1982-1986 27 72 1 112 149 30 163 39 364 17 77 1,051

1987-1991 16 12 1 79 100 19 104 20 208 9 44 611

1992-1996 14 13 1 86 93 20 113 26 224 9 43 641

1997-2001 13 13 1 170 92 26 115 20 175 1 10 636

2002-2006 12 10 3 146 70 25 76 13 257 1 10 624

2007-2013 14 13 5 208 92 25 96 17 350 1 12 834

All Years 386 346 24 1,434 2,113 457 2,032 485 6,628 199 975 15,081

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 1.3 $ 1.0 $ 0.1 $ 5.3 $ 9.2 $ 2.7 $ 6.9 $ 1.4 $ 16.7 $ 1.6 $ 3.9 $ 50.0

1972-1976 $ 3.0 $ 2.7 $ 0.2 $ 6.6 $ 19.8 $ 6.2 $ 15.1 $ 3.5 $ 46.5 $ 3.9 $ 9.0 $ 116.5

1977-1981 $ 1.3 $ 2.2 $ 0.2 $ 8.1 $ 13.5 $ 3.8 $ 10.3 $ 2.1 $ 28.8 $ 2.1 $ 6.3 $ 78.6

1982-1986 $ 0.2 $ 1.5 $ 0.1 $ 3.6 $ 4.6 $ 1.2 $ 3.5 $ 0.7 $ 8.3 $ 0.9 $ 2.1 $ 26.8

1987-1991 $ 0.1 $ 0.2 $ 0.0 $ 2.4 $ 3.6 $ 0.7 $ 2.4 $ 0.4 $ 5.2 $ 0.5 $ 1.4 $ 16.9

1992-1996 $ 0.2 $ 0.3 $ 0.0 $ 3.0 $ 3.5 $ 0.9 $ 2.7 $ 0.6 $ 6.1 $ 0.6 $ 1.4 $ 19.4

1997-2001 $ 0.1 $ 0.2 $ 0.0 $ 6.0 $ 3.7 $ 1.2 $ 2.9 $ 0.5 $ 5.4 $ 0.1 $ 0.4 $ 20.7

2002-2006 $ 0.1 $ 0.4 $ 0.1 $ 5.2 $ 3.3 $ 1.2 $ 2.4 $ 0.4 $ 8.5 $ 0.1 $ 0.4 $ 22.1

2007-2013 $ 0.2 $ 0.5 $ 0.2 $ 7.5 $ 4.6 $ 1.2 $ 3.2 $ 0.5 $ 11.2 $ 0.1 $ 0.6 $ 29.8

All Years $ 6.5 $ 9.1 $ 0.9 $ 47.6 $ 65.9 $ 19.1 $ 49.4 $ 10.2 $ 136.7 $ 9.8 $ 25.4 $ 380.7
126

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Mississippi’s COS was 20.50 in 1965, increased over the subsequent 4 periods, 

then declined to a value of 20.13 in the most recent period.

Mississippi Coefficient of Specialization

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Mississippi COS
127

New York: Total Estimated Impacts

The production impacts for New York naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 332 jobs and 

$10.7 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

New York: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)

Improving Physical 

Accessibility

27%

Developing Human 

Resource Capabilities

45%

Developing 

Appalachian 

Economies

12%

Catalyzing Regional 

Development

16%

New York ARC Funds Expended by Investment Classification (Cumulative Total) 
128

New York: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 3,825 $ 122,422 $ 32.0 $ 114.3 $ 20.0

1972-1976 5,429 $ 172,195 $ 31.7 $ 164.6 $ 38.5

1977-1981 3,569 $ 111,918 $ 31.4 $ 129.7 $ 42.7

1982-1986 915 $ 28,475 $ 31.1 $ 31.4 $ 15.1

1987-1991 624 $ 21,111 $ 33.8 $ 20.9 $ 11.7

1992-1996 645 $ 23,031 $ 35.7 $ 21.6 $ 14.3

1997-2001 472 $ 17,080 $ 36.2 $ 23.1 $ 16.9

2002-2006 360 $ 13,727 $ 38.1 $ 17.0 $ 13.6

2007-2013 419 $ 16,268 $ 38.8 $ 21.5 $ 19.6

Summary 16,259 $ 526,228 $ 32.4 $ 544.0 $ 192.4

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 88 92 7 171 533 156 444 123 1,812 38 360 3,825

1972-1976 117 129 9 161 693 274 603 176 2,693 55 518 5,429

1977-1981 74 79 7 267 467 150 458 200 1,496 32 340 3,569

1982-1986 20 24 3 58 127 33 127 58 373 8 85 915

1987-1991 12 19 1 37 69 22 85 27 293 5 53 624

1992-1996 12 28 1 32 71 29 92 20 301 6 54 645

1997-2001 7 16 1 55 54 23 69 17 218 1 9 472

2002-2006 5 7 1 35 32 12 35 7 218 1 6 360

2007-2013 5 9 1 42 31 13 36 9 265 0 7 419

All Years 340 403 33 860 2,078 713 1,949 637 7,669 146 1,432 16,259

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 1.9 $ 2.4 $ 0.3 $ 8.2 $ 24.4 $ 8.2 $ 11.9 $ 2.0 $ 47.2 $ 2.0 $ 14.0 $ 122.4

1972-1976 $ 2.5 $ 3.3 $ 0.4 $ 7.7 $ 31.7 $ 14.3 $ 16.1 $ 2.9 $ 70.2 $ 2.8 $ 20.2 $ 172.2

1977-1981 $ 0.6 $ 1.5 $ 0.5 $ 10.7 $ 22.7 $ 8.3 $ 11.3 $ 3.1 $ 37.8 $ 1.9 $ 13.4 $ 111.9

1982-1986 $ 0.2 $ 0.6 $ 0.2 $ 2.1 $ 6.5 $ 1.8 $ 2.9 $ 1.0 $ 9.5 $ 0.5 $ 3.3 $ 28.5

1987-1991 $ 0.2 $ 0.4 $ 0.1 $ 1.5 $ 3.9 $ 1.3 $ 2.0 $ 0.7 $ 8.3 $ 0.3 $ 2.3 $ 21.1

1992-1996 $ 0.2 $ 0.6 $ 0.0 $ 1.2 $ 4.3 $ 1.5 $ 2.2 $ 0.6 $ 9.4 $ 0.4 $ 2.6 $ 23.0

1997-2001 $ 0.0 $ 0.4 $ 0.1 $ 2.1 $ 3.2 $ 1.3 $ 1.7 $ 0.6 $ 7.3 $ 0.1 $ 0.4 $ 17.1

2002-2006 $ 0.0 $ 0.2 $ 0.1 $ 1.5 $ 2.1 $ 0.6 $ 1.0 $ 0.2 $ 7.6 $ 0.0 $ 0.3 $ 13.7

2007-2013 $ 0.1 $ 0.2 $ 0.1 $ 1.7 $ 2.1 $ 0.6 $ 1.0 $ 0.3 $ 9.8 $ 0.0 $ 0.4 $ 16.3

All Years $ 5.8 $ 9.6 $ 1.7 $ 36.8 $ 100.8 $ 38.0 $ 50.1 $ 11.5 $ 207.0 $ 8.2 $ 56.9 $ 526.2
129

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. New York’s COS values ranged from 9.93 in the first period to 11.11 in the latest 

period. This indicates the region is becoming more specialized.

New York Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

New York COS
130

North Carolina: Total Estimated Impacts

The production impacts for North Carolina naturally follow the investment trend, which results 

in larger impacts in earlier years. On average annually, the ARC funds supported 425 jobs and 

$11.7 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

North Carolina: ARC Funds Expended for Area Development Program by Investment 

Classification (Cumulative Total 1965-2013)
131

North Carolina: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 5,885 $ 152,806 $ 26.0 $ 184.2 $ 32.2

1972-1976 7,356 $ 189,937 $ 25.8 $ 225.1 $ 52.7

1977-1981 3,457 $ 96,832 $ 28.0 $ 123.8 $ 40.7

1982-1986 1,067 $ 28,778 $ 27.0 $ 35.8 $ 17.3

1987-1991 674 $ 21,175 $ 31.4 $ 24.3 $ 13.7

1992-1996 799 $ 26,127 $ 32.7 $ 28.4 $ 18.8

1997-2001 555 $ 19,211 $ 34.6 $ 26.4 $ 19.3

2002-2006 490 $ 18,967 $ 38.7 $ 23.5 $ 18.7

2007-2013 524 $ 19,696 $ 37.6 $ 29.0 $ 26.5

Summary 20,806 $ 573,529 $ 27.6 $ 700.5 $ 239.8

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 139 137 5 291 821 224 750 192 2,852 83 393 5,885

1972-1976 170 171 5 266 997 284 908 240 3,722 105 486 7,356

1977-1981 76 72 3 243 475 134 479 113 1,572 39 251 3,457

1982-1986 24 48 1 96 142 37 165 49 419 11 75 1,067

1987-1991 14 20 1 76 98 25 113 22 255 7 43 674

1992-1996 14 25 1 100 103 33 141 27 296 8 50 799

1997-2001 9 19 1 94 68 22 91 18 225 1 7 555

2002-2006 7 9 1 76 46 13 54 10 268 1 6 490

2007-2013 7 9 1 95 44 15 53 11 283 1 6 524

All Years 459 510 19 1,338 2,795 786 2,753 682 9,892 254 1,317 20,806

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 2.0 $ 2.8 $ 0.2 $ 8.7 $ 26.8 $ 10.3 $ 19.5 $ 4.1 $ 63.2 $ 3.3 $ 11.8 $ 152.8

1972-1976 $ 2.5 $ 3.5 $ 0.2 $ 8.0 $ 32.6 $ 13.1 $ 23.6 $ 5.2 $ 82.5 $ 4.2 $ 14.6 $ 189.9

1977-1981 $ 1.1 $ 1.1 $ 0.3 $ 7.4 $ 17.3 $ 6.7 $ 12.0 $ 2.3 $ 39.1 $ 1.9 $ 7.6 $ 96.8

1982-1986 $ 0.2 $ 0.8 $ 0.1 $ 2.6 $ 5.4 $ 1.9 $ 3.7 $ 1.0 $ 10.3 $ 0.6 $ 2.2 $ 28.8

1987-1991 $ 0.2 $ 0.4 $ 0.0 $ 2.6 $ 4.4 $ 1.3 $ 2.8 $ 0.5 $ 7.2 $ 0.3 $ 1.5 $ 21.2

1992-1996 $ 0.4 $ 0.4 $ 0.0 $ 3.2 $ 4.7 $ 1.6 $ 3.5 $ 0.8 $ 9.2 $ 0.5 $ 1.9 $ 26.1

1997-2001 $ 0.3 $ 0.3 $ 0.0 $ 3.5 $ 3.3 $ 1.1 $ 2.4 $ 0.6 $ 7.4 $ 0.1 $ 0.3 $ 19.2

2002-2006 $ 0.1 $ 0.2 $ 0.0 $ 3.1 $ 2.5 $ 0.8 $ 1.8 $ 0.5 $ 9.6 $ 0.1 $ 0.3 $ 19.0

2007-2013 $ 0.1 $ 0.2 $ 0.0 $ 3.6 $ 2.5 $ 0.8 $ 1.7 $ 0.3 $ 10.0 $ 0.1 $ 0.3 $ 19.7

All Years $ 6.9 $ 9.7 $ 0.9 $ 42.7 $ 99.5 $ 37.4 $ 71.0 $ 15.3 $ 238.6 $ 11.0 $ 40.4 $ 573.5
132

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. North Carolina’s COS was 16.37 in 1965, remained steady over the subsequent 3 

periods, then declined to a value of 9.40 in the most recent period.

 

North Carolina Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

North Carolina COS
133

Ohio: Total Estimated Impacts

The production impacts for Ohio naturally follow the investment trend, which results in larger 

impacts in earlier years. On average annually, the ARC funds supported 445 jobs and $14.1 

million (in 2013 dollars) in earnings. The economic model examined only ARC investments so 

these estimate job and earnings impacts reflect only the results from the ARC portion of larger 

projects. Given that the average leverage ratio may be several times the ARC investment, the 

actual project impacts may be several times higher than these estimates.

Ohio: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)

Improving Physical 

Accessibility

31%

Developing Human 

Resource Capabilities

49%

Developing 

Appalachian 

Economies

9%

Catalyzing Regional 

Development

11%

Ohio ARC Funds Expended by Investment Classification (Cumulative Total)
134

Ohio: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 6,645 $ 200,207 $ 30.1 $ 200.1 $ 35.0

1972-1976 7,027 $ 212,025 $ 30.2 $ 211.9 $ 49.6

1977-1981 3,370 $ 111,909 $ 33.2 $ 116.0 $ 38.2

1982-1986 1,008 $ 32,981 $ 32.7 $ 32.2 $ 15.5

1987-1991 700 $ 23,183 $ 33.1 $ 24.8 $ 13.9

1992-1996 1,034 $ 36,153 $ 35.0 $ 35.0 $ 23.1

1997-2001 749 $ 26,110 $ 34.9 $ 35.7 $ 26.1

2002-2006 620 $ 22,952 $ 37.0 $ 30.5 $ 24.3

2007-2013 647 $ 23,696 $ 36.7 $ 36.4 $ 33.2

Summary 21,799 $ 689,217 $ 31.6 $ 722.5 $ 258.9

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 136 201 39 231 867 270 793 221 3,391 73 423 6,645

1972-1976 143 246 41 237 905 308 835 233 3,553 77 449 7,027

1977-1981 72 94 36 250 454 132 461 108 1,510 29 224 3,370

1982-1986 23 32 14 92 139 41 155 34 398 9 71 1,008

1987-1991 16 26 7 80 96 29 114 27 252 6 46 700

1992-1996 22 43 9 122 139 41 177 33 370 9 69 1,034

1997-2001 14 34 6 147 100 30 124 26 256 1 11 749

2002-2006 11 14 4 113 68 18 70 13 299 1 9 620

2007-2013 10 15 6 137 65 17 69 13 305 1 8 647

All Years 447 705 162 1,410 2,832 887 2,797 708 10,335 207 1,310 21,799

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 1.7 $ 5.9 $ 2.0 $ 11.6 $ 41.6 $ 13.6 $ 20.6 $ 4.6 $ 81.5 $ 3.8 $ 13.1 $ 200.2

1972-1976 $ 1.8 $ 7.2 $ 2.1 $ 11.9 $ 43.5 $ 15.5 $ 21.7 $ 4.9 $ 85.4 $ 4.0 $ 14.0 $ 212.0

1977-1981 $ 0.7 $ 2.2 $ 2.3 $ 12.5 $ 24.9 $ 7.4 $ 12.1 $ 2.2 $ 38.6 $ 1.7 $ 7.2 $ 111.9

1982-1986 $ 0.1 $ 0.7 $ 0.8 $ 4.1 $ 8.1 $ 2.2 $ 3.5 $ 0.6 $ 10.0 $ 0.6 $ 2.3 $ 33.0

1987-1991 $ 0.1 $ 0.4 $ 0.4 $ 3.2 $ 5.7 $ 1.5 $ 2.6 $ 0.5 $ 6.6 $ 0.4 $ 1.7 $ 23.2

1992-1996 $ 0.2 $ 0.8 $ 0.4 $ 5.0 $ 8.7 $ 2.1 $ 4.3 $ 0.8 $ 10.4 $ 0.7 $ 2.8 $ 36.2

1997-2001 $ 0.1 $ 0.6 $ 0.2 $ 6.0 $ 5.8 $ 1.6 $ 3.2 $ 0.7 $ 7.5 $ 0.1 $ 0.5 $ 26.1

2002-2006 $ 0.1 $ 0.4 $ 0.1 $ 5.1 $ 4.2 $ 1.0 $ 2.3 $ 0.5 $ 9.0 $ 0.1 $ 0.4 $ 23.0

2007-2013 $ 0.1 $ 0.4 $ 0.2 $ 5.5 $ 4.2 $ 0.9 $ 2.1 $ 0.4 $ 9.3 $ 0.1 $ 0.4 $ 23.7

All Years $ 4.9 $ 18.7 $ 8.4 $ 64.8 $ 146.6 $ 45.7 $ 72.4 $ 15.2 $ 258.3 $ 11.3 $ 42.5 $ 689.2
135

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Ohio’s COS was 9.98 in 1965, increased over the subsequent 2 periods, fell in 

period 4, then increased steadily to a value of 11.10 in the most recent period.

Ohio Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Ohio COS
136

Pennsylvania: Total Estimated Impacts

The production impacts for Pennsylvania naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 858 jobs and 

$17.2 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

Pennsylvania: ARC Funds Expended for Area Development Program by Investment 

Classification (Cumulative Total 1965-2013)
137

Pennsylvania: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 14,006 $ 485,131 $ 34.6 $ 466.7 $ 81.7

1972-1976 14,258 $ 503,325 $ 35.3 $ 462.3 $ 108.2

1977-1981 6,194 $ 231,874 $ 37.4 $ 236.7 $ 77.9

1982-1986 2,045 $ 72,342 $ 35.4 $ 65.3 $ 31.5

1987-1991 1,413 $ 50,121 $ 35.5 $ 48.4 $ 27.2

1992-1996 1,488 $ 57,997 $ 39.0 $ 53.1 $ 35.1

1997-2001 1,002 $ 40,071 $ 40.0 $ 48.1 $ 35.2

2002-2006 765 $ 33,430 $ 43.7 $ 35.8 $ 28.6

2007-2013 880 $ 39,272 $ 44.6 $ 44.0 $ 40.2

Summary 42,051 $ 1,513,563 $ 36.0 $ 1,460.5 $ 465.4

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 169 405 516 913 1,983 618 1,839 514 5,999 185 865 14,006

1972-1976 197 433 115 1,557 2,425 653 2,072 468 5,241 181 915 14,258

1977-1981 80 162 94 659 926 301 923 303 2,275 76 395 6,194

1982-1986 29 59 26 73 311 86 293 76 938 27 126 2,045

1987-1991 18 44 10 41 144 50 191 81 735 22 76 1,413

1992-1996 16 51 8 33 136 53 210 66 821 22 73 1,488

1997-2001 11 38 4 102 91 39 143 34 527 2 12 1,002

2002-2006 8 14 3 71 53 21 71 16 499 1 8 765

2007-2013 7 17 3 75 51 22 73 19 603 1 9 880

All Years 535 1,223 780 3,524 6,120 1,843 5,815 1,578 17,639 517 2,478 42,051

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 2.2 $ 12.7 $ 28.7 $ 45.5 $ 88.5 $ 32.3 $ 52.8 $ 13.0 $ 170.0 $ 9.8 $ 29.7 $ 485.1

1972-1976 $ 2.5 $ 13.6 $ 6.4 $ 77.6 $ 108.2 $ 34.2 $ 59.5 $ 11.8 $ 148.5 $ 9.6 $ 31.4 $ 503.3

1977-1981 $ 1.1 $ 3.9 $ 7.9 $ 33.1 $ 48.3 $ 17.3 $ 26.1 $ 7.5 $ 67.4 $ 4.7 $ 14.4 $ 231.9

1982-1986 $ 0.3 $ 1.3 $ 1.8 $ 3.4 $ 16.6 $ 4.6 $ 7.6 $ 1.9 $ 28.4 $ 1.7 $ 4.6 $ 72.3

1987-1991 $ 0.2 $ 0.7 $ 0.4 $ 2.0 $ 7.8 $ 2.7 $ 5.1 $ 2.2 $ 24.3 $ 1.4 $ 3.3 $ 50.1

1992-1996 $ 0.3 $ 1.2 $ 0.6 $ 1.6 $ 7.7 $ 3.1 $ 5.9 $ 2.2 $ 30.1 $ 1.6 $ 3.6 $ 58.0

1997-2001 $ 0.1 $ 0.8 $ 0.3 $ 4.7 $ 5.5 $ 2.3 $ 4.2 $ 1.5 $ 20.0 $ 0.2 $ 0.6 $ 40.1

2002-2006 $ 0.0 $ 0.6 $ 0.2 $ 3.7 $ 3.4 $ 1.3 $ 2.5 $ 0.8 $ 20.4 $ 0.1 $ 0.4 $ 33.4

2007-2013 $ 0.1 $ 0.6 $ 0.3 $ 3.8 $ 3.3 $ 1.3 $ 2.7 $ 0.8 $ 25.8 $ 0.1 $ 0.5 $ 39.3

All Years $ 6.9 $ 35.4 $ 46.7 $ 175.4 $ 289.2 $ 99.1 $ 166.5 $ 41.7 $ 535.0 $ 29.2 $ 88.4 $ 1,513.6
138

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Pennsylvania’s COS was 8.95 in 1965, decreased over the subsequent 6 periods, 

then increased to a value of 5.77 in the most recent period.

Pennsylvania Coefficient of Specialization

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

Pennsylvania COS
139

South Carolina: Total Estimated Impacts

The production impacts for South Carolina naturally follow the investment trend, which results 

in larger impacts in earlier years. On average annually, the ARC funds supported 365 jobs and 

$10.8 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

South Carolina: ARC Funds Expended for Area Development Program by Investment 

Classification (Cumulative Total 1965-2013)
140

South Carolina: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 5,470 $ 151,325 $ 27.7 $ 184.1 $ 32.2

1972-1976 6,169 $ 170,145 $ 27.6 $ 202.5 $ 47.4

1977-1981 3,217 $ 93,592 $ 29.1 $ 121.9 $ 40.1

1982-1986 773 $ 23,432 $ 30.3 $ 29.4 $ 14.2

1987-1991 517 $ 17,846 $ 34.5 $ 20.8 $ 11.6

1992-1996 662 $ 24,249 $ 36.6 $ 25.2 $ 16.6

1997-2001 435 $ 17,675 $ 40.6 $ 22.4 $ 16.4

2002-2006 289 $ 13,156 $ 45.4 $ 16.0 $ 12.8

2007-2013 375 $ 16,606 $ 44.3 $ 22.2 $ 20.2

Summary 17,906 $ 528,026 $ 29.5 $ 644.5 $ 211.6

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 49 161 3 270 786 173 707 176 2,802 42 299 5,470

1972-1976 57 180 4 226 938 190 783 199 3,207 48 336 6,169

1977-1981 31 84 2 232 470 96 455 98 1,531 17 201 3,217

1982-1986 10 25 1 100 155 25 141 23 236 4 54 773

1987-1991 5 18 0 63 96 17 92 16 173 3 32 517

1992-1996 7 25 1 75 110 26 121 19 235 4 41 662

1997-2001 5 17 0 102 85 19 82 13 106 1 6 435

2002-2006 3 5 0 69 45 8 38 6 111 0 4 289

2007-2013 3 6 0 106 37 9 43 7 159 0 4 375

All Years 169 523 11 1,242 2,721 565 2,462 558 8,560 118 977 17,906

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 0.5 $ 3.6 $ 0.2 $ 9.4 $ 25.4 $ 7.7 $ 19.1 $ 4.9 $ 69.6 $ 2.2 $ 8.8 $ 151.3

1972-1976 $ 0.6 $ 4.0 $ 0.2 $ 7.9 $ 30.4 $ 8.4 $ 21.1 $ 5.6 $ 79.7 $ 2.5 $ 9.9 $ 170.1

1977-1981 $ 0.2 $ 1.6 $ 0.1 $ 8.5 $ 17.0 $ 4.8 $ 12.1 $ 2.4 $ 39.7 $ 1.0 $ 6.1 $ 93.6

1982-1986 $ 0.0 $ 0.5 $ 0.0 $ 3.5 $ 6.0 $ 1.3 $ 3.5 $ 0.5 $ 6.1 $ 0.2 $ 1.7 $ 23.4

1987-1991 $ 0.0 $ 0.4 $ 0.0 $ 2.6 $ 4.4 $ 0.9 $ 2.5 $ 0.5 $ 5.1 $ 0.2 $ 1.2 $ 17.8

1992-1996 $ 0.1 $ 0.6 $ 0.0 $ 2.9 $ 5.5 $ 1.4 $ 3.5 $ 0.6 $ 7.6 $ 0.3 $ 1.7 $ 24.2

1997-2001 $ 0.0 $ 0.4 $ 0.0 $ 4.6 $ 4.6 $ 1.0 $ 2.6 $ 0.5 $ 3.7 $ 0.1 $ 0.3 $ 17.7

2002-2006 $ 0.0 $ 0.1 $ 0.0 $ 3.3 $ 2.9 $ 0.5 $ 1.6 $ 0.2 $ 4.3 $ 0.0 $ 0.2 $ 13.2

2007-2013 $ 0.0 $ 0.2 $ 0.0 $ 4.9 $ 2.4 $ 0.5 $ 1.8 $ 0.2 $ 6.2 $ 0.0 $ 0.2 $ 16.6

All Years $ 1.5 $ 11.4 $ 0.6 $ 47.6 $ 98.5 $ 26.5 $ 67.6 $ 15.5 $ 222.1 $ 6.5 $ 30.1 $ 528.0
141

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. South Carolina’s COS values ranged from 22.26 in the first period to 11.00 in the 

latest period. This indicates the region is becoming more diversified. 

South Carolina Coefficient of Specialization

0.00

5.00

10.00

15.00

20.00

25.00

South Carolina COS
142

Tennessee: Total Estimated Impacts

The production impacts for Tennessee naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 512 jobs and 

$16.0 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

Tennessee: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)
143

Tennessee: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 6,156 $ 178,754 $ 29.0 $ 195.9 $ 34.3

1972-1976 8,090 $ 233,840 $ 28.9 $ 254.4 $ 59.5

1977-1981 4,846 $ 154,359 $ 31.9 $ 181.7 $ 59.8

1982-1986 1,541 $ 49,862 $ 32.4 $ 54.0 $ 26.1

1987-1991 1,065 $ 37,426 $ 35.1 $ 39.7 $ 22.3

1992-1996 1,120 $ 40,526 $ 36.2 $ 40.4 $ 26.7

1997-2001 830 $ 31,637 $ 38.1 $ 40.3 $ 29.4

2002-2006 575 $ 23,185 $ 40.3 $ 29.2 $ 23.3

2007-2013 853 $ 34,557 $ 40.5 $ 49.4 $ 45.1

Summary 25,077 $ 784,146 $ 31.3 $ 885.0 $ 326.5

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 172 143 17 398 910 199 795 201 2,837 116 368 6,156

1972-1976 222 181 22 464 1,163 260 1,025 281 3,839 155 479 8,090

1977-1981 122 100 15 624 763 160 745 222 1,670 94 331 4,846

1982-1986 38 38 4 246 242 52 272 49 468 27 105 1,541

1987-1991 25 28 3 178 152 36 187 35 341 17 64 1,065

1992-1996 23 32 2 142 144 41 193 42 420 18 65 1,120

1997-2001 15 24 1 241 103 32 149 25 229 1 10 830

2002-2006 10 10 1 139 61 17 70 11 248 1 7 575

2007-2013 12 14 2 244 73 22 96 17 363 1 9 853

All Years 638 570 68 2,676 3,612 818 3,531 881 10,414 430 1,438 25,077

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $1.8 $ 4.2 $ 1.1 $ 14.4 $ 32.7 $ 9.1 $22.0 $ 4.4 $ 71.4 $ 6.6 $ 11.1 $178.8

1972-1976 $2.3 $ 5.3 $ 1.4 $ 16.8 $ 41.8 $11.9 $28.4 $ 6.2 $ 96.6 $ 8.7 $ 14.4 $233.8

1977-1981 $0.7 $ 2.4 $ 1.9 $ 22.8 $ 30.6 $ 7.8 $20.5 $ 4.6 $ 46.4 $ 6.4 $ 10.2 $154.4

1982-1986 $0.2 $ 1.0 $ 0.5 $ 8.8 $ 10.5 $ 2.4 $ 6.8 $ 1.0 $ 13.4 $ 1.9 $ 3.3 $ 49.9

1987-1991 $0.1 $ 0.7 $ 0.2 $ 6.6 $ 7.1 $ 1.8 $ 5.0 $ 0.9 $ 11.3 $ 1.3 $ 2.4 $ 37.4

1992-1996 $0.2 $ 0.6 $ 0.2 $ 5.3 $ 7.1 $ 2.1 $ 5.3 $ 1.3 $ 14.4 $ 1.6 $ 2.5 $ 40.5

1997-2001 $0.0 $ 0.4 $ 0.1 $ 9.7 $ 5.3 $ 1.5 $ 4.5 $ 0.9 $ 8.6 $ 0.1 $ 0.4 $ 31.6

2002-2006 $0.0 $ 0.2 $ 0.0 $ 6.2 $ 3.5 $ 0.6 $ 2.6 $ 0.5 $ 9.3 $ 0.1 $ 0.3 $ 23.2

2007-2013 $0.3 $ 0.3 $ 0.1 $ 10.1 $ 4.4 $ 0.7 $ 3.6 $ 0.6 $ 13.7 $ 0.1 $ 0.4 $ 34.6

All Years $5.7 $ 15.1 $ 5.5 $100.7 $143.0 $38.0 $98.7 $20.5 $285.1 $ 26.9 $ 45.0 $784.1
144

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Tennessee’s COS values ranged from 13.23 in the first period to 11.03 in the 

latest period. This indicates the region is becoming more diversified. 

Tennessee Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Tennessee COS
145

Virginia: Total Estimated Impacts

The production impacts for Virginia naturally follow the investment trend, which results in larger 

impacts in earlier years. On average annually, the ARC funds supported 353 jobs and $9.2

million (in 2013 dollars) in earnings. The economic model examined only ARC investments so 

these estimate job and earnings impacts reflect only the results from the ARC portion of larger 

projects. Given that the average leverage ratio may be several times the ARC investment, the 

actual project impacts may be several times higher than these estimates.

Virginia: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)
146

Virginia: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 5,338 $ 127,950 $ 24.0 $ 158.6 $ 27.8

1972-1976 5,541 $ 134,899 $ 24.3 $ 167.2 $ 39.1

1977-1981 2,906 $ 84,837 $ 29.2 $ 103.9 $ 34.2

1982-1986 709 $ 19,455 $ 27.5 $ 24.3 $ 11.7

1987-1991 559 $ 15,862 $ 28.4 $ 22.0 $ 12.3

1992-1996 789 $ 23,150 $ 29.4 $ 27.5 $ 18.2

1997-2001 541 $ 16,602 $ 30.7 $ 25.4 $ 18.5

2002-2006 465 $ 14,333 $ 30.8 $ 23.0 $ 18.3

2007-2013 455 $ 14,386 $ 31.6 $ 24.4 $ 22.3

Summary 17,303 $ 451,475 $ 26.1 $ 576.3 $ 202.5

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 135 110 33 117 681 204 655 179 2,746 54 423 5,338

1972-1976 149 113 38 229 772 226 718 185 2,602 55 454 5,541

1977-1981 77 58 44 400 488 113 484 94 848 23 278 2,906

1982-1986 17 16 11 91 110 28 125 37 202 6 66 709

1987-1991 12 14 6 81 74 20 98 41 162 5 46 559

1992-1996 16 21 7 112 104 29 146 31 251 7 65 789

1997-2001 10 15 4 144 70 20 100 17 150 1 10 541

2002-2006 8 10 3 105 48 15 58 9 201 1 8 465

2007-2013 6 10 4 103 37 12 52 9 215 0 7 455

All Years 430 365 151 1,382 2,383 666 2,437 602 7,376 153 1,357 17,303

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 1.4 $ 2.1 $ 1.8 $ 3.4 $ 20.6 $ 8.6 $ 15.4 $ 3.8 $ 56.2 $ 2.9 $ 11.7 $ 127.9

1972-1976 $ 1.6 $ 2.1 $ 2.1 $ 6.6 $ 23.4 $ 9.5 $ 16.9 $ 3.9 $ 53.3 $ 3.0 $ 12.5 $ 134.9

1977-1981 $ 0.3 $ 0.9 $ 3.3 $ 17.5 $ 15.8 $ 5.4 $ 11.6 $ 1.8 $ 19.0 $ 1.4 $ 7.9 $ 84.8

1982-1986 $ 0.0 $ 0.3 $ 0.8 $ 3.1 $ 3.6 $ 1.4 $ 2.7 $ 0.6 $ 4.4 $ 0.4 $ 2.0 $ 19.5

1987-1991 $ 0.1 $ 0.3 $ 0.4 $ 2.5 $ 2.7 $ 1.1 $ 2.1 $ 0.8 $ 3.8 $ 0.3 $ 1.8 $ 15.9

1992-1996 $ 0.1 $ 0.4 $ 0.5 $ 3.1 $ 4.0 $ 1.4 $ 3.2 $ 0.8 $ 6.5 $ 0.5 $ 2.6 $ 23.2

1997-2001 $ 0.0 $ 0.2 $ 0.3 $ 4.6 $ 2.7 $ 1.0 $ 2.3 $ 0.6 $ 4.3 $ 0.1 $ 0.4 $ 16.6

2002-2006 $ 0.0 $ 0.2 $ 0.2 $ 3.2 $ 2.3 $ 0.7 $ 1.6 $ 0.6 $ 4.9 $ 0.1 $ 0.3 $ 14.3

2007-2013 $ 0.1 $ 0.2 $ 0.3 $ 3.0 $ 2.1 $ 0.7 $ 1.4 $ 0.5 $ 5.6 $ 0.0 $ 0.4 $ 14.4

All Years $ 3.7 $ 6.8 $ 9.7 $ 47.0 $ 77.3 $ 29.8 $ 57.3 $ 13.5 $ 158.1 $ 8.7 $ 39.5 $ 451.5
147

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. Virginia’s COS was 19.81 in 1965, increased over the subsequent 3 periods, 

declined steadily for 3 periods, rose in period 8, then fell to a value of 17.86 in the most recent 

period.

Virginia Coefficient of Specialization

16.00

17.00

18.00

19.00

20.00

21.00

22.00

Virginia COS
148

West Virginia: Total Estimated Impacts

The production impacts for West Virginia naturally follow the investment trend, which results in 

larger impacts in earlier years. On average annually, the ARC funds supported 500 jobs and 

$15.7 million (in 2013 dollars) in earnings. The economic model examined only ARC 

investments so these estimate job and earnings impacts reflect only the results from the ARC 

portion of larger projects. Given that the average leverage ratio may be several times the ARC 

investment, the actual project impacts may be several times higher than these estimates.

West Virginia: ARC Funds Expended for Area Development Program by Investment Classification 

(Cumulative Total 1965-2013)

Improving Physical 

Accessibility

46%

Developing Human 

Resource Capabilities

36%

Developing 

Appalachian 

Economies

8%

Catalyzing Regional 

Development

10%

West Virginia ARC Funds Expended by Investment Classification 

(Cumulative Total)
149

West Virginia: Summary of All Impacts

Period

Employment 

Impacts

Earnings 

Impacts 

(2013$K)

Earnings / 

Employee 

(2013$K)

Investment 

(2013$M)

Investment 

(Current$M)

1965-1971 6,642 $ 189,778 $ 28.6 $ 251.9 $ 44.1

1972-1976 8,569 $ 248,108 $ 29.0 $ 321.4 $ 75.2

1977-1981 3,429 $ 115,836 $ 33.8 $ 162.1 $ 53.3

1982-1986 1,206 $ 41,389 $ 34.3 $ 55.7 $ 26.9

1987-1991 763 $ 26,975 $ 35.4 $ 36.4 $ 20.4

1992-1996 1,227 $ 44,528 $ 36.3 $ 57.0 $ 37.7

1997-2001 1,043 $ 38,689 $ 37.1 $ 61.4 $ 44.9

2002-2006 788 $ 31,604 $ 40.1 $ 46.3 $ 36.9

2007-2013 828 $ 34,036 $ 41.1 $ 52.9 $ 48.3

Summary 24,495 $ 770,943 $ 31.5 $ 1,045.1 $ 387.6

Detail of Employment Impacts

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 134 93 39 239 673 212 704 190 3,883 124 350 6,642

1972-1976 179 122 53 390 921 277 931 267 4,810 159 459 8,569

1977-1981 58 84 51 407 399 108 434 114 1,519 55 200 3,429

1982-1986 22 33 16 197 130 40 177 44 457 18 71 1,206

1987-1991 14 14 8 154 79 24 126 30 260 11 44 763

1992-1996 20 24 10 250 113 40 205 55 425 19 67 1,227

1997-2001 14 20 7 294 97 36 169 37 354 2 14 1,043

2002-2006 10 10 5 184 57 16 80 15 400 1 9 788

2007-2013 7 11 5 261 41 15 82 15 380 1 9 828

All Years 459 411 195 2,376 2,510 768 2,908 767 12,488 390 1,223 24,495

Detail of Income Impacts (millions)

Period Farm AgForFish Mining Constr Mfg TPU Trade FIRE Service Fed Gov't S&L Gov't Total

1965-1971 $ 0.7 $ 2.2 $ 2.2 $ 11.5 $ 30.0 $ 10.7 $ 19.1 $ 4.7 $ 92.6 $ 6.2 $ 9.8 $ 189.8

1972-1976 $ 0.9 $ 2.9 $ 3.0 $ 18.8 $ 41.1 $ 14.0 $ 25.3 $ 6.6 $ 114.7 $ 8.0 $ 12.8 $ 248.1

1977-1981 $ 1.0 $ 1.7 $ 3.5 $ 19.4 $ 20.6 $ 6.1 $ 12.0 $ 2.7 $ 39.2 $ 3.2 $ 6.3 $ 115.8

1982-1986 $ 0.3 $ 0.7 $ 1.2 $ 8.0 $ 7.3 $ 2.3 $ 4.6 $ 1.0 $ 12.6 $ 1.1 $ 2.4 $ 41.4

1987-1991 $ 0.0 $ 0.3 $ 0.5 $ 6.1 $ 4.5 $ 1.5 $ 3.2 $ 0.7 $ 7.8 $ 0.7 $ 1.6 $ 27.0

1992-1996 $ 0.1 $ 0.5 $ 0.8 $ 10.0 $ 6.5 $ 2.3 $ 5.1 $ 1.5 $ 13.5 $ 1.4 $ 2.7 $ 44.5

1997-2001 $ 0.0 $ 0.4 $ 0.6 $ 12.4 $ 5.4 $ 2.0 $ 4.3 $ 1.1 $ 11.7 $ 0.1 $ 0.6 $ 38.7

2002-2006 $ 0.2 $ 0.4 $ 0.3 $ 8.7 $ 3.7 $ 2.2 $ 1.2 $ 0.9 $ 13.4 $ 0.1 $ 0.5 $ 31.6

2007-2013 $ 0.2 $ 0.3 $ 0.4 $ 12.8 $ 2.6 $ 2.1 $ 1.2 $ 0.9 $ 12.9 $ 0.1 $ 0.5 $ 34.0

All Years $ 3.3 $ 9.4 $ 12.6 $ 107.8 $ 121.9 $ 43.0 $ 76.1 $ 20.2 $ 318.4 $ 20.9 $ 37.2 $ 770.9
150

Coefficient of Specialization 

The coefficient of specialization (COS) reflects the industry structure of a region. COS is a 

measure that compares a region’s distribution of employment to the national distribution. 

Typically, higher regional self-sufficiency and higher multiplier effects are associated with lower 

COS values. More specialized regions are indicated by higher COS values and are considered by 

some to be more susceptible to economic downturns. COS values range from a low of zero to a 

maximum 100. West Virginia’s COS values ranged from 11.62 in the first period to 8.78 in the 

latest period. This indicates the region is becoming more diversified. 

West Virginia Coefficient of Specialization

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

West Virginia COS
151

Appendix B: QEM Sub-Period Analysis Findings

Sub-period 1965-1974

The data for the sub-period from 1965 to 1974 comes from the economic structure for the year 

1965, defined as the share of the income by sector and the rest of the variables are measured 

from the 1960 U.S. Census. The treatment group includes only those counties that received 

investments in the years 1966 to 1968. The control group excludes those counties within 60 

miles to counties that are members of the ARC and this exclusion is repeated for all sub-periods 

hereafter. The rates of growth use 1965 per capita income as base year and 1969 employment as 

base year to measure the change in the periods. 

The selection of which matching procedure is best is straightforward for this analysis because the 

procedure that had the lowest number of variables that differ between treated and control, the 

lowest pseudo R2, and the smallest mean in the biases is the nearest neighbors matching 

procedure (see Tables 1 and 2). The results also show that there is a difference between percapita income growth between treated and control counties, with treated counties having higher 

per-capita income.

The results from the fitted models highlight that per capita income in the treated counties (Table 

6) and employment in the treated counties (Table 7) have a higher rate of growth and that the 

difference between the two groups of counties is positive and significant. 

Sub-period 1975-1984

The data for the sub-period from 1975 to 1984 comes from the economic structure for the year 

1974, as explained in the last sub-period and the rest of the variables are calculated from the 

1970 U.S. Census. The treatment group includes only those counties that received ARC 

investments in the years 1975 to 1978. The measured rates of growth for per-capita income and 

employment use 1974 as the base year in this sub-period analysis.

To decide which model performs better we analyze the results from Tables 10 and 11. The first 

table shows that the two best matching measures are the Mahalanobis distance metric and the 

nearest neighbors metric. The procedures with the lower pseudo R2and likelihood ratio are 

nearest neighbors with kernel matching. Therefore, we use nearest neighbor matching for this 

sub-period.

The observed results in Table 15 show that the per capita income has a higher and significant 

growth rate for treated counties than for the control counties. However, the growth rate of 

employment is lower for treated counties than the control counties (see Table 16), and this result 

is only statistically significant for the years 1983 and 1984. 
152

Sub-period 1985-1994

The data for the sub-period from 1985 to 1994 comes from the economic structure for the year 

1984 and other variables are measured from the 1980 U.S. Census. The treatment group includes 

as treated only those counties that received ARC investments in the years 1985 to 1988. The year 

1984 is used as the base year in calculating the growth rate in per-capita income and employment 

for this sub-period.

We determine the best matching algorithm by following the previous procedure, i.e. we chose 

the method that has the fewest number of variables that are significantly different from zero, 

which are the Mahalanobis distance metric and the nearest neighbor matching algorithm (see 

Tables 19 and 20). The methods with the lowest pseudo R2and likelihood ratio test statistics are 

the nearest neighbor with kernel matching, and we chose nearest neighbors as the method for this 

sub-period.

The observed results in Table 24 show that the per capita income has a higher and significant 

growth rate for treated counties than the control counties. However the growth rate of 

employment is lower and not significant for treated counties compared to the control counties 

(see Table 25). 

Sub-period 1995-2002

The data for the sub-period from 1995 to 2002 comes from the economic structure for the year 

1994 and the rest of the variables are measured from the 1990 U.S. Census. The treatment group 

includes as treated only those counties that received investments in the years 1995 to 1997. The 

growth rates for per-capita income and employment use 1994 as the base year in the calculations 

for this sub-period. 

Tables 28 and 29 contain the results for the Mahalanobis distance matric and nearest neighbors 

matching algorithm. Again, the procedure with the lowest pseudo R2and likelihood ratio test 

statistic is nearest neighbors with kernel matching, and we select the nearest neighbors matching 

methodology for this sub-period.

The results in Table 35 show that per capita income has a lower and for most years not 

statistically significant (it is only statistically significant for 2001) growth rate for treated 

counties relative to the control counties. However the growth rate of employment is negative and 

decreases for treated counties relative to control counties (see Table 36), but these results are not 

statistically significantly different from zero. 
153

Sub-period 2003-2012

The data for the sub-period from 2004 to 2012 comes from 2002 and the rest of the variables are 

measured from the 2000 U.S. Census. The treatment group includes as treated only those 

counties that received investments in 2003 because from the year 2003 onward all counties in the 

Appalachian Region received investments. The growth rate for per-capita income and 

employment use 2001 as the base year in this sub-period analysis.

Table 37 indicates that the models that have the fewest variables that are significantly different 

from zero is the kernel matching methods. Table 38 indicates that the procedure with the lowest 

pseudo R2and likelihood ratio test statistics are associated with the kernel matching algorithm, 

and based on this we use kernel matching for this sub-period. It should also be noted that the pretreatment analysis points to the kernel matching algorithm since there are no significant 

differences between treatment and control.

One noteworthy aspect of Table 44 is that the per capita income has a negative difference that is 

not significantly different from zero and from Table 45 employment has a positive and 

significant difference that shows that employment grows faster in the ARC counties. 
154

Table 1: Comparison of averages variables used for estimating the matching procedure between 

treated and control and significance levels for sub-period 1966 to 1974.

* = 10% ** = 5% and *** = 1%

Variable Mean 

Treated

Mahalanobis distance Nearest Neighbors Kernel Matching

Mean 

Control Bias T-stat

Mean 

Control Bias T-stat

Mean 

Control Bias T-stat

freeway 0.53 0.57 -7.60 -0.87 0.51 3.00 0.35 0.38 30.20 3.50***

lpop59 10.40 10.43 -2.30 -0.28 10.38 2.00 0.27 9.35 96.10 8.65***

spc65 0.14 0.15 -12.00 -2.02** 0.15 -12.90 -2.13** 0.21 -87.40

-

10.36***

rtot59 58.62 58.88 -0.50 -0.05 57.32 2.60 0.45 140.85 -162.5 -4.50***

rpop59 -0.95 3.21 -17.60 -3.14*** 3.12 -17.20 -3.32*** -6.44 23.20 2.97***

dens59 111.69 120.12 -1.60 -0.52 102.08 1.80 0.62 79.00 6.10 1.83*

pov59 17.90 15.21 32.90 3.13*** 15.16 33.50 3.39*** 19.22 -16.20 -1.45

pc1760 37.76 37.11 16.50 2.03*** 36.51 31.60 4.41*** 37.05 18.00 1.97**

pc6560 9.41 10.38 -34.20 -4.81*** 11.07 -58.50 -8.91*** 8.52 31.20 4.54***

black60 5.55 7.11 -12.60 -1.81* 5.95 -3.20 -0.52 9.11 -28.70 -4.09***

city2560 29.92 31.25 -4.30 -0.59 30.58 -2.20 -0.35 57.03 -88.50 -6.90***

city10060 65.05 63.08 3.00 0.58 61.90 4.80 1.08 85.28 -30.80 -4.40***

city25060 109.46 104.84 5.40 0.88 101.81 9.00 1.63 119.73 -12.10 -1.88*

city50060 188.11 177.28 7.00 1.07 182.70 3.50 0.59 326.92 -89.30 -9.02***

city100060 358.14 337.66 9.00 1.22 366.35 -3.60 -0.50 519.39 -71.00 -7.78***

psvc65 0.07 0.07 2.30 0.45 0.07 0.80 0.16 0.05 32.30 4.83***

prtl65 0.08 0.08 -17.10 -2.21** 0.08 -21.70 -3.26*** 0.08 9.40 1.13

ptpu65 0.04 0.04 16.60 2.02** 0.04 10.70 1.25 0.02 64.00 8.03***

pmfg65 0.24 0.25 -5.60 -0.60 0.22 12.90 1.42 0.16 60.80 6.04***

pcon65 0.04 0.04 -10.00 -1.43 0.04 -3.80 -0.66 0.03 32.80 4.68***

pfar65 0.06 0.07 -13.40 -2.36** 0.08 -18.20 -3.41*** 0.12 -56.70 -5.79***

ptrf65 0.12 0.11 27.50 2.86*** 0.11 16.60 1.75 0.11 24.50 2.71***

pdir65 0.10 0.11 -34.50 -5.87*** 0.12 -49.30 -8.34** 0.10 -5.80 -0.83

pres65 0.06 0.06 1.50 0.18 0.07 -3.50 -0.46 0.05 4.30 0.53

pmil65 0.00 0.01 -3.80 -1.07 0.00 -0.90 -0.40 0.01 -3.20 -0.75

pfed65 0.02 0.02 -0.60 -0.09 0.02 5.10 0.73 0.02 -2.50 -0.33

pstl65 0.08 0.08 11.60 1.39 0.07 16.10 2.14** 0.15 -170.4 -9.25***

pwhl65 0.02 0.02 -4.70 -0.56 0.02 -4.10 -0.52 0.01 45.00 5.57***
155

Table 2: Tests of fitting between matching procedures for sub-period 1966-1974.

Pseudo R2 LR Chi2 Mean 

Bias

Variables 

Significantly 

Different

Mahalanobis

Distance 0.45 327.08 -1.75 10

Nearest neighbors 

without 

replacement

0.25 181.57 -1.61 9

Kernel Matching 0.30 220.99 -12.40 20

Table 3: Comparison of objective variables before treatment between treatment and control 

counties and significance level. Rate of growth of matched counties rate of growth of per capita 

income for period 1962-1965. * = 10% ** = 5% and *** = 1%

Matching 

procedure Treated Control Difference T-stat

Mahalanobis 

Distance 0.182 0.178 0.004 0.47

Nearest 

Neighbors 

without 

replacement

0.182 0.112 0.070 2.6*

Kernel 

Matching 0.182 0.171 0.011 1.79*

Table 4: Mahalanobis distance results for growth rate of per capita income for sub-period 1969 

to 1974 with respect to the base year 1965. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1969 0.389 0.365 0.025 1.99**

1970 0.469 0.429 0.040 2.75***

1971 0.541 0.503 0.038 2.6***

1972 0.637 0.584 0.052 3.48***

1973 0.752 0.711 0.041 2.59***

1974 0.848 0.788 0.060 3.76***
156

Table 5: Mahalanobis distance results for growth rate of employment for sub-period 1969 to 

1974 with respect to the base year 1969. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1970 0.007 0.005 0.002 0.32

1971 0.873 0.766 0.107 0.81

1972 0.067 0.051 0.015 1.75*

1973 0.112 0.088 0.023 2.25**

1974 0.125 0.101 0.024 2.1**

Table 6: PSM Nearest neighbors without replacement results for growth rate of per capita 

income for sub-period 1969 to 1974 with respect to the base year 1965. 

* = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1969 0.389 0.382 0.008 0.86

1970 0.469 0.442 0.027 2.56**

1971 0.541 0.517 0.024 2.24**

1972 0.637 0.610 0.027 2.27**

1973 0.752 0.734 0.018 1.42

1974 0.848 0.811 0.037 2.83***

Table 7: PSM-Nearest Neighbors results for growth rate of employment for sub-period 1969 to 

1974 with respect to the base year 1969. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1970 0.0069 0.0019 0.0050 1.36

1971 0.8729 0.7084 0.1645 1.58

1972 0.0665 0.0493 0.0171 2.41**

1973 0.1115 0.0886 0.0229 2.65***

1974 0.1247 0.1018 0.0228 2.43**

Table 8: PSM-Kernel matching results for growth rate of per capita income for sub-period 1969 

to 1974 with respect to the base year 1965. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1969 0.3893 0.4750 -0.0857 -2.63***

1970 0.4689 0.5424 -0.0735 -2.06**

1971 0.5414 0.5980 -0.0567 -1.53

1972 0.6366 0.7630 -0.1264 -3.18***

1973 0.7520 0.8688 -0.1167 -2.41**

1974 0.8480 0.9542 -0.1062 -2.48**
157

Table 9: PSM-Kernel matching results for growth rate of employment for sub-period 1969 to 

1974 with respect to the base year 1969. * = 10% ** = 5% and *** = 1%

Year Treated Controls Difference T-stat

1970 0.007 0.025 -0.018 -1.65

1971 0.873 1.710 -0.837 -2.43**

1972 0.067 0.123 -0.057 -2.41**

1973 0.112 0.161 -0.050 -1.69*

1974 0.125 0.184 -0.059 -1.81*
158

Table 10: Comparison of averages variables used for estimating the matching procedure between 

treated and control and significance levels for sub-period 1975 to 1984.

* = 10% ** = 5% and *** = 1%

Variable Mean 

Treated

Mahalanobis distance Nearest Neighbors Kernel Matching

Mean 

Control Bias T-stat Mean Control Bias T-stat Mean Control Bias T-stat

freeway 0.456 0.464 -1.700 -0.220 0.536 -16.000 -2.15** 0.320 27.400 3.80***

lpop65 10.243 10.253 -0.900 -0.140 10.357 -10.300 -1.450 9.978 23.700 3.55***

spc74 0.355 0.356 -0.300 -0.060 0.391 -18.000 -2.80*** 0.593 -117.700 -9.81***

rtot65 0.328 0.343 -12.500 -2.30** 0.344 -13.100 -2.21** 0.330 -2.000 -0.300

rpop65 4.641 4.654 -14.400 -2.80*** 4.662 -22.700 -3.81*** 4.660 -20.400 -2.77***

dens65 104.700 97.691 1.300 0.350 140.110 -6.300 -1.530 104.520 0.000 0.010

pov70 23.723 20.790 26.200 3.48*** 20.874 25.400 3.29*** 27.450 -33.200 -3.83***

rural70 73.886 68.096 22.100 3.28*** 67.064 26.100 3.62*** 79.262 -20.500 -2.98***

pc1770 34.151 34.156 -0.100 -0.020 34.619 -13.000 -1.77* 31.743 66.800 7.72***

pc6570 11.035 12.132 -33.200 -6.26*** 11.226 -5.800 -0.910 9.810 37.200 5.28***

black70 6.226 6.944 -5.900 -0.940 9.395 -26.000 -3.62*** 17.202 -90.200 -8.03***

city2560 31.959 33.019 -3.500 -0.680 32.975 -3.300 -0.570 33.934 -6.500 -1.090

city10060 66.276 65.667 0.900 0.240 63.049 4.900 1.140 66.161 0.200 0.040

city25060 105.700 98.481 8.600 1.85* 100.52 6.100 1.180 104.290 1.700 0.330

city50060 188.080 174.450 8.800 1.86* 184.94 2.000 0.350 163.140 16.000 3.18***

city100060 357.890 347.58 4.500 0.830 343.860 6.100 1.010 420.160 -27.200 -4.13***

psvc74 0.078 0.080 -3.200 -0.610 0.087 -14.500 -1.620 0.079 -2.200 -0.230

prtl74 0.075 0.078 -15.600 -2.56*** 0.078 -13.100 -1.85* 0.069 24.300 3.19***

ptpu74 0.048 0.045 11.500 1.650 0.045 10.200 1.350 0.039 28.300 3.96***

pmfg74 0.223 0.215 5.600 0.720 0.215 5.400 0.650 0.287 -46.000 -4.72***

pcon74 0.046 0.045 2.800 0.400 0.048 -3.900 -0.520 0.044 6.900 0.900

pfar74 0.031 0.057 -27.400 -7.96*** 0.041 -10.900 -3.47*** 0.047 -16.600 -4.40***

ptrf74 0.158 0.149 18.800 2.58*** 0.142 32.300 4.01*** 0.142 33.500 3.92***

pdir74 0.106 0.122 -45.000 -8.53*** 0.121 -40.900 -7.86*** 0.116 -27.500 -5.41***

pres74 0.090 0.088 1.400 0.200 0.093 -1.600 -0.200 -0.005 61.000 5.68***

pmil74 0.005 0.004 1.700 1.220 0.006 -3.100 -1.250 0.006 -2.000 -0.740

pfed74 0.021 0.018 6.800 1.370 0.022 -2.100 -0.370 0.022 -1.400 -0.220

pstl74 0.088 0.081 15.700 2.44*** 0.091 -5.500 -0.720 0.159 -162.100 -9.73***

pwhl74 0.021 0.022 -7.600 -1.160 0.023 -12.500 -1.76* 0.016 26.800 4.37***
159

Table 11: Tests of fitting between matching procedures for sub-period 1975-1984.

Pseudo R2 LR Chi2

Mean 

Bias

Variables 

Significantly 

Different

Mahalanobis 

Distance 0.354 358.88-1.193 10

Nearest Neighbors 

without 

replacement 

0.244 247.44

-4.279 10

Kernel Matching 0.224 227.39 -7.645 20

Table 12: Comparison of objective variables before treatment between treatment and control 

counties and significance level. Rates of growth of per capita income and employment between 

1971-1974. * = 10% ** = 5% and *** = 1%

Method Treated Controls Difference T-stat

Mahalanobis 

Distance 0.308 0.292 0.015 1.97**

Nearest Neighbors 

without 

replacement 0.308 0.289 0.019 3.84***

Kernel Matching 0.308 0.305 0.003 0.1

Table 13: Mahalanobis distance matching results for growth rate of per capita income and 

significance levels for sub-period 1979 to 1984 with respect to the base year 1974. 

* = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1979 0.499 0.467 0.032 4.28***

1980 0.594 0.550 0.045 5.17***

1981 0.693 0.660 0.033 3.98***

1982 0.751 0.717 0.034 3.8***

1983 0.799 0.768 0.031 3.24***

1984 0.897 0.879 0.017 1.93*
160

Table 14: Mahalanobis distance matching results for growth rate of employment for sub-period 1979 to 

1984 with respect to the base year 1974. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1979 0.123 0.096 0.027 2.39**

1980 0.117 0.087 0.030 2.34**

1981 0.116 0.086 0.030 2.08**

1982 0.096 0.070 0.025 1.49

1983 0.104 0.089 0.015 0.82

1984 0.138 0.127 0.011 0.57

Table 15: PSM-Nearest Neighbors matching results for growth rate of per capita income for sub-period 

1979 to 1984 with respect to the base year 1974. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1979 0.499 0.473 0.026 4.02***

1980 0.594 0.556 0.038 5.39***

1981 0.693 0.663 0.030 3.97***

1982 0.751 0.712 0.038 4.99***

1983 0.799 0.768 0.030 3.99***

1984 0.897 0.872 0.025 3.25***
161

Table 16: PSM- Nearest Neighbors matching results for growth rate of employment for sub-period 1979 

to 1984 with respect to the base year 1974. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1979 0.123 0.125 -0.002 -0.24

1980 0.117 0.121 -0.004 -0.39

1981 0.116 0.127 -0.011 -1.05

1982 0.096 0.110 -0.015 -1.23

1983 0.104 0.132 -0.028 -2.13**

1984 0.138 0.170 -0.032 -2.31**

Table 17: PSM-Kernel matching results for growth rate of per capita income for sub-period 1979 

to 1984 with respect to the base year 1974. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1979 0.499 0.414 0.085 2.15**

1980 0.594 0.537 0.057 1.19

1981 0.693 0.706 -0.013 -0.33

1982 0.751 0.737 0.014 0.36

1983 0.799 0.821 -0.023 -0.55

1984 0.897 0.881 0.016 0.41

Table 18: PSM-Kernel matching results for growth rate of employment for sub-period 1979 

to 1984 with respect to the base year 1974. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1979 0.123 0.129 -0.007 -0.21

1980 0.117 0.210 -0.093 -2.63***

1981 0.116 0.254 -0.139 -3.53***

1982 0.096 0.245 -0.149 -3.46***

1983 0.104 0.274 -0.170 -3.85***

1984 0.138 0.317 -0.179 -3.79***
162

Table 19: Comparison of averages variables used for estimating the matching procedure between 

treated and control and significance levels for sub-period 1985-1994. 

* = 10% ** = 5% and *** = 1%

Variable Mean 

Treated

Mahalanobis distance Nearest Neighbors Kernel Matching

Mean 

Contro

l

Bias T-stat

Mean 

Control Bias T-stat

Mean 

Control Bias T-stat

freeway 0.426 0.419 1.6 0.18 0.488 -12.5 -1.41 0.33 19.5 2.26**

lpop74 10.243 10.231 1.1 0.14 10.371 -11.2 -1.32 9.867 32.7 4.05***

spc84 0.788 0.798 -2.1 -0.34 0.838 -10.7 -1.58 0.839 -10.8 -1.13

rtot74 0.939 0.927 6.2 0.82 0.917 11.8 1.26 0.806 71.1 3.35***

rpop74 0.072 0.083 -7.6 -1.26 0.097 -17.8 -1.87* -0.072 104.3 3.99***

dens74 112.73 100.62 2.2 0.44 133.86 -3.9 -0.8 102.77 1.8 0.35

pov70 25.758 22.673 26.6 2.91*** 21.063 40.5 4.56*** 25.707 0.4 0.05

rural80 28.989 27.558 10.3 1.32 28.469 3.7 0.4 32.061 -22.1 -2.37**

pct1780 29.66 29.29 11.5 1.67 29.237 13.2 1.67 29.078 18.1 2.21**

pc6580 12.213 13.305 -31.4 -5.06*** 12.614 -11.5 -1.55 13.265 -30.2 -3.41***

black80 6.123 6.37 -2.1 -0.27 7.819 -14.1 -1.79* 7.976 -15.4 -1.99**

city2579 10.456 12.494 -8.8 -1.72* 9.893 2.4 0.49 15.496 -21.7 -4.1***

city10079 40.769 48.056 -16.6 -3.2 40.373 0.9 0.17 48.197 -16.9 -3.09***

city25079 66.874 73.277 -9.7 -2.04** 67.049 -0.3 -0.05 73.862 -10.6 -2.02**

city50079 105.37 106.46 -1.4 -0.23 103.23 2.7 0.4 105.73 -0.5 -0.07

city100079 309.94 285.54 16.5 1.9* 271.16 26.2 3.00*** 306.63 2.2 0.27

psvc84 0.081 0.083 -3.5 -0.61 0.085 -6.3 -1.09 0.078 4.4 0.81

prtl84 0.065 0.066 -3.4 -0.45 0.065 -0.6 -0.08 0.059 26.9 3.33***

ptpu84 0.042 0.038 11.9 1.67 0.04 5.3 0.58 0.04 4.9 0.55

pmfg84 0.187 0.18 5.8 0.63 0.204 -13.8 -1.35 0.169 14.8 1.5

pcon84 0.03 0.031 -1.6 -0.60 0.034 -7.9 -2.04** 0.028 3.8 1.13

pfar84 0.017 0.026 -14.6 -4.06*** 0.018 -1.2 -0.36 0.016 1.3 0.37

ptrf84 0.188 0.18 15.1 1.7* 0.174 27.1 2.86*** 0.201 -26.3 -2.19**

pdir84 0.166 0.192 -49.1 -9.06*** 0.181 -27.9 -4.98*** 0.178 -24.1 -4.20***

pres84 0.085 0.089 -1.9 -0.26 0.088 -1.9 -0.24 0.082 2 0.23

pmil84 0.006 0.006 0 -0.02 0.005 1.5 0.66 0.011 -9.9 -2.59***

pfed84 0.02 0.018 4.5 0.57 0.017 5.6 0.77 0.028 -15.6 -1.91*

pstl84 0.082 0.078 10.5 1.5 0.082 -0.1 -0.01 0.092 -26.8 -1.67

pwhl84 0.019 0.022 -15.6 -2.07** 0.022 -15.6 -1.81* 0.016 15.7 2.1**
163

Table 20: Tests of fitting between matching procedures for sub-period 1985-1994.

Pseudo R2LR Chi2 Mean 

Bias

Variables 

Significantly 

Different

Mahalanobis Distance 0.356 254.79 -1.572 7

Nearest Neighbors without replacement 0.171 122.2 -0.566 5

Kernel Matching 0.124 88.89 3.207 15

Table 21: Comparison of objective variables before treatment between treatment and control 

counties and significance level. Rates of growth of per capita ncome and employment between 

1981-1984. * = 10% ** = 5% and *** = 1%

Procedure Variable Treated Controls Difference T-stat

Mahalanobis 

Distance

PCI 0.213 0.209 0.004 0.57

Employment 0.029 0.029 0.000 -0.05

Nearest 

Neighbors 

without 

replacement

PCI 0.213 0.212 0.001 0.24

Employment 0.029 0.040 -0.011 -1.62

Kernel 

Matching

PCI 0.213 0.221 -0.008 -0.44

Employment 0.029 0.041 -0.013 -0.88

Table 22: Mahalanobis distance matching results for growth rate of per capita income and 

significance levels for sub-period 1989 to 1994 with respect to the base year 1984. 

* = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1989 0.276 0.269 0.007 0.91

1990 0.330 0.305 0.024 3.00***

1991 0.369 0.346 0.023 2.76***

1992 0.430 0.407 0.023 2.76***

1993 0.463 0.437 0.026 2.92***

1994 0.506 0.483 0.022 2.4**
164

Table 23: Mahalanobis distance matching results for growth rate of employment for sub-period 

1989 to 1994 with respect to the base year 1984. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1989 0.094 0.084 0.010 0.91

1990 0.115 0.100 0.015 1.22

1991 0.110 0.102 0.008 0.63

1992 0.127 0.111 0.016 1.15

1993 0.153 0.138 0.015 1

1994 0.173 0.166 0.007 0.48

Table 24: PSM Nearest neighbors Without replacement matching results for growth rate of per 

capita income for sub-period 1989 to 1994 with respect to the base year 1984. 

* = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1989 0.276 0.261 0.014 2.56***

1990 0.330 0.301 0.029 4.81***

1991 0.369 0.341 0.028 4.56***

1992 0.430 0.401 0.029 4.72***

1993 0.463 0.433 0.030 4.52***

1994 0.506 0.481 0.024 3.53***
165

Table 25: PSM Nearest neighbors Without replacement matching results for growth rate of 

employment for sub-period 1989 to 1994 with respect to the base year 1984. 

* = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1989 0.094 0.095 -0.001 -0.06

1990 0.115 0.111 0.004 0.38

1991 0.110 0.113 -0.002 -0.21

1992 0.127 0.134 -0.007 -0.52

1993 0.153 0.162 -0.010 -0.7

1994 0.173 0.194 -0.020 -1.34

Table 26: PSM-Kernel matching results for growth rate of per capita income for sub-period 1989 

to 1994 with respect to the base year 1984. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1989 0.276 0.258 0.017 1.02

1990 0.330 0.304 0.026 1.58

1991 0.369 0.354 0.015 0.87

1992 0.430 0.412 0.018 1.05

1993 0.463 0.440 0.023 1.21

1994 0.506 0.485 0.021 1.07
166

Table 27: PSM-Kernel matching results for growth rate of employment for sub-period 1989 to 

1994 with respect to the base year 1984. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1989 0.094 0.102 -0.007 -0.33

1990 0.115 0.128 -0.013 -0.54

1991 0.110 0.122 -0.012 -0.46

1992 0.127 0.148 -0.021 -0.72

1993 0.153 0.173 -0.020 -0.67

1994 0.173 0.196 -0.022 -0.68
167

Table 28: Comparison of averages variables used for estimating the matching procedure between 

treated and control and significance levels for sub-period 1995-2002. 

* = 10% ** = 5% and *** = 1%

Variable Treated

Mahalanobis distance Nearest Neighbors Kernel Matching

Control Bias T-stat Control Bias T-stat Control Bias T-stat

freeway 0.43 0.41 2.30 0.27 0.49 -12.50 -1.41 0.33 19.50 2.26**

lpop90 10.35 10.30 4.50 0.65 10.51 -13.10 -1.63 10.02 28.00 3.60***

spc90 1.22 1.24 -3.40 -0.61 1.30 -12.30 -1.84* 1.28 -9.30 -1.11

rtot90 0.66 0.66 3.70 0.49 0.67 -2.50 -0.33 0.69 -11.90 -1.51

rpop90 0.02 0.03 -5.90 -0.89 0.05 -21.50 -2.88*** 0.05 -20.10 -2.70***

dens90 123.69 87.24 2.80 1.59 144.91 -4.10 -0.78 121.66 0.40 0.07

pov89 19.71 17.61 26.80 3.22*** 16.83 36.70 4.33*** 19.32 5.00 0.52

rural90 74.25 70.12 15.30 1.97** 64.06 38.00 4.41*** 73.31 3.50 0.40

pc1700 26.82 28.14 -23.30

-

3.11*** 28.35 -27.10 -3.35*** 28.84 -35.70 -4.31***

pc6500 15.77 16.16 -9.90 -1.42 15.51 6.60 0.86 16.18 -10.20 -1.22

black90 6.12 5.57 4.50 0.61 7.43 -10.90 -1.41 7.56 -11.90 -1.56

city2596 9.18 9.91 -3.50 -0.70 8.46 3.40 0.68 14.30 -24.00 -4.34***

city10096 38.03 41.57 -9.10 -1.67 36.60 3.70 0.64 46.09 -20.70 -3.42***

city25096 63.77 64.70 -1.40 -0.31 63.12 1.00 0.20 71.65 -12.00 -2.44**

city50096 87.26 85.83 1.90 0.41 98.23 -15.00 -2.37** 100.49 -18.10 -3.20***

city100096 236.67 194.63 34.80 4.15*** 205.34 25.90 2.99*** 189.90 38.70 4.59

psvc94 0.10 0.10 6.10 1.21 0.11 -7.60 -0.99 0.10 -2.10 -0.19

prtl94 0.06 0.06 9.80 1.28 0.06 -2.60 -0.31 0.05 29.70 3.68***

ptpu94 0.04 0.04 3.00 0.40 0.04 -4.60 -0.54 0.04 6.50 0.81

pmfg94 0.17 0.16 12.10 1.45 0.18 -10.10 -1.06 0.15 19.30 2.00**

pcon94 0.03 0.03 3.90 0.73 0.04 -6.60 -1.19 0.03 3.80 0.67

pfar94 0.02 0.02 -7.80 -2.08** 0.02 -1.10 -0.32 0.01 4.70 1.22

ptrf94 0.23 0.21 22.50 2.48** 0.20 39.20 4.30*** 0.23 -9.20 -0.80

pdir94 0.15 0.16 -28.00

-

4.78*** 0.16 -18.60 -3.06*** 0.16 -13.30 -2.11**

pres94 0.09 0.12 -14.60 -2.31** 0.10 -1.80 -0.26 0.10 -5.00 -0.67

pmil94 0.00 0.00 0.90 0.49 0.01 -1.60 -0.54 0.01 -10.70 -2.97***

pfed94 0.02 0.01 5.50 0.93 0.02 2.40 0.41 0.02 -11.60 -1.80

pstl94 0.09 0.09 3.70 0.57 0.09 -5.20 -0.62 0.10 -22.20 -1.77

pwhl94 0.02 0.02 1.00 0.12 0.02 -8.80 -1.01 0.02 25.30 3.13**
168

Table 29: Tests of fitting between matching procedures for sub-period 1995-2002.

Pseudo R2 LR Chi2 Mean 

Bias

Variables 

Significantly 

Different

Mahalanobis Distance 0.225 253.61 2.01 8

Nearest neighbors without 

replacement 0.212 238.08 -1.06 8

Kernel Matching 0.123 138.03 -2.19 14

Table 30: Comparison of objective variables before treatment between treatment and control 

counties and significance level. Rates of growth of per capita income and employment between 

1991-1994 * = 10% ** = 5% and *** = 1%

Procedure Variable Treated Controls Difference T-stat

Mahalanobis 

Distance

PCI 0.135 0.136 -0.001 -0.19

Employment 0.063 0.066 -0.004 -0.75

Nearest 

Neighbors 

without 

replacement

PCI 0.135 0.132 0.003 0.96

Employment 0.063 0.075 -0.013 -2.25**

Kernel Matching PCI 0.135 0.129 0.006 0.45

Employment 0.063 0.054 0.008 0.51

Table 31: Mahalanobis distance matching results for growth rate of per capita income and 

significance levels for sub-period 1998 to 2002 with respect to the base year 1994. 

* = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1998 0.170 0.176 -0.006 -1.58

1999 0.202 0.209 -0.007 -1.65

2000 0.252 0.262 -0.010 -1.99**

2001 0.318 0.339 -0.021 -3.81***

2002 0.333 0.349 -0.016 -2.82***

Table 32: Mahalanobis distance matching results for growth rate of employment for sub-period 

1998 to 2002 with respect to the base year 1994. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1998 0.070 0.088 -0.019 -2.49**

1999 0.081 0.099 -0.019 -2.2**

2000 0.094 0.116 -0.022 -2.31**

2001 0.086 0.107 -0.021 -1.81**

2002 0.080 0.099 -0.019 -1.58
169

Table 33: PSM--Nearest Neighbors matching results for growth rate of per capita income for 

sub-period 1998 to 2002 with respect to the base year 1994. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1998 0.170 0.170 0.000 -0.07

1999 0.202 0.205 -0.003 -0.79

2000 0.252 0.258 -0.005 -1.39

2001 0.318 0.335 -0.017 -3.76***

2002 0.333 0.345 -0.012 -2.46**

Table 34: PSM-Nearest Neighbors matching results for growth rate of employment for subperiod 1998 to 2002 with respect to the base year 1994. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1998 0.070 0.086 -0.017 -2.78***

1999 0.081 0.099 -0.018 -2.68***

2000 0.094 0.113 -0.019 -2.54**

2001 0.086 0.107 -0.021 -2.37**

2002 0.080 0.102 -0.022 -2.26**

Table 35: PSM-Kernel matching results for growth rate of per capita income for sub-period 1998 

to 2002 with respect to the base year 1994. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1998 0.170 0.173 -0.003 -0.25

1999 0.202 0.209 -0.007 -0.49

2000 0.252 0.266 -0.014 -0.84

2001 0.318 0.343 -0.025 -1.41

2002 0.333 0.353 -0.020 -1.04

Table 36: PSM-Kernel matching results for growth rate of employment for sub-period 1998 to 

2002 with respect to the base year 1994. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

1998 0.070 0.077 -0.007 -0.41

1999 0.081 0.090 -0.009 -0.43

2000 0.094 0.102 -0.008 -0.36

2001 0.086 0.087 -0.001 -0.04

2002 0.080 0.084 -0.004 -0.15
170

Table 37: Comparison of averages variables used for estimating the matching procedure between 

treated and control and significance levels for sub-period 2003-2012. 

* = 10% ** = 5% and *** = 1%

Variable Mean 

Treated

Mahalanobis distance Nearest Neighbors Kernel Matching

Mean 

Control Bias T-stat

Mean 

Control Bias T-stat

Mean 

Treated

Mean 

Control Bias T-stat

freeway 0.44 0.44 0.50 0.07 0.48 -6.90 -0.98 0.47 0.52 -9.50 -1.23

lpop90 10.33 10.32 0.80 0.15 10.40 -5.70 -0.94 10.41 10.27 11.40 1.62

spc00 1.85 1.94 -7.50 -1.50 2.04 -16.90 -3.39*** 1.93 1.92 1.10 0.20

rtot90 0.66 0.65 4.20 0.70 0.67 -8.70 -1.40 0.66 0.67 -9.00 -1.34

rpop90 0.02 0.02 -3.80 -0.73 0.04 -14.40 -2.45*** 0.02 0.03 -8.10 -1.26

dens90 114.17 91.32 1.80 1.45 117.52 -0.30 -0.19 124.78 108.45 1.30 0.51

pov99 16.40 14.33 32.50 5.02*** 14.67 27.10 3.96*** 15.56 15.88 -5.10 -0.74

rural00 70.52 66.17 15.40 2.43** 64.16 22.50 3.45*** 67.63 68.84 -4.20 -0.59

pc1700 26.48 27.51 -18.70 -3.31*** 27.44 -17.40 -3.04*** 26.56 27.52 -17.30 -2.58***

pc6500 15.96 16.20 -6.30 -1.19 16.07 -2.80 -0.43 16.00 15.79 5.60 0.79

black00 6.18 5.71 3.70 0.61 8.76 -20.70 -2.95*** 6.71 8.46 -13.90 -1.79*

city2501 8.59 9.71 -5.30 -1.30 7.92 3.20 0.80 8.04 9.33 -6.10 -1.30

city10001 36.23 40.55 -11.10 -2.72*** 35.62 1.60 0.36 35.28 36.22 -2.40 -0.49

city25001 60.79 66.42 -8.80 -2.44** 61.23 -0.70 -0.19 61.22 63.05 -2.80 -0.65

city50001 87.90 90.14 -3.10 -0.78 83.49 6.00 1.47 86.88 82.87 5.50 1.15

city100001 221.58 187.52 29.30 4.55*** 192.44 25.10 3.85*** 205.55 214.66 -7.80 -1.12

psvc00 0.11 0.11 4.90 1.13 0.12 -11.90 -0.93 0.12 0.11 8.00 0.78

prtl00 0.06 0.05 6.20 1.01 0.06 -0.70 -0.10 0.06 0.05 13.30 1.79*

ptpu00 0.04 0.04 -1.80 -0.31 0.04 -5.70 -0.87 0.04 0.04 -0.30 -0.04

pmfg00 0.14 0.14 0.30 0.05 0.14 0.40 0.05 0.14 0.13 12.00 1.47

pcon00 0.03 0.03 -0.60 -0.10 0.04 -9.70 -1.31 0.03 0.03 4.90 0.56

pfar00 0.01 0.01 -7.00 -2.96** 0.01 1.10 0.21 0.01 0.01 4.20 0.57

ptrf00 0.23 0.20 32.50 4.64*** 0.20 35.80 5.08*** 0.22 0.21 3.10 0.42

pdir00 0.16 0.17 -23.40 -5.06*** 0.17 -23.50 -4.07*** 0.17 0.17 4.20 0.60

pres00 0.11 0.12 -9.10 -1.73* 0.10 5.10 0.63 0.10 0.12 -11.20 -1.48

pmil00 0.00 0.00 0.40 0.40 0.00 -0.10 -0.11 0.00 0.00 -1.70 -0.68

pfed00 0.01 0.01 1.10 0.27 0.02 -7.10 -0.70 0.02 0.02 0.90 0.11

pstl00 0.09 0.09 0.70 0.13 0.10 -10.80 -1.93 0.09 0.09 -1.60 -0.25

pwhl00 0.02 0.02 2.60 0.44 0.02 -13.60 -2.07 0.02 0.02 8.50 1.19
171

Table 38: Tests of fitting between matching procedures for sub-period 2003-2012.

Pseudo R2LR Chi2 Mean Bias

Variables 

Significantly 

Different

Mahalanobis

Distance 0.229 260.23 1.05 9

Nearest 

neighbors 

without 

replacement

0.166 188.24 -1.71 10

Kernel

Matching 0.033 31.19 -0.59 1

Table 39: Comparison of objective variables before treatment between treatment and control 

counties and significance level. Rates of growth of per capita income and employment between 

2000-2003. * = 10% ** = 5% and *** = 1%

Procedure Variable Treated Controls Difference T-stat

Mahalanobis 

Distance

PCI 0.080 0.089 -0.008 -1.87*

Employment -0.014 -0.021 0.006 1.17

Nearest 

Neighbors 

without 

replacement

PCI 0.080 0.089 -0.009 -2.44*

Employment

-0.014 -0.016 0.002 0.36

Kernel 

Matching

PCI 0.079 0.087 -0.008 -0.98

Employment -0.013 -0.017 0.005 0.64

Table 40: Mahalanobis distance matching results for growth rate of per capita income and 

significance levels for sub-period 2004 to 2012 with respect to the base year 2001. 

* = 10% ** = 5% and *** = 1%

Year Treated Controls Difference T-stat

2004 0.077 0.084 -0.007 -2.00**

2005 0.113 0.116 -0.002 -0.65

2006 0.156 0.161 -0.005 -1.12

2007 0.200 0.209 -0.009 -1.70*

2008 0.241 0.255 -0.015 -2.15**

2009 0.239 0.247 -0.008 -1.04

2010 0.261 0.268 -0.007 -0.89

2011 0.307 0.319 -0.011 -1.28

2012 0.344 0.354 -0.011 -1.19
172

Table 41: Mahalanobis distance matching results for growth rate of employment for sub-period 

2004 to 2012 with respect to the base year 2001.

year Treated Controls Difference T-stat

2004 0.017 0.009 0.008 2.14**

2005 0.035 0.023 0.012 2.32**

2006 0.049 0.035 0.013 2.15**

2007 0.061 0.049 0.012 1.66

2008 0.051 0.040 0.012 1.39

2009 0.016 0.009 0.007 0.79

2010 0.012 0.001 0.011 1.2

2011 0.028 0.013 0.015 1.6

2012 0.039 0.023 0.015 1.54

Table 42: PSM Nearest Neighbors matching results for growth rate of per capita income for subperiod 2004 to 2012 with respect to the base year 2001.

year Treated Controls Difference T-stat

2004 0.077 0.089 -0.012 -4.95***

2005 0.113 0.121 -0.008 -2.91***

2006 0.156 0.165 -0.010 -3.03***

2007 0.200 0.215 -0.015 -4.13***

2008 0.241 0.261 -0.021 -4.67***

2009 0.239 0.252 -0.013 -2.57***

2010 0.261 0.273 -0.012 -2.15**

2011 0.307 0.325 -0.017 -2.84***

2012 0.344 0.360 -0.016 -2.62***

Table 43: PSM Nearest Neighbors matching results for growth rate of employment for subperiod 2004 to 2012 with respect to the base year 2001. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

2004 0.017 0.013 0.004 1.57

2005 0.035 0.027 0.008 2.03**

2006 0.049 0.039 0.010 2.05**

2007 0.061 0.053 0.009 1.56

2008 0.051 0.045 0.006 1.02

2009 0.016 0.015 0.001 0.1

2010 0.012 0.010 0.002 0.31

2011 0.028 0.023 0.005 0.76

2012 0.039 0.033 0.006 0.79
173

Table 44: PSM-Kernel matching results for growth rate of per capita income for sub-period 2004 

to 2012 with respect to the base year 2001. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

2004 0.075 0.087 -0.012 -1.77*

2005 0.111 0.114 -0.004 -0.45

2006 0.155 0.155 0.000 0.00

2007 0.200 0.203 -0.004 -0.37

2008 0.240 0.252 -0.012 -0.92

2009 0.238 0.247 -0.010 -0.74

2010 0.259 0.270 -0.011 -0.78

2011 0.307 0.320 -0.013 -0.73

2012 0.344 0.357 -0.013 -0.69

Table 45: PSM-Kernel matching results for growth rate of employment for sub-period 2004 to 

2012 with respect to the base year 2001. * = 10% ** = 5% and *** = 1%

year Treated Controls Difference T-stat

2004 0.017 0.004 0.012 2.47**

2005 0.034 0.009 0.025 3.57***

2006 0.048 0.019 0.029 3.23***

2007 0.063 0.033 0.030 2.81***

2008 0.054 0.030 0.024 2.02**

2009 0.020 0.002 0.018 1.44

2010 0.017 0.002 0.015 1.16

2011 0.034 0.019 0.015 0.98

2012 0.044 0.029 0.015 0.93
174

Matching maps

Maps 1 through 5 plot the various counties that were deemed to be a good match (i.e. the control 

counties) for the counties that were contained in the ARC investment areas. The maps show that 

the control counties selected by the match have broader geographic dispersion as the time frame 

moves forward. 

Map 1: matched counties for full period matching from data for 1965
175

Map 2: matched counties for full period matching from data for 1975

Map 3: matched counties for full period matching from data for 1985
176

Map 4: matched counties for full period matching from data for 1995

Map 5: matched counties for full period matching from data for 2002
177

Appendix C: QEM Regression Results

Table 1 shows the regression models to determine if higher rates of growth of per capita income 

and employment are correlated to ARC investments and total investment from ARC projects and 

other sources. The four columns represent different specifications that were utilized in the 

empirical analysis. In each specification, 16 different control variables that represent such things 

as the presence of a highway, population measures, and other demographic characteristics were 

used as were two variables related to ARC investments. The top number next to each variable 

name is the coefficient estimate with the bottom number in parentheses being the p-value.

The main variables of interest are the two different ARC investment variables. The first ARC 

investment variable consists of just funds from ARC alone, while the other variable is ARC 

funds plus funds from other sources such as local and state government spending on programs 

such as job training, education, and water treatment to name just a few examples. The sample of 

counties used in the regression results consist of only those counties that are contained in the 

ARC region and thus the sample size is 420 counties. The reason for this choice is twofold. 

First, we do not have data on investments for counties that are not part of the ARC region and 

second, the sample needs to be restricted to ARC counties to determine if the investments that 

are specifically targeted to ARC counties are effective.

The results indicate that counties that received ARC funds alone experienced a positive and 

statistically significant increase in both per-capita income growth and employment growth over 

the period 1965 to 2005. Counties that received a combination of ARC and other local 

government funds experienced a positive and statistically significant increase in employment 

over this same period. The only exception to this pattern is that counties that received a 

combination of ARC and other government funds experienced a positive increase in per-capita 

income growth over this time period, although this result was not statistically significant.
178

Table 1: Regression results for the rate of growth of the per capita income and the employment. 

The regression results include coefficients and the p-values in the parentheses

Variable

Per 

capita 

income

Per 

capita 

income

Employment Employment

(Intercept) 2.984 3.164 -0.569 -0.483

(0.000) (0.000) (0.340) (0.413)

freeway -0.015 -0.013 0.083 0.081

(0.420) (0.465) (0.055) (0.062)

city2560 0.002 0.002 0.004 0.004

(0.000) (0.000) (0.005) (0.007)

city10060 0.000 0.000 -0.001 -0.001

(0.521) (0.577) (0.076) (0.074)

city25060 0.001 0.001 -0.001 -0.001

(0.000) (0.000) (0.039) (0.053)

dens59 0.000 0.000 0.000 0.000

(0.176) (0.157) (0.037) (0.032)

rpop59 -0.003 -0.003 0.010 0.010

(0.001) (0.001) (0.000) (0.000)

black60 0.002 0.002 -0.005 -0.004

(0.037) (0.027) (0.009) (0.016)

pov59 0.001 0.001 -0.004 -0.004

(0.162) (0.118) (0.039) (0.048)

pfed65 0.514 0.486 1.874 1.879
179

(0.050) (0.065) (0.003) (0.003)

pmfg65 -0.109 -0.119 -0.373 -0.362

(0.218) (0.184) (0.076) (0.087)

pres65 -0.296 -0.296 1.432 1.446

(0.197) (0.199) (0.009) (0.008)

pwhl65 -0.685 -0.638 1.341 1.392

(0.168) (0.200) (0.255) (0.238)

perw65 -0.590 -0.575 0.148 0.163

(0.012) (0.015) (0.791) (0.771)

pmil65 0.627 0.737 -2.405 -2.233

(0.484) (0.412) (0.259) (0.293)

pfar65 0.708 0.685 0.617 0.616

(0.000) (0.000) (0.042) (0.043)

pstl65 1.533 1.517 1.810 1.823

(0.000) (0.000) (0.005) (0.005)

log(investment from ARC) 0.024 0.058

(0.038) (0.031)

log(Total Investment ARC projects) 0.010 0.047

(0.304) (0.046)

R

2

0.569 0.566 0.369 0.368

N 420 420 420 420

F-stat 31.220 30.780 13.810 13.740
180
STATE MEETINGS REPORT

FEBRUARY 2015

Appalachia Examining Changes to the Appalachian Region Since 1965

THEN AND NOW

Prepared by the Center for Regional Economic Competitiveness 

and West Virginia University for the Appalachian Regional Commission
1

 It is impossible to drink water, flush a toilet, or drive down a highway in Appalachia

 without seeing first-hand the result of an ARC investment.

 - Common sentiment expressed at Stakeholder Meetings
2

Contents

About the ARC Focus Groups .................................................................................................... 3

Alabama ARC Focus Group Report............................................................................................ 9

Georgia ARC Focus Group Report ........................................................................................... 14

Kentucky ARC Focus Group Report ........................................................................................ 18

Maryland ARC Focus Group Report ........................................................................................ 22

Mississippi ARC Focus Group Report...................................................................................... 26

New York ARC Focus Group Report ....................................................................................... 30

North Carolina ARC Focus Group Report ............................................................. 34

Ohio ARC Focus Group Report................................................................................................ 38

Pennsylvania ARC Focus Group Report................................................................................... 42

South Carolina ARC Focus Group Report................................................................................ 46

Tennessee ARC Focus Group Report ....................................................................................... 50

Virginia ARC Focus Group Report........................................................................................... 53

West Virginia ARC Focus Group Report ................................................................................. 57

Development Districts Association of Appalachia (DDAA) ARC Focus Group Report ......... 61

Acknowledgements.......................................................................................65
3

About the ARC Focus Groups

Stakeholder meetings were held in all 13 ARC states to afford key stakeholders the opportunity 

to provide insights about their region’s ARC-related activities (see Figure 1). Participants were 

asked to provide feedback on several issues including:

 The ARC-sponsored programs and other initiatives that made significant differences in their 

community,

 The community’s changing involvement with ARC over time,

 How ARC can strengthen their programs and initiatives, and

 Current and emerging issues that might motivate additional, future investments to alleviate 

economic distress in Appalachia.

Each facilitated session lasted roughly two hours and involved roughly 10 to 20 participants. 

Combined, over 220 people participated in these stakeholder meetings. This document contains 

reports describing the substance of these conversations and the participants who attended. 

Figure 1: ARC 50th Anniversary State Focus Groups

State Location Date

Tennessee Alcoa, TN May 16, 2014

North Carolina Boone, NC July 11, 2014

Development Districts Association 

of Appalachia

Marietta, OH July 28, 2014

West Virginia Charleston, WV August 8, 2014

Virginia Roanoke, VA September 18, 2014

Mississippi Tupelo, MS October 15, 2014

Kentucky Slade, KY October 21, 2014

Georgia Atlanta, GA October 22, 2014

Maryland Cumberland, MD October 28, 2014

Ohio Zanesville, OH November 14, 2014

New York Binghamton, NY December 8, 2014

Pennsylvania Lewisburg, PA December 9, 2014

Alabama Decatur, AL December 15, 2014

South Carolina Greenville, SC December 17, 2014

Several common themes emerged from these meetings. Overall, participants noted that ARC 

investments have made a significant impact on the Region over the past half century. Several 

factors contributed to this impact including the federal-state-regional partnerships, the flexibility 

of ARC funding, and the ability for ARC investments to complement other projects. While much 
4

has been accomplished over the past half century, challenges remain for the Appalachian Region 

as it faces new and emerging issues. Below are the common themes that arose during the course 

of the focus groups.

The ARC Model 

 Stakeholders were quick to mention ARC’s catalyzing role in spurring regional 

development. Regional stakeholders shared many examples of relatively small ARC 

investments that planted the seed for significantly more investment and growth. In many 

of these examples, later investments would not have occurred without the initial ARC 

investment. This was especially true for many investments in the Region’s most 

distressed communities, which have few accessible funding sources because of the 

difficulty they have in raising even a modest amount of local matching funds. 

 ARC does more than fund projects—it also provides leadership, advocacy, planning, 

research, and timely seed investments to advance these efforts, and has done so with 

countless federal, state, non-profit, and private partners. This model has proven effective 

over the past 50 years and may become even more important for achieving the Region’s 

development goals in the future.

 Stakeholders noted that over the past 50 years, ARC’s federal-state-local partnership 

model has proven effective in helping Appalachian communities advance efforts with 

great local support and impact.

 ARC allows states and regions to set their own priorities and make their own decisions 

about how ARC funding is used. This model has allowed states to shift their focus in 

response to changing economic, political, or fiscal conditions. Diminished funds have led 

a number of states to refocus their ARC investments from relatively expensive physical 

infrastructure projects to business enterprise and tourism development, workforce 

training, and health promotion activities. Many stakeholders see this new generation of 

investments as having greater regional impact with the available funds. 
5

Flexibility of ARC Funding

 Local and regional stakeholders universally praise ARC’s flexibility. Unlike other 

funding programs, ARC funding allows regions to think creatively about how best to 

address pressing regional challenges. 

 Many stakeholders spoke of ARC’s role in supporting the expansion of health-care and 

education facilities and the importance of those investments to the well-being of the 

Region’s population. In many ways, these efforts helped address serious market failures 

that would have significantly diminished the health and welfare of the Region’s residents. 

 Several focus groups participants said that economic transformation in Appalachia is less 

about diversification, and more about forging entirely new economies. To help facilitate 

this process, ARC has made investments that support these transformations. This includes 

investing in the preparation of industrial sites, but also providing support for 

entrepreneurship, tourism destination development and promotion, export expansion, and 

business development programs, helping companies access programs that support 

technology acceleration or advanced manufacturing processes. 

State and Local Partnerships

 ARC’s approach to development prioritizes partnerships with other federal, state, private,

and nonprofit partners. Therefore ARC funding aligns with and complements state 

development initiatives, such as New York State’s Regional Economic Development 

Councils and Kentucky’s Shaping Our Appalachian Region (SOAR) initiative. 

 Stakeholders from every state noted the importance of ARC support for the Region’s 73 

local development districts (LDDs). Several focus group participants cited the critical 

ability of LDDs to serve as an interagency connection between different service providers 

and local jurisdictions within the Region; and to connect local residents and businesses 

with resources at the state, regional, and federal levels, as well as with private resources. 

ARC funding also allows the LDDs to assist communities with project development, 

including assistance in grant writing for common funding sources. Without this support, 

stakeholders maintained, many of the most successful ARC projects would never have 

started. 
6

ARC Investments Complement One Another

 While the challenges facing Appalachia shift over time, ARC continues to lay the 

groundwork for future development by making investments that are designed to advance 

the Commission’s strategic goals. Few investments embody this more than those that 

improve the Region’s broadband infrastructure. Efforts to increase broadband access and 

speed have proven to be a vital foundation in addressing many other issues, including 

entrepreneurship development, tourism, telemedicine, distance learning, and even inperson education. Stakeholders throughout the Region noted that this was one of the most 

prominent issues that required ARC attention and investment. 

 ARC investments in basic physical infrastructure, such as highways; water and 

wastewater; and, more recently, broadband, have allowed Appalachian communities to 

lay the basic foundation for additional development. Without this assistance, these 

communities would be at a significant disadvantage in taking advantage of future 

economic development opportunities.

 ARC has made investments to advance education and training programs to prepare the 

Region’s workers for the jobs of tomorrow. These investments are not only for curricula 

and educational programs, but also for the construction of new facilities and the purchase 

of training equipment, with an emphasis on assisting the Region’s most economically 

distressed or underserved communities.

 Place-making projects are another area where ARC investments not only create new 

economic activity, but also preserve Appalachian culture and improve the Region’s 

overall quality of life. Focus group participants noted several ARC-supported projects 

that helped to preserve historic buildings or revitalize downtowns. Similarly, efforts to 

develop local food systems are beneficial in that they can serve as an attraction to visitors 

while creating new economic opportunities and healthier food options for residents. 

Focus group participants noted that these kinds of ARC-supported projects contribute to 

the Region’s tourism infrastructure.

 ARC has invested in efforts to link individual projects, such as Virginia’s Crooked Road,

which connects heritage music venues and events; and the Great Allegheny Passage trail,

which provides bike access from Pittsburgh to Cumberland, Maryland. These projects 

leverage many individual attractions to make the Region a more compelling destination 

for visitors since they can take advantage of many attractions rather than just one.
7

Accomplishments and Challenges

 Many communities said they are continuously trying to do more with less. In order to do 

this, they must find ways to partner and leverage other public and private funding 

opportunities. For example, ARC launched multi-year Global Appalachia grants to foster 

member states’ rural trade development efforts, building on the State Trade and Export 

Promotion Program of the U.S. Small Business Administration. Another example is

ARC’s participation in the Rural Jobs and Innovation Accelerator Challenge, in 

partnership with the U.S. Economic Development Administration and the U.S. 

Department of Agriculture.

 Communities can find projects that fit into other ARC or regional investments. Tourism 

and cultural projects provide a structure that allows regions to connect individual 

attractions into a bigger idea that makes the region itself a more attractive destination for 

visitors. By leveraging other investments, the sum of the parts becomes greater than the 

whole and is an effective way of doing more with less. 

 Stakeholders recognize the importance of prioritizing ARC funding based on need. This 

does not mean, however, that the classification of counties by distress levels is free from 

challenges. For instance, persistent pockets of poverty can remain even in counties that 

overall are performing well relative to other counties. Additionally, while not a common 

occurrence, fluctuations in county economic status from year to year can also pose

challenges for communities. Given that a change in status results in a change in matching 

funds requirements, this can introduce some long-term planning difficulties for some 

counties.

 Finally, stakeholders also suggested that ARC recognize that its impact has been well 

beyond what might be easily measured. As noted in several states and in similar ways, it 

is impossible to drink water, flush a toilet, or drive down a highway without seeing firsthand the result of an ARC investment. Given this significant impact on the Region, many 

stakeholders thought that ARC should more actively promote its accomplishments in 

ways similar to what other agencies do (e.g., signs that read “This road was paid for in 

part through ARC Funds”). 
8

Figure 2: The Appalachian Region
9

Alabama ARC Focus Group Report

On December 15th, 2014 key ARC stakeholders in the state of Alabama met to discuss the impact 

of ARC investments in their communities. The Alabama Department of Economic and 

Community Affairs (ADECA) helped to organize the meeting that was hosted by the North 

Central Alabama Regional Council of Governments in Decatur, Alabama. Participants were 

asked to give their input on several broad issues including:

 ARC-sponsored programs use in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall there was enthusiastic support for ARC programs and investments. ARC remains vitally 

important to the communities it serves, and this is especially true for its most rural communities. 

Additionally, the consistency and predictability of ARC funding and services was cited as being 

critical to assisting rural and distressed communities. Participants also noted that among the 

reasons ARC investments have been impactful to the state are its ability to let investment 

priorities be “locally-driven” and the Commission staff really “cares” about Appalachia Alabama 

and the Region’s ability to improve the economy and quality of life for residents, communities, 

and businesses throughout the area.

Current role of ARC funding

Participants noted that ARC funding plays a critical role in getting projects off the ground and as 

a tool for addressing core economic development challenges that provide the foundation for later 

development. Among the range of projects that ARC investments continue to help fund include 

water and sewer, education, healthcare, business access to capital via revolving loan funds, 

workforce training and community development. These investments make securing additional 

investment and activity easier for distressed communities with minimal access to resources. 

Not all impactful investments require large outlays of funds, noted participants. A fruitful effort 

underway in the state includes ARC projects in Madison and Jefferson counties on integrating 

local food systems by partnering with local industries, schools, food banks and other community 

based organizations. This is a part of the Appalachian Regional Commission’s larger effort of 

“Growing the Appalachian Food Economy,” which is meant to foster economic diversification 

and revitalization by promoting local food systems and sustainable agriculture. In Alabama, 

participants discussed how this effort held enormous promise matching local growers with local 

buyers and that the project needed the boost provided through ARC to get it connected. An 

effort like this creates jobs and also helps with health, as the Region has a high incidence of 

obesity. 
10

The collaboration model established by ARC for its investments is also bearing fruit in high-tech 

business development. For example, the Economic Development Partnership of Alabama 

Foundation, an ARC partner, collaborates with seven research universities in Alabama to 

promote high-growth and innovative start-up companies. Collectively they have established 

Alabama “Launchpad”, which provides startup capital and expert guidance to budding 

companies. Alabama ARC supports such efforts through its participation in Revolving Loan 

funds and Angel funds in Appalachian Alabama. 

Additionally, ARC investments have complemented many important state initiatives to develop 

communities and grow the economy. For instance, ARC has been heavily involved with 

supporting the work of the Alabama State Energy Office, which is working on a State Energy 

Program to increase the use of renewable energy and promote energy efficiency in Alabama. 

State officials also noted how the Alabama ARC program is making an important contribution to 

the successful execution of the Alabama State Department of Education’s Plan 2020 to improve 

student growth and achievement, increase the graduation rate, close the achievement gap and 

increase the number of high school graduates that are college and/or career-ready and compete in 

the global economy. The state’s international trade and export development efforts in 

Appalachia Alabama are also being supported by ARC. The flexibility of ARC funding made 

these types of investments possible.

ARC funding also continues to play a crucial role for many rural and/or distressed counties

because there remains tremendous pockets of need, which are often isolated. Several participants 

described ARC as being the champion in the rural development world. Helping to form this 

perspective were not just singular projects but the continuing commitment to fund the local 

planning councils, which are on the front lines to make things happen in Alabama. The funds 

provided to the Local Development Districts are important because rural areas often lack the 

staff capacity and resources to meet all their planning needs. The LDD funding provided by ARC 

allows for consistent and reliable planning and government service to these rural counties. 

Impacts of past ARC investments

At the time of its launch, ARC sought to bring the Appalachian Region to parity with the rest of 

the country. While the work is far from finished, the focus group participants thought that

Alabama’s Appalachian communities had made meaningful progress toward fulfilling that goal. 

ARC investments allowed many rural residents access to clean water and sewer, and helped to 

put in place an infrastructure to support industry that created local employment opportunities.

ARC has also played a leadership role in Alabama in bridging non-traditional economic 

development issues such as local food, healthcare, and alternative energy with traditional 

economic development activities.

Past ARC investments in Alabama have worked in building the foundation and capacity to 

provide needed services to residents. Among the most basic, water and sewer investments have 
11

been an important part of ARC investments over the years, which participants agreed “have 

come a long way over the past 30-40 years due to ARC.” But ARC has done much more than 

this. For instance, ARC funds were used to purchase cargo cranes at the International Intermodal 

Center (IIC) at Huntsville International Airport and also helped to establish the Jetplex Industrial 

Park. Participants discussed how these investments helped open things up economically at the 

Huntsville International Airport to the benefit of the entire region. At this global logistics park,

rail, air, and highway all come together which attracts major companies. The Jetplex Industrial 

Park, which opened in 1974, currently has more than 60 tenants, 24-hour U.S. Customs services, 

USDA inspectors, freight forwarders and a Foreign Trade Zone. The IIC, Jetplex and airport 

occupy more than 6,000 acres of the 10,000-acre master plan.

Perhaps the biggest economic development advancement for the Region in the past 50 years has 

been the growth of the state’s automotive industry. Participants noted ARC’s role in this as well, 

such as funding a water line from Tuscaloosa to the Mercedes-Benz plant. Alabama now has 

three major international auto manufacturers: Mercedes-Benz, Hyundai and Honda. MercedesBenz is located in Vance, which is in two ARC counties, Tuscaloosa and Bibb, while Honda 

Manufacturing of Alabama is in Lincoln, which is in the ARC county of Talladega. Other ARC 

counties in Alabama also benefit from these automotive manufacturers through the Tier 1, Tier 2 

and Tier 3 supplier businesses serving them. 

While never a large component of the total funding package, ARC funding—both in the past and 

present—has served as important seed funding to get larger projects started. Several participants 

offered examples demonstrating the importance of early ARC investments. For instance, ARC 

funded the purchase of diagnostic equipment for the area’s one stop center for justice. This 

center started as a unique concept, and is one of only two in Alabama – for those who are victims 

of abuse, instead of going to hospital or police, have all of their care and needs met in one 

location. Others have leveraged ARC investments to create revolving loan funds to offer area 

businesses access to needed capital. Several of these loan funds have turned small initial ARC 

investments into multi-million dollar capital funds for the ongoing benefit of the local economy. 

One example was given of funds going to a business filling a vital need for physician assistants 

in the Region and that business grew quickly to 15 jobs from 4, and they were able to pay back a 

three year loan in a year and a half.

ARC investments in highway infrastructure are also seen as crucial past investments as they 

provide important connections. This is particularly true for rural, mountainous counties where 

roads are limited in number, difficult to build, and expensive to maintain. This can make rural 

living even more expensive and difficult. ARC Highway funds are therefore seen as important to 

expanding and strengthening the Region’s highway infrastructure. Participants noted past road 

improvements as critical for the area but also looked forward to the final completion and opening 

of the Appalachian Development Highway System (ADHS) Corridors X and V which will offer 

an important contribution to continued economic growth in Appalachian Alabama. ADHS 
12

Corridors X (future Interstate 22) and V will provide major new connections to Memphis, 

Tennessee in the west and Atlanta, Georgia to the east.

Moving forward

Participants were all adamant about the continued importance of ARC funding to the state. As a 

result, they would like to see current investments continued, particularly as they relate to 

infrastructure-related investments such as water, sewer and highways. Although participants 

noted here that more requests are now for industrial access roads and maintenance; while 

water/sewer is usually for capacity increase or new projects, not maintenance. However with 

ARC funds, participants noted that in some places infrastructure projects are stalled due to 

trouble with meeting matching fund requirements. 

In addition to these ongoing investments, the participants expressed general consensus about 

expanding investments in several other emerging areas. For instance, developing the 

Appalachian Region’s human capital and health and wellness efforts was viewed as increasingly 

crucial. Expanded broadband access was mentioned by several participants as a particular area of 

investment that needs continuous attention from ARC. Broadband potentially supports many 

other priority areas including entrepreneurship, home-based businesses, and the overall ability to 

compete in the global economy. 

Continuing to invest in education funding, including technology and workforce programs for 

schools was another important potential focus area for ARC investments. State officials noted 

how workforce development and training efforts will focus on high growth and high demand 

occupations, including green and renewable energy related occupations and industrial technology 

and maintenance specialties related to Alabama’s specific industrial sectors, including auto 

manufacturing. Workforce development was seen by participants as helping with the problem of 

youth brain drain by keeping talented people in the Region rather than leaving to find jobs 

elsewhere. 

The discussion often noted the need to generally improve the quality of life within the region in 

order to retain and assist current residents, as well as attract new residents, especially young 

workers. Connected to this, creating places where people want to live and spend time was seen 

as a vital condition for success in other strategies related to tourism and entrepreneurship and 

others. A participant noted how important it was to maintain a group of 20-50 year old residents

in your community to develop future leaders and support services like volunteer fire 

departments. Other participants also noted the need to work on job access by getting workers out 

of impoverished communities and getting them to job opportunities in the surrounding 

communities. Many residents need viable transportation options – having no money for vehicles 

and poorly developed public transit systems. Also prevalent was the need to train the workforce 

to have soft skills (e.g., timeliness, drug- free).
13

Going forward, health care is a growing concern for the citizens of Appalachian Alabama. 

Challenges included low levels of dentists per capita; a lack of hospital-affiliated substance 

abuse treatment services; a lack of hospital-affiliated psychiatric services; and a lack of obstetric 

care, particularly in economically distressed counties. On the business development side, 

finding suitable land is a big issue for industrial expansion as landowners want to keep land. In 

other areas, it’s topographic. However, throughout the conversation on future investments, 

participants noted how difficult it is to predict, and that a great benefit is the flexibility of ARC 

funding, as that flexibility (unlike many other funding sources) allowed them to effectively 

respond to their region’s many critical development challenges and to future challenges as they 

arise. They also encouraged ARC to keep fostering a climate for creative ideas to take hold, 

especially if they can create change in the way things are done, and maintaining the wide range 

of projects that can be funded.

Meeting Attendees

 Lucas Blankenship, Top of Alabama Regional Council of Governments

 Joey Hester, North Central Alabama Regional Council of Governments

 Brenda Jones, Alabama Department of Economic and Community Affairs

 Keith Jones, Northwest Alabama Council of Local Governments 

 Jimmy Lester, Alabama Department of Economic and Community Affairs

 Michael Mills, North Central Alabama Regional Council of Governments

 Yvonne Murray, Regional Planning Commission of Greater Birmingham 

 Jeffrey Pruitt, North Central Alabama Regional Council of Governments

 Nancy Robertson, Top of Alabama Regional Council of Governments 

 Jeff Schwartz, Appalachian Regional Commission

 Marilyn Smith, Northwest Alabama Council of Local Governments

 Max Snyder, West Alabama Regional Commission
14

Georgia ARC Focus Group Report

On October 22nd, 2014 key ARC stakeholders in the state of Georgia met to discuss the impact 

of ARC investments in their communities. The Georgia Department of Community Affairs 

helped to organize and host the meeting in Atlanta, Georgia. Participants were asked to give their 

input on several broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall, focus group participants expressed enthusiastic support for ARC programs and 

investments. Participants regularly noted the importance of the program’s flexibility and how 

that flexibility allowed them to find creative solutions for projects with no obvious funding 

stream. They also noted the importance of the support provided by the state’s Local 

Development Districts (LDDs), the State and ARC’s Washington office, and how that expertise 

was vital to ensuring that projects not only got off the ground, but were also completed. 

Current role of ARC funding

Georgia has historically used its ARC funding to make investments to address key development 

challenges, often related to basic economic development infrastructure. Through the Appalachian 

Highway Development System, ARC has helped to fund the development of Corridor A (GA 

400, US 19, GA 515, US 76) and A1 (GA 400, US 19) that have improved the connection 

between Atlanta and North Georgia. 

Many participants cited examples of ARC-supported water, sewer and access road projects that 

were instrumental in creating sites for new businesses or ensuring that existing employers can 

remain competitive in their existing locations. In partnership with other funders, ARC 

investments in access road construction paved the way for an expansion of the King’s Hawaiian 

Bakery in Hall County and subsequent expansion. Similarly, ARC investments in upgrades to the 

sewer system in Barrow County allowed the Atlantic Engineering Group to retain 60 jobs and 

create 50 new jobs in Braselton. 

Participants all noted that ARC investments were unique because they provided important seed 

money for projects with no guaranteed return. Relative to many other funding sources, ARC 

funds allow communities to take some risks and be occasionally speculative with their economic 

development projects. Few projects are as illustrative of this as ARC’s involvement with the 

Chateau Elan Winery. In 1982, ARC partnered with the Town of Braselton to expand the town’s 

existing water and sewer system to accommodate what at the time was a two person winery. This 

initial seed money, as well as subsequent investments, laid the foundation for what has become a 
15

3,500 acre world class resort, conference center and leisure destination. None of this was 

guaranteed in 1982, but these early somewhat speculative investments in Chateau Elan led to 

over 300 jobs and significant local, state and Federal tax revenues.

In addition to providing key seed money, ARC funding has also filled important holes in the 

current funding landscape. For instance, participants noted the growing need to preserve historic 

landmarks within their communities, and the importance of these vestiges in defining the 

community’s cultural identity and contribution. Given the flexibility of ARC funding, 

communities can develop projects that can address some of these issues and ARC funds have 

been used for a number of tourism projects throughout Appalachian Georgia. The purchase of 

four-acres of land in Gainesville, Georgia for the development of Paradise Gardens in 2011 has 

been important for that area as it has allowed it to emerge as a leading folk artist site. Other 

tourism-related projects include restoring three barns in Blairsville, Georgia on the property 

formerly owned by Georgia poet, Byron Herbert Reece in 2005; the creation of a phone-guided 

tour and related signage of the Shields-Etheridge Farm in Jefferson, Georgia in 2010; and 

preserving two slave cabins in Snellville, Georgia in 2010. Developing these attractions 

strengthens the ability of these communities to grow their revenues from tourism.

A number of Georgia communities have used ARC funding as part of their efforts to revitalize 

their downtown. For instance, ARC investments helped to renovate Jackson County’s historic 

courthouse which has been a key element in revitalizing Jefferson’s downtown. ARC 

investments in the renovation of the Somerville theater has also had an impact on that 

community as that facility is now capable of hosting conferences and other events. These 

projects are important to their communities because they make them more attractive places to 

live and therefore serve the dual purpose of attracting new residents and retaining existing 

residents. 

ARC investments have also made a difference beyond just economic and business development. 

ARC investments have also helped to strengthen the Region’s education and health 

infrastructure. Construction of Lanier Technical College’s Manufacturing Development Center 

(MDC) in Gainesville, Georgia was in part made possible by an ARC grant towards sewer 

construction. Similarly, ARC was one of the investors in the construction of Northeast Georgia 

Health System’s Gainesville Campus. This project not only increased the Region’s access to 

healthcare services, but it also created 450 jobs in the process. ARC funds, in conjunction with 

USDA Rural Development funds, were also utilized to improve sewer improvements for 

Cedartown’s new hospital in Polk County. 

ARC has also invested in non-infrastructure efforts such as programs that address the Region’s 

substance abuse problems. Developing the Region’s local food system is another way in which 

ARC funds have been leveraged to improve the Region’s overall wellbeing. ARC helped to fund 

the Northwest Georgia Regional Commission’s North Georgia Local Food Assessment Guide. 
16

This guide allowed the Commission to identify and assemble the right partners to move their 

local foods efforts forward. It gave the Region a roadmap from which to begin, for instance, farm 

to school programs by working with Georgia Organics. 

Pulling these kinds of projects together often requires a level of capacity greater than what many 

smaller communities (particularly those with fewer than 5,000 residents) are capable. 

Participants noted that ARC investments in the Region’s Local Development Districts have been 

vital to ensuring that these small, rural communities received consistent service in areas such as 

grant writing, project development and basic community planning. ARC has also helped to 

expand the GIS capacity within the LDDs, which better enables them to undertake master 

planning throughout the Region. In addition to the support within the Region, participants were 

grateful for the assistance that they receive from ARC representatives both in Georgia and in 

Washington, DC. Assistance related, for instance, to the sharing of best practice or for preparing 

successful grants and applications is greatly appreciated.

Moving forward

Diminished resources have led to a shift in focus. While infrastructure projects remain an 

important element in Georgia’s ARC program, growing costs and fewer resources have led 

communities to look more to other funding sources like the Georgia Environmental Finance 

Agency to fund traditional water and sewer projects. Consequently, Georgia’s communities are 

now looking at other types of projects for which to invest their ARC resources. These projects 

have been related to downtown revitalization, historic preservation, tourism, or local foods. What 

many of these projects have in common is that they do not have obvious funders for which to 

turn. As a result, the flexibility of ARC’s funding allows local communities the opportunity to 

find locally-driven solutions to their challenges, rather than trying to shoehorn their efforts into a 

more rigid state or federal funding program.

Moving forward, participants expressed great interest in expanding and strengthening the 

Region’s broadband capacity. This is not only needed to support economic and entrepreneurial 

development, but also to improve the Region’s quality of life both in rural locations and 

revitalized downtowns. Aside from broadband, workforce training and retention are ongoing 

challenges, and participants were particularly concerned about youth leaving the Region if they 

cannot find the right opportunities. 

Participants noted several ways in which Georgia might refine its ARC program. Participants 

expressed a desire for more flexible timing for applications, possibly two or more application 

dates as the need for assistance can occur at any time throughout the year. Several communities 

also noted that they had communities in their county that did not reflect the attainment status of 

their counties. These persistent pockets of poverty are therefore more difficult to serve using 

ARC funds. Finally, several participants noted that ARC needs more visibility for its efforts not 
17

only for what ARC resources and programs can be used for, but also to draw attention to what 

they have already accomplished.

Meeting Attendees

 J.R. Charles, Hambersham County Economic Development Department

 Gretchen Corbin, Georgia Department of Community Affairs

 Jennifer Dees, Town of Braselton

 Jim Dove, Northeast Georgia Regional Commission

 Beth Eavenson, Elbert County, Joint Development Authority of Northeast Georgia

 Jeff Ellis, City of Rockmart (Written Input)

 Bill Fann, Cedartown, GA (Written Input)

 Heather Feldman, Georgia Mountains Regional Commission

 Pat Graham, Barrow County Chairperson

 Jon Herschell, City of Cornelia

 Guy Herring, Barrow County Economic and Community Development

 Jim Henry, Northwest Georgia Regional Commission (Written Input)

 Jerry Hood, Town of Braselton

 David Howerin, Northwest Georgia Regional Commission (Written Input)

 Julie Meadows, Northwest Georgia Regional Commission (Written Input)

 Kostas Skordas, Appalachian Regional Commission

 Phil Smith, Georgia Department of Community Affairs 

 Saralyn Stafford, Georgia Department of Community Affairs

 Bob Thomas, Elbert County Manager

 Burke Walker, Northeast Georgia Regional Commission

 Mayor John Weaver, City of Jasper (Written Input)

 Dan Wright, City of Ringgold (Written Input)
18

Kentucky ARC Focus Group Report

On October 21st, 2014 key ARC stakeholders in the state of Kentucky met to discuss the impact 

of ARC investments in their communities. The Kentucky Department for Local Government 

(DLG) helped organize the meeting which was hosted by Natural Bridge State Resort Park in 

Slade, Kentucky. Participants were asked to give their input on several broad issues including:

 ARC-sponsored programs used in their region and community.

 Changes in ARC-sponsored programs and funding over time.

 Notable and important ARC investment, and

 Current and emerging issues that might motivate additional, future investments.

Overall, participants were in agreement that ARC has played a central role in addressing many of 

Eastern Kentucky’s development challenges. The impacts of ARC investments are visible 

throughout Appalachian Kentucky, as they have helped strengthen the Region’s connectivity, 

improve the health of its citizens, create economic opportunities, and bring its infrastructure 

closer to parity with the rest of the country. In spite of this progress, focus group participants 

noted that much work remained and that ARC remains relevant and vital in moving forward. 

Impacts of past ARC investments

Over the past half century, few places have been as impacted by ARC and ARC investments than 

Appalachian Kentucky. Focus group participants all recognized this legacy, and its 

transformative impact on the Region. To paraphrase one participant, you cannot drive down a 

highway, drink a glass of water or flush a toilet in Eastern Kentucky without seeing the impact of 

ARC investments. These investments in the Region’s basic infrastructure have been striking. 

While investments in water and sewer systems are now typified by their influence on industrial 

development, in Eastern Kentucky these early ARC investments brought clean drinking water 

and indoor plumbing to people who previously lacked even these basic services. It was noted that 

in some communities, ARC investments helped facilitate basic garbage disposal as well.

ARC investments have also reduced the Region’s overall isolation. A quick scan of an interstate 

map shows that the Region lacks any real interstate access, and this minimal access severely 

limits its economic development potential. Roads constructed in part with ARC funding like the 

Bert T. Combs Mountain Parkway (Corridor I) or the Country Music Parkway (US 23-Corridor 

B) near Pikeville have created real development opportunities for previously disconnected, 

isolated regions of Appalachia.

ARC investments have also improved the overall wellness of the Region’s population by 

improving access to healthcare. The Region struggles with higher than average rates of diabetes, 

obesity and cancer. ARC investments are evident by the construction of health clinics and by 

funding programs that address core healthcare challenges. For instance, ARC has invested in 
19

Morehead State’s Center for Health Education and Research which has increased the number of 

nurses and radiographic technicians in the Region. In partnership with the University of 

Kentucky, Morehead State has also been actively involved in the Appalachian Rural Dental 

Education Partnership Program. Oral healthcare is an often overlooked issue, but can have an 

important influence on people’s wellbeing. This program not only provides people access to oral 

health screening by dental students from the University of Kentucky, but it also supports 

awareness programs for parents and children about the importance of oral health. Several 

participants also noted that in addition to improving the overall wellbeing of the Region’s 

population, healthcare also provided an important source of jobs and regional employment.

Overall regional wellbeing requires economic opportunities for its citizens. Participants noted 

that ARC had also been instrumental in the development of industrial sites that have laid the 

foundation for job opportunities in the Region in areas such as advanced manufacturing and 

distribution. For workers to take advantage of these job opportunities they must have the proper 

skills, and as a result participants did praise ARC investments that helped build a campus of 

Ashland Community and Technical College on the grounds of EastPark industrial site. This 

campus will provide easier access to training for companies and workers at EastPark.

Participants noted a number of workforce and training programs where ARC investments made a 

significant difference. For instance, they noted ARC’s involvement in the expansion of the 

University of Pikeville or the creation of the University Center of the Mountains in Hazard. The 

latter of which provides residents access to programs available through eight different 

institutions of higher education. Participants also noted the importance of ARC investments in 

youth programs like Roger’s Scholars or Morehead State’s efforts to promote Science, 

Technology, Engineering and Math (STEM) education.

For all of these efforts, ARC funding has rarely been the sole source of funding. However, 

participants noted that ARC funding often served as important seed funding that got a project off

the ground or allowed communities to leverage other funds. In other instances, it may have 

served as closing funds that ensured that a project was completed. They also noted how the 

flexibility of funding allowed them to address challenges in a way not possible through other 

funding sources. Participants appreciate this flexibility as it enables communities to create 

locally driven solutions to regional challenges. 

In addition, financial support for Kentucky’s Area Development Districts was also appreciated. 

This support allows the ADDs to provide consistent and reliable support to communities that 

generally lack any real capacity to write grants or develop projects. It also allows them to 

provide basic GIS or planning services to these communities who are often unable to afford 

them. At a broader level, focus group participants also praised ARC staff in Washington for 

providing them with additional expertise or being able to connect them to other experts 
20

elsewhere in the Appalachian region or beyond. This expertise can help strengthen the design of 

different projects and improve the likelihood that they will be successful and impactful.

Moving forward

While much progress has been made toward achieving ARC’s mission over the past 50 years, the 

focus group participants were in general consensus that much work remains. By way of 

illustration, one participant noted that Kentucky now has three times as many distressed counties 

as West Virginia, yet in 1990 it was the same. Therefore there is a need to complete the job that 

was started, be it in the highway infrastructure or achieving greater parity in health outcomes. 

This will require continuing ongoing infrastructure investments, but also finding creative 

approaches to addressing other ongoing regional challenges like public or workforce 

transportation, meeting the care needs of an aging population, improving the Region’s affordable 

housing stock, or battling health issues such as obesity or substance abuse.

The work remains, but participants were aware and realistic about the challenges that lie ahead. 

Most notably, the resources are not available in the way they once were. As a result, it is 

important for the Region to leverage as many different opportunities as possible. This will 

require regional stakeholders to forge new partnerships and effectively communicate in order to 

avoid duplication. This will involve effectively leveraging the opportunities provided through 

Community Development Block Grants (CDBG) investments or Kentucky’s Promise Zone. It 

will also require finding ways to activate private investors or local foundations. Participants 

noted that the state’s Shaping Our Appalachian Region (SOAR) effort provided an effective 

venue to not only develop these partnerships, but also to have a larger discussion about the future

of the Region. Participants expressed gratitude for the Federal Co-Chair contributing an 

additional $750,000 to support the SOAR initiative. 

One of the clear benefits of the SOAR initiative has been a larger discussion about the Region’s 

future. The Region’s economy is undergoing significant transformation at the moment as the 

accelerating decline of the coal industry has removed one of its major economic drivers. While 

some participants noted that Appalachian Kentucky needed a more diversified economy, others 

responded by saying that the Region needed to create an entirely new economy. In either case, 

ARC can play an important role in this transformation through its support for entrepreneurial 

support efforts or tourism development initiatives. It can also promote economic growth through 

continued investments in industrial site development and workforce training. Moreover, future 

investments must continue to address issues of connectivity and isolation. As a result, expanding 

and strengthening the Region’s broadband infrastructure will be vital for the future.

The Region must also continue to build capacity and develop regional leaders and continued 

support for the ADDs remains one important element of this effort. However, participants also 

noted for ARC to continue to play an important role in the Region’s future there needs to be 

more new leaders from the Appalachian region stepping forward to advocate on the Region’s 
21

behalf. Congressman Hal Rogers has been a great advocate for the Region, but participants noted 

that more leaders will be required to promote these efforts over the next fifty years.

Meeting Attendees

 Rocky Adkins, Majority Leader of Kentucky House of Representatives

 Wayne Andrews, Morehead State University (Phone interview)

 David Barber, Kentucky Speaker’s Office

 Amy Barnes, Kentucky Department for Local Government

 Al Cross, Institution for Rural Journalism at University of Kentucky

 Ron Daley, Hazard Community and Technical College

 Scott Hamilton, Appalachian Regional Commission

 Peter Hillie, Mountain Association for Community Economic Development

 Jerry Johnson, University of Louisville

 Lonnie Lawson, The Center for Rural Development

 Lynn Latrell, Kentucky Department for Local Government

 Hilda Legg, Legg Strategies

 Paul Patton, Former Governor of Kentucky (Phone Interview)

 Vonda Poynter, Federation of Appalachian Housing Enterprises

 Roger Recktenwald, Kentucky Association of Counties

 Jerry Rickett, Kentucky Highlands Investment Corporation

 Sandra Runyon, Big Sandy Area Redevelopment District

 Peggy Satterly, Kentucky Department for Local Government

 Kostas Skordas, Appalachian Regional Commission

 Al Smith, Former Appalachian Regional Commission Federal Co-Chair

 Greg Stumbo, Speaker of the Kentucky House of Representatives 

 Jim Ward, Letcher County Judge/Executive

 Tony Wilder, Kentucky Department for Local Government

 Gail Wright, Gateway Area Development District

 
22

Maryland ARC Focus Group Report

On October 28th, 2014 key ARC stakeholders in the state of Maryland met to discuss the impact 

of ARC investments in their communities. Maryland Department of Planning’s Al Feldstein

helped to organize the meeting that was held at the Allegheny Arts Council in Cumberland, 

Maryland. Participants were asked to give their input on several broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall ARC investments are valued by members of the local community and are seen as an 

important resource for the community moving forward. Participants were especially appreciative 

of ARC’s ability to catalyze regional collaboration and by the service and leadership provided by 

ARC federal and state representatives. 

Current role of ARC funding

Focus group members recognize the interrelatedness of ARC investments and appreciate ARC’s 

wide-ranging mission. Focus group participants highlighted the flexibility of investments and 

ability to make small, focused investments. ARC funds also frequently provide the leverage 

necessary to complete key projects. Many local leaders note that ARC’s requirement to leverage 

multiple resources has helped them accomplish their mission – and ARC’s matching investments 

often facilitated this leverage. 

The ARC mission of “expanding the capacity of the people” gives room for a wide range of 

investment opportunities. Furthermore, local leaders noted that the Maryland’s ARC Program 

Manager played an important role in educating new partners (especially those not located within 

the Region) about the issues and challenges facing Maryland’s Appalachian communities. Focus 

group participants also praised federal and state ARC staff. Furthermore, their institutional 

knowledge made the grant process work very smoothly.

ARC investments have also played an important role in the Region’s development. It offers the 

flexibility of funding that allows local partners to develop creative solutions to local problems. 

The Region’s economic development leaders play a particularly important role in Maryland in 

managing the process of determining whether an ARC investment makes sense for a project. 

These three organizations serve as the go-between for marketing the project and helping 

organizations with the application process. These organizations know that the ARC cannot fund 

every project that comes along, so the economic development organizations help to identify 

which investments are likely to have the largest impact. State and local leaders consider 
23

themselves stewards of the ARC funds and take great pride in the fact that their investments are 

effective and efficient.

Another way that ARC makes an impact on the community is through the connections that it 

facilitates. ARC’s model for leveraged investment has spurred stronger collaboration amongst 

federal, state, local and private sector partners who have been forced to work together in order to 

secure these investments. For example, the Region’s approach to supporting entrepreneurship 

came about through ARC facilitated collaboration among area colleges. Local leaders noted that 

this was less about the financial resources and more about the leadership and connections that 

ARC resources helped to facilitate.

The Allegheny County Human Resources Development Commission provides another 

compelling example of how a local organization can benefit from ARC’s leverage and 

networking capabilities. HRDC staff described how the agency was asked to organize a thirteenday medical clinic, with over a thousand people receiving medical services. A five-figure ARC 

grant helped to leverage more than $1 million in local investment. Numerous people at the focus 

group session participated in a collaborative eight month planning process.

Impacts of past ARC investments

Initially, ARC investments in Maryland focused on housing and traditional infrastructure such as 

water, sewers, and access roads. More recently, ARC has shifted to a greater share of its 

Maryland investment to supporting the provision of high speed broadband designed to better 

connect the Region to the wider world, much in the same way that highway investments once 

did. Maryland’s greatest legacy includes the construction of I-68, which has been instrumental in 

connecting the Regions east and west of the Appalachians through the Cumberland Gap as well 

as connecting communities across a once isolated region. For large infrastructure projects like 

this, small investments from ARC may not seem significant at first, but they can be a key factor 

in catalyzing a larger investment or accomplishing regional goals. For smaller projects, the 

investments in access roads and related infrastructure have helped to open up key sites to 

development along the Region’s interstate corridor.

The Region’s educational institutions have also seen substantial expansions due to ARC 

investment. For instance, Frostburg State University expanded its nursing program from four to 

over 300 students with some assistance from ARC. Local public schools expanded their 

broadband infrastructure with help from ARC grants. In addition, local community colleges and 

technical schools have used ARC to develop new training programs and purchase equipment. 

Local leaders also cited a number of “legacy” investments made by ARC that have helped to 

redefine the Region. For instance, the bike trail from Pittsburgh to Washington and the related 

Trail Town Program have been a boon for local business. The trail brings in people from all 

over the world to spend money at local businesses. It also has helped to attract major events, 
24

including local cancer walks and kidney rides. Those events have helped to raise awareness 

about the Region and bring the community together. One example cited of this impact involved 

a tourist on the trail was so impressed by the Town of Hancock that he bought and rehabbed a 

building there.

Moving forward

While ARC has significantly impacted Maryland’s Appalachian communities, the organizations 

delivering services to local residents see a variety of future investment opportunities. ARC has 

long helped smaller communities meet basic water, wastewater and storm water infrastructure 

needs. These issues remain important, but participants also believe that ARC funds could better 

be used to leverage more private investment. This will be important in the area of 

telecommunications where rapidly changing technology will require continued investment. An 

expanded broadband infrastructure will be required to support greater tele-medicine, or distance 

learning. In addition, the leaders noted the need for programs aimed at attracting and retaining 

quality healthcare professionals. 

Many participants also described the importance of broadband and related infrastructure 

improvements to support post-secondary education in the Region. In fact, continued investment 

in education was a recurring theme during the meeting. There was an agreement that the 

Region’s sparse and declining population affects the area’s ability to make a case for greater 

public investments. Consequently, the state’s school funding formula does not favor local 

schoolchildren because the area’s declining population has led to budget cutting for schools and 

limiting opportunities for the educational system to invest as a way to remain competitive.

Related to this last point, local leaders believe that it is vital that the Region attract and retain 

young people, and suggested a number of ways to provide education, public transportation, arts 

and culture, and other amenities. Broadband infrastructure was mentioned by multiple parties as 

a particularly important tool for both homes and businesses. This would be beneficial for 

education, healthcare and other vital areas. In addition, participants recognize the hidden costs 

for area residents to live, especially for those who cannot afford a car, because the area’s 

underfunded public transit system limits route options, leading to fewer riders and less funding. 

Participants mentioned a number of areas in which ARC can improve or make the application 

process easier. Most of these concerns related to the application process, where ARC’s basic 

agency requirement tied to federal procurement regulations can present challenges. At times, 

grantees have a difficult time finding a logical agency to serve as their basic agency. 

Understaffed state agencies have a hard time helping due to paperwork and other issues tied to 

the combination of federal and state procurement rules. That being said, ARC received positive 

reviews for the new online application system, quick turnaround, flexibility, and customer 

service. With Al Feldstein’s retirement, participants are greatly concerned about ensuring that the 
25

state can hire a new program manager. They hoped that she or he will bring a strong local 

knowledge and will proactively work to develop a strong relationship with regional stakeholders.

Meeting Attendees

 Bill Atkinson, Maryland Department of Planning

 Matt Diaz, Allegany County Economic and Community Development

 State Senator George Edwards, District 1, Garrett, Allegany, and Washington Counties 

 Paul Edwards, Mayor of Garrett County 

 Al Feldstein, ARC/ Maryland Department of Planning

 Nil Grove, Allegany County Public Schools

 Scott Hamilton, Appalachian Region Commission

 Shawn Hershberger, City of Cumberland Development Office 

 Joe Hoffman, Frostburg State University 

 Sonny Holding, Congressman John Delaney’s Office

 Ann Jacobs, Senator Ben Cardin’s Office

 Linda Janey, Maryland Department of Planning 

 David Jones, Allegany College of Maryland

 Janice Keene, Evergreen Heritage Center Foundation

 Rick MacLennan, Garrett College

 Jay Oliver, City of Cumberland

 Vic Rezendes, Allegany Arts Council

 Cindy Sharon, Garrett County Economic Development 

 Anne Shepard, Hagerstown Community College

 Kostas Skordas , Appalachian Region Commission 

 Chris Sloan, Allegany Arts Council

 Elizabeth Stahlman, City of Frostburg 

 Robin Summerfield, Senator Ben Cardin’s Office

 Courtney Thomas, Allegany County HR Development Commission

 Guy Winterberg, Tri-County Council for Western Maryland

 
26

Mississippi ARC Focus Group Report

On October 15th, 2014 key ARC stakeholders in the state of Mississippi met to discuss the 

impact of ARC investments in their communities. The Mississippi Development Authority 

helped to organize the meeting that was hosted by the Community Development Foundation in 

Tupelo, Mississippi. Participants were asked to give their input on several broad issues 

including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall ARC investments, particularly related to support for the Planning and Development 

Districts (PDDs), are seen as vitally important to the communities they serve, and this is 

especially true for the most rural communities. Additionally, the flexibility and consistency of 

ARC funding as making an important contribution to Appalachian Mississippi’s rural and 

distressed communities. 

Current role of ARC funding

Mississippi has focused much of its past and current ARC funding on infrastructure development 

programs. Participants value the flexibility and broad parameters of ARC funding that allow 

local stakeholders to use their local knowledge to structure their efforts so that they best meet 

their unique challenges. ARC funding for the state’s PDDs is particularly important to these 

efforts, as it allows the PDDs to provide services to communities in a consistent, reliable, and 

integrated fashion. Participants praised the responsiveness of their ARC representatives, 

especially during emergency situations. For example, ARC funding supported one PDD’s GIS 

staff, which in turn allowed them to provide a detailed assessment of the areas affected by 

devastating tornadoes. One participant suggested that the ARC acronym should stand for “active, 

reliable, and caring.”

Participants noted the value of being able to learn about ARC investments and strategies in other 

regions, and this knowledge allowed them to better craft their own strategies and metrics for 

success. They also stated that the ARC requirement to create community wide action plans helps 

them to prioritize and plan for future regional investments. Planning and technical assistance is 

particularly important for many small and rural communities that lack resources and capacity. 

ARC funding in Mississippi helped create the Appalachian Community Learning project—a selfhelp, results-based approach to community and economic development. This popular program 

has helped provide seed money for various community projects, including after school programs, 

farmers’ markets and high school student career recruitment. 
27

Small ARC investments can prove vital for moving larger projects forward, as they can provide 

gap financing, seed funding for pilot projects, or contributions to larger projects. ARC funding 

often provides the initial infrastructure or business assistance investment in an area, which can 

lead to securing larger private investments in the future. For instance, ARC invested in industrial 

pads as part of an industrial land development project in Panola County. These investments 

contributed to a company deciding to build in the Region soon afterwards because the county 

had a site readily available. As another example, ARC funding for water tank projects in 

Yalobusha County have recently led to three major company expansions in that area. 

Impacts of past ARC investments

Several participants offered examples demonstrating the importance of early ARC investments. 

During the early 1980’s, the Mississippi Forestry Commission was able to use ARC funding to 

participate in the federal Forest Legacy Program. This ongoing ARC funding investment has 

returned millions of dollars to both the private and public sectors. Other notable early ARC 

investments contributed to the Red Hills/Mississippi Lignite Mining project in Choctaw County, 

which has been one of that region’s major economic drivers. Similarly, ARC investments 

contributed to the National Hills Heritage Area, an important regional cultural resource. 

The state has used ARC funding for a wide range of infrastructure projects related to 

transportation, water and sewage, civic/community centers, communications, natural resource 

conservation, and health care. Participants emphasized the major impact ARC investments, such 

as local access roads and water and sewage lines, have had in drawing businesses to the Region. 

In addition to these kinds of infrastructure projects, ARC has funded a large number of smaller 

industrial infrastructure projects contributing to industrial parks, incubators, and spec and 

industrial buildings that have collectively had an outsized impact on the Regional economy.

ARC funding for infrastructure improvements has been especially vital for rural and distressed 

counties that do not receive enough tax revenue for funding even minor infrastructure upgrades. 

Funding from the program has gone toward not only building infrastructure, but also 

consolidating and coordinating regional infrastructure systems, like different water systems. 

Funding has also been used for some major infrastructure projects, including the highway 

Interstate-22 in northern Mississippi. Many of the infrastructure investments such as sewer and 

water line and access road have led to many small investments, rather than large investments by 

major businesses. Participants noted that infrastructure investments through programs like Main 

Street Batesville in Batesville, MS can help municipalities quickly increase their tax revenue due 

to the influx of several new small businesses in an area newly served by infrastructure. 

Participants noted the great value of ARC funding for technology infrastructure projects, 

including telecommunications infrastructure and Enhanced 911. The latter is especially 

important due to the Region’s frequency of tornados. ARC also helped fund MEGAPOP 

(Mississippi Economic Growth Alliance and Point of Presence), a partnership between the 
28

Mississippi Economic Growth Alliance and Telepak Networks to deliver advanced broadband 

telecommunications services. ARC funding also helped the Northeast Mississippi Planning and 

Development District partner with Blue Mountain Community College and Northeast 

Community College to further advance the Region’s broadband capacity. 

ARC funding has been particularly important for initiating workforce development programs. 

ARC provided gap funding so the state could match a grant from the Carl D. Perkins Vocational 

and Technical Education Act, which the state has used to create several workforce development 

programs and educational institutions. In 2006, Mississippi received an ARC construction grant 

related to Hurricane Katrina, which helped them serve about 1,900 people and leverage close to 

$1 million in addition to ARC funding over three years. Noxubee and Clay Counties then started 

a construction skills program, which proved vital for retraining workers after a major plant 

closing in 2007 in West Point. The program has also been used for retraining ex-offenders from 

Noxubee Country Correctional Facilities, which has been “life changing” for graduates. ARC 

investments also supported an industry partnership between Itawamba Community College and 

various industry representatives to establish a mobile welding lab and other training programs to 

help meet their manufacturing training needs. 

Participants noted the major return on investment they have experienced from using ARC 

technology grants for small workforce training related projects, such as mobile science labs, 

software equipment purchases and community college training programs. ARC funding has also 

helped them create a mobile career aspiration lab to inform high school students about careers in 

manufacturing, and an interactive conference system to support their training activities. 

Moving forward

ARC investments have helped to spur investment and address many development challenges in 

Mississippi’s Appalachian communities. In spite of this, there remain many enduring pockets of 

poverty through the Region and there are challenges yet to be addressed. A few participants 

stated that it was difficult to meet ARC funding job creation requirements for projects intended 

more for quality of life improvements. Participants also noted the need to maintain the flexibility 

of ARC funding, as that flexibility (unlike many other funding sources) allowed them to 

effectively respond to their region’s many critical development challenges.

In addition to maintaining many of the current ARC investments, participants noted several other 

ongoing and emerging priority areas. Because much of the infrastructure in the Region was put 

in place during the 1960’s and 70’s, participants asked for support in funding physical 

infrastructure replacement projects. Participants also asked for increased funding for road, water 

and sewer infrastructure projects, especially highway building. Additionally, there was particular 

interest in expanding and strengthening the broadband infrastructure. All of these emerging 

areas point to the oft noted need to generally improve the Region’s quality of life to retain 

existing residents and attract new ones. This is especially true for young workers that have left 
29

the Region. In addition, participants noted the importance of bridging the gap in employment for 

workers who are between projects, so that the Region can retain highly skilled workers. 

Meeting Attendees

 Mike Armour, MDA/ARC Regional Office, Tupelo, MS

 Phylis Benson, Golden Triangle Planning and Development District, Starkville, MS

 Robert (Bob) E. Borton, Mayor – City of Macon, Macon, MS

 Spencer Brooks, Golden Triangle Planning and Development District, Starkville, MS

 John Byers, Three Rivers Planning and Development District, Pontotoc, MS

 George Crawford, Golden Triangle Planning and Development District, Starkville, MS

 Joseph P. Dodson, Golden Triangle Development LINK, Starkville, MS

 Steve Hardin, Mississippi Development Authority, Jackson, MS

 Larry Hart, Mayor – City of Water Valley, Tupelo, MS 

 Kyle L. Jordan, Office of Congressman Harper, Starkville, MS

 Nancy Knight, Former ARC Director, Tupelo, MS

 Mindy Maxwell, Office of Senator Cochran, Columbus, MS

 Kawana McCary, East Central Planning and Development District, Kemper County

 Jamie McCoy, Northeast Mississippi Planning and Development District, Booneville, MS 

 Sue Moreland, Appalachian Regional Commission 

 Hank Moseley, Office of Congressman Harper, Starkville, MS

 Dr. Mabel Murphree, Office of Congressman Nunnelee, Former ARC Director, Tupelo, 

MS

 William (Boo) Oliver, Noxubee County – Board of Supervisor, Macon, MS

 Larry Otis, Former ARC Director, Starkville, MS

 Sandra Perkins, MDA/ARC Regional Office, Tupelo, MS

 Drew Robertson, Office of Senator Wicker, Tupelo, MS

 David P. Rumbarger, Community Development Foundation, Tupelo, MS

 Dr. Raj Shaunak, East Mississippi Community College, Starkville, MS

 Susan Shedd, Mississippi Development Authority, Jackson, MS

 Sonny Simmons, Panola Partnership, Batesville, MS

 James Williams, Itawamba Community College, Tupelo, MS

 
30

New York ARC Focus Group Report

On December 8th, 2014 key ARC stakeholders in New York State met to discuss the impact of 

ARC investments in their communities. The New York Department of State helped to organize 

the meeting that was hosted by the Southern Tier East Regional Planning and Development 

Commission at the Doubletree by Hilton Hotel Binghamton. Participants were asked to give their 

input on several broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall, there was great enthusiasm for ARC investments, as it is one of the few programs that 

afforded them the ability to address their unique regional and local needs. Participants noted that 

ARC funding was vital for providing seed funding to move bigger projects or funding pilot 

projects that allowed communities to test innovative new ideas. Importantly ARC has also been 

instrumental in helping communities—in part through its support of the Region’s Local 

Development Districts (LDDs)—build their capacity to respond to both new and persistent 

challenges. They also appreciated the stability of the program over the past several decades and 

particularly how the federal-state-local collaboration allowed them the flexibility to address 

challenges that required creative responses.

Impacts of past ARC investments

In many places throughout Appalachia, the impact of ARC funding can be found in the hard 

infrastructure projects pertaining to water, sewer and roads. While similar investments have been 

made in New York State, the state has never been one of the largest recipients of ARC funding. 

As a result, ARC investments in the state have often focused on a different set of issues but 

ARC’s impact on the Region remains evident. For instance, past ARC investments supported the 

creation of social welfare and child development programs that are still in operation today. 

ARC has also been a leader in supporting new development ideas. For example, ARC funded 

many of the early planning grants related to broadband deployment and those plans allowed the 

Region to get a jump on installing what has become a vital piece of infrastructure. The range of 

ARC-supported projects underway in New York State demonstrates how this flexible funding 

source can be used to address a wide array of regional challenges. New York State uses its ARC 

funding for several purposes including seed funding, to pilot innovative projects, to help build 

community capacity, and to promote regionalism.

ARC investments have allowed New York State to seed programs that seek to address key 

regional challenges. Within the area of healthcare, for instance, ARC investments seeded social 
31

service and child development programs that are still in operation today. It has been used to fund 

healthcare-related programs such as a mobile cancer screening unit that since 2009 has screen 

12,000 people. ARC funds have also seeded the establishment of an accelerated nursing 

curriculum at Hartwick College that will allow graduates to complete their program earlier and 

more quickly to begin to meet the Region’s need for nurses.

ARC investments have also allowed New York State to pursue innovative pilot projects in 

emerging areas of economic development. For instance, ARC investments have supported Finger 

Lakes ReUse’s deconstruction program, which is a one -of -a kind program in upstate New York. 

This program helps deconstruct homes and buildings, or refurbish computers, so that many of the 

materials are reused rather than creating additional waste for landfills. These deconstruction 

projects also provide workforce development opportunities for workers, as they gain practical 

experience in the construction and other technical trades.

New York State is also attempting to leverage its significant agricultural assets for economic 

development. ARC has assisted this effort through several pilot projects. For instance, ARC has 

helped to support the Groundswell Center for Local Food and Farming. Working with partners 

like the Cornell Cooperative Extension and the New York Department of State Office of New 

Americans, Groundswell is a 10 Acre Farm Incubator that provides support, resources, and 

training for new farmers. These services may range anywhere from basic farming techniques to 

English as a Second Language for farmers. In doing so, it hopes to contribute to a more 

sustainable food system. 

ARC investments have also been used to create support for producers of craft food and 

beverages through funding creating the Hartwick Center for Craft Food and Beverage. In 

partnership with Hartwick College and Brewery Ommegang, ARC funding will contribute to 

staff support and equipment that will provide testing, business development and education for 

small breweries, wineries, distilleries, and other craft food producers. These activities have 

grown in upstate NY, and these services will make it easier for these enterprises to retain and 

grow their employment.

In addition to these pilot projects, ARC investments have been instrumental in promoting 

regional efforts. For instance, ARC funding in conjunction with a New York State Local 

Government efficiency grant helped underwrite a planning project that examined the water 

systems in northwest Chautauqua County and see how eight different local governments could 

work together to create a more efficient and integrated water system. ARC investments have also 

supported less traditional economic development efforts like the Career Opportunities in Rural 

Education (CORE) program in Otsego County. This program has helped to promote career 

awareness among students in multiple school districts and start a conversation between schools 

and businesses about what students need to succeed. ARC funding allowed the Milford School 

District to hire a coordinator that helps to facilitate and sustain the conversation and activities 
32

that occur between the various regional partners. Programs like this have helped to give a new 

regional vision for school districts that do not always think regionally.

ARC investments have also been critical in building the capacity of communities throughout 

New York State’s Appalachian counties. This is most evident through its support of the New 

York State’s Regional Planning and Development Boards. This funding allows the RPDBs to 

better assist their communities by underwriting some of the costs involved in grant writing, 

project development or complying with state and Federal guidelines. Similarly, ARC funding 

offsets some of the costs they incur to provide planning and support services like the use of 

Geographic Information Systems (GIS). This GIS capacity has allowed the RPDBs to map water 

and sewer systems or the Region’s broadband network. This information is a vital input to 

crafting more regional solutions to these challenges. ARC involvement has also allowed New 

York State communities to both access and share best practices not only throughout Appalachian 

New York (e.g., the planner’s roundtables), but throughout the entire region as well. This 

planning support has also been vital for the Regions because it has allowed them to strengthen 

their proposals to their respective regional economic development councils, which in turn has 

made them more competitive for state economic development funding.

Moving forward

Looking to the future, the participants were in general consensus that ARC investments in the 

Region were an important source of funding and should continue. As noted above, ARC 

investments help spur innovative approaches to solving regional development challenges. 

Participants did note that there were several areas where ARC might want to consider placing its 

attention in the future. 

Issues pertaining to sustainability and addressing information related to climate change were 

noted as an area that requires creative thinking for addressing a multi-faceted problem. This not 

only means funding innovative programs like Finger Lakes ReUse, but also looking for other 

ways in which to reduce the environmental impact of economic development activities. In a 

somewhat related manner, participants also noted the need to find better local and regional 

approaches to storm water run-off and resiliency planning. Like many other parts of the ARC 

region, this region has been severely impacted by flooding and preparing for and responding to 

these events may be another area meriting greater ARC attention.

Another challenge facing rural regions is public transportation. New York State’s rural areas 

make people car dependent. While mobility services are available in places for the aged and 

elderly, this is less the case for workers. As a result, workers lacking reliable personal 

transportation are at a real disadvantage in finding work opportunities. ARC funding might be 

used to better understand this issue and fund practical solutions. 
33

For all the reasons mentioned above (seed funding, pilot project opportunities, capacity building, 

region building) participants remain enthusiastically supportive of ARC funding and the 

program’s structure. Even though the funding is less than it has been in the past, it remains a vital 

to addressing the Region’s challenges. The flexibility of ARC funding allows New York State’s 

Appalachian communities to do more with less, and it is an important tool for leveraging 

additional funding opportunities. It not only helps fund innovative pilot projects, but its 

contributions to the Region’s planning capacity makes the communities in New York State’s 

Appalachian communities better able to respond when new opportunities present themselves.

Meeting Attendees

 Maggie Arthurs, Hartwick College

 Diane Cohen, Finger Lakes ReUse

 Frank Evangelisti, Broome County Planning

 Carlena Ficano, Hartwick College

 Joanna Green, Groundswell Center

 Jen Gregory, Southern Tier East Regional Planning and Development Board

 Erik Miller, Southern Tier East Regional Planning and Development Board

 Dan Neff, Appalachian Regional Commission

 Mark Pattison, New York State Department of State

 Carolyn Price, Town of Windsor

 Liz Rickard, Milford Central School

 Jack Salo, Rural Health Network SCNY

 Kevin Stevens, Milford Central School

 Joanne Tobey, Bassett Healthcare Network

 Marcia Weber, Southern Tier Central Regional Planning and Development Board
34

North Carolina ARC Focus Group Report

On July 11th, 2014 key ARC stakeholders in the state of North Carolina met to discuss the impact 

of ARC investments in their communities. The North Carolina Department of Commerce helped 

to organize the meeting that was hosted by the High Country Council of Governments in Boone, 

North Carolina. Participants were asked to give their input on several broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investment.

Overall ARC investments, particularly related to support for the Local Development Districts 

(LDDs) and water and sewer projects and local food systems, are seen as vitally important to the 

communities they serve, and this is especially true for the most rural communities. Additionally, 

the flexibility and consistency of ARC funding and services were cited as being critical to 

meeting the needs of rural and distressed communities. The participants also noted the ARC 

programs will have more significant impacts in their communities in the future with a more 

explicit focus on the return of investment.

Current role of ARC funding

Participants noted ARC funding has played a crucial role in North Carolina’s Appalachian 

region. Support for the Local Development Districts (LDDs) was seen as one of the most 

important elements of ARC’s efforts in North Carolina. Relative to many other states, North 

Carolina devotes more of their ARC funds to their LDDs. This funding allows the LDDs to 

provide planning services and assistance with grant writing and project development for many of 

their more rural communities. Several participants mentioned ARC investments in the 

community planning has increased local jurisdiction’s capacity in project management and fund 

raising. In addition, ARC projects are seen the catalyst for communities to work together. 

Without this funding, the LDDs would be less able to provide consistent and reliable service to 

their smaller and more distressed communities. Due to this funding, it was noted that the LDDs 

in the western part of the state were in many instances, like writing CDBG grants, able to 

provide a greater level of service than their rural counterparts in eastern North Carolina.

Relative to many other forms of federal funding, North Carolina’s ARC funding is relatively 

small. However, ARC funding can provide a critical gap funding for a number of regionally 

important projects. This funding ensures that projects get completed, or enables communities to 

leverage other forms of needed funding. These investments have shifted over time, and while 

North Carolina continues to use ARC funds for water, sewer and other large infrastructure 

projects these investments are increasingly being used to support efforts related to local food 

systems, education and workforce development, and entrepreneurial development among others. 
35

ARC investments often provide important early funding which can have important ancillary 

benefits. One good example is the Home of the Perfect Christmas Tree in Mitchell County. ARC 

provided resources that helped to modernize a sewer line in Spruce Pine. This project laid the 

foundation for the establishment of the Home of the Perfect Christmas Tree, which provides a 

facility for local crafts people and small businesses to sell their handmade products. Since the 

project was started in 2003, 60 individual small businesses – many are owned by crafts people 

who lost their jobs in the furniture and manufacturing industries due to outsourcing in the past 

decades – have been created. Proceeds and a portion of the royalties from the product sales are 

used to fund scholarships at the local high school. ARC investments have also been used to 

support other entrepreneurial support efforts. For instance, ARC investments contributed to the 

Piedmont Angel Network that ARC has helped fund. The Piedmont Angel Network has invested 

in 23 companies since 2002 with a primary focus on the life sciences, technology, software, and 

advanced materials fields.

North Carolina has also used ARC funding to grow the local food systems in its Appalachian 

region. ARC’s support has been particularly vital for small and independent farms to grow and 

expand. For instance, ARC, along with other North Carolina organizations such as North 

Carolina Golden LEAF Foundation and North Carolina Rural Economic Development Center, 

funded the Foothills Pilot Plant, a small scale slaughtering and processing facility serving 

independent growers of poultry and rabbits. Foothills Pilot Plant is the only USDA inspected 

small-animal slaughter facility in North Carolina. 

North Carolina’s ARC investments have also been utilized to retain some of the Region’s larger 

businesses as well. For example, ARC funding supported renovating a major water pipe line in 

Wilkesboro, NC to meet increased demand in water from a large food company (Tyson Foods). 

With this ARC funding, the community was able to leverage more financial resources for this 

project. These investments were an important reason the community was able to retain over 200 

jobs. 

Impact of Past ARC Investments

North Carolina’s ARC investments have shifted over time. Past ARC investments were focused 

on a wide range of traditional economic development projects related to water and sewage, 

transportation, and community facilities. These investments were helpful in filling critical gaps, 

often to support the Region’s manufacturing sector which has historically driven the Region’s 

economy. Given the sharp declines in the textiles and furniture industries and the lack of large 

new manufacturing projects, the focus of North Carolina’s ARC investments has somewhat 

shifted. 

While ARC investments continue to go into infrastructure projects (e.g. water, sewer, access 

roads), these investments have become more diverse over time. North Carolina’s ARC 

investments now are directed toward projects that address issues related to entrepreneurship, 
36

local food systems, and community planning. This transition of ARC investments in North 

Carolina from traditional economic development projects to less tangible projects is largely due 

to declines in traditionally competitive industries such as furniture and manufacturing and more 

limited resources. As a result, there are fewer opportunities to develop these kinds of projects.

Participants provided examples of several past ARC projects that were critical to the 

development of their communities. For instance, combined with investments from other sources 

such as the U.S. Economic Development Administration and North Carolina Rural Center, ARC 

funding supported building the Granite Falls water tower in Caldwell County. This water tower 

was a critical element of much subsequent commercial and residential development. The Hot 

Springs Health Center in Madison County is another ARC supported effort that has had lasting 

impact. This health center was initially built in the early 1970s and since then has served as one 

of the primary medical and healthcare providers for Madison County and its surrounding 

counties. 

Moving forward

ARC investments have played a critical role in major economic development efforts in North 

Carolina’s Appalachian region. All participants noted ARC’s continued support was important to 

their communities. They also wished to see ARC funding and services remain flexible, 

consistent, and predictable going forward. This was especially important for the support for the 

LDDs. As participants noted, ARC’s future investment in community planning is critical for 

LDDs to improve their staff capacity and build sustainable models for project management in 

longer term. In addition, many participants emphasized ARC should expand its investments to 

help LDDs implementing their plans and fulfill the entire planning process.

In addition to maintaining many of the current ARC investments, participants noted several other 

ongoing and emerging priority areas, most of which are related to improving the Region’s 

quality of life and competitiveness. Almost all participants agree ARC expanding the reach of 

the Region’s broadband infrastructure was an increasing priority moving forward. Last mile 

broadband is particularly challenging for many of the Region’s more isolated areas. Broadband 

infrastructure was viewed as foundational for other initiatives related to local food systems, 

entrepreneurship, or the delivery of rural healthcare. Healthcare-related issues were another 

identified issue requiring greater attention. This effort will not only improve access to basic 

healthcare such as proximity to urgent care centers and general practitioners but also stimulate 

economic growth in distressed region. 

There was also a consensus among the participants that ARC should increase its investments in 

local food systems, particularly in North Carolina’s rural communities. For instance, the 

Foothills small animal pilot plant has proven successful and more help would be needed to allow 

it to serve more small animal producers. ARC investments might also be used to support the 
37

Regional livestock market, or craft food and beverage producers in order to create more 

opportunities for local growers and producers. 

The participants also noted the future ARC investment should place priority on supporting 

education and workforce development in distressed regions. Future ARC investments might help 

fund projects like the Spring Creek Literacy Project. This is an ongoing effort to help girls and 

young women from rural communities of Hot Springs, Laurel, and Spring Creek gain the skills 

they need for success in high school and beyond. As in other states, participants appreciated how 

ARC investments allow local communities to address this diversity of challenges and think it is 

important that these programs continue in a manner consistent with the past.

Meeting Attendees:

 Dee Blackwell, Western Piedmont Council of Governments

 Richard Canipe, High Country Council of Governments

 Kristy Carter, ARC Regional Planner, NC Department of Commerce

 Kelly Coffey, High Country Council of Governments

 Olivia Collier, ARC State Program Manager, NC Department of Commerce

 Matthew Dolge, Piedmont Triad Regional Council

 Mickey Duvall, High Country Council of Governments

 Jim Edwards, Isothermal Planning and Development Commission

 Sarah Graham, Southwestern Commission

 Sherry Long, Western Piedmont Council of Governments

 Ken Noland, High Country Council of Governments

 Ryan Sherby, Southwestern Commission

 Danna Stansbury, Land of Sky Regional Council

 Gary Steely, Piedmont Triad Regional Council

 Phil Trew, High Country Council of Governments
38

 Ohio ARC Focus Group Report

On November 14th, 2014 key ARC stakeholders in the state of Ohio met to discuss the impact of 

ARC investments in their communities. The Governor’s Office of Appalachia helped to organize 

the meeting that was hosted by Zanesville Community College in Cambridge, Ohio. Participants 

were asked to give their input on several broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall ARC investments are valued by members of the local community and are seen as an 

important resource for the community moving forward. Meeting participants noted the 

importance of Local Development District funding to building community capacity throughout 

the Region. Additionally, it was noted that the ARC federal-state-local model allowed Ohio’s 

Appalachian communities not only respond to traditional economic development challenges, but 

also to ongoing and emerging development challenges in areas such as healthcare and workforce 

development. 

Current role of ARC funding

As in many states, investments in the Local Development Districts were cited as one of the 

important uses of ARC funds. With diminishing budgets for local governments, these 

investments allow LDDs to provide consistent services for local governments that lack capacity. 

As a result, LDDs can assist local governments with activities such as grant writing and project 

development. This administrative funding also allows Ohio’s Appalachian LDDs to provide 

other services like GIS, community planning, and data analysis for their communities. Providing 

and building this kind of community capacity ensures that small, rural communities have better 

access to resources, and can at least keep pace with other communities. Extending these kinds of 

basic services to underserved areas was a common theme repeated by the participants. 

ARC funding also plays several important pieces of the funding puzzle. Communities have used 

ARC funding to fill funding gaps in projects to ensure that they are completed. More often, 

however, ARC funds are used to leverage additional funding and resources. One participant 

noted that ARC investments often gain up to 10:1 leverage. In an era of scarce resources, the 

ability to pull together a variety of funding sources is crucial to completing most projects.

A keyword for ARC impacts in Ohio is diversity. ARC funding has been used toward a variety 

of efforts ranging from workforce development to water and sewer expansion to growing 

broadband access. All of these efforts are intended to improve quality of life, economic 

development efforts, and job retention. This diversity of issues reflects the flexibility afforded by 
39

the ARC federal-state-local partnership model that allows communities to create unique 

solutions to local problems. For instance, health and wellness issues related to substance abuse, 

obesity and other issues related to poverty present critical challenges to the Region’s 

development. ARC funds allowed the Lawrence Economic Development Corporation to conduct 

a feasibility study for a medical wellness center in Lawrence County. Raising funds had been a 

challenge because the facility does not qualify as either a hospital or educational institution.

Impacts of past ARC investments

Similar to many other ARC states, basic infrastructure investments typified early ARC 

investments. Water lines, sewage systems, and highway systems have been typical projects 

undertaken in the Region. These basic infrastructure investments can make a large impact on a 

community. For example, ARC helped fund a new wastewater treatment plant in a town that had 

lost an existing employer and needed assistance to maintain existing businesses. Basic 

infrastructure has been vital for key economic development and business retention projects –

FedEx brought over 750 jobs to Lawrence County partially due to the development of an 

industrial park that was funded in part by ARC investments.

These kinds of infrastructure and construction projects were aided by the state’s ability to serve 

as the basic agency for these efforts. When these projects had to rely on large Federal agencies to 

administer these grants, the process may have moved at too inefficient a pace. Companies were 

ready to create jobs in the area, but they were on a faster schedule than the local organizations’ 

federal partners, and these delays might cause them to move forward with their efforts in the 

Region. As the state assumed this basic agency role, these projects were able to move at a 

somewhat quicker pace and ensure that projects were completed more efficient manner.

These investments in the Region’s economic development infrastructure made a significant 

difference in its development trajectory. However, due to more limited resources it is 

increasingly difficult to provide these services and undertake these projects in communities that 

are more remote and have smaller populations. As a result, ARC investments in Ohio have 

shifted to fund a wider array of projects related to healthcare, workforce development/education, 

and business assistance. Healthcare projects may, for instance, involve increasing access to 

healthcare services by investing in telemedicine or equipping clinics. 

Similarly, ARC investments have strengthened the Region’s education and workforce 

infrastructure. For example, Zanesville Community College was once just a business and 

industry training center, but it has now been transformed into a more comprehensive campus. A 

critical link in the transformation was an ARC grant that helped build a state of the art science 

lab. This development paved the way for a fundraising campaign and building expansion. ARC 

funding also helped the college expand its welding program in support of the Region’s 

manufacturing sector. Other efforts have been made to support entrepreneurial and business 

development opportunities. For instance, ARC funding has helped to leverage local private and 
40

foundation money for export promotion programs for family-owned businesses and youth 

development programs. All of these programs are important for changing the culture of the 

Region from one often typified by dependency and instead create one based on self-sufficiency.

Potential future investments 

Moving forward, the ARC Ohio focus group attendees expressed an interest to expand workforce 

and entrepreneurial development efforts in their communities. The community college system 

was mentioned as a possible source for new leadership training opportunities and workforce 

development. Focus group participants were also uniformly interested in changing the culture of 

the Region by improving education, health outcomes, and self-sufficiency/entrepreneurship. 

Workforce development was a recurring theme among participants, in both specific industries 

and general issues related to culture and basic education. Medical wellness will continue to be an 

issue in the area, and investments in drug prevention, nutrition, and more will help both quality 

of life and business retention and expansion, as this is a workforce development issue. 

Participants were also concerned about youth retention and attraction. They believe that quality 

of life and amenities are vital to convincing young people with potential to stay in the area. The 

area’s relative isolation is a recurring theme and traditional remedies for addressing this isolation 

(e.g., highways) needs to be mixed more with newer solutions (e.g., broadband). 

Participants also sought to find ways to further growth the Region’s local craft industries, such as 

those producing locally made furniture. Focus group participants believe that entrepreneurship 

and small firms are important, as they would change the character of the Region from relying on 

company relocation to the Region for employment to self-sufficiency. Participants also sought 

greater investment in business incubators and other investments in entrepreneurship that might 

help revitalize downtown areas, diversify the economy, and expand businesses. In addition, 

participants would like to see a greater focus on the logistics needs for cooperative multi-county 

businesses (e.g., supply chain networks for furniture makers). Entrepreneurship will be a focus 

moving forward, as participants recognize that small communities have a difficult time attracting 

large businesses.

Participants note that money continues to be tight, and that the needs that they are currently 

meeting will continue to be issues. Some participants would like to see more small capacitybuilding projects in communities, which was something ARC had funded in the past. A number 

of participants also mentioned the need for flexibility in their revolving loan fund requirements. 

One organization’s fund has essentially turned into a grant program due to program limitations, 

and they would find it helpful, for example, to be able to change interest rates. In addition, some 

believe that equity is more of a need for businesses than loans, and they are interested creating 

more connections for equity investors.
41

Meeting Attendees

 Bret Allphin, Buckeye Hills-Hocking Valley Regional Development District

 Cara Brook, Foundation for Appalachian Ohio

 Paul Brown, Zane State College

 Peggy Carlo, County Commissioner Ashtabula County

 Misty Casto, Buckeye Hills-Hocking Valley Regional Development District

 Greg DiDonato, Ohio Mid-Eastern Governments Association

 Bill Dingus, Lawrence Economic Development Corporation

 Ray Eyler, Holmes County

 Karen Fabiano, ODSA Community Services Division

 Senator Lou Gentile, Ohio Senate

 John Hemmings, Ohio Valley Regional Development Commission

 Ralph Kline, Lawrence County Community Action

 John Molinaro, Appalachian Partnership for Economic Growth

 Mary Oakley, Ohio Development Services Agency

 Joy Padgett, Governor's Office of Appalachia

 Ron Rees, Corporation for Ohio Appalachian Development

 Molly Theobald, Appalachian Regional Commission

 Viviane Vallance, Lawrence Economic Development Corporation

 Perry Varnadoe, Meigs County

 Jeannette Wierzbicki, Ohio Mid-Eastern Governments Association

 Jason Wilson, Governor's Office of Appalachia

 Mike Workman, Contraxx Furniture 

 Kathy Zook, Eastgate Regional Council of Governments
42

Pennsylvania ARC Focus Group Report

On July 9th, 2014 key ARC stakeholders in the state of Pennsylvania met to discuss the impact of 

ARC investments in their communities. The Pennsylvania Department of Community and 

Economic Development helped to organize the meeting that was hosted by SEDA-COG in 

Lewisburg, Pennsylvania. Participants were asked to give their input on several broad issues 

including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall, ARC investments, particularly related to support for the Local Development Districts 

(LDDs) and enterprise development programs, are seen as vitally important to the communities 

they serve, and this is especially true for the most rural communities. Additionally, the 

flexibility, consistency and predictability of ARC funding and services was cited as being critical 

to meeting the needs of rural and distressed communities. 

Current role of ARC funding

In the past, Pennsylvania’s ARC funding focused on infrastructure projects, but over the past few 

decades has transitioned to more of a service delivery role, especially through the Local 

Development Districts (LDDs). Participants noted that moving from narrowly-focused 

infrastructure projects to more service delivery has allowed the program to have a broader impact 

throughout the Region. Consequently, Pennsylvania’s current ARC funding supports several 

broad issues including funding for LDDs, Enterprise Development, supplementing and 

complementing larger projects with targeted investments, and providing seed funding for small, 

often pilot projects. 

Most participants identified support for the LDDs as the most valuable role that ARC plays in the 

state’s Appalachian region. ARC funding allows the LDDs to provide consistent and reliable 

service to the communities that they serve. LDDs can provide local communities with a range of 

planning services related to numerous disciplines (e.g., economic development, workforce 

development, transportation, tourism, environmental, etc.). ARC funding allows LDDs to do this 

in a consistent, reliable, and integrated fashion. This technical assistance is particularly important 

for many small and rural communities that lack resources and capacity. Several participants cited 

the critical ability of LDDs to serve as an interagency connection between different service 

providers and local jurisdictions within the Region. LDDs also connect local residents and 

businesses with resources at the state, regional and federal levels, as well as private resources. 
43

Pennsylvania has also focused much of its ARC funding on enterprise development programs. 

Specifically these funds complement other funding sources in support of three primary areas—

government procurement, export financing and business financing. The increased usage of ARC 

funding for enterprise development has led to small seed financing packages and assistance that 

help businesses expand their markets and internal capacity, which in turn advances the Region’s 

business retention and job creation efforts. Participants value the flexibility and broad parameters 

of ARC funding that allow the structure of projects to be formed by the local stakeholders that 

have a stronger understanding of the issues facing the Region. 

Small amounts of ARC funding can also prove vital for moving larger projects forward. ARC 

funding for local access roads has been especially important. For instance, an access road project 

has supported the creation of a new Life Science industrial park in Mifflin County that will 

include a health center and create more than 100 jobs. Similarly, funding for an access road has 

furthered the development of the recently opened Anthracite Outdoor Adventure Area in 

Northumberland County. Built on abandoned coal mining land, this park provides opportunities 

for hiking, camping, horseback riding and other recreational activities. Additionally, ARC has 

supported a technology project at the park that tests new ways of using cell phone coverage to 

increase safety and security for visitors. 

ARC funding has also been used to seed smaller, often pilot projects. Participants mentioned 

several examples of these smaller projects including the New Berlin Energy Independence 

Project. This 3-year project, funded by ARC and other partners (including PPL Electric Utilities, 

Bucknell University, and the Pennsylvania Department of Community and Economic 

Development among others) sought to achieve greater energy independence in the small rural 

community of New Berlin, PA (pop. 900). This project allowed the community numerous ways 

to conserve energy, and these measures have gone on to save New Berlin $200,000 in energy 

costs annually. The project resulted in the publication “Energizing Small Communities” which 

serves as a guide for other communities. The ability to pursue this kind of project speaks to the 

overall flexibility of ARC funding.

Impacts of past ARC investments

Pennsylvania’s current usage of ARC funding with its emphasis on supporting the LDDs and 

enterprise development funding differs from its usage during the 1970s and early 1980s. This is 

due in part to a relatively smaller amount of available funding. In the past, Pennsylvania used 

ARC funding for a wide range of infrastructure projects related to transportation, water and 

sewage, communication, recreation, business assistance, community facilities, and healthcare. 

Several participants offered examples demonstrating the importance of early ARC investments. 

In Lackawanna County, ARC provided the first funding in 1978 for an access road to the 

Montage Mountain ski resort, which was completed and opened in 1984. Participants appreciated 

not only this initial project investment, but also ARC’s unwavering commitment throughout the 
44

long public hearing process. Combined with four or five later investments, ARC eventually 

invested about $3 million into the project. Opening up this land for development eventually led 

to more than $500 million in private investment and four to five thousand jobs, as well as spinoff development in the area, such as golf courses, retail, housing, hotels, a stadium and service 

businesses. 

In 1983, ARC provided a grant for a feasibility study on whether a public-private initiative could 

sustain rail in the Region, which lead to the formation of the SEDA-COG Joint Rail Authority, 

an 8-county rail organization. The SEDA-COG Joint Rail Authority has invested over $100 

million in the Region for providing rail service, including refurbishment and capital upgrades, 

and extending new service to industrial parks. This investment has “created a whole different 

outlook in some of those towns” because of the current increase in the use of freight rail traffic 

over trucking. The World Bank recently used the SEDA-COG Joint Rail Authority as an 

example of successful regional rail model in a manual (prepared by Cambridge Systematic) for 

developing countries on models transportation initiatives. 

In addition to these kinds of infrastructure projects, ARC has funded a large number of small 

infrastructure projects throughout the Region, such as Brownfield redevelopments and health 

centers, which have collectively had an outsized impact on the Regional economy. Participants 

noted that ARC’s early funding for rural healthcare facilities and career and technical education 

facilities have made a lasting impact and contributed to the overall health and capacity of the 

Region’s people. 

Moving forward

ARC investments have helped to spur investment and address many development challenges in 

Pennsylvania’s Appalachian communities. In spite of this, there remain enduring pockets of 

poverty through the Region and there are challenges yet to be addressed. To this end, there was a 

consensus among the participants that the most vital future ARC investments should place 

priority on maintaining the support for the LDDs. LDD support was seen as providing one of the 

greatest returns on ARC-related investments. Increasing investments in broadband access was 

seen as another critical investment area that fits well within ARC’s overall mission. Participants 

also noted the need to maintain the flexibility of ARC funding, as that flexibility (unlike many 

other funding sources) allowed them to effectively respond to their region’s many critical 

development challenges.

In addition to maintaining many of the current ARC investments, participants noted several other 

ongoing and emerging priority areas. Toward the top of this list was the need for increased 

support for expanding and strengthening the broadband infrastructure. Broadband infrastructure 

was viewed as foundational for other initiatives related to local food systems, entrepreneurship, 

or the delivery of rural healthcare. Healthcare-related issues were another identified issue 

requiring greater attention. This ranged from ensuring adequate access to basic healthcare such 
45

as proximity to urgent care centers and general practitioners to reducing the incidence of obesity, 

diabetes and substance abuse among the Region’s population.

Participants also noted the importance of supporting projects that address issues related to quality 

of life or quality of place. These types of projects may take on several forms. For instance, they 

may address water resources or sustainable development issues. They may incorporate projects 

like the Susquehanna Greenway Partnership and the multi-county Pennsylvania Wilds project 

that build on the Region’s natural assets for recreation, tourism and other local business 

opportunities. All of these emerging areas point to the oft noted need to generally improve 

quality of life within the Region in order to retain and assist current residents, as well as attract 

new residents, especially young workers that have left the Region. 

Meeting Attendees

 Kevin D. Abrams, Northern Tier Regional Planning & Development Commission 

Kim D. Barnes, Northern Tier Regional Planning & Development Commission 

 Jeffrey K. Box, Northeastern Pennsylvania Alliance 

 Eric M. Bridges, North Central Pennsylvania Regional Planning & Development 

Commission 

 Lisa Davis, Pennsylvania Office of Rural Health

 Neil Fowler, Pennsylvania Department of Community & Economic Development 

 Jill Foys, Northwest Pennsylvania Regional Planning & Development Commission 

 Ned Goucher, Northwest Pennsylvania Regional Planning & Development 

Commission

 Jim Hassinger, Southwestern Pennsylvania Commission 

 Yvonne Lemel, Pennsylvania Department of Community & Economic Development 

 Elizabeth Lockwood, SEDA-Council of Governments 

 Teri MacBride, PPL Electric Utilities

 Julie Marshall, Appalachian Regional Commission

 Robert Postal, Mifflin County Industrial Development Corporation 

 Tom Pellegrini, Northeastern Pennsylvania Alliance 

 Deborah L. Prosser, Southern Alleghenies Planning & Development Commission 

 Dennis E. Robinson, SEDA-Council of Governments 

 Lorri Shaver, Pennsylvania Department of Community & Economic Development 

 Lew Villotti, Southwestern Pennsylvania Commission 

 Jerry S. Walls, SEDA-COG Joint Rail Authority Board
46

South Carolina ARC Focus Group Report

On December 17th, 2014 key ARC stakeholders in the state of South Carolina met to discuss the 

impact of ARC investments in their communities. The South Carolina Department of Commerce 

helped to organize the meeting that was hosted by the South Carolina Appalachian Council of 

Governments (ACOG) in Greenville, South Carolina. Participants were asked to give their input 

on several broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall, ARC investments have been important in spurring economic development throughout 

the upstate South Carolina region. ARC has been an important source of funding for rural 

projects that do not always have a guaranteed return on investment. Participants were also 

especially grateful for the assistance that they receive from their local development district, the 

state department of commerce and ARC’s Washington-based staff. 

Current role of ARC funding

South Carolina has focused much of its ARC funding on infrastructure development programs. 

Participants value the flexibility and broad parameters of ARC funding that allow the structure of 

projects to be formed by local stakeholders. They highly valued the open communication channel 

and technical assistance provided by ACOG, especially for learning about public and private 

grant opportunities, and assistance with grant writing. 

ARC funding is often used as simultaneous match funding for grants from other federal agencies, 

such as the U.S. Economic Development Administration or the USDA Rural Development 

agency. Participants noted that the largest challenges for accessing ARC funding are lining up 

partners, getting match funding, and understanding the complex regulations that accompany 

applying for and administering federal grants. ACOG brings invaluable expertise for helping 

applicants navigate these challenges and forging partnerships between different economic 

development stakeholders in the Region. ACOG’s provision of grant information has created a 

“true support system for smaller communities” in the Region. 

Participants appreciated the helpfulness of ARC staff in Washington, as well as the ARC 

program manager with the South Carolina Department of Commerce. This assistance was 

especially appreciated during the application process, where participants noted their appreciated 

for the willingness to provide feedback on how to improve their applications and projects. This 

type of communication helps create an “open forum for South Carolina”. By serving as the basic 

agency for construction projects that are partially funded by ARC, the state has also helped to 
47

make the administration of these projects relatively easier and more efficient. Participants also 

noted the value of being able to learn about ARC investments and strategies in other regions, 

especially through peer-to-peer networking at ARC workshops. For instance, one participant 

mentioned that attending an ARC-sponsored workshop on local foods in West Virginia proved 

very helpful to their efforts locally. This kind of information sharing allows stakeholders to see 

what types of projects work in other regions and provides them with ideas for additional ways 

they can use ARC funding in South Carolina. However, some noted that they would like to see 

additional opportunities to share best practices between different regions. 

Impacts of past ARC investments

Since the start of the ARC program in South Carolina, the state has used ARC funding for a wide 

range of projects related to workforce training, economic and industrial development, food 

systems, education, healthcare, water and sewer systems, and transportation. ARC funding for 

infrastructure improvements has been especially vital for rural and distressed counties. One 

participant noted that ARC funding was integral to the upstate South Carolina region. 

Several participants offered examples demonstrating the importance of early ARC investments. 

In addition to basic infrastructure, ARC provided funding support for many economic and 

workforce development projects in the Region. Greenville Technical College received 

construction and equipment funding for multiple workforce training related projects, including 

the Quick Jobs with a Future, allied health, dental worker, and truck driver training programs, as 

well as campus expansion projects. Tri-County Technical College has received funding from 

ARC for welding laboratories. Participants noted that the Quick Jobs with a Future proved 

especially beneficial for training dislocated workers during the recent downturn. One participant 

stated that without ARC funding for workforce training programs at the Region’s technical 

colleges, large manufacturing plants like BMW would not have chosen to locate in the Region. 

ARC funding for infrastructure in underserved communities has led to economic and industrial 

development in areas that otherwise would not have received that investment. Participants were 

particularly appreciative of being able to use ARC funds for these kinds of rural projects, as 

other federal grant programs generally do not invest in areas not already served by infrastructure. 

Moreover, few funding sources are able to be relatively speculative in nature, but ARC is one of 

them. Specific examples in the Region include road improvements to a publicly funded industrial 

park in Pickens County that have resulted in thousands of jobs. Other examples are water lines 

along Highway 5 and providing infrastructure that supported the creation of a regional outlet 

mall on Highway I-85. 

Education grants from ARC and others helped ACOG develop various technology driven 

business development tools for regional businesses. These include business planning technical 

assistance and InfoMentum, a GIS-based data and mapping service that allows investors to 

generate maps and reports on demographic and market conditions, as well as access a database of 
48

industrial sites and buildings for the ACOG region. ACOG is also developing a new technologydriven project to support entrepreneurship in the Region, and a website for taking counties 

through steps to become more business friendly, which is currently being piloted by three rural 

communities in the Region. 

ARC funding in South Carolina has evolved from mainly meeting the basic needs of rural 

communities to funding more of a mix of projects, such as tourism and other quality of life 

improvements. Multiple recent ARC projects in the Region have focused on food systems 

planning, including a small animal facility program in Greenville, a planning technology project 

at Tri-County Technical College, and business plan development assistance for communities 

looking to become “farm-to-table” enterprises. ARC has also provided assistance with 

identifying gaps in their regional food system infrastructure.

Moving forward

Participants also noted that continued ARC investments will play an important role in 

diversifying the Region’s economy. Its flexibility can fund projects related to quality of life and 

tourism that often do not have any obvious sources of funding. These projects are not only 

important for creating jobs and attracting visitors and residents to the Region, but they also help 

establish a new regional identity. ARC investments can be helpful in developing the Region’s 

local food system or promoting attractions related to the Region’s Revolutionary War history. 

Several participants noted the success of the Greenville Health System Swamp Rabbit Trail, a 

Rails-to-Trails, multi-use trail system in Greenville County, South Carolina. Not only has this 

trail drawn tourists to the Region, but it has provided a framework that has allowed communities 

throughout the Region to connect their attractions and make the Region a more attractive 

destination. Other communities in the Region are following the Swamp Rabbit Trail model. For 

instance the “Blueways” (i.e., Water Trails) river recreation initiative in Spartanburg and Upstate 

Forever connects water attractions with tourism and environmental efforts. 

Participants also noted that ARC investments might be useful for downtown redevelopment 

initiatives. Communities like Gaffney hoping to replicate the downtown revitalization success of 

Greenville through investing in streetscape improvements and taking part in the National Main 

Street Center redevelopment process. Like many ARC investments, these initiatives touch upon 

many development challenges. A collection of nice downtowns can make the Region a tourist 

destination. They also provide an attractive environment for new residents or entrepreneurs, and 

denser development can improve the public transportation infrastructure, which is relatively 

underdeveloped in the Region. 

Participants are also looking for further economic and workforce development assistance through 

entrepreneurship training and after school education, especially for building the Region’s 

Information Technology (IT) workforce. ARC could also help expand the Region’s IT capacity 
49

through broadband infrastructure development projects, as there remain many gaps in broadband 

coverage. 

In order to market the Region globally, South Carolina is looking to attract more foreign direct 

investment and developing export plans through public-private partnerships like the Upstate 

South Carolina Alliance. Although participants were very interested in exploring opportunities 

for ARC investment needs, they also suggested increased ARC support for increasing 

collaboration between economic development partners on new regional initiatives. This was 

especially true for smaller, rural communities.

Meeting Attendees

 Jill Francisco, South Carolina Department of Commerce

 Jane Hall, South Carolina Appalachian Council of Governments

 Tracy U. Martin, Town of Blacksburg

 Michael McInerney, South Carolina Department of Commerce

 Scott Park, Greenville County

 Dirk Reis, South Carolina Appalachian Council of Governments

 David Shellhorse, South Carolina Appalachian Council of Governments

 Kostas Skordas, Appalachian Regional Commission 

 Brian Swords, Tri-county Technical College

 James R. Taylor, City of Gaffney

 Elizabeth Varga, Greenville Technical College

 Caroline Wilson, South Carolina Appalachian Council of Governments

 
50

Tennessee ARC Focus Group Report

On May 16th, 2014 key ARC stakeholders in the state of Tennessee met to discuss the impact of 

ARC investments in their communities. The Tennessee Department of Community and 

Economic Development helped to organize the meeting that was hosted by the East Tennessee 

Development District in Alcoa, Tennessee. Participants were asked to give their input on several 

broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall there was enthusiastic support for ARC programs and investments. ARC is seen as 

vitally important to the communities it serves, and this is especially true for the most rural 

communities. Additionally, the consistency and predictability of ARC funding and services was 

cited as being critical to meeting the needs of rural and distressed communities. Participants also 

noted that they appreciated that the majority of ARC funding ends up “in the ground” in the form 

of, for instance, actual water and sewer lines, and not lost to substantial administrative costs. 

Current role of ARC funding

Participants noted that ARC funding remains a relatively small part of their region’s overall 

funding portfolio, but they all noted that it nevertheless plays a critical role in getting projects off 

the ground. For the participants, ARC funding was a tool for addressing core economic 

development challenges that require medium and long-term thinking. This funding often plays an 

important seed role—like installing water and sewer lines—that provide the foundation for later 

development. Without these initial investments, communities would find it more difficult to 

secure additional investment and activity.

ARC funding also continues to play a crucial role for many rural and/or distressed counties 

because there remains tremendous pockets of need, which are often isolated pockets of need. As 

one participant noted, “ARC means more for rural counties than you can imagine.” Meeting the 

match requirements for many potential funding sources can often prove challenging for the most 

distressed counties. ARC’s favorable match requirements therefore make it a crucial funding 

source for many smaller communities. 

For instance, Pickett County is Tennessee’s least populous county with just over 5,000 residents. 

One participant noted that ARC was one of that county’s only hopes to fund infrastructure 

projects because they lack the resources to meet the match requirements needed to access other 

forms of infrastructure funding. Another participant noted that there would be no public water 
51

system in rural Cocke County if not for ARC. Similarly, ARC funding has been important to 

Hancock County which has the lowest percentage of public water access in Tennessee.

Participants also view the relatively small amount of funding provided to the Local Development 

Districts as important for rural communities. Rural counties often lack the staff capacity and 

resources to meet all of their planning needs. The LDD funding provided by ARC allows the 

Local Development Districts to provide consistent and reliable planning and government 

services to their rural counties. In doing so, rural counties can access these services without 

having to potentially rely on more expensive consultants. 

Impacts of past ARC investments

At the time of its launch, ARC sought to bring the Appalachian Region to parity with the rest of 

the country. While the work is far from finished, the focus group participants thought that 

Tennessee’s Appalachian communities had made meaningful progress toward fulfilling that goal. 

ARC investments allowed many rural Tennessee residents access to clean water and sewer, 

renovated schools in order to keep them open, and helped to put in place an infrastructure to 

support industry that created local employment opportunities. 

While never a large component of the total funding package, this funding—both in the past and 

present—has served as important seed funding to get larger projects started. Several participants 

offered examples demonstrating the importance of early ARC investments. For instance, in 1986 

ARC investments helped to fund the installation of a new sewer line that helped to prepare the 

current site for Pellissippi State Community College in Knox County. At the time, that $500,000 

investment looked small, but Pellissippi State now serves over 11,000 students and is a core 

component of the Region’s higher education and workforce development systems. More 

recently, ARC investments were instrumental in getting the Kingsport Center for Higher 

Education facility off the ground. Kingsport has traditionally been underserved by higher 

education institutions, and this facility made it possible for Kingsport resident to access higher 

education from six different institutions in one location. 

Several other residents noted that ARC investments in water and sewer lines allowed for the 

establishment of industrial corridors that have made economic development possible. This was 

certainly the case in Meigs County—a rural county without interstate access. ARC funding 

allowed for the creation—and over time expansion—of water and sewer lines to industrial sites. 

Without this infrastructure, the county would have limited opportunities to build its industrial 

base. Similar stories were shared by representatives from other rural locations like Polk County. 

The initial ARC investments made later investments possible. 

ARC investments in highway infrastructure are also viewed as crucial as they provide important 

connections. This is particularly true for rural, mountainous counties where roads are limited in 

number, difficult to build, and expensive to maintain. In Polk County, several rock slides along 
52

US 64 caused travelers to face an 80 mile detour. This can make rural living even more 

expensive and difficult. ARC Highway funds are therefore seen as important to expanding and 

strengthening the Region’s highway infrastructure. 

Moving forward

Participants were all adamant about the importance of ARC funding. As a result, they would like 

to see current investments continued, particularly as they relate to infrastructure-related 

investments such as water, sewer and highways. In addition to these ongoing investments, the 

participants had general consensus about expanding investments in several other emerging areas. 

For instance, developing the ARC Region’s human capital was viewed as increasingly important. 

These kinds of investments may take several forms. It may involve supporting efforts to provide 

workers with in-demand skills such as ARC’s support for Motlow State’s new Mechatronics 

program. These investments may also speak to general health and wellness efforts, such as 

increased support for substance abuse programs. 

Investing in downtown redevelopment was another potential emerging focus area. Creating 

places where people want to live and spend time was seen as a vital condition for success in 

other strategies related to tourism, entrepreneurship and others. Increasing investments in 

broadband access was another critical investment area that fits well within ARC’s overall 

mission. Broadband supports many other priority areas whether it related to local foods and agritourism or support for entrepreneurship and home-based businesses. 

Meeting Attendees

 Morris Baker, City of Kingsport

 Terry Bobrowski, East Tennessee Development District

 Brooxie Carlton, Tennessee Department of Community and Economic Development

 Hoyt Firestone, Polk County Executive

 Lorie Fisher, South Central Tennessee Development District

 Donald Hurst, Cocke County Partnership

 Garland Lankford, Meigs County Mayor

 Paula Lovett, Tennessee Department of Community and Economic Development

 Leigh McClure, Southeast Tennessee Development District

 Michelle Price, Upper Cumberland Development District

 Ken Rea, First Tennessee Development District
53

Virginia ARC Focus Group Report

On September 18th, 2014 key ARC stakeholders in the state of Virginia met to discuss the impact 

of ARC investments in their communities. The Virginia Department of Housing and Community 

Development helped to organize the meeting that was hosted by the Roanoke Valley-Alleghany 

Regional Commission in Roanoke, Virginia. Participants were asked to give their input on 

several broad issues including:



 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall ARC investments are seen as vital to the Region’s Appalachian communities, 

particularly those in the Virginia coal fields. ARC funds have been vital to laying the foundation 

for future development, leveraging key regional assets to spur new economic activities and 

ongoing health and workforce development challenges. In addition, investments in the Local 

Development Districts (LDDs) affords these communities access to the planning and support 

services they need to take advantage of opportunities as they arise. The flexibility, consistency 

and predictability of ARC funding and services was cited as being critical to meeting the needs 

of rural and distressed communities. 

Current role of ARC funding

Virginia uses its ARC funding to spur investments in building community capacity and in both 

traditional and non-traditional economic development activities. As in many states, the 

participants agreed that ARC’s support of the local development districts was a vitally important 

element of the program. This funding—which for some of the more rural LDDs is an important 

source of non-local funding—allows LDDs to provide consistent planning services to rural 

communities throughout western and southwestern Virginia. These services might take the form 

of grant writing or more time consuming activities like project development. Without these 

services, many rural communities would be at an even greater disadvantage when it comes to 

leveraging additional funding. 

ARC investments are rarely the sole source of funding. They can either serve as seed funding for 

an innovative project or it may represent the closing money needed to ensure that a project 

moves forward. The participants noted that ARC funding often can be used to attract other 

funders, such as the Economic Development Administration, Virginia Tobacco Commission, 

Foundations or private funds. ARC interest in a project can provide a signal to these other 

funders that the project is a ‘real’ project that is viable and has the ability to provide regional 

impact. For instance, in Danville, ARC funding helped bring foundation and private funds to the 

table for several projects.
54

The flexibility of ARC funding has allowed Virginia communities to undertake this wide range 

of projects that do not neatly fit into typical funding programs (e.g., USDA Rural Development, 

U.S. Economic Development Administration). This has been particularly true for efforts 

pertaining to cultural and heritage-related tourism. The Region’s culture represents one of its 

great place-based assets. To better leverage these assets, ARC has invested in programs like the 

Crooked Road. The Crooked Road connects 19 counties, 4 cities and dozens of smaller 

communities in a regional effort to make southwest Virginia a destination for heritage music 

enthusiasts. ARC provided the ‘glue’ money that binds regional actors (including the 5 LDDs) 

around these efforts and it also helped to complement other funding sources like the Virginia 

Tourism Commission, The Virginia Department of Housing and Community Development, the 

National Endowment of the Arts, and several foundations, among others. In doing so, Virginia 

has used its ARC funding to create a structure around which other funders can invest, and 

communities can turn their individual attractions into a larger regional destination. The Crooked 

Road is therefore emblematic of how the larger federal-state-local ARC model can work in an 

era of scarce resources. 

Infrastructure projects related to water and sewer and roads remain important, and the impacts of 

past ARC investments in these areas are wide- ranging. One participant noted that is difficult to 

travel far within southwest Virginia and not see a project that had been supported by ARC. The 

ARC impact on the Region is everywhere, and this is particularly true in the western Virginia 

coalfields. Participants noted several highway projects that have created opportunities in western 

Virginia. For instance, when completed the Corridor Q project (US Route 460) will connect 

Pikeville, KY to Interstate 81 in Christiansburg, VA. The improvements on this route have 

already facilitated better access to the interstate for many rural communities. Similarly, creation 

of four lanes on U.S. Routes 23 and 58 (which intersect in Duffield, VA) has allowed these roads 

to become more significant arteries that can create greater development opportunities. Similar 

investments in water and sewer systems have also created development opportunities for 

Appalachian Virginia. 

The administration of these large infrastructure projects can be costly and inefficient. Virginia 

has been a leader amongst Appalachian states by assuming the role of the basic agency for its 

construction projects. In doing so, the state manages the construction contracts rather than larger 

Federal agency like the Department of Housing and Urban Development or the Department of 

Transportation. This often makes the contract administration more efficient and projects are 

completed earlier thereby reducing the risk of costly delays. A number of other states are now 

following Virginia’s lead in adopting the basic agency role.

Moving forward

Meeting participants confirmed the federal-state-local model used by ARC has worked well for 

Virginia’s Appalachian region. They found the feedback that they received from both ARC and 
55

the Virginia Department of Housing and Community Development to be very beneficial in terms 

of developing projects and making sure they were structured in a way that would enable a 

successful outcome. Moreover, they mentioned that Virginia assessed projects not just on a year 

to year basis, but with a somewhat longer time horizon. A project may not fit with Virginia’s 

current ARC funding program, but that project may be revisited in subsequent years. 

Moving forward, Virginia will continue to use its ARC funding to support key infrastructure 

projects related to water and sewer or road access. However, the state is also making investments 

that will help lay the foundation for a new economy that is less dependent on traditional 

manufacturing and natural resource extraction. Doing so requires addressing a variety of current 

and emerging challenges facing Virginia’s Appalachian region. 

The diversity of these responses can be illustrated by several projects undertaken by the West 

Piedmont Planning District Commission’s service area (Patrick and Henry counties, and the city 

of Martinsville). This region, which joined the ARC region in 2009, has used ARC funding to 

address a variety of development challenges. For instance, ARC investments contributed to the 

completion of the New College Institute which is a center for higher education that provides the 

Region’s citizens easier access to college and university education. In partnership with the 

GENEDGE Alliance (Virginia’s Manufacturing Extension Partnership affiliate), ARC 

investments have supported the delivery of E3 (Energy, Environment, Economy), a program that 

helps the Region’s manufacturers adopt more sustainable and efficient manufacturing processes. 

In the future, Virginia will continue to use ARC investments to support many other efforts to 

create a new economy. For instance, investments designed to expand the Region’s broadband 

infrastructure have contributed to this goal in several ways. Expanded broadband access has 

allowed the Region to attract more retirees to the Region because it is one of several basic 

amenities required for those retirees who have many options for their retirement destination. It is 

also vital to the Region’s entrepreneurial ecosystem as it creates more small business 

opportunities. This is best illustrated in Galax, VA where investments in efforts like the 

Crossroads Institute have helped the Region overcome the loss of 2,000 furniture jobs by 

assisting in the creation of 1,200 new small businesses.

ARC funding has also been used to support the provision of healthcare throughout the Region

and this is another area where the expansion of the Region’s telecommunications infrastructure 

can have wide ranging impact. For instance, Virginia was one of the first states to fund 

broadband when it funded the installation of fiber for the Haysi Clinic. This kind of connectivity 

is particularly important for delivering health services in rural areas. Virginia’s ARC funds have 

also been used to fund the St. Mary’s Health Wagon that brings basic health care services to 

rural, under-served, high poverty areas in southwest Virginia.
56

Meeting participants were overwhelmingly positive about ARC programs, but there were a 

couple of issues that they thought required some additional consideration. While not a 

widespread issue, several participants did note that the change in county economic status from 

year to year can cause some issues in project development and planning. For some smaller, 

distressed counties raising matching funds is an ongoing challenge, so a change in economic 

status that requires them to raise more can pose challenges. This is particularly true for counties 

looking to develop more long-term projects. 

Moreover, the change in economic status may not reflect actual progress in the Region. For 

instance, a decrease in unemployment may mask underemployment and people falling out of the 

labor force altogether. In addition, participants noted that within Virginia’s Appalachian 

counties—even the more prosperous counties like Botetourt County—there remain a number of 

persistent pockets of poverty. Given that funding is often prioritized by county economic status, 

using ARC funding to address these pockets of poverty remains incredibly difficult.

Meeting Attendees

 Jim Baldwin, Cumberland Plateau Planning District Commission 

 Patrick Burton, New River Valley Planning District Commission

 Leah Manning, West Piedmont Planning District Commission

 Duane Miller, LENOWISCO Planning District Commission

 William Shelton, Virginia Department of Housing and Community Development

 Wayne Strickland, Roanoke Valley-Alleghany Regional Commission

 Matt Weaver, Virginia Department of Housing and Community Development

 Ed Wells, Roanoke Valley-Alleghany Regional Commission

 
57

West Virginia ARC Focus Group Report

On August 7th, 2014 key ARC stakeholders in the state of West Virginia met to discuss the 

impact of ARC investments in their communities. The West Virginia Development Office helped 

to organize the meeting that was hosted by the West Virginia Economic Development Authority 

in Charleston, West Virginia. Participants were asked to give their input on several broad issues 

including:

 ARC-sponsored programs use in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

Overall there was enthusiastic support for ARC programs and investments. ARC is seen as 

vitally important to the communities it serves, and this is especially true for the most rural 

communities. Additionally, the consistency and predictability of ARC funding and services was 

cited as being critical to meeting the needs of rural and distressed communities. Participants also 

noted that while ARC investments have been enormously impactful to the state, the most 

significant legacy of ARC was not necessarily financial related. “It changes the culture,” 

participants agreed. ARC helped create a culture of collaboration across agencies and levels of 

government, between the private, public and non-profit sectors, and in communities throughout 

West Virginia. 

Current role of ARC funding

Participants noted that ARC funding plays a critical role in getting projects off the ground and as 

a tool for addressing core economic development challenges that provide the foundation for later 

development. ARC investments continue to help fund a wide array of projects across West 

Virginia including water and sewer, education, healthcare, business access to capital via 

revolving loan funds, workforce training and community development. Without these 

investments, communities would be unable to get these projects started or completed. 

Several participants noted the importance of small-scale investments. For instance, one program 

underway includes the state’s Flex-E-Grant program. This program provides small grants, up to 

$10,000, that may be used to support local leadership, civic engagement and capacity building 

efforts. These grants foster civic entrepreneurship with leadership training for residents of West 

Virginia's ARC-designated distressed counties so they become more effective participants in the 

civic life and leadership of their communities. This approach contributes to the development of 

the community and the community members. Flex-E-Grant is a joint effort of the West Virginia 

Development Office (WVDO), the Appalachian Regional Commission (ARC) and the Claude 

W. Benedum Foundation. The collaboration of WVDO, ARC, and the Benedum Foundation is 

helping to address some of the state’s healthcare needs.
58

ARC funds contribute to this effort through investments that focus on improving access to oral 

healthcare. The number of days missing work because of bad oral health is actually a workforce 

issue, and West Virginia falls below the national standard for oral health. The Benedum 

Foundation representatives indicated that the ARC investment has been crucial to this effort to 

improve oral health across the state. 

Additionally, ARC investments have supported many important state initiatives to develop 

communities and grow the economy. For instance, ARC has been heavily involved with the 

state’s Main Street Program as one of the main funders since the creation of the program. ARC 

also helped fund a new and incumbent worker training program for existing manufacturers. This 

program provides cross training for manufacturing workers. West Virginia has an aging 

workforce and is looking to work on replacing retirees in manufacturing with younger workers. 

ARC hired the trainers to teach managers how to write job descriptions, standardize the 

production process, and also reimburse companies with costs related to new equipment training. 

The state’s international trade office was also initially started and supported by ARC. The 

flexibility of ARC funding made these types of investments possible.

ARC funding also continues to play a crucial role for many rural and/or distressed counties 

because there remains tremendous pockets of need, that are often quite isolated. Helping to form 

this perspective were not just singular projects but the continuing commitment to fund the local 

planning councils, which are on the front lines to make things happen in West Virginia. The

funds provided to the Local Development Districts are important because rural areas often lack 

the staff capacity and resources to meet all their planning needs. The LDD funding provided by 

ARC allows for consistent and reliable planning and government service to these rural counties. 

Impacts of past ARC investments

At the time of its launch, ARC sought to bring the Appalachian region to parity with the rest of 

the country. While the work is far from finished, the focus group participants thought that West 

Virginia’s Appalachian communities had made meaningful progress toward fulfilling that goal. 

ARC investments allowed many rural residents access to clean water and sewer, and helped to 

put in place an infrastructure to support industry that created local employment opportunities. 

ARC has also played a leadership role in West Virginia in bridging non-traditional economic 

development issues such as local food, healthcare, and alternative energy with traditional 

economic development activities.

Past ARC investments in West Virginia have worked in building the foundation and capacity to 

provide needed services to residents. Among the most basic, water and sewer investments have 

been an important part of ARC investments over the years in West Virginia, necessitated in 

many instance by the impact of the state’s mining industry on water quality. But ARC has done 

much more; for instance, Appalachian ambulance service was supported by ARC back in 1970s. 

ARC also invested significantly in building career centers in West Virginia, while many 
59

community health clinics would not exist today if not for ARC funding. As one participant 

summarized, ARC has been involved with all the major impactful projects in West Virginia in 

the past few decades.

While never a large component of the total funding package, ARC funding—both in the past and 

present—has served as important seed funding to get larger projects started. Several participants 

offered examples demonstrating the importance of early ARC investments. For instance, ARC 

funded the planning for the Bakers Island community park project. The community got in total a 

half million dollar investment for that project to build a parking lot and other facilities for that 

park. This park is now the key community recreational facility and amenity. Leveraged ARC 

investments have also been used to create revolving loan funds to offer area businesses access to 

needed capital. Several of these loan funds have turned small initial ARC investments into 

multi-million dollar capital funds for the ongoing benefit of the local economy. 

ARC investments in highway infrastructure are also seen as crucial past investments as they 

provide important connections. This is particularly true for rural, mountainous counties where 

roads are limited in number, difficult to build, and expensive to maintain. This can make rural 

living even more expensive and difficult. ARC Highway funds are therefore seen as important to 

expanding and strengthening the Region’s highway infrastructure. 

Moving forward

Participants would like to see current investments continued, particularly as they relate to 

infrastructure-related investments such as water, sewer and highways. In addition to these 

ongoing investments, the participants had general consensus about expanding investments in 

several other emerging areas. For instance, developing the ARC Region’s human capital and 

health and wellness efforts, was viewed as increasingly crucial. Expanded broadband access was 

mentioned by several participants as a particular area of investment that needs continuous 

attention from ARC. Broadband potentially supports many other priority areas whether it related 

to local foods and agri-tourism or support for entrepreneurship and home-based businesses.

Continuing to invest in downtown redevelopment was another important focus area for ARC 

investments. Creating places where people want to live and spend time was seen as a vital 

condition for success in other strategies related to tourism and entrepreneurship and others. 

Participants also encouraged ARC to continue to take leadership in helping the state move new 

initiatives forward such as small business incubators. ARC brought this topic to the table. It 

helped to find mentors and innovation ideas for small businesses. The use of Flex-E-grant 

funding to help develop the local food system was given as another example of ARC bringing 

new ideas to the table. The local food projects happened because of a handful of grants and 

leadership from ARC.
60

All of these emerging areas point to the oft noted need to generally improve quality of life within 

the Region in order to retain and assist current residents, as well as attract new residents, 

especially young workers that have left the Region. One participant mentioned the findings from 

a study on household wealth and financial security in Appalachia which ARC helped fund. In 

the coming years, an estimated $45 trillion is transferring from the older generation to younger 

generation. How can we prepare to make sure the money stays and benefits West Virginia 

communities? Participants also noted the need to maintain the flexibility of ARC funding, as 

that flexibility (unlike many other funding sources) allowed them to effectively respond to their 

region’s many critical development challenges. They also encouraged ARC to remain the 

“Voice of Rural” going forward. 

Meeting Attendees

 Sharon Adams, West Virginia Development Office

 James Bush, West Virginia Development Office 

 Becky Ceperley, Greater Kanawha Valley Foundation

 David Cole, Region 1-Planning and Development Council

 Michele Craig, Region 2-Planning and Development Council

 Ralph Goolsby, RHGoolsby Consulting

 Jeff Herholdt, West Virginia Division of Energy

 Mary Hunt, Benedum Foundation

 Monica Miller, West Virginia Development Office 

 WD Smith, Region 4-Planning and Development Council (retired)

 Kim Tieman, Benedum Foundation

 Geary Weir, Webster County Economic Development Authority
61

Development Districts Association of Appalachia (DDAA) 

ARC Focus Group Report

On July 28th, 2014 the Board of the Development District Association of Appalachia (DDAA) 

met to discuss the impact of ARC investments in their communities. This meeting was held at 

the Lafayette Hotel in Marietta, Ohio. Participants were asked to give their input on several 

broad issues including:

 ARC-sponsored programs used in their region and community,

 Changes in ARC-sponsored programs and funding over time,

 Notable and important ARC investments, and

 Current and emerging issues that might motivate additional, future investments.

The discussion focused both on the impact of ARC investments in the participant’s region, but 

also on its importance to their organizations. ARC funding allows Local Development Districts 

(LDDs) to provide a wider array of services, but also provide more reliable and consistent 

service to their more rural and disadvantaged communities. ARC investments have also been 

important in creating a community of stakeholders in their region and play a role in building 

consensus about regional economic and community development. The flexibility afforded by 

ARC funding allows local stakeholders the ability to implement programs that therefore align to 

regional and state priorities. 

Current role of ARC funding

Although a relatively small portion of overall ARC funding, support to the Region’s Local 

Development Districts is a vital element of ARC’s overall investment portfolio. The participants 

all spoke of ways in which ARC funding advances their organizations’ economic and community 

development goals. LDD support allows these organizations to better serve local governments by 

providing important technical assistance such as grant writing, project development and grant 

administration. Without these services, many local governments would be unable to undertake 

these activities in an effective or affordable manner. Several participants from states that do not 

provide extensive resources to LDDs noted that the ARC funds were instrumental for them to 

provide a basic level of services given their current staffing levels. 

The funding that ARC provides to LDDs also allows them to strengthen their internal capacity. 

Participants from numerous states noted that their organizations’ ability to provide regional GIS 

(Geographic Information Systems) support was a direct consequence of their ARC funding. In 

addition, ARC-sponsored events provide opportunities for LDD staff to obtain the professional 

development and training necessary to do their jobs. This training may include guidance on how 

to develop competitive federal grant proposals or how to undertake data-driven economic 

development planning. ARC also provides LDD staff members the opportunity to learn more 

about emerging issues such as building local food systems or engaging local youth. 
62

In addition to strengthening internal organization, the LDD directors also noted that ARC plays a 

vital role in building the capacity of the broader communities that they serve. This additional 

support allows LDDs to provide education and training to their region’s local governments to 

improve their ability to address critical local issues. It also enables them to educate and prepare

their local elected officials, so that they can make more informed decisions. These issues have 

been particularly important in rural localities, where local government and elected officials are 

stymied by loss of young people that has hurt the region’s capacity to transition toward a new 

generation of leaders. 

ARC funding also allows the LDDs to play an important role in promoting regional approaches 

to development challenges. Its ability to leverage other sources of public, private and 

philanthropic resources allows LDDs to assemble coalitions that can address broader regional 

challenges. One participant noted that these coalitions are built not just by bringing partners 

together for one project, but through hundreds of various ARC-sponsored projects over many 

decades. As a result, these ongoing investments have created a community of ARC stakeholders. 

This cumulative effect creates a platform for more easily achieving regional consensus on the 

nature and direction of regional economic and community development. ARC investments and 

projects can therefore provide important ‘glue’ money for promoting regional efforts. One 

participant noted that in his state there was a trend toward the ‘deregionalizing’ of economic 

development efforts in the areas outside of the ARC region, while in the ARC region it was just 

the opposite.

Impacts of past ARC investments

Participants generally agreed that many of the issues that in the past drove their region and 

state’s ARC investments remained just as important today. For instance, connectivity and road 

access are critical prerequisites for development, so completing the Appalachian Development 

Highway System (ADHS) remains an important priority. Similarly, it has been important for 

financing much of the regional economic development infrastructure such as water, wastewater, 

industrial parks and access roads.

However, ARC funding has been reduced significantly from what it was in ARC’s early history 

and these relatively more limited resources change the nature of ARC-funded projects. While 

infrastructure projects remain important, ARC investments have shifted away from large 

physical infrastructure projects toward a more diversified array of development efforts. For 

instance, one participant noted that their state used ARC funds to invest in the region’s health 

infrastructure. In the past, this meant large-scale investments in hospitals and health clinics. 

Investments in strengthening regional health systems continue, but with more limited funding 

those investments now go into activities related to telemedicine or programs that expand access 

to basic health services. 
63

The changing nature of ARC investments was not just the result of diminished resources. In 

some instances, the changing economic environment and a diminished need for newer, greenfield 

industrial sites led to fewer investments in physical infrastructure and a greater focus placed on 

expanding planning and technical assistance , enterprise and business development, and 

workforce training. In other instances, the shifting focus reflects the priorities of the states’

governors. One participant indicated that their state had used its ARC funding to support 

programs related to microenterprise development, but a change in the Governor’s office shifted

focus toward water and wastewater projects. The ability for ARC funds to reflect evolving state 

priorities is important to its overall success with state and regional partners.

These shifting priorities can also impact the composition of a region or state’s ARC stakeholder 

group. As one participant explained, the shift away from physical infrastructure projects meant 

less involvement from local developers, but increased investments related to education and 

workforce development brought more educators into the coalition. Bringing these entirely new 

sets of stakeholders into the coalition ultimately broadens the wider ARC stakeholder 

community. This ever-changing stakeholder group is one of the lasting elements of ARC’s 

legacy in the Region.

Moving forward

Overall the participants noted that the consistency and reliability of ARC funding was important 

to their ability to serve their region’s communities. It allows them to maintain this service and it 

affords them the ability to find creative ways to successfully address local development 

challenges. 

There was a general consensus among participants that ARC should continue to emphasize 

ongoing efforts related to infrastructure development and poverty reduction. At the same time 

there remains a need to balance these efforts with emerging issues that are arising throughout the 

region. For instance, representatives from Pennsylvania and West Virginia were keenly 

interested in learning more about unconventional natural gas development (i.e., hydraulic 

fracturing). Since these types of energy projects are significantly reshaping regional economies 

in these states, there was interest in learning how to better pursue this development in a way that 

avoids some of the mistakes or unintended consequences experienced by other extractive

industries.

Representatives from states such as New York and Alabama reinforced healthcare and workforce 

issues as growing in importance and were looking to utilize ARC investments to expand their 

efforts in those areas. Other participants were also interesting in placing greater emphasis on 

other non-infrastructure issues related to workforce development, tourism development, and 

growing local foods systems.
64

Meeting Attendees

 Eric Bridges, North Central Pennsylvania Regional Planning and Development 

Commission

 Misty Casto, Buckeye Hills–Hocking Valley Regional Development District 

 Michele Craig, Region 2–Planning and Development Council

 Jim Dove, Northeast Georgia Regional Commission

 John Hemmings, Ohio Valley Regional Development Commission

 Rudy Johnson, Golden Triangle Planning and Development District

 Keith Jones, Northwest Alabama Council of Local Governments

 Danny Lewis, Georgia Mountains Regional Commission

 Leanne Mazer, Tri-County Council for Western Maryland, Inc.

 Sherry McDavid, FIVCO Area Development District

 Joe McKinney, National Association of Development Organizations

 Erik Miller, Southern Tier East Regional Planning Development Board

 Dan Neff, Appalachian Regional Commission

 Steve Pelissier, South Carolina Appalachian Council of Governments

 Susan Reid, First Tennessee Development District

 Kostas Skordas, Appalachian Regional Commission

 Richard Zink, Southern Tier West Regional Planning & Development Board
65

Acknowledgements

The completion of this research project to examine the significant structural and socioeconomic 

changes to the Appalachian Region since 1965 and to determine to what extent the ARC’s 

economic development investments have contributed to these changes could not have been 

accomplished without the assistance of many talented individuals working in concert.

The project team included: from the Center for Regional Economic Competitiveness in 

Arlington, Virginia: Ken Poole, Marty Romitti, Mark White, William Cook, Sarah Gutschow, 

Brendan Buff, Randall Arthur, and Wen Sun; from the Regional Research Institute at West 

Virginia University in Morgantown, West Virginia: Randy Jackson, Don Lacombe, Gianfranco 

Piras, Juan Tomas Sayago Gomez, and Caleb Stair; from the Bureau of Business and Economic 

Research at West Virginia University: John Deskins, Jane Ruseski, Christiadi, Eric Bowen, Brian 

Lego, and Patrick Manzi. 

We would also like to thank the Appalachian Regional Commission, and particularly Earl Gohl, 

Kostas Skordas, Julie Marshall, and Keith Witt, for their support, valuable insights and 

assistance provided over the course of this project. A special thanks as well to Ed Feser, 

University of Illinois; Henry Renski, University of Massachusetts; and Troy Mix, University of 

Delaware for their insights, contributions to, and review of various parts of this report. 

We also very much appreciated the comments we received and the guidance provided by our 

research advisory team, including: Ed Feser, Roger Stough, Amy Glasmeier, Elizabeth Mack, 

Terry Clower, Nichola Lowe, Dave Peters, Matt Murray, Henry Renski, Geoff Hewings, Jim 

Futrell, Jennifer Zeller, and Jim Held.

Finally, our sincere thanks to those who participated in the state meetings held throughout the 

Region. Your willingness to share your experiences on the dynamics of change in Appalachia 

breathed life into the data points and provided invaluable context for our analysis and findings.