The third group has mean index scores that are significantly worse than the top two groups of
counties, with averages generally in the 135 to 145 range. The fourth group exhibits very high
mean index scores throughout the time period, with averages ranging from about 170 to over
190. It also is interesting to note that the volatility within this group is significantly higher than
in any of the other three groups. In all, the separation among the four groups is consistent
through time. The “best” counties in 1960 are still the best counties in 1995. No systematic
convergence of counties within the region is evident, nor does there appear to be convergence
within any individual group towards national averages.

4.2.5 The Relationship Between County Economic Health Score and Economic Base.
There is longstanding evidence to support the hypothesis that the fate of many Appalachian
distressed counties is related to the underlying economic base. The prosperous areas of the
region have been beneficiaries of the decentralization of manufacturing over the post-war
period. Additionally, as the case studies point out, many areas of the region that have been
lifted out of distress were blessed with pleasing environments and beautiful scenery ripe for
tourism development. At the other extreme, however, are areas of Appalachia that have been
abused by natural resource exploitation that has seriously damaged the environment and left
few alternative economic development possibilities in its wake. The region’s central area and
parts of the old plantation south have seen a slow and inexorable decline in their economies as
the coal industry declined in size and extensiveness and agricultural lands were exhausted and
became unfertile. Thus, we would expect a significant correlation between economic base and

ranking on the index of economic health.

53


Table 1. Case Studies for 14 Counties
(cont.)

Randolph County, Alabama

Randolph County, Alabama, was well-positioned to benefit from post-war growth in the southern
United States. Like Bibb, in the 1950s, Randolph County was primarily agricultural and relatively
poor. The poverty rate was 49.3 percent and income was half the national average. Over the
subsequent 35 years, economic development transformed the county’s economic base into a services,
tourist, and manufacturing center. In 1980, the county’s poverty rate was 23 percent. A decade later,
this rate had dropped substantially to barely above the state average (18% vs. 17%). The county’s
per-capita income level is within 80 percent of the state average. Net commuter income contributes
20 percent to the county’s total income. Investments in tourism infrastructure and worker training
have made Randolph an attractive destination location for vacationers and manufacturers alike.

Pike County, Kentucky

Pike County, Kentucky, is well known for its severe problems and the remarkable and heroic
gestures made by the ARC (cutting down a mountain, re-routing a river, and re-routing railroad
access), and the federal government more broadly, to redefine the economic options facing the
county. Despite very large sums of money spent to bring jobs and provide access to the county, Pike
remains distressed: its poverty rate has been as high as 25 percent and its unemployment rate, 10
percent—more than five percentage points above the national level.

Doddridge County, West Virginia

Doddridge County, West Virginia, fares little better, with an average per-capita income of less than
$8,700 in 1990, a poverty rate of 23 percent, and an unemployment rate of almost 10 percent.
Traditional resource jobs are being replaced with marginal service, textile, and government jobs.
Many residents commute out of the county; 33 percent of net income is derived from such workers.

19



Executive Summary
Building on Past Experiences: Creating a New Future for Distressed Counties

Appalachia has seen a recent increase in the number of distressed counties within
the Region, partly as a result of congressional action that added seven new
counties to the Region, with three designated as distressed. Distressed counties
are those with poverty and unemployment rates that are 150 percent or more of
the national rates and a per capita market income (i.e. per capita income less
transfer payments) that is no more than two-thirds of the national average. In FY
1999, 108 counties (27 percent) of the Appalachian Region's 406 counties are
designated as distressed. This compares with the national rate of 13 percent (when
not counting the Appalachian counties).

In light of these trends, the Appalachian Regional Commission (ARC) requested
this study of the development experience of the distressed counties with the aim
of identifying the lessons that might be learned from those counties that have
changed socioeconomic status. The research consisted of four parts: the first
phase examined the origins of the distressed counties program, its purposes,
policies and objectives; the second part examined the common factors and trends
of 21 counties that had left distressed status since 1988; the third stage analyzed
ARC’s method of determining and tracking distress and developed an alternative
index that could be more completely updated on an annual basis; the final phase
of research examined all ARC counties including the distressed counties to
determine the major factors that predict persistent distressed status.

Case Studies

Nineteen of the 21 counties that moved out and stayed out of the severely
distressed category share common themes. Many enjoyed an early foundation of
agriculture based upon private land ownership, infrastructure investments
including Interstate and Appalachian highways construction, and investments in
educational institutions and medical facilities. These counties are located
predominantly in the southern sub-region and have benefitted from manufacturing
relocation over the post-war period. The counties tend to be either smaller
urbanized areas or are adjacent to metropolitan areas. Almost 15 percent of the
income circulating in these counties represents a net inflow of commuter incomes
into the local jurisdiction.

An Alternative Distress County Indicator

Next, 399 of the ARC counties were examined based on the traditional distress
measures and the differing experiences of distressed and non-distressed counties
of the last 20 years. Because of the limits of the existing ARC distressed index,
tied as it is to a county-level poverty rate updated only once every ten years, an
alternative index is proposed based on unemployment rates, labor force
participation rates (percent of population in the labor force), and dependency rates
as measured by per capita transfer payments in relation to per-capita income. This
alternative measure of economic health is very accurate in portraying and tracking


health." In addition, a higher percentage of establishments with fewer than 10 employees and
higher percentages of county income derived from the government sector also have negative
associations with economic health.

In addition to the socioeconomic characteristics, five geographic variables were
significant in the final regression model. Counties adjacent to a metro area were positively
related to better economic health index scores.'* Conversely, residence within the central
region had a negative relationship with economic health.’* The metro area, adjacency to the
edge of the region, and southern region dummies were significant’® and had positive
associations with economic health.

5.2 Interpretation of the Regression Results

We believe these results can be used to infer some of the major challenges facing
residents of distressed counties. Thus, we are carrying the analysis an additional step by
suggesting that the socioeconomic condition of an individual in a distressed county carries
along with it special burdens encumbering his or her ability to secure a satisfying, fulfilling,
and self-determined life experience. First and foremost, as the county index of economic health
deteriorates, residents in distressed counties display special circumstances. Distressed counties
have a higher dependent population compared with more prosperous counties in the region

consisting of single female-headed households with dependent children under the age of 17 and

8 Significant at the p < 0.01 level.
Significant at the p < 0.05 level.
'S Significant at the p < 0.01 level.
'© Significant at the p < 0.01 level.

62


region’s residents. Unlike areas in the region which are more metropolitan and therefore
attractive to external investment, deeply distressed counties are often remote and distant from
markets. An interim, grassroots job development program, building on the needs of residents
in the region for quality housing, public facilities, and infrastructure, could help establish the
base of skills needed to be successful in the future. The region boasts many community groups
that provide job training and employment opportunities. These organizations need and deserve
ARC support. Along with programs that are traditionally male-oriented, jobs programs also
must serve the needs of women. Child care, in-home care for the elderly, and community
mentoring are just a few examples of activities that could help support the labor market success
of women.

In summary, it is worthwhile to look back at the ARC’s original design, which has
many merits. The problems of the most economically challenged areas are reminiscent of the
past. Many of the original interventions could be reexamined and, where appropriate,
reapplied, but with a new commitment to local involvement in planning and implementation.
As an advocate for the region, the ARC must look back to its proud, meritorious, and
comprehensive early view of the region’s problems and act accordingly. While additional funds
would be helpful, the lack of funds alone is not the primary problem. Instead, the challenge is
to find the courage to embark upon a new plan that builds on the region’s philosophical intent

and built-up expertise.

7B


remained unaffected by the ARC’s emphasis on concentrated investment, uniform
infrastructure planning, and roads. The growth-center investment policies that characterized
the early history of the program were designed to move people out of places of little potential
to those having more. As Jim Pickford, ARC staff member, noted in testimony before the
Commission in 1981, “We are basically dealing with those counties we bypassed in terms of
projects that were funded” (Appalachian Regional Commission, 1982, p. 56)2

These enduring problems did not go unnoticed by ARC Washington staff. Indeed, over
the course of the 1970s, internal documents attest to the recognition of very serious differences
in social and economic conditions found in various parts of the region. Jerome Pickard,
Geographer and Demographer at the ARC, experimented in the early 1970s with the
development of indexes of distress, focusing on a variety of characteristics selected to capture
differences within the region. He developed four indices: socioeconomic, labor force capacity,
housing quality, and a composite of these three. His findings cast early light on the deep
problems of the core distressed areas of the region (Pickard, 1974; see Table 3 for a rank order
listing of counties based on Pickard’s 1960s composite index). Although Pickard’s findings
were items of discussion within the Commission, another eleven years would pass before his

seminal insights informed deliberations leading up to the creation of the Finish-up Program.

> Further complicating, and in some sense diminishing, the significance of even the
initial set-aside, the original computation of the distressed county measure was most noticeably
affected by changes in the unemployment rate. Hence, through time, additions to the original
67 counties occurred as business cycle downturns and structural change led to increased
unemployment over the subsequent two decades.

9


6.1 Revisit the Original ARC Program Design

While there are many possible explanations for the persistence of distressed counties,
for the majority of such counties, the positive benefits of ARC programs have been muted by
the fact that many were unable to receive funds under the initial program design. These places
were small, isolated, and lacking in urban concentrations. They did not qualify under the
growth center strategy embedded in the program from the start. Over time, in the absence of
needed social and human infrastructure, these places have been unable to successfully compete
with more urban-oriented counties found on the edges of the region and around its metro areas.
They have not attracted the mobile jobs so important to the success of other portions of the
region.

An acknowledgment that these counties have shown little progress in the last two
decades should not be interpreted to mean that nothing can be done to change their current
situation. Indeed, the fact that several counties have left the distressed status behind suggests
that changes can and do occur. Moreover, unlike many of the counties that have left the group
having benefitted from the early ARC program (ARC Staff Evaluation, 1968), the initial
experimental design of the ARC has yet to be applied to these more remote locations. Thus,
there is ample room to take the tremendous learning that has occurred over the intervening 30
years and use it constructively to build community-based programs that can help foster a spirit
of new opportunity, new hope, and greater dignity.

ARC’s legacy has included the role of opportunity broker. As an organization, the
Commission has experience with many elements needed to overcome the region’s serious

economic health circumstances. Unlike in the past, where the focus was on developing the

68


Part If: The Distressed Counties Based on the ARC’s Index

The case studies just presented provide interesting though limited insights into why
some counties have transitioned out of the most severely distressed county category while
others have remained distressed. These results, however, provide only a glimpse into the
experience of less than 10 percent of the total counties, thus making generalizations hazardous.
The current system of designation has serious limitations and makes timely tracking of changes
in distressed county status difficult. A key limitation of the existing system is the use of the
poverty level as a primary indicator. As mentioned previously, the poverty rate, being
collected only once every ten years, tends to dampen or arbitrarily overstate circumstances
depending on the conditions in a county once every ten years during a census-taking year.
Recognizing the problems of single indicators, previous analysis conducted by ARC staff
demographer Jerome Pickard attempted to overcome this problem by developing a combined
index that captured the complex conditions found in the region in the 1960s. Building on this
past experience, in the next section, we first examine the ARC’s current index to determine the
broad outlines of distressed county geography and behavior. This is followed by an alternative
index developed by us that permits the tracking of distress on an annual basis.

3.0 A Retrospective View of the ARC’s Distressed Counties

Having described the program, and its history and composition, we now present an
analysis of the characteristics of the ARC distressed counties over the 1992-1998 and
1980-1995 periods. Our intention in the rest of this report is to explore differences between
counties that have remained distressed throughout the period and those that have moved toward
national averages. Our analysis sought to determine differences between distressed counties and

26


Appendix G. Counties with Index Changes > 10%

(cont.)
EPS County State 1980-1985 1990-1995 Percentage Change
47029 Cocke TMN 188.60 165.51 -.1224 Positive
21165 Menifee KY 229.84 201.94 -1214 Positive
13055 Chattooga GA 142,36 125.45 ~.1188 Positive
1093 Marion AL 155.21 136.92 ~1178 Positive
28069 Kemper MS 188.34 166.22 -.1174 Positive
37011 Avery NC 140.19 123.74 - 1173 Positive
47111 Macon 1TMN 144.04 127.29 -.1163 Positive
1125 Tuscaloosa AL 123.54 109.21 -.1160 Positive
1089 Madison AL 103.76 91.81 -.1152 Positive
13139 Hall GA 99.01 87.73 -.1140 Positive
37009 Ashe NC 144.88 128.50 -.1130 Positive
13281 Towns GA 161.93 143.79 -.1120 Positive
uu Randolph AL 161.37 143.31 -.1119 Positive
28081 Lee MS 111.09 98.75 -LLL Positive
39025 Clermont OH 106.67 95.04 -.1090 Positive
47123 Monroe TN 164.00 146.14 -.1089 Positive
47179 Washington TN 115.37 102.84 -.1086 Positive
37171 Surry NC 115.40 102.90 -.1083 Positive
47057 Grainger TN 160.92 143.92 -.1056 Positive
13137 Habersham GA 113.45 101.66 -.1039 Positive
45073 Oconee sc 122.93 110,32 -.1026 Positive
13313 Whitfield GA 96.77 86.90 -.1021 Positive
21171 Monroe KY 157.46 141.54 -.1011 Positive
47035 Cumberland TN 163.55 147.13 -. 1004 Positive
54067 Nicholas wv 175.13 192.95 1018 Negative
54023 Grant wv 131.91 145.43 1025 Negative
24001 Allegany MD 141.08 156.01 1059 Negative
13143 Haralson GA 121.81 134.76 1063 Negative
54077 Preston wv 153.46 169.83 .1067 Negative
1057 Fayette AL 119.73 132.56 -1071 Negative
21121 Knox KY 173.45 192.06 1073 Negative
37175 Transylvania NC 101.99 113.07 1087 Negative
47061 Grundy TMN 170.99 190.47 1139 Negative
39131 Pike OH 157.54 175.69 1152 Negative
37075 Graham NC 167.40 187.30 1189 Negative
54109 Wyoming WV 197.53 221.30 1203 Negative
21135 Lewis KY 174.48 195.64 1213 Negative
28025 Clay MS 131.45 147.54 21224 Negative
42127 Wayne PA 123.50 138.91 1248 Negative
47091 Johnson TN 174.80 196.63 1249 Negative
54083 Randolph wy 156.02 175.62 1257 Negative
51169 Scott VA 116.61 131.29 £1259 Negative
54095 Tyler wv 138.81 156.33 1263 Negative
28155 Webster MS 141.05 159.61 1316 Negative
21065 Estill KY 169.88 192.61 1338 Negative
21237 Wolfe KY 213.46 242.79 1374 Negative
42121 Venango PA 141.45 161.11 1390 Negative
54073 Pleasant wy 143.42 163.42 1394 Negative

G-2


it difficult to pinpoint secondary variables that make significant contributions to status level
and status-level changes.

To isolate the problems described above, we began our exploratory analysis by
computing the average distress level of the 399 ARC counties along the 5-point scale over the
relevant time period. Based upon this calculation, we then considered distress level as a binary
variable: counties with average distress values of greater than or equal to 3 were considered
non-distressed for the time period. Those counties with a distress level of less than 3 were
considered distressed. Although a rating of lower than 2 is needed to be considered distressed
by the ARC, we placed those counties with average values less than 3 into the distressed
group, as counties falling into this range must be considered in jeopardy. We do this primarily
for statistical purposes.

Figure 3 (Average Distress Status, 1992-1997) is a map displaying the spatial
distribution of the two groups of counties. There is a relatively well-defined pattern of non-
distressed counties. Much of the northern portion of the ARC region— specifically, New York
and Pennsylvania— is non-distressed, as is a significant grouping of counties along the
eastern and southeastern margins of the region. Distressed counties are most clearly evident in
the dissected plateau regions of eastern Kentucky and portions of West Virginia. There are
pockets of both types of counties in the deep south. This general pattern of distress and non-
distress echos that described by Isserman (1996a,b) and Couto (1994), where it is shown that

the central portion of the ARC region is, and has been historically, the subregion displaying

33


continuous,’ we employ (OLS) regression to predict index scores—using several of the same
independent variables utilized in previous analysis. These variables include a number of 1990
Census variables and several measures of 1994 income characteristics. Socioeconomic
variables include income from the manufacturing, services, retail, and government sectors,
percent of income earned by residents working outside the county of residence, percent of the
population with a college education, percent single female-headed households with children
under 17, percent adults over 65, females as a percent of the labor force, and the share of
establishments with less than 10 employees. (See Figure 11 for a description of the variables.)
In addition, we include several geographic variables in the model. We include a variable that
allows us, for example, to distinguish between counties that are on the edge of the region
versus in the interior, and we also use a variable that allows us to examine the influence of
intra-regional location (north, central versus southern, subregion). The variables included in
the analysis are presented in Figure 11 (see table notes for technical details of variable
inclusion). Additionally, a variable indicating proximity to a metro area also was included
combined with a variable indicating whether the county is considered metropolitan based on

the ERS Beale coding system.

analysis of county distress for that year. In the logistic analysis we attempted to predict county
distress or non-distress based upon 1992-1997 mean ARC distress rankings (measured on a
1-5 scale). In that analysis, we labeled all counties with a mean distress score of < 3.0
distressed, and all others non-distressed. The model was quite successful.

7 The dependent variable is the natural log of the 1994 index values.

58


Table 3. ARC Distressed County Designation Criteria

Unemployment: Market Income: Poverty Rate:
Designation
3-year avg. Per capita 1990 Census
L. Severely >= 150% of >= 150% of 2x U.S. poverty
Distressed US. <= 67% of US. | & US. Or | plus one other indicator
2. Distressed >= 125% of <= 67% of U. & >= 125% of & Not severely distressed
US. US.
3. Middle All other counties
4. Strong <= 100% of >= 80% of US. | & | <= 100%0f | & Not very strong
US. US.
5. Very Strong <= 75% of >= 80% of US. | & <= 75% of
US. US.

Source: ARC documents

29



Figure 12. Linear Regression Model:
Predicting the 1994 County-level Index

Dependent Variable: Log 1994 Index

Standardized
Variable Coefficient Significance
Percentage of population with BS degree -0.339 0.000
Percentage population female, single, with children < 17 years 0.314 0.000
Female labor force as percentage of total -0.249 0.000
Percentage of population aged 65+ 0.223 0.000
Percentage of income from the government sector , 0.212 0.000
County location: “Southern” ARC subregion -0.207 0.000
Percentage of income from the manufacturing sector -0.157 0.000
County location: Metropolitan area -0.151 0.000
County location: Along the “edge” of the ARC region -0.126 0.000
Percentage of establishments < 10 employees 0.088 0.007
County location: “Central” ARC subregion 0.078 0.013
County location: Adjacent to metropolitan area -0.055 0.052
Percentage of income from the retail sector -0.032 0.271
Percentage of income from the services sector 0.000 996

N = 387 due to missing data on source of income data for several counties.
Model Fit:

R? = 0.797

R’adjusted = 0.789

Source: Authors’ calculations on data described in text.

61



Appendix G. Counties with Index Changes > 10%
(cont.)

Index Average:. = Index Average: Change. = “Direction” of.

EPS County State 1980-1985 1990-1995 Percentage Change
54041 Lewis wv 157.53 179.80 1413 Negative
54049 Marion wv 143.46 164.79 1487 Negative
51185 Tazewell VA 123.79 142.21 1488 Negative
21189 Owsley KY 286.59 332.49 1602 Negative
21115 Johnson KY 141.90 165.61 1671 Negative
54103 Wetzel wv 141.70 165.52 1681 Negative
54045 Logan wv 175.27 205.63 1732 Negative
47143 Rhea TN 133.73 156.90 1733 Negative
21071 Floyd KY 161.46 189.66 1746 Negative
39053 Gallia OH 139.82 165.49 -1836 Negative
13149 Heard GA 117.28 139.03 1854 Negative
21195 Pike KY 132.27 157.10 1877 Negative
28019 Choctaw MS 141.93 169.27 1926 Negative
54075 Pocahontas wv 167.41 201.13 -2014 Negative
21013 Bell KY 170.95 205.84 2041 Negative
21175 Morgan KY 181.54 218.69 2046 Negative
54021 Gilmer wv 157.52 191.58 2162 Negative
39163 Vinton OH 149.16 181.52 .2170 Negative
39115 Morgan OH 135.84 165.34 2172 Negative
21087 Green KY 123.33 151.67 .2298 Negative
21051 Clay KY 178.52 221.63 2415 Negative
51017 Bath VA 86.57 108.15 2493 Negative
54047 McDowell wv 233.04 293.69 -2603 Negative
21095 Harlan KY 180.16 228.41 .2678 Negative
39105 Meigs OH 144.94 184.76 2747 Negative
21063 Elliott KY 184.31 235.78 .2793 Negative
54097 Upshur wv 137.05 175.81 -2828 Negative
54005 Boone wv 140.99 181.47 .2871 Negative
21193 Perry KY 139.00 180.45 -2982 Negative
21057 Cumberland KY 160.85 209.98 3054 Negative
54001 Barbour wv 164.23 217.10 3219 Negative
51051 Dickenson VA 135.37 180.13 3306 Negative
51027 Buchanan VA 117.64 161.03 -3689 Negative
21025 Breathit KY 159.30 225.22 4138 Negative
S1195 Wise VA 109.01 156.55 4362 Negative
21153 Magoffin KY 170.92 252.15 4752 Negative
21159 Martin KY 103.68 187.25 8059 Negative


economic health. Although 28 of the original 67 designated counties are in the top 50 counties
with the highest scores on our index of economic health (see Appendix B for Pickard’s index
ranking; see Appendix G for the 15-year average index of economic health), there has been
movement for some of the original counties. Twelve of the original 67 counties had scores less
than 150; several, such as Dawson, Georgia, Coosa, Alabama, and Clay Counties in
Tennessee, were considered distressed in 1980 because in the year the first distressed counties
were identified, these had an unusually high infant mortality rate which subsequently decreased
significantly by the next census.

At the same time, however, a clear spatial pattern of persistent distress is evident.
Problems are particularly acute for nonmetropolitan (nonmetro) counties and counties with
small populations. Second, there is significant socioeconomic differentiation among the
counties in the region, based on their relative score. Those places with the highest scores
(worst condition) tend to be located in the central portion of the region, embrace resource-
based economies, and are geographically remote. Distressed counties in the southern portion of
the region represent old agricultural economies that did not move to newer forms of economic
development, particularly services or tourist development. Many depend on low-wage
manufacturing, which is highly susceptible to fluctuations in business cycles and longer-term
economic tends.

4.2.1 Aggregate Regional Health Through Time: County-level Experience Controlling
for Size of Population. A primary question of interest is whether counties in the Appalachian
region have significantly improved their status today compared with 1980, just prior to the
Distressed Counties Program. To assess the overall economic health of the region, the raw

39


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Walker AL 146.90 Smith TN 204.9
Cameron PA 146.66 Gilmer wv 204.9
Prentiss MS 146.64 Pocahontas wv 204.6
Alcorn MS 146.35 White GA 204.3
Pulaski KY 146.26 Raleigh wv 204.3
Somerset PA 146.21 St. Clair AL 203.7
Union GA 145.76 Wyoming wv 203.4
Mineral wv 145.67 Coosa AL 203.4
Chickasaw MS 145.56 Randolph AL 202.9
Harrison wv 145.44 Tucker WV 202.8
Sullivan PA 145.27 Cullman AL 202.8
Russell VA 145.14 Monroe wv 201.5
Morgan OH 145.10 Jackson NC 201.2
Rhea TTMN 144.95 Murfay GA 201.0
Franklin AL 144.78 Cherokee AL 200.9
Armstrong PA 144.38 Hawkins TN 200.4
Marshall wv 144.25 Putnam TN 200.4
Fleming KY 144.15 Blount AL 200.3
Grant wv 143.66 Chickasaw MS 199.1
Jefferson OH 143.42 Fannin GA 198.8
Allegany NY 143.40 Grayson VA 198.7
Highland OH 143,33 De kalb AL 198.5
Garrett MD 143.31 Preston wv 197.9
Lawrence PA 142.87 Wayne wv 197.5
Unicoi TN 142.85 Pickens GA 197.4
Montgomery KY 142.83 Rabun GA 197.3
Union TN 142.61 Tippah MS 197.2
Polk GA 142.37 Cannon TN 196.9
Schuylkill PA 142.33 Dade GA 193.4
Clearfield PA 142.07 Wythe VA 193.1
Northumberland PA. 141.91 Wilkes NC 192.8
Lawrence AL 141.79 Green KY 192.8
White TN 141.49 Greene IN 192.6
Clay MS 141.14 Randolph wv 191.3
Green KY 141.12 Upshur wv 191.3
Yancey NC 140.99 Monroe MS 191.2
Morgan wv 140.95 Lewis wv 190.6
Blair PA 140.95 Sevier TN 190.4
Jackson AL 140.81 Alleghany NC 190.2
Buchanan VA 140.21 Gallia OH 190.0
Chattooga GA 140.00 Lumpkin GA 189.2
Cannon TN 139.99 Alcorn MS 189.1
Marion TN ‘ 139.95 Shelby AL 188.9
Etowah AL 139.54 Limestone AL 188.8
Mifflin PA 139.44 Stokes NC 188.1
Carbon PA 139.26 Paulding GA 187.9
Potter PA 138.76 Garrett MD 186.4
Luzerne PA 138.38 Elmore AL 186.3

B4


the poverty rate, we propose an alternative index based on unemployment and labor force
participation rates, levels of dependency in the population (percent of population in the labor
force), and per-capita income. Using this index, we are able to identify counties in distress on
an annual basis. In doing so here, we explore some of the characteristics of such counties in
the hope of providing a clearer explanation of why many have failed to make progress toward
economic health. Based on both statistical and case study evidence, the report concludes with a
series of suggestions for augmenting the ARC’s existing distressed counties policy to better
enable counties to graduate from the program.'
Part I: The Origins and Evolution of the Distressed Counties Program

We begin this section with a brief overview of the use of special set-asides by federal
agencies concerned with the fate and development options of areas of high economic need. We
then discuss the history of the ARC program in this area, highlighting both the origins of the
ARC Distressed Counties Program as well as the earlier discussions leading up to the Finish-
up Program begun in 1981. We conclude this section with a discussion of some of the
strengths and limitations of the ARC’s distress designation.

1.0 Identification of Counties in Distress

Like the ARC, a number of federal agencies loosely involved in economic development
provide special set-asides for rural areas in great need. A survey of other agencies” distressed
area definitions requested by the ARC in 1995 suggests that a diverse set of agencies at the

federal and state levels make distinctions among locations based on the severity of economic

' This study is based on case studies of 32 distressed counties and statistical analysis of
data describing the distressed counties group.


Table of Contents

Page
List of Tables and Figures ...........0ccsceceeeseeeeeecee cece teen eee nn eee ee ene eeeeeeee a eeeeeeninneees vii
Acknowledgement ...............c:scceeeeecceeeeecenececeeeeseaeesueeesuesseneeeseeseeeeeeeaeeseeeeenees viii
Tintroduction............ccccceeeeeeee cence eeeeeeceaeeeeeeeeseeeeeeaeseeneseseeeeseeseeeeeaneeeaeeeeeneeaeeeeee 1
Part I: The Origins and Evolution of the Distressed Counties Program.............0...::00eeeeeee 2
1.0 Identification of Counties in Distress ..............00cccceeeeececee cee ecceceeteeeeeeeeeeeeees 2

1.1 The Historical Basis of the Distressed County Designation: ARC and
the Finish-up Program .............cececseececseececeeeeeeeeeeueeeaaeeseaeeeaeeeeaeeeeteeeneeees 5
1.2 Implementation of the ARC’s Distressed Designation ...............0000.eeceeeeeeeeeee il
1.3 Benefits from Receipt of the Distressed Designation.................::ceeeeeeeeeeeeeees 11
1.4 SUMMALY 00.0... ee eee eee cee cc ee eeccneeeeeeeeeeueeeeaeeeeueceeaeeeaeeeeaeeeeaeeeaaeeeaee 14
Part II: Case Studies of Formerly Distressed Counties ..............0ccsccceceeceeeeeeeeueeneeueees 14

2.0 Introduction

2.1 The Fortunate “21” Counties that Left the Distressed Category Between
1988 and 1995.0... ...cccccee cece seeecceeeeeeaeeeeeeeeseneeseeeeeseeeseeeeseneeeateeeaeeeaaees 20

Part III: The Distressed Counties Based on the ARC’s Index .

3.0 A Retrospective View of the ARC’s Distressed Counties........0.......00ccceeeeeeeee 26

Part IV: The Development of an Alternative Distress Index: An Index of
Economic Health............ccccccccceesseeeeeeeeesseeeeeeeeeeseeeeeeeeseeeeeessesaaeeceeeanaeeees 35

4.0 Introduction...

4.1 Evaluation of the Index



effectiveness of the local labor market (percent of population that is economically dependent
and the share of income from transfer payments).

There is precedent for including other variables in the distress index. In 1974, ARC
Demographer Pickard experimented with the construction of a composite indicator of
socioeconomic status. He constructed four indexes: poverty-population; low educational status;
labor force participation and unemployment; and substandard housing. He combined these
factors into an additive index. Based on this analysis, he rank-ordered the counties (see table in

Appendix B). Like our index, a good score equals a low number and a bad score equals a high

number. The results are predictable given the conditions of the region. Using Pickard’s index,
counties with high scores, and therefore low socioeconomic status, were similar in ranking
with those designated 20 years later by the ARC as severely distressed counties. In the top 50
counties based on Pickard’s ranking, 29 were in the state of Kentucky and another 12 were in
Tennessee. Additionally, 14 of the original 18 Kentucky counties designated severely
distressed were in the top 50 troubled counties based on Pickard’s 1960 combined index. Of
the original 13 ARC distressed counties in Tennessee, nine were in the top 50 on Pickard’s
index. Familiar names such as Owsley, Magoffin, McCreary, and McDowell (counties in
Kentucky) and Lincoln, Webster, and Calhoun (counties in West Virginia) exhibit low levels
of economic health over the entire 35-year period.

4.0.1 Elements of Index of Economic Health. After considerable deliberation, we
chose to develop an additive index that includes four ratios. Drawing in part on the ARC’s
traditional measures of economic health, but with some significant additions, the four
individual measures are: a per-capita market income index which compares a county’s income

36


formulating policies targeted toward developmental versus short-term labor market adjustment
problems.

The development of an annual index which has a significant range allows greater
specificity in categorizing distressed counties. Clearly, some counties are on the verge of
leaving behind conditions of distress. Many are in all likelihood actually doing comparatively
well, given cost of living differences in the region compared with the nation. These counties
would be close to the national average and thus may need specific assistance in the form of
entrepreneurial training, leadership building, and skills development. At the other end of the
spectrum are those counties whose extreme problems are associated with a degraded
environment, small populations, and underdeveloped social institutions. For these
communities, policies will have to be especially sensitive to the desires and capabilities of
residents. Engendering new ways to build capacity and foster local empowerment are critical
realms of consideration.

Finally, an annual measure will help identify those counties on the edge of distress.
Such a finding could lead to programs specifically designed to push counties “over the edge”
and out of distress. Regardless of the type of county, the ability to be more timely and precise
in determining the characteristics of counties will help clarify and therefore ultimately
overcome many of the challenges facing them. We recommend that the ARC conduct an
assessment of an alternative index designed to capture distressed status. The ARC should

consider weighting schemes as well as additional variables as part of a new index construction.

67


Russell, J. 1998. Personal correspondence with the author (June).

U.S. Department of Commerce. Various years. Regional Economic Information System.
Washington, DC: Author.

U.S. Department of Commerce. 1997. Consolidated Federal Funds Reports 1987-1996.
Washington, DC: Author.

Whisnant, D. 1994. Modernizing the Mountaineer: People, Power, and Planning in
Appalachia. Knoxville, TN: University of Tennessee Press.

76


PENNSTATE

BOS

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

Amy K. Glasmeier and Kurtis G. Fuellhart
A Report to the Appalachian Regional Commission
John D. Whisman Scholar Program
January 1999

Institute for Policy Research and Evaluation
The Pennsylvania State University


Metropolitan Versus Nonmetropolitan Status

NEGCHG | POSCHG TOTAL
Not metro 51 (46.2) 35 (39.8) 86
Metro 0 (4.8) 9 (4.2) 9
TOTAL 31 44 95

chi-square p = 0.001

Fisher’s p = 0.001 (used when there are expected cell values below 5)

The fourth evaluation of counties examined levels of change and location in one of the
ARC’s three subregions. The results suggest the following.’ Negative changes are
overrepresented in the north and central parts of the region compared with the south.
Conversely, southern counties were overrepresented in the positive change category given their

presence in the entire group of distressed counties.

Subregion
NEGCHG | POSCHG TOTAL
North 17 (11.3) 4 (9.7) 21
Central 26 (19.9) 11 (17.1) 37
South 8 (19.9) 29 (17.1) 37
TOTAL 51 44 95
chi-square p = 0.000

1-3


Implications. The positive benefits of ARC programs have been muted by the fact
that many distressed counties were unable to receive funds under the initial
program design because remote, isolated counties did not qualify under the
growth center strategy as set forth at the inception of the program. The fact that
some distressed counties have left distressed status suggests that positive changes
can and do take place. Thus, there is ample room to the take learning that has
occurred over the last 30 years to amplify on the distressed counties program.

The development of an alternative economic index to identify, characterize, and
track distressed counties allows for much greater precision in sorting out short-
and long-term problems. An annual index helps to focus policy concerns on the
underlying challenges facing distressed counties and should help better direct the
pursuit of solutions tailored more precisely to systemic problems.

The high share of small establishments that are emblematic of distressed counties
economic bases could be harnessed through the Commission's entrepreneurship
initiative to develop a self-employment learning program. The most challenged
entrepreneurs, those with limited incomes and in isolated areas, require
considerable support services in addition to the more typical small firm support
activities.

Another important difference that separates these poorer communities from those
that are more resilient is the absence of social and community capital that is so
necessary for developing livelihoods, incomes and quality of life. Social capital
such as community leadership, entrepreneurial resources for managerial and
technical assistance, access to health care and health education, and student
mentoring programs for high school and college-bound students are key examples
of an assets-based approach to economic development. The social capital
approach complements ARC’s leadership and entrepreneurship initiatives and
emphasizes building community and individual assets to deepen the development
potential of the region, and further capitalize its existing social and financial base.

Given the higher poverty rates among single, female-headed households and
lower labor force participation rates, jobs programs also must serve the needs of
women. Child care, in-home care for the elderly, and community mentoring are
just a few examples of activities that could help support the labor market success
for women. Such initiatives should extend beyond welfare-to-work activities.

Finally, the study recommended that the Local Development Districts be given
incentives to partner with new players in their communities and obtain the
training necessary to partner with foundations so that planning districts can
leverage more private funding.

iti


Mean _ Mean_

Variable NEGCHG POSCHG ANOVA p value

BS degree 0.033 0.044 0.004
Females with children < 17 0.035 0.032 0.245
Percent establishments < 10 employees 0.794 0.796 0.874
Percent income residential adjustment 0.039 0.161 0.000
Percent income retail 0.057 0.587 0.675
Percent income services 0.099 0.085 0.191
Percent income government 0.109 0.088 0.002
Percent income manufacturing 0.086 0.169 0.000
Percent population 65+ 0.139 0.146 0.178
Labor force—percent female 0.410 0.443 0.000

1-4



average income level is $10,300, and the standard deviation is less than $800. Like the two
previous measures, counties exhibit very similar unemployment rates of 8 percent with a
standard deviation of one percentage point. The similarity in the group and the close levels of
prosperity are more than coincidental. In many respects, given their geography and their
previous economic history, the entire group of 21 exhibited surprisingly similar development
trajectories.

Other comparable characteristics can be found as well. Many of the counties,
particularly those located on the Cumberland Plateau and within the TVA watershed,
experienced significant public-sector investment in water impoundment infrastructure. In
addition to flood control, these investments proved to be important tourist attractions (Pickett
County, for example). Also among the counties that left the distressed categories are areas of
significant scenic beauty, which served to support tourist development (Pendelton, WV). Even
other counties such as Montgomery County, KY, have taken advantage of changing
demographics and built housing developments and infrastructure to attract retirees to the area.
Thus, in addition to the more conventional sources of economic development such as
manufacturing, these counties are fortunate to have nice environments to sell.

These counties are important examples of community and economic development
success. For all of them, the 1980s and 1990s have been very good years. While there are still
realms in which they lag behind both state and national averages, nonetheless their emergence
from periods of economic stagnation appears irrefutable. These counties are increasingly
focusing on filling in those areas that complement the infrastructure and institutional
investments that they received over the last 30 years.

24


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Greenup KY 129.10 Franklin TN 166.4
Marshall AL 129.10 Pleasants WV 166.1
Muskingum OH 129.05 Pulaski VA 165.3
Lamar AL 129.02 Rutherford NC 165.2
McMinn TN 128.32 Jackson wv 164.5
Cullman AL 128.28 Montgomery KY 164.1
Gilmer GA 128.20 Fulton PA 163.8
Hancock wv 128.07 Washington TN 163.7
Macon NC 127.94 Jackson. OH 163.3
Haralson GA 127.88 Loudon TN 163.3
Avery NC 127.88 Roane TN 161.9
De kalb AL 127.83 Giles VA 160.8
Delaware NY 127.55 Greene PA 160.4
Oktibbeha MS 127.39 Somerset PA 159.9
Franklin GA 126.93 Scioto OH 159.8
Columbia PA 126.75 Putnam wv 159.6
Sevier TN 126.32 Surry NC 159.3
Elmore AL 126.30 Brown OH 159.2
Cleburne AL 126.01 Botetourt VA 159.2
Wayne PA 126.00 Huntingdon PA 159.0
Lauderdale AL 125.92 Lee MS 158.3
Schoharie NY 125.86 Stephens GA 158.2
Washington PA 125.83 Marshall wv 158.1
Coshocton OH 125.33 Davie NC 158.0
Carroll OH 125.16 Yadkin NC 157.3
Smyth VA 124.63 Etowah AL 157.1
Chautauqua NY 124.52 Jefferson wv 156.6
Hamblen TN 124.36 Lowndes MS 155.4
Pontotoc MS 124.31 Burke NC 154.9
Lycoming PA 124.19 Bradley TN 154.7
Stephens GA 124.17 Whitfield GA 154.5
Highland VA 124.07 Guernsey OH 154.0
Walker GA 123.77 Hall GA 153.9
Dade GA 123.76 Berkeley wv 153.8
Blount AL 123.67 Hamblen TN 153.6
Roane TN 123.63 Morgan OH 153.5
Tallapoosa AL 123.63 Habersham GA 153.4
Scott VA 123.60 Hocking OH 153.1
Hawkins TN 123.50 Highland OH 152.8
Jackson NC 123.36 Lawrence OH 152.7
Boyd KY 123.28 Caldwell NC 151.4
Union MS 123.13 Gwinnett GA 150.9
Alleghany VA 122.80 Bedford PA 150.6
Bradford PA 122.48 Calhoun MS 150.0
Wood wv 122.46 Perry OH 149.4
Chemung NY 122.37 Spartanburg sc 149.1
Tuscarawas OH 122.25 Athens OH 149.0
Alleghany NC 122.13 Transylvania NC 148.7

B-6


Appendix B. Comparison of Rank-ordered Indicies

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Owsley KY 309.85 Magoffin KY 330.6
McCreary KY 284.85 McCreary KY 315.7
McDowell wv 276.33 Leslie KY 315.3
Webster wv 270.31 Breathitt KY 315.0
Clay wv 256.00 Knott KY 313.3
Wolfe KY 239.89 Martin KY 309.7
Summers wv 236.44 Owsley KY 308.3
Calhoun wv 236.00 Clay KY 300.9
Lee KY 234.37 Wolfe KY 292.3
Hancock TN 225.11 Jackson KY 291.0
Magoffin KY 224.35 Fentress TN 288.0
Lincoln wv 223.30 Knox KY 284.9
Elliott KY 222.74 Lee KY 283.4
Jackson KY 218.73 Elliott KY 283.3
Fayette wy 218.19 Morgan 1N 280.7
Wyoming wv 216.41 Webster wv 280.5
Letcher KY 215.37 Lawrence KY 279.5
Clinton KY 212.80 Lincoln wv 276.2
Menifee KY 211.88 Clay wv 273.1
Clay KY 209.94 Van Buren TN 272.8
Knott KY 209.01 Rockcastle KY 272.2
Morgan KY 206.98 Scott TN 269.8
Braxton wv 206.40 Perry KY 267.7
Adams OH 204.80 Menifee KY 266.5
Harlan KY 201.97 Lee VA 266.0
Barbour wv 201.84 Letcher KY 265.8
Wirt wv 201.46 Grundy TN 264.7
Leslie KY 201.25 Bell KY 264.0
Noxubee MS 199.72 Wayne KY 262.0
Campbell TN 199.15 Hancock, IN 261.7
Logan wv 198.98 Powell KY 260.4
Lawrence KY 198.89 Pike KY 258.9
Roane WV 198.85 Pickett TN 257.2
Breathitt KY 196.15 Clinton KY 256.5
Scott TN 196.04 Morgan KY 256.4
Bell KY 193.86 Meigs TN 255.1
Whitley KY 193.27 Bland VA 255.0
Graham NC 192.92 Jackson TN 254.2
Johnson IN 192.18 Floyd KY 253.9
Mingo wv 191.37 Johnson KY 253.7
Lewis KY 189.74 Whitley KY 252.4
Fentress TN 189.07 Overton TN 252.2
Wayne KY 187.95 Campbell TMN 251.1
Carter KY 187.73 Casey KY 250.4
Knox KY 187.25 Bledsoe IN 249.5
Nicholas wv 186.93 Harlan KY 249.2
Swain NC 186.64 Calhoun wv 248.9
Kemper MS 186.04 Claiborne TN 248.1
Taylor WV 185.93 Braxton wv 247.9
Estill KY 185.18 Estill KY 247.8

B-1


Yet, the defining moment for the creation of the distressed county designation cannot be
understood outside the pressures the agency felt as it stared down its possible imminent
demise. Creation of the designation can be understood as a final effort to secure some level of
support for those counties in the 13-state region that had not benefitted from the early days of
the program due to their small, rural, and remote conditions (Appalachia, 1984, p. 5;
Appalachian Regional Commission, 1982). As stated in the 1981 report:

There has been some criticism of the ARC in the past for failure to provide sufficient

help to the most distressed counties in the Region. The Appalachian Regional

Development Act, however, established ARC as an economic development agency and

requires it to focus its investments where they can be expected to bring about the

highest economic development returns. Few of these investment opportunities are found
in the most distressed and underdeveloped counties (Appalachian Regional

Commission, 1981, p. 39).

As if to stand apart from the agency’s original mandate and place responsibility for the
care of those areas and communities with problems not susceptible to this mandate with the
federal government and the states, the document goes on,

We believe, however, that in a finish-up program the Appalachian Regional

Commission and the Federal government must assume some responsibility to assist

these counties prior to the termination of the ARC, to give them some basis for hope in

the future. We propose, therefore, that the Appalachian Regional Development Act be
amended to authorize $15 million annually for five years to provide special assistance
to the 60 or so most distressed and underdeveloped of the 397 (at the time) Appalachian

counties (Appalachian Regional Commission, 1991, p. 39).

At the time of its creation, the Distressed Counties Program explicitly cited as an area
of greatest need the provision of clean water and adequate sewers. Hence, the program’s
emphasis has been and continues to be primarily on support for water and sewer systems. The
initiative set aside a share of existing funds, but did not step outside of the broad institutional

mandates that had come to characterize the program after 1975.

6


the general economic malaise experienced in this part of the ARC region since the early 1980s,
which is tied directly to the decline of major industrial employers such as IBM and General
Electric, as well as to fabricated metal producers such as U.S. Steel (according to personal
interviews with county officials in the southern tier region). Only a smattering of counties
experienced a decline in economic health south of the southern Kentucky and Virginia state
borders. On the contrary, much of the southern portion of Appalachia experienced an
improvement in economic health, as did many counties in central Pennsylvania. These results
only serve to confirm those of the Economic Research Service, which suggests a widespread
improvement in the fortunes of formerly persistently impoverished counties in the southern
U.S. over the 1960-1990 period.

To see where relatively significant changes occurred between the beginning and end of
the study period, a second map was created (see Figure 7. County-level Improvement or
Decline > 10%, and Appendix G) to single out those counties where mean index values had
changed by more than + 10%. Approximately two-thirds of all ARC counties had less than a
10 percent change between the beginning and end of the period. Of the counties with
significant negative changes in economic health, the vast majority were again in the states of
Kentucky, Ohio, and West Virginia. Counties exhibiting significant improvement were highly
clustered in the southern portion of the region, including many in the states of Alabama,
Georgia, and Tennessee.

To assess the relative stability of ARC county-level economic health throughout the
study period, as well as to compare our index of economic health with that created by ARC
staff demographer Jerome Pickard in 1960, Spearman rank-order correlation coefficients were

48


The combined index score is simply calculated as the mean of the four individual
indices as described above. As a reminder to the reader, a score > 100 indicates a county has
poor economic health relative to national averages; conversely, a score < 100 reflects better

economic health in a county.

C-4


areas most likely to succeed, the time has come to apply the wealth of experience gained by
the Commission to emphasize a locally-based development process for those places left behind.
While there is ample evidence to suggest that insights from past experience can be useful in
facilitating change in distressed counties, the problems in these areas call for a new formula
based on local capacity and assets-based development.

6.2 Using an Assets Approach to Development

Conventional economic and development practice typically revolve around identifying
the weaknesses in the economic and social systems of communities and then prescribing
ameliorative actions to make up those perceived deficiencies. These proposed “solutions”
usually are in the form of some type of state (or firm) intervention that imposes cost reductions
for greater economic efficiency. On the one hand, in the more advanced countries, regions,
and cities, such actions have contributed to the generation of jobs and in many cases have
contributed to improvements in the living experiences of community residents. On the other
hand, for many regions, and especially communities that remain outside the mainstream of
economic development, such measures have had surprisingly little effect, even after long
periods of government intervention.

Recognizing the inability of conventional practice to resolve many of the development
problems confronting distressed communities, a series of new policy initiatives are focusing on
developing assets and community capacity from the ground up. There is a comparatively new,
growing, and evolving framework of development practice emphasizing community assets in
all their multifold dimensions. Beginning from a perspective that suggests that access to

monetary assets is what separates poor communities from those more capable of and resilient

69


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30


recovery in economic health steadily reaches 1980 levels (and even dips slightly below).
Measured in this way, it would seem that overall regional economic health is about the same
today as it was in 1980. In an important sense this is partly due to stagnation of incomes
nationally. Prior to 1980, national income levels were continuing to rise, although they were
slowing by the beginning of the decade. After 1980, with the deep economic recession of 1982,
followed by economic turmoil throughout most of the 1980s, income levels stagnated and job
generation became much more erratic. Thus, to some extent, what we see happening in
distressed counties mirrors broader national trends.

There is a sharp increase in index levels between 1994 and 1995, reminiscent of
economic decline in the early 1980s. This trend has been observed by ARC staff investigating
basic statistics on the region. As Greg Bischak, ARC senior economist, suggests, it appears
that the trends in the region are counter-cyclical relative to national conditions. The nation is
pulling away from ARC counties in a period of significant national development.

These results are not unique and are in fact mirrored by those of other researchers
examining the experience of all counties considered distressed by the U.S. Department of
Agriculture. Mark Nord, ERS economist, noted in his study of persistently distressed areas
that:

In the 1980s, both of these patterns (decline in poverty rates and persistent poverty

counties in the 1950s and 1970s) disappeared or were greatly attenuated. The mean

poverty rate of non-metro counties increased by about one percentage point from

1979-1989, and that of persistent poverty counties increased about 1.5 percentage

points. Although, as in the previous two decades, the counties escaping from persistent

poverty status were at the fringes of the high poverty cores (including central

Appalachia), those counties represented a much smaller proportion of persistent poverty
counties than in the previous two decades (Nord, 1998, p. 10).

42


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The decline of persistent and severely distressed counties in the 1950s and 1970s is
correlated with the significant expenditure of national funds for the eradication of poverty.
Looking back on the expenditure levels of the ARC alone, funds in the program’s early years
dwarf those of later years, particularly for social welfare improvement and community well-
being. As our results will show, the edges of the ARC region have dramatically improved over
the last 30 years, paralleling Nord’s findings for persistent poverty counties throughout the
nation.

4.2.2 The Spatial Pattern of “Distress” in the Appalachian Region. To examine the
patterns of average distress levels in the region over the study period, the average index score
for Appalachian counties was calculated for the time period 1980-1995 and mapped (see Figure
5. Mean County-level Index, 1980-1995). Based on our index, the patterns of distress look
quite similar to those based on the ARC’s distressed measure. Counties in central West
Virginia, eastern Kentucky, and northeastern Tennessee comprise the focal areas of “hard core”
distress. Examining the top and bottom 25 percent of counties based upon mean index scores
reveals a now familiar pattern. Counties in the top 25 percent based on mean index score (those
most closely resembling national conditions) are primarily located along the southeastern
margins of Appalachia. This includes many counties in Georgia, all of Appalachian South
Carolina, and eastern regional sections of North Carolina and Virginia. The greater Pittsburgh,

PA, and Binghamton, NY areas also are among the “healthiest” places as measured by the

43


compare with those in the rest of the country. Second, the ARC should provide a forum for
training local officials and educators to improve their ability to win more external program
funds for early childhood development. The ARC could identify best-practice programs and
establish a demonstration program to filter such experiences throughout the region. Third, the
ARC could sponsor demonstration programs that seek to include parents in their children’s
school experience. Fourth, high school students need mentors and access to health education.
The ARC could be instrumental in exploring financing alternatives to ensure that support staff
are available in every school. Fifth, high school students need meaningful and pride-filled
alternatives to college. The ARC could sponsor a demonstration program to highlight and
disseminate model school-to-work programs throughout the region. Sixth, some evidence
suggests that Appalachian students begin college but drop out by the end of their second year.
Dropout rates are related to lack of resources and problems of transition. The ARC could
stimulate the development of programs designed to help students stay in college and finish
degree programs. These programs could take two forms. First, Appalachian students lack
funding for higher education. The ARC could help identify funding sources, scholarship
programs, and other support mechanisms to encourage students to pursue college. Second, a
bigger and more immediate problem is keeping students who start college in college to
successfully complete advanced education. Mentoring and transitioning programs are
important to ensure students who are college-bound are able to make the transition
successfully.

6.2.5 Community-based Jobs: The Right Alternative. In many parts of the region, the

lack of skills, education, and previous work experience limits economic options for the

72


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Indiana PA 137.74 Cherokee sc 185.4
Snyder PA 137.46 * Clay MS 185.4
Jefferson TN 137.40 Warren IN 185.4
Sequatchie TN 137.29 Bath VA 184.5
Chilton AL 137.14 Pike OH 183.8
Macon TN 136.76 Washington VA 182.3
Cattaraugus NY 136.69 Smyth VA 182.1
Clarion PA 136.65 Taylor wv 181.6
Calhoun AL 136.37 Greenbrier WV 181.5,
Crawford PA 136.06 Forsyth GA 181.2
Rabun GA 136.03 Meigs OH 181.1
Colbert AL 135.89 Oktibbeha MS 180.9
Clay TN 135.80 Bartow GA 180.1
Fulton PA 135.72 McDowell NC 180.1
Mitchell NC 135.49 Carter TN 180.1
Brooke wv 135.38 Tyler wv 179.8
Clay AL 135.30 Garrard KY 179.5
Cherokee AL 135.15 Mason wv 179.5
Cabell wv 135.02 Oconee sc 179.3
Monroe MS 134.96 Unicoi TMN 179.3
Wise VA 134.90 Craig VA 178.3
Carter TN 134.26 Ritchie wv 177.6
Warren TN 134,14 Jackson GA 177.5
Tioga PA 133.72 Watauga NC 177.3
Jefferson PA 133.63 Cherokee GA 176.8
Columbiana OH 133.54 Polk GA 175.9
Fayette AL 133.48 Gordon GA 175.2
Beaver PA 133.33 Haralson GA 174.6
Tazewell VA 133.10 Carroll GA 174.5
Coosa AL 132.73 Douglas GA 173.6
De Kalb IN 132.65 Jefferson IN 173.1
Schuyler NY 132.50 Mercer wv 172.8
Hardy wv 132.06 Talladega AL 172.8
Chambers AL 132.00 Greenup KY 171.4
Ashe NC 131.55 Chattooga GA 1711
Madison NC 131.31 Monroe OH 170.8
Greene TN 131.26 Alleghany VA 170.8
McKean PA 130.90 Madison KY 170.7
Ross OH 130.65 McMinn TMN 170.6
Brown OH 130.64 Tallapoosa AL 169.7
Heard GA 130.52 Fayette PA 169.6
Lackawanna PA 130.48 Polk NC 169.2
Laurel KY 130.41 Tuscaloosa AL 169.1
Ttawamba MS 130.11 Alexander NC 167.7
Mercer PA 129,98 Barrow GA 167.5
Franklin IN 129.89 Chambers AL 167.4
Garrard KY 129.44 Marshall AL 166.9
Juniata PA 129.31 Noble OH 166.5

B-5


level to the national level (PCMI,,,); an unemployment rate index which compares the county-
level unemployment rate to the national unemployment rate (URT,,,); a labor force to total
population ratio index (LFPOP,,,); and a per-capita transfer payments to per-capita market
income ratio index (TFP,,,). The use of these four indicators was designed to shed light on the
degree to which the experience of individual counties deviates from national norms. The
inclusion of a measure of transfer payments and of a labor force participation was designed to
assess the extent to which the population depends on external sources of unearned income
(transfer payments) and the share of the population that depends on the labor of others. We
were concerned about the availability of underutilized human resources; similarly, we also
were interested in the extent to which large segments of the population were not participating
for any reason in wage-earning activities. For more detail on and further specification of the
variables in the index, see Appendix C.

4.1 Evaluation of the Index

The strength of our index is that it not only allows annual analysis of a county’s
condition, but it also allows us to examine county conditions which were heretofore obscured
by the ARC five-category designation. We can now examine each county relative to others and
we can evaluate changes in county scores through time. At the same time, the index allows us
to examine the condition of counties on an annual basis. The intent in developing the index of
economic health is to assist the ARC in improving its ability to rank counties more precisely
with the most current and accurate data. With our index we can examine absolute and relative
conditions of counties through time. We can examine county scores on the overall index and
the underlying index components. This measure also allows us to use the most recent county-

37


distressed county progress over time, and has the additional merit that it can be
updated on an annual basis.

Factors Predicting Distressed Status

The study then analyzed the determinants of the economic status of all ARC

counties for the 1994 period. The statistical analysis sought to predict economic

status using 12 socioeconomic variables.

e Percentage of population with four-year college degrees, percentage of
income from manufacturing, and percentage of net income from commuting
were statistically significant and positively associated with county economic
health.

e The percentage of population of single mothers with children under 17, the
percentage of females in the labor force, and the percentage of population
over 65 years of age have negative relationships with economic health.

e A higher percentage of establishments with fewer than ten employees and
higher percentages of county income derived from the government sector
have negative associations with economic health.

e Counties adjacent to a metro area were significantly and positively related to
better economic health index scores.

¢ Conversely, residence within the central region had a negative relationship
with economic health.

This analysis yields the following findings. Compared with more prosperous
counties in the region, distressed counties have a higher dependent population
consisting of single female-headed households with dependent children under the
age of 17 and the population over 65. We know from national studies that single
mothers have a higher incidence of very low incomes and poverty, and in order to
work effectively in the wage-earning economy often require access to support
programs such as child care services and health care.

Distressed counties lack manufacturing jobs. The association between poor
economic health and the share of small establishments with ten or fewer
employees also signifies two trends. First, the absence of larger employers may
signal that small establishments represent high levels of self-employment.
Second and related, given the major role branch plant employment has played in
the development of the ARC over the last 30 years, by implication distressed
counties have not shared equally in this source of employment growth.

Finally, low levels of women in the labor force signify impediments to female
labor force participation. Such limitations include a lack of employment
opportunities, social and cultural restrictions, and incompatibilities between the
primary and secondary wage earners’ work schedules. The geography of
distressed counties is distinct and well-known. Distressed counties tend to be
very rural, remote from metro areas or the edge of the region, and are not adjacent
to metro areas.

ii


Appendix I. Change in Index Scores
(mean 1991-1995 versus mean 1980-1984)

Step 1: Calculation of mean and standard deviation of change in distress index

N Mean change SD

399 0.1223 18.019

To determine groups that changed by more than one standard deviation
Mean + 18.01989 = 18.142194 and higher changed significantly for the worse (N = 51)
Mean - 18.01989 = -17.89786 and lower changed significantly for the better (N = 44)
Henceforth: Group that changed for the better: POSCHG
Group that changed for the worse: NEGCHG
To see if the counties started and ended at the same place we conducted two analysis of
variance tests comparing the mean index score for the POSCHG counties and the NEGCHG

counties for the beginning period (1980-1984) and the end period (1991-1995).

NEGCHG | POSCHG | ANOVA p value
Mean index 1980-1984 | 157.08 156.14 0.809

Mean index 1991-1995 | 190.65 129.76 0.000

This indicates that there was no difference between these counties at the beginning of

the period, but divergence occurred during the period.

L-1


5.1 Model Results

Complete results for the regression model are presented in Figure 12 (Linear
Regression Model: Predicting the 1994 County-level Index). Overall, the model was
statistically significant.’ It was extremely successful in predicting index values: the model
successfully predicted 80 percent of the cases.’ The table breaks the significant independent
variables into two categories: socioeconomic and geographic." Variables within each category
are listed in descending order of strength in measuring the relationship between the dependent
and independent variables.'!

Eight socioeconomic variables were significant predictors of distress status. Percent of
population with four-year college degrees, percentage of income from manufacturing, and
percentage of income from residential adjustment were significant” and have positive
relationships with county economic health. (Note that since a lower index value indicates
relatively greater economic health, a negative coefficient is associated with better economic
performance.)

The percentage of the population which are single mothers with children under 17,

females in the labor force, and over 65 years of age have negative relationships with economic

* Significant at the p < 0.001 level.
° The R? was 0.797 and the adjusted R? was 0.789.

’° The “t” statistics is a measure of the strength of the relationship between the
dependent and independent variables.

" See Appendix J for a discussion of regression diagnostics.
” Significant at the p < 0.01 level.

60


were counties along the region’s edges. Thus, it comes as little surprise that these locations
have done relatively well over the last 20 years.

4.2.3 ARC’s Distressed Measure Compared with Our Index of Economic Health. In
an effort to determine the overlap between the ARC distressed measure and our index, we
compiled a graph (see Appendix F) that compares the ARC rating with our index. The results
are quite striking. Those counties designated 2 or lower also score very high on our index of
health, signifying that these locations suffer from poor or low economic health (high score
indicating poor economic health). The results suggest, too, that like the ARC’s designation,
those counties with a level 5 rating perform at or below national averages based on our
measure. These are the more urbanized and border portions of the region. Both our measures
clearly identify places with very good and very poor economic health.

4.2.4 Which Counties are Improving . . . and Declining? As part of the original
conception of the distressed index, the ARC envisioned being able to discern counties that have
done well versus those that have failed to make progress toward national standards. In previous
sections we have shown that places with larger populations have consistently done better (on
average) than those with smaller populations. To visualize change over time, a mean county-
level index was calculated for the first five years in the period (1980-1984) and compared with
the most recent five years for which data are available (1991-1995). The map in Figure 6
(County-level Improvement or Decline) shows the change over time on a simple dichotomous
scale. Declining counties are highly clustered in central Appalachia, primarily in Kentucky,
Ohio, and West Virginia, though significant clusters of decline also are apparent in western
Pennsylvania and in the southern tier counties of New York state. We attribute this decline to

46


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health. The results of this project suggest that many important challenges facing young children
in the region relate to fundamental problems of insufficient quality schools, lack of access to
health care, and the lack of stable economies in which to grown up and be a part. While we do
not detail these case studies, we do return to issues of children’s health and education in
distressed counties in the conclusions to this report.

2.1 The Fortunate “21” Counties that Left the Distressed Category Between 1988
and 1995

Common among the case study counties that successfully transitioned from extreme
distress was an early foundation of agriculture based upon private land ownership,
infrastructure investments (including interstate and Appalachian highways construction), and
investments in educational institutions and medical facilities. A brief examination of 21
counties suggests both impressionistically and factually that these counties shared a similar
potential which helps explain their emergence from the distressed county group.

A picture of the fortunate 21 counties tells a story of a relatively homogeneous group of
locations, many of which as early as the 1950s were destined to make major strides toward
economic health. Table 2 presents a series of statistics about these counties. An obvious
characteristic linking the 21 counties is their geographic location. More than half are located in
the southern subregion of Appalachia. Set apart from the rest of Appalachia, this fortuitous
location has been the beneficiary of the general decentralization of manufacturing over the
post-war period. While the public sector in many of the ARC counties was an active recruiter
of branch plants, nonetheless, manufacturing decentralization has unfolded as part of a larger

process of reorganization of the national economy, and thus is the result of forces originating

20


Table 1. Case Studies for 14 Counties

Bibb County, Alabama

In 1960, Bibb County, Alabama, was a poor, isolated, largely agricultural county with a poverty rate
(43.2%) significantly above national averages and a per-capita income level half the national
average. By 1994, the county’s unemployment rate was 7.6 percent, still above the national average
but converging on regional and national norms. Per-capita market income was 80 percent of the state
average and climbing. The county has benefitted from highway construction and Alabama’s
industrial training programs, which have made the county’s labor pool attractive to employers
relocating to the region. Today, the location of a new Mercedes Benz manufacturing plant in the
adjacent county is expected to add to the already significant amount of net local income from
commuters traveling to job destinations outside the county (27%). Given growth in manufacturing
both within and outside the county, Bibb is expected to add to the region’s good economic fortunes.

Chickasaw County, Mississippi

Chickasaw County, Mississippi, has benefitted handsomely from the filtering of manufacturing
employment from nearby Tupelo, a major manufacturing center in the northwest corner of the state.
The county enjoys a high-quality transportation infrastructure, including an interstate, two state
highways, and an historic parkway. Historically dependent on agriculture, Depression-era programs
reduced the available crop land, thereby driving up land prices and stabilizing the agriculture sector.
The county was an early site for southern industrial plants; textiles, furniture, and plastics firms
offered jobs to county residents. Today, the county boasts 18 percent of the state’s jobs in furniture
manufacturing. These jobs account for 70 percent of the county’s total manufacturing employment.
People commute into Chickasaw for jobs in manufacturing; thus, it is one of the 21 counties that
exports more income than it imports through commuter income. Income levels in 1990 were
reaching state averages.

Cumberland County, Tennessee

Cumberland County, Tennessee, halfway between Nashville and Knoxville, sits atop fertile
agricultural land of the Cumberland Plateau. Bisected by Interstate 40, Cumberland County has
experienced a dramatic change in economic circumstances over the last 35 years. In 1960, like many
Appalachian counties remote from metropolitan areas, more than 36 percent of Cumberland’s
population lived in poverty and many had an average per-capita income level less than half the
national average. This forested county, blessed with rolling hills and abundant lakes, ponds, and
streams, has turned its natural beauty into an asset. In the 1960s, mining and agriculture were
mainstays of the local economy, along with timber and furniture manufacturing. In the intervening
38 years, the county’s economy has been transformed into a destination for tourism and retirement-
related activities. Within 50 minutes of Tennessee’s major population centers, Cumberland attracts
old and young alike. This growth has brought with it a rise in the per-capita income level, which is
converging toward the state average, and poverty rates only two percentage points above the state
average (18 vs. 16). The unemployment rate is converging on the state average.

16



Appendix H. Logistic Analysis of ARC Distressed
Designation Binary Dependent Variable

Logistic Regression Analysis”

Standardized Likelihood Ratio

Variable Coefficient Significance
Percentage of population with BS degree -0.412 0.000
Percentage population female, single, with children < 17 0.256 0.000
years

County location: metropolitan area -0.232 0.000
Female labor force as percentage of total -0.207 0.000
Percentage of income from government sector , 0.199 0.000
County location: adjacent to metropolitan area -0.118 0.002
County location: “central” ARC subregion 0.101 0.025
Percentage of income from manufacturing sector -0.076 0.058
County location: along the “edge” of the ARC region -0.064 0.067
Percentage of income from the services sector -0.091 0.185
Percentage of population aged 65+ 0.058 0.203
County location: “southern” ARC subregion 0.025 0.583
Percentage of establishments < 10 employees -0.001 0.988
Percentage of income from the retail sector -0.000 0.992

“Dependent variable: mean distress scores were calculated based upon the ARC’s 5-category county
rating system for the years 1992-1997 (1 = worst; 5 = best). Mean scores were dichotomized as
follows: scores < 3 = “distressed / high risk” (coded 1); scores 3 = “healthy” (coded 0).

N = 387 due to missing values on source of income variables for several counties.

Summary Statistics:

Model Fit Prediction Rates
-2LL initial: 524.838 Nondistressed: 89.87%
-2LL final: 225.948 Distressed: 84.38%
Model chi-square: 298.889 (p = 0.000) Overall: 87.60%

R?, = 0.570

0.626



(2) how many of the three measures are below threshold values in comparison with national
averages. Table 3 summarizes the criteria for designation along the 5-point scale.

Under this rating scheme, for analytical purposes, counties with a distress scale rating
of 1 or 2 are considered distressed, while those attaining ratings > 2 are considered non-
distressed. Category 1 distressed counties are the only group eligible to receive special set-
aside funds. A summary of the number of counties in each of the five distress categories over
the period 1992-1998 can be seen in Figure 1 (Distress Ratings by Year, 1992-1998). A
comparison of distressed (rating <= 2) versus non-distressed counties (rating > = 2) is
provided in Figure 2 (Distress Status by Year, 1992-1998). As these figures show, the
distribution of counties in the various distress categories has been relatively constant over time,
though a slight overall improvement is detectible in recent years. This may be related to the
robust nature of the U.S. economy as a whole.

In recent years, the ARC has combined categories 2 and 3 into one category. In our
analysis, for reasons of historical comparability, we initially retained the 5-point designation.
With this gesture, our results serve to retain the meaning of the ARC’s original designation
system. As the discussion in the subsequent analysis demonstrates, the key distinction in the
designation of distressed is found in those counties that score less than 3 and those which score
3 and higher. Thus, our results remain true to the original principle behind the categorization,
while providing greater clarity about the central core distressed counties as well as those

counties that have gone in and out of distress through time.

28


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to changing global economic conditions, a growing body of research is recognizing the
importance of social capital and other less tangible assets as bases for development.

Several policy options are embedded in the notion of assets-based development which
complements existing ARC programs. ARC’s recent experience with leadership programs
provides a sound basis for formulating activities that emphasize the special needs of distressed
counties. In order for communities to formulate realistic and meaningful proposals for their
future, new leadership is needed. The ARC can begin the process through its LDDs. Within
the area of leadership, some suggestions include the following.

6.2.1 Building New Leadership Through LDD Development. For the ARC to be more
effective in formulating community-based development strategies, LDDs must broaden their
base by working with new groups, citizens, non-governmental organizations, churches, and
private funders in planning for development. The conventional skills associated with
infrastructure development and branch plant attraction, while not irrelevant, must take second
place to new capabilities and sympathies needed to facilitate the success of programs designed
to assist distressed and high-need counties. LDDs should be given encouragement and
incentives to partner with new players in their communities. They need training to partner with
foundations to build on a new conception of development that emphasizes the building of
community and individual assets in their multifold dimensions.

6.2.2 Using the Entrepreneurial Program to Help Distressed Counties. The high share
of small establishments which are emblematic of distressed counties’ economic bases should be
harnessed through the development of a self-employment learning program. The most

challenged entrepreneurs, those with limited incomes and in isolated areas, require consider-

70


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income earned from wages, dividends, and rent, adjusted for residence and exclusive of

transfer payments. The county-level index in year j is calculated as follows:
100

,_[(PCMI, - PCMI,)
- PCMI,

PCM, =

where PCMI,, = U.S. per-capita market income in year j and PCMI, = county per-capita
market income in year j. Counties with lower per-capita market income in comparison with
national levels will have an index score > 100 on this measure.

The second individual county index is again an extension of a traditional ARC
measure: the unemployment rate in comparison with national averages. The derivation of the

index is as follows:
URTwx. = 100URT.
° URTu
where URT,, = county unemployment rate in year j and URT,, = U.S. unemployment rate in
year j. Counties with unemployment levels greater than the national average will have an index
score > 100 on this measure.

The third county-level index reflects the number of persons in the labor force in
comparison with the total population. This measure is defined to capture the “dependent”
population of a county. As the relative number of (for example) children, non-working
mothers, elderly, discouraged workers, and other non- (or low-) wage-earning persons

increases for a given population, the further the wage earners’ incomes will be stretched.

C2


circumstances (Fullenbaum and McNeill, 1995). Programs of the U.S. Department of
Agriculture (USDA) maintain special set-aside provisions in rural development programs
covering housing, electricity, water and sewer, empowerment zones, and enterprise
communities. The Economic Development Administration (EDA), under numerous program
titles, makes special provisions to fund projects in areas of economic distress. The Housing
and Urban Development (HUD) Agency’s Community Development Block Grant (CDBG)
program contains a special provision for areas with persistent economic problems or distress.
Other HUD programs that recognize special local circumstances include the Home Investment
Partnership, urban revitalization, and Moving to Opportunity programs. Under its emergency
food stamp and shelter program, the Federal Emergency Management Agency (FEMA)
provides resources for areas with high levels of need. Finally, other agencies, including the
Bureau of Indian Affairs, Tennessee Valley Authority (TVA), and the World Bank,
acknowledge the special circumstances of distressed areas.

Within all 13 Appalachian states, economic development programs (either coupled with
federal programs or freestanding state-funded programs) make special provisions for areas of
economic distress. This practice also is common in states outside the ARC region. A
designation of distress is most commonly linked with federal agency mandates such as the
Enterprise Zone program.

No single indicator is used exclusively to define distress at the federal or state levels.
Some programs use absolute measures such as the incidence of poverty in the population, while
others identify distress based on area ranking or score (e.g., income levels or unemployment
rates). The ARC survey is by no means totally inclusive, but it does provide a sense for the

3


ble support services in addition to the more typical small firm support activities. There is
considerable knowledge of best practice in this area. The ARC could team with well-known
practitioner groups to further develop ARC’s recently established entrepreneurial regional
initiative. In addition to the self-employment learning activity, the ARC should sponsor
support programs that provide services to groups of firms. In the remote areas of the region
there are numerous examples of firms imitating one another. In such a situation, economies of
scale can be realized by identifying small clusters of firms that need basic business services.

6.2.3 ARC Cares About Health Care. Serious investments in health care infrastructure
have not been made in many of the region’s most remote locations since the 1970s. The ARC
needs to revisit its original metric of having a clinic within 30 minutes of all residents.
Hospital and clinic consolidations, combined with the movement toward group health care,
have left many of the region’s remote areas far from providing decent, accessible, and
affordable health care. Today’s problems take three forms: quality, cost, and accessibility.
Importantly, ARC assistance is not needed to start health care services from scratch. However,
assistance is needed to provide the technical support to insure that health care providers in the
region are capable of applying for and successfully receiving competitive awards for remote
service delivery and facilities support. The abundance of funding for transportation innovations
in the new transportation bill may be used to fund mobile health clinics and other community
health access approaches.

6.2.4 Children and Young Adults are the Region’s Future. The conditions for children
in the region are difficult, and in some cases need urgent attention. Six policy areas are
critical. First, there is a need to assess how well the physical facilities for schools in the region

71


Appendix A. Beale Codes

Metro counties

wWNrO

Central counties of metro areas of 1 million population or more
Fringe counties of metro areas of 1 million population or more
Counties in metro areas of 250,000 to 1 million population
Counties in metro areas of fewer than 250,000 population

Nonmetro counties

OMIDNA

Urban population of 20,000 or more, adjacent to a metro area

Urban population of 20,000 or more, not adjacent to a metro area

Urban population of 2,500 to 19,999, adjacent to a metro area

Urban population of 2,500 to 19,999, not adjacent to a metro area

Completely rural or fewer than 2,500 urban population, adjacent to a metro area
Completely rural or fewer than 2,500 urban population, not adjacent to a metro
area

Source: Butler, M. A., and C. L. Beale. 1994. Rural-Urban Continuum Codes for Metro and

Nonmetro Counties, 1993. Beltsville, MD: Agriculture and Rural Economy Division,
Economic Research Service, U.S. Department of Agriculture. Staff report no. 9425
(September).

A-l


Acknowledgements

This report could not have been written without the help of many individuals. The distressed
counties analysis began two years ago when Amy K. Glasmeier was appointed the John D.
Whisman Appalachian Scholar. The project became the central focus of her two-year effort as
professional support staff to the Appalachian Regional Commission (ARC). Throughout the
project, countless members of the ARC staff contributed to the report’s development. Judith
Maher was the project’s first program officer. Judy provided countless hours of her time and
generously imparted a small amount of her vast knowledge of the Commission’s history and
development. Many additional staff members also assisted, including Jeffery Thompson,
Charles Howard, Robert Sokolowsky, Jack Russell, Robert Decker, Monroe Newman (former
staff member), Judy Rae, and Cary Morningstar. We also would like to acknowledge Gregory
Bischak, senior economist at the Commission. Greg came on during the execution of the
project. He added enormously to the rigor and precision of the work. He was the first to
suggest that we consider the development of an index to examine more precisely the economic
health of the region. Greg dutifully read every draft and organized a round of internal and
external reviewers to ensure the work was clear and carefully written. It was an ongoing
pleasure to work with him. Another person who added greatly to the cheerful and increasingly
effective analytical setting was Keith Witt, who is the purveyor of data and graphical
representation. Keith provided data and geographical analysis willingly, promptly, and kindly.
He provides the Commission, and all who work with it, excellent analytical support and
guidance. There were countless others, too numerous to mention. We owe these people
enormous thanks, and, as with all documents, all errors are our own.

This project benefitted mightily from resources and assistance from other individuals and
organizations. The project was the recipient of the 1998 Center for Undergraduate Studies and
Research Award for field course support, from the College of Earth and Mineral Sciences,
The Pennsylvania State University. Project assistants included Martha Traverse, Amanda
MacQuade, Tim Robinson, Willie Vancurra, and countless staff from the Department of
Geography’s computer mapping center. The course was co-taught by two remarkably able and
supportive colleagues, Drs. Cindy Brewer of the Department of Geography and Dr. Stephen
Mathews of the Population Research Institute. The undergraduate course was supported by a
parallel graduate research seminar that generated the Atlas of Appalachia. Students used the
Atlas to shape their projects during the semester. Students who worked hard and diligently
included John Bardugone, Steven Brown, Julie Del Muto, Elaine Dennehy, Kara Deutsch, Joe
Frieben, Matt Funk, David Grabel, Illene Harrington, Paul Mitchell, Wendy Mokes, Matt
Schell, Sarah Szalankeiwicz, Roy Tiley, Elliott Westermann, and Wendy Zeller. Students in
the graduate seminar included: Cory Eicher, Shawn Faith, Mark Harrower, Christy Jocoy,
Becky Reifenstahl, Risa Whiston, and Larry Wood.

viii


Standardized Predicted Value

Actual and Predicted Values

4.0 4.2 4.4 4.6 4.8 5.0 5.2

Log 1994 Index

J-2

5.4

5.6

5.8


Gaventa, J. 1980. Power and Powerlessness in Appalachia, Champaign-Urbana: University of
Illinois Press.

Glasmeier, A., and N. Glickman. 1989. The International Economy and the American South.
In L. Rodwin and H. Sazanami (Eds.), Deindustrialization and Regional Economic
Transformation: The Experiences of the United States. Boston: Unwin and Hyman, pp.
60-89.

Glasmeier, A., and R. M. Leichenko. 1996. From Free Market Rhetoric to Free Market
Reality: The Rise of the New South. International Journal of Urban and Regional
Research 20(4).

Hansen, N. 1972. Growth Centers in Regional Development. New York: The Free Press.

Isserman, A. 1996a. Socioeconomic Review of Appalachia: The Evolving Appalachian
Regional Economy. Paper prepared for the ARC in partial fulfillment of ARC Contract
No. 95-13.

Isserman, A. 1996b. Socioeconomic Review of Appalachia: Appalachia Then and Now. An
Update of ‘The Realities of Deprivation’ Reported to the President in 1964. Paper
prepared for the Appalachian Regional Commission in partial fulfillment of ARC
Contract No. 95-13.

Kusmin, L., J. Redman, and D. Sears. 1996. Factors Associated with Rural Economic Growth:
Lessons from the 1980s. Economic Research Service, U.S. Department of Agriculture.
Washington, DC.

Markusen, A., P. Hall, S. Campbell, and S. Detrick. 1994. The Rise of the Gunbelt. New
York: Basic Books.

Nord, M. 1998. Overcoming Persistent Poverty and Sinking Into It. Paper presented at the
Annual Meetings of the Southern Rural Sociological Association. Little Rock, AR.

Norusis, M. J. 1997. SPSS Professional Statistics 7.5. Chicago: SPSS, Inc.

Pickard, J. 1970. Socioeconomic Indicators-1960, Appalachian Data for Counties. ARC
computer printouts.

Pickard, J. Undated. A Comparison of Conditions of Distressed Counties 1959-1960 and
1980-1986. Appalachian Regional Commission, Washington, DC.

Pickard, J. 1960. Data files, Appalachian Regional Commission, Washington, DC.

75


Statistical tests between groups
Step II: Crosstabulations of county characteristic and change in index (expected

values in parentheses).

Adjacency to the Edge of the Region

NEGCHG | POSCHG TOTAL
Not Adjacent 44 (38.7) 28 (33.3) 72
Adjacent 7 (12.3) 16 (10.7) 23
TOTAL 51 44 95

chi-square p = 0.010

We then compared change in economic health score among counties with county
characteristics that have been identified as important predictors of distressed status. The second
table indicates that counties experiencing a negative change in health were more likely than
predicted not to be adjacent to urban areas. The reverse was true for counties that improved
their status over the study period.

A third evaluation of characteristics distinguishing among major changes, positive and
negative, was metro status. Table 4 suggests that nonmetro counties were more likely than
expected to be associated with negative change. Fewer nonmetro counties than expected also
showed positive change. These results suggest there is a significant negative effect of being a

nonmetro county.

12


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Stokes NC 93.25 Chenango NY 90.9
Whitfield GA 91.96 Butler PA 90.7
Alexander NC 91.79 Cortland NY 88.6
Tompkins NY 90.81 Beaver PA 86.7
Greenville sc 90.11 Erie PA 86.4
Davie NC 88.72 Tioga NY 86.2
Shelby AL 88.24 Allegheny PA 85.1
Douglas GA 86.59 Chemung NY 83.8
Botetourt VA 83.46 Mercer PA 83.2
Cherokee GA 79.94 Hancock wv 81.6
Forsyth NC 79.84 Cameron PA 73.5
Forsyth GA 76.69 Broome NY 71.3
Gwinnett GA 67.92 Tompkins NY 65.8


To assess the performance of counties with different economic emphases, we used the
economic county typology devised by the Economic Research Service (ERS) to distinguish
among ARC counties, and plotted (unweighted) county performance from 1980-1995 (see
Figure 10. County Performance by ERS Type). We chose the unweighted index in order to
preserve the differences among counties regardless of their population. This ensures that
smaller counties have equal representation in the analysis. We also wish to emphasize or at
least treat equally smaller counties to preserve the ability to compare these results with
Pickard’s earlier work on distressed counties in which he pointed out that the counties of
greatest distress and those that worsened over the study period grew slowly and were very
small in size at both the beginning and end of the period (Pickard, undated).

The typology distinguishes between metro and nonmetro counties with various
economic foci, including manufacturing, mining, farming, services, government, and “non-
specialized.” Metro counties have clearly fared better than rural counties over the time period
of interest. Counties with a reliance on mining and the government sector have fared very
poorly, with mean county indices > 180 for most of the 1980s and all of the 1990s. Over the
remaining ERS types, manufacturing counties have performed the best over the time period,
with mean scores at or near 140 for most years. Other county specializations are highly
clustered in terms of index scores throughout the time period, with mean scores generally
between 140 and 160. In summary, a strong positive correlation appears to exist between
metro status and county performance. In addition, a manufacturing concentration also appears

to have a positive relationship with economic health. Conversely, reliance on extractive

54


calculated between index scores for 1960 and the average of our index score for 1980-1995.
As seen in Figure 8, the first result to point out is that the rank correlation between the two
indexes is surprisingly high. The correlations between the 1980-1995 index and Pickard’s
1960 index of economic health data also are quite high. Between 1960 and 1995, the range of
coefficients between the current index and the 1960 data is 0.517-0.606. This is quite
interesting given that it has been 35 years since the data used by Pickard were compiled by the
census, and also because his index measured different county-level statistics than does the
current measure. This suggests that the underlying economic conditions tapped by Pickard’s
index and our own are surprisingly similar. To the extent that there are differences, some of
the improvements that have already occurred in the region, such as improvements in counties
of the south and along the edge of the region, could help to distinguish between the two points
in time. As Pickard’s own analysis makes plain, while changes did occur in the region over the
1959-1960 and 1980-1986 period, the poverty axis taking into account West Virginia, the coal
fields of Kentucky and Tennessee, and portions of the Mississippi Delta, remained largely
intact and otherwise unchanged.

To assess the performance of counties over the 1980-1995 time period in relation to
Pickard’s index, we divided counties into four equal size groups, ranging from the lowest to
the highest based upon their distressed index score as calculated in 1960. We then plotted the
unweighted mean index score for each of the four groups throughout the study period. The
results are shown in Figure 9 (County Performance, 1980-1995: Pickard Quartiles). The two
“best” groups of counties remain relatively clustered through time— with mean scores mainly
falling in the 120-130 range—and are, at times, virtually indistinguishable from one another.

50


distressed counties for each state” (Decker, 1994, p. 2). Each factor is weighted equally in the
formula. The distribution formula is voted on annually by all Commission members.

1.3.1 Eligible Counties. The number of counties designated distressed and the amount
of annual funding for the set-aside have varied through time. Of the original 397 counties, 67
were given the original designation. Although the original cutoff procedure was loosely based
upon a distribution of all counties into four groups of equal frequency (quartiles), the existing
cutoff procedure set thresholds for the distress criteria and then the number of counties in each
category was free to change with economic conditions.

Although the governors initially sought approximately $15 million annually for the
program, the funding level has varied through time and makes up approximately 20 percent of
the total funds available for Area Development. Given that the ARC budget has fluctuated over
the life of the special designation, approximately $236 million have been spent in those
counties exhibiting high levels of economic hardship. The vast bulk of these funds have been
spent on water projects (85%; ARC, 1998). Since the set-aside was established, a number of
modifications have been made in the original formulation. In 1994, 115 counties qualified. In
1998, 106 counties were designated as distressed. The enlarged list of counties was due to an
increase in the flexibility of qualifying criteria, whereby a county that met two of three criteria
could be eligible for special designation, provided it had at least twice the national poverty
rate. This adjustment was made when it became evident that counties with high levels of
discouraged workers experienced lower unemployment rates.

The poverty rate looms large as a major weight in the determination decision, although

it is collected only once every ten years as part of the decennial census. The lack of timely data

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combined index. Marginal counties—in the middle two quartiles of mean index scores—are
spread relatively evenly throughout the region.°

The improved economic health of the counties along the edge of the region and those in
the southern subregion are the result of two separate and in some cases related trends. The first
and overarching trend is the industrialization of the U.S. South in the second half of the 20"
century. Public-sector investments in infrastructure, including roads (interstate highway
program), power provision (the TVA), and military base training facilities, served to fuel
development of the South (Glasmeier and Leichenko, 1996; Glickman and Glasmeier, 1989;
Markusen, Hall, Campbell, and Detrick, 1994). One need only consider the meteoric rise of
the skylines of cities such as Charlotte, NC, along Interstate Highway 85 (which have sprung
up in the last 20 years), to gauge the tremendous transformation of the South.

Improvements along the edge of the ARC region are more properly the result of the
luck of location. Along both the western and eastern edges of the region major urban centers
such as Washington, DC, Roanoke, VA, Greensville/Spartansburg, NC, and Cincinnati, OH,
have grown prosperous over the post-war period. In some sense, the probability of this growth
was preordained, in part because the edge locations also were those that received funds under
the growth centers program (ARC Staff Evaluation, 1965-1968, 1968; Burlage, 1972). In the
original program conception—that by concentrating investments, development would occur—

those places most connected to the rest of the national economy and with larger populations

°A quartile is calculated by dividing a data distribution into four equal size groups.
Quartiles are commonly referred to by their order in the distribution (i.e., top quartile), bottom
quartile.

45


References

Appalachian Regional Commission. 1995. A Proud Past, A New Vision: Profile of a Program.
Washington, DC: Author.

Appalachian Regional Commission. 1995. Internal Memorandum: Distressed Counties Policy
Overview. Washington, DC: Author.

Appalachian Regional Commission. 1992. Synopsis: Distressed County Designations.
Unpublished report, August 27.

Appalachian Regional Commission. 1982. Transcript from the May 4" Commission meeting.
ACE Federal Reports, Inc.

Appalachian Regional Commission. 1981. A Report to Congress: From the Appalachian
Governors Concerning the Appalachian Regional Commission (December).
Washington, DC: Author.

Appalachian Regional Commission 1968. Staff Evaluation, Appalachian Regional Development
Program, 1965-1968. Mimeo. Washington, DC.

Bischak, G. 1998. Personal correspondence with the author.

Burlage, R. 1971. The People’s ARC. In D. Walls and J. Stephanson (Eds.), Appalachia in the
Sixties. Lexington, KY: University of Kentucky Press.

Butler, M., and C. Beale. 1993. Rural-Urban Continuum Codes for Metro and Nonmetro
Counties, 1993. Washington, DC: U.S. Department of Agriculture.

Couto, R. A. 1994. The Future of the Welfare State: The Case of Appalachia. In P.
Obermiller and W. Philiber (Eds.), Appalachia in an International Context: Cross-
national Comparisons of Developing Regions. Westport, CT: Praeger, pp. 1-28.

Decker, R. 1994. Internal Memorandum: Distressed Counties. Washington, DC: Appalachian
Regional Commission.

Fullenbaum, R., and M. McNeill. 1995. Survey and Comparison of Distressed Area

Definitions. Report submitted to the Appalachian Regional Commission in partial
fulfillment of Contract No. ARC 95-19.

74


Emphasis on water and sewer provision made sense in the program’s early years. As
the ARC noted in its spring 1984 issue of Appalachia, the agency’s recurring magazine,
“Previously, due to limited economic growth potential, these counties had been unable to
qualify for most ARC or other kinds of development funding.” Thus, basic infrastructure
remained a critical and unaddressed need of these communities. A broader mandate, though
discussed among the ARC staff, was difficult to justify given the very basic needs of the
poorest parts of the region.

With the acceptance of the 1981 report, the Finish-up Program planned around a five-
year cycle gave the Commission time, but more importantly it established the cycle of program
development for a series of new initiatives, one of which was to provide special assistance to
areas deemed of greatest need. The set-aside’s mandate coincided with a drastic decline in
overall program funds. Any hope of coupling substantial development funding with water and
sewer funding was never realized. Funds were reduced from a high of approximately $135
million in 1976 for Area Development activities to $55 million in 1994. To gauge the
significance of this decline, in the early 1970s, prior to the removal of categorical investment
requirements in such realms as education and training, health and child development alone
received $70 million per year, a large sum even by today’s standards. An annual set-aside
assured that a share of the agency’s funds (approximately 20%of the Area Development funds
regardless of the number of counties qualifying) would be targeted towards counties
experiencing unusual economic hardship.

Unfortunately, implementation of the program did not emphasize the complex and
multi-faceted needs of residents of counties with the most severe problems, nor were the

7


a

P86T-O86T SNSAIA S6GI-1661 XOPUT ULITA] 2% OT < aulpsoq 10 yUOWIDAOAdUTT [aAa]-AQuNOD */ aANSIy


Table of Contents

(cont.)

Page

4.2 Examining an Alternative Measure of Economic Health of ARC Counties......... 38

4.3 SUMMATY 00... eee eee eee eee EEE EEE EEE EE eet Here eee aaa eee eet eees 56

Part V: Predicting Economic Distress: A Regression Analysis of Distressed Counties ........ 56
5.0 A Statistical Model Estimating County-level Economic Health ...................005 57

5.1 Model Results ..........cccccceeccsseecceeeeeeeeeeeeeeeee eee teeeeeee nessa eeeeaeeeeeeeneeeen eee 60

5.2 Interpretation of the Regression Results .............::00::::ssceeeeeseetseeeeeeeeeeneeeees 62

Part VI: Summary and Policy Reflections ..............::ssscceeeeeeseeeeeeeeeeeeeeeeeeseeeeeeeneneees 66
6.0 Using an Annualized Index ............. 00s cceeeeceseetteeettetteeeeeeeeeeeeeneeeeeeeeeeeeees 66

6.1 Revisit the Original ARC Program Design .............:00ccceeeeeseeeeeesseaeeeeeene nee 68

6.2 Using an Assets Approach to Development...

References 00... ceccecece cece e cece eee ee eee eee e ene EEE EEE EEE TEESE EEE EEE EE EEE EERE EEE EE EEE EEG THEE EEE 74
Appendices
Appendix A. Beale Codes ............cccccceeeeeeeee ee eeneeeeee nen eeeeeeaeeeeeeeeaneeeeee anes A-1

Appendix B. Comparison of Rank-ordered Indicies ..

Appendix C. Alternative Index Specification .............::cceseeeeeseeeeeeeetteeeeeeeeees C-l
Appendix D. Evaluation of the Combined Score Index ............:::::::eseeeeeeeeeeeeeee D-1
Appendix E. Index Volatility ............ccceeee cece cece eect eee e eee eeeeeeeeeeeeeeeeneeee E-1


successful counties highlights the importance of the early years of the ARC program where
expenditures for education and community welfare enjoyed equal footing with infrastructure.
Part VI: Summary and Policy Reflections

The findings of this report can be stated simply: while some counties have made very
good progress toward economic health, the majority of distressed counties continue to be held
back by inadequate social and human infrastructures. Adequate infrastructures are needed to
move toward a new future. The fates of those counties that have made it out of the most
difficult conditions have been tied to a significant degree to early development trajectories,
fortuitous locations, and access to funds for economic development and poverty alleviation
much in excess of existing resources available today. There is little question that ARC, and
federal programs more broadly, have certainly contributed to the change that has occurred in
these counties. Nonetheless, other factors outside the control of the local area also have had an
important influence on their success.

6.0 Using an Annualized Index

The development of an alternative index to identify, characterize, and track distressed
counties provides a new window onto a serious problem. Building on the concept of economic
health helps to focus policy concerns on the underlying challenges facing distressed counties
and should help better direct the pursuit of solutions tailored more precisely to systemic
problems. The development of an annual measure of economic health allows for much greater
precision in sorting between short- and long-term problems. An annual index helps distinguish
between cyclical changes (i.e., downturns in the business cycle) and structural changes (i.e., a

shift from a goods-producing to service-producing economy) which should assist in

66


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this development are quite significant. Any attempt at job attraction requires a community to
have an adequate labor supply. Thus, changing demographics will place a premium on
employer retention efforts through job training and skills upgrading as well as export
promotion. More problematic and costly is the fact that an aging population is going to
generate more demand for medical and social services, which will only further strain the local
health care financing system. A growing elderly population will be least able to financially
support the need for more social services and therefore is going to further burden the region’s
tax base.

The economy of distressed counties illustrates additional distinct attributes not usually
associated with conditions of growth and development. These results suggest that, compara-
tively speaking, the sources of income relied upon by residents are not dynamic, but in fact
reflect dependency. Distressed counties have not benefitted equally from the growth of manu-
facturing in the region over the last 30 years. Distressed counties lack manufacturing jobs, a
quality seen as important in the success of other counties that have left the distressed category,
as well as those rural counties that have more generally experienced significant progress over
the last 18 years (Economic Research Service, 1996). The strong association between
distressed status and income from the government sector has been interpreted by others as
signifying the absence of other sources of employment (Economic Research Service, 1994). In
many of the ARC distressed counties, government has become the employer of last resort.

The organizational structure of distressed county economies also presents significant
challenges. The positive association between distressed counties and the share of small

establishments with 10 or fewer employees signifies at least two trends. First, some have

64


have shown distinct spatial patterns in economic distress throughout the ARC region, and
traced the performance of various groups of ARC counties through time. In this section, we
devise a model to predict county index scores using a variety of relevant socioeconomic and
geographic data.

5.0 A Statistical Model Estimating County-level Economic Health

Based on the index we have developed, we are now in a position to examine the index
in relation to different socioeconomic variables which are important to the performance of
regional economies and that simultaneously describe characteristics of Appalachian counties. In
earlier analyses reported in Appendix H, we examined the one-to-one relationship between
characteristics of Appalachian counties and our index. In the discussion in this section, we
examine the contribution or influence of several variables simultaneously. This type of
investigation allows us to determine the relative importance of multiple social, geographic, and
economic characteristics on the economic health of Appalachian counties. This type of analysis
is quite common, particularly as a basis for policy discussions. For example, ERS developed
the same type of model to explore factors associated with the growth of rural areas in the
1980s. Multivariate regression analysis provides us with one means of summarizing the
importance of characteristics of places in relation to their relative economic performance.

With the spatial and temporal patterns of distress identified, we developed a regression

model to predict 1994 index levels.° In the current analysis, since the dependent variable is

° Appendix I presents findings from an earlier logistic regression analysis based on
ARC distressed categories which were combined into a binary analysis. The results of this
analysis are quite similar to those reported here. The year 1994 was chosen because a variety
of county-level variables had already been collected in conjunction with the logistic regression

57


1.4 Summary

In summary, the use of special designations to identify areas of greatest need and the
selection of criteria to capture differences across locations is a common, longstanding practice
of development agencies. ARC’s Distressed Counties Program can be seen as part of a larger
movement in economic development to identify and track conditions in areas with persistent
problems with the intent of providing special assistance. Over the last 18 years, the number of
counties designated distressed has changed in response to national as well as local economic
conditions.

Before turning to a region wide assessment of the distressed counties, a review of a
sample of the 21 counties that have left the designation since 1988 is in order. From this set of
successful experiences come insights that can inform a more comprehensive future Distressed
Counties Program.

Part II: Case Studies of Formerly Distressed Counties
2.0 Introduction

We begin the analytical portion of this report by examining cases of success among
formerly distressed counties. The designation of distress was designed to situate the more
economically challenged counties within the context of larger national trends. In so doing we
are able to examine through time the conditions of counties that share common characteristics.
A second level of analysis can identify which, if any, counties left the distressed county group
and to the extent possible help identify underlying factors that lay behind their improvement.
Admittedly, this type of analysis can tell us something about changing conditions in a small
subset of counties at a single point in time, but it cannot explain earlier changes that might

14


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Table 1. Case Studies for 14 Counties
(cont.)

Marion, Tennessee

Marion, Tennessee, which is proximate to Chattanooga, began as a strong agricultural region with
flat fertile land. The county experienced population growth throughout the post-war era, is served by
several highways and an interstate, and has railroad access. The county has been a manufacturing
center since the turn of the century, and has substantial infrastructure, including a technical
community college. Net income due to commuting has increased over time.

Montgomery, Kentucky

Montgomery, Kentucky, which is proximate to Lexington, began as a strong agricultural region with
flat land, has experienced population growth throughout the post-war period, and is serviced by
several highways and an interstate as well as having railroad access. The county has embraced
manufacturing jobs since the 1950s. Most of the housing stock in the 1950s was of sound construc-
tion. Net income from commuting has increased through time.

Monroe County, Tennessee

Monroe County, Tennessee, is located halfway between Knoxville and Chattanooga, on the southeast
border of the state. The county is served by an interstate and several major state highways.
Agriculture and manufacturing were equally important in the 1950s. As agriculture declined in the
1950s, manufacturing continued to grow. Commuting-related income has been a major component of
local income since at least the 1970s.

Pendelton County, West Virginia

Pendelton County, West Virginia, has emerged from years of economic stagnation to become a
region of West Virginia where poverty, unemployment rates, and income levels are converging on
state averages. The experience of county residents is quite different today than in 1960, when almost
half of the population lived in poverty and per-capita income was less than half the national average.
Today, thanks to a growing tourist economy, modest increases in manufacturing employment, and an
expanding service sector, unemployment rates are as low as 4.6 percent. Per-capita income figures
are surprisingly robust ($16,839), equaling 92 percent of the state average. The county’s educational
attainment levels continue to climb, with student test scores above the national average. Most
remarkable, poverty rates have fallen to 17 percent, almost three percentage points below the state
average.

17



Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Berkeley wv 122.01 Clearfield PA 148.2
Union PA 121.89 Clark KY 148.0
Montour PA 121.52 Ross OH 146.2
Washington OH 121.39 Anderson sc 145.6
Susquehanna PA 121.28 Mineral wv 144.7
St. Clair AL 121.07 Forest PA 144.5
Westmoreland PA 120.96 Juniata PA 144.1
Steuben NY 120.93 Indiana PA 143.4
Cortland NY 120.78 Wetzel wv 143.3
Ohio wv 120.63 Sullivan PA 142.1
Putnam wv 120.15 Holmes OH 141.9
Putnam TN 119.67 Coffee TMN 141.8
Haywood NC 119.22 Lauderdale AL 141.1
Wyoming PA 119.12 Calhoun AL 141.0
Chenango NY 118.82 Morgan AL 140.8
Lowndes MS 118.76 Floyd GA 140.7
McDowell NC 118.51 Schuylkill PA 140.4
Bland VA 118.03 Walker GA 139.5
Smith IN 117.98 Blount TN 138.4
Monongalia wv 117.64 Snyder PA 138.3
Kanawha wv 117.47 Monongalia wv 137.7
Grayson VA 117.30 Montour PA 137.4
Giles VA 117.25 Pickens sc 136.6
Pulaski VA 116.23 Colbert AL 136.3
Carroll VA 116.23 Wayne PA 135.4
Tuscaloosa AL 116.22 Armstrong PA 134.8
Morgan AL 116.09 Belmont OH 134.5,
Loudon TN 116.00 Columbiana OH 133.0
Otsego NY 115.80 Potter PA 132.9
Warren PA 115.72 Union PA 132.4
Erie PA 115.43 Cambria PA 132.3
Pickens GA 114.95 Carbon PA 131.5
Rutherford. NC 114.89 Schoharie NY 131.4
Elk PA 114.11 Haywood NC 131.2
Washington MD 113.72 Harrison OH 130.6
Madison GA 113.54 Jefferson PA 130.3
Madison KY 113.53 Washington MD 126.9
Butler PA 113.17 Luzerne PA 125.9
Limestone AL 113.16 Jefferson AL 125.3
Oconee sc 112.58 Henderson NC 125.0
Wythe VA 111.88 Wyoming PA 124.6
Jefferson wv 111.75 Harrison wv 124.4
Floyd GA 111.40 Buncombe NC 124.3
Coffee TN 111.16 Clarion PA 124.2
Bartow GA 110.79 Susquehanna PA 123.5
Cherokee sc 110.57 Perry PA 123.0
Barrow GA 110.34 Northumberland PA 122.7
Blount TN 110.00 Sullivan TN 122.7

B-7


level data available, and the continuous nature of the scale allows differences between counties
to be viewed more easily.

Overall, this revised measure appears to reflect the concerns of the current
methodology well, and has high overall reliability (see Appendix D for a review of reliability
measures associated with the index; see Appendix E for a discussion of the relative volatility of
the index components). Our measure also allows us to compare its powers of classification
with the original ARC county groups. There is a high degree of overlap between counties
that score high on distressed measures using the ARC definition versus our index of economic
health (see Appendix F). Thus, we present the index in a suggestive way as an alternative to
current practice.

In the following pages, using the index, based on income, unemployment, share of
transfer payments to total income, and labor force participation rates, we analyze the spatial
and temporal patterns of distressed status in the Appalachian region over the period 1980-
1995, or roughly, the period of time the Distressed Counties Program has been in existence at
the Commission. Finally, we present an econometric analysis of ARC county-level distress,
using socioeconomic and geographic variables to predict distress index scores.

4.2 Examining the Alternative Measure of Economic Health of ARC Counties

The combined scale index is utilized to examine economic trends in ARC counties over
the past 15 years and to further illuminate temporal and spatial patterns of economic health.
Results indicate several important trends. First, there has been little discernable change in
aggregate regional health since the inception of the Distressed Counties Program in the early
1980s. A large number of the original ARC designated counties still exhibit low levels of

38


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Ritchie wy 185.10 Dickenson VA 247.8
Cumberland KY 183.34 Carter KY 247.3
Pocahontas wy 183.04 Summers wy 245.6
Gilmer wv 182.34 Johnson TN 243.2
Scioto OH 181.48 Buchanan VA 243.1
Grundy TN 180.79 Clay NC 243.0
Cocke 1N 180.12 Benton MS 242.8
Rockcastle KY 178.06 Mingo wv 242.7
Morgan TN 178.05 Cumberland TN 242.2
Mason wy 176.87 Cumberland KY 241.2
Pickens AL 176.49 Lewis KY 240.7
Floyd KY 175.97 Russell KY 240.6
Casey KY 175.48 Laurel KY 237.7
Tucker wy 175,05 Grainger TN 237.7
Benton MS 174.48 Macon IN 237.3
Pike OH 174.16 Union TN 237.1
Russell KY 173.60 Noxubee Ms 236.0
Randolph wv 172.67 Marshall MS 235.7
Lewis wy 171.52 Cherokee NC 235.3
Powell KY 171.13 Clay IN 235.2
Overton TN 170.96 Graham NC 234.6
Harrison OH 169.36 Swain NC 234.0
Monroe wy 169.28 Gilmer GA 233.6
Winston MS 168.75 Choctaw MS 233.5
Vinton OH 168.59 McDowell wy 233.0
Bath KY 168.50 Bibb AL 233.0
Pickett TN 168.36 Wise VA 231.6
Greenbrier wv 168.00 Bath KY 231.3
Van Buren TN 167,39 Barbour wy 231.0
Jackson TMN 167.16 Monroe KY 230.6
Greene PA 167.12 Adair KY 229.7
Fayette PA 167.05 Roane wy 229.3
Meigs OH 166.94 Dawson GA 228.5
Tishomingo MSs 166.80 Lawrence AL 228.0
Forest PA 166.48 White TN 227.7
Lee VA 166.46 Cocke TN 227.6
Raleigh wv 166.39 Lincoln KY 227.3
Marshall MS 165.76 Pendleton wv 226.6
Lincoln KY 165.53 Scott VA 225.9
Boone wy 165.40 Avery NC 224.6
Cherokee NC 164.69 Yancey NC 224.3
Perry ort 164.42 Ashe NC 224.2
Choctaw MS 163.90 Wirt wy 224.1
Doddridge wv 163.73 Cleburne AL 220.6
Jackson OH 162.44 Fayette wy 220.5
Perry KY 162.29 Heard GA 220.3
Dickenson VA 161.70 Monroe 1N 220.3
Preston wy 161.61 Madison NC 219.1

B-2


industries or the government sector has a negative relationship with county-level economic
health.

4.3 Summary

The information provided in this section is extensive. We have examined the ARC’s
index and noted its limitations, particularly those related to its categorical nature and its
dependence on the decennial census poverty rate measure. We have offered an alternative
measure of economic health that takes into account factors such as unemployment, per-capita
market income, labor force participation, and share of income from transfer payments. This
alternative index preserves the broad patterns of the ARC’s index while allowing for annual
inter-county comparisons over time. The pattern of distress is by now a familiar one: distressed
counties form a core that has experienced little change over the last 20 years. Counties in West
Virginia, Kentucky, Tennessee, and the Delta area of Mississippi have remained stagnant and
largely unchanged over the last 20 years. The most improved counties are located along the
edges and in the southern portion of the region. Comparisons of earlier ARC experimentation
with indexes and the resulting analysis of distressed counties over the 1960-1986 period with
our own analysis demonstrate considerable overlap. These results also suggest that the
conditions of many of the most economically distressed counties indicate little change over the
1960-1995 period. In the next section we explore socioeconomic and geographic
characteristics that seem to describe severely distressed counties.
Part V: Predicting Economic Distress: A Regression Analysis of Distressed Counties

In the previous sections we described the derivation of a continuous measure of county-
level distress that can be calculated from readily available data on an annual basis. Further, we

56


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Jackson wv 161.19 Fleming KY 218.8
Upshur wy 161.05 Kemper MS 217.8
Johnson KY 160.69 Webster MS 217.6
Bibb AL 159.84 Russell VA 217.2
Cambria PA 159.62 Marion. AL 216.6
Pleasants wv 159.47 De Kalb TN 215.5
Lawrence OH 158.77 Pontotoc MS 215.1
Guernsey OH 158.75 Union GA 215.0
Marion wv 158.19 Pulaski KY 214.6
Clay NC 156.90 Grant wv 214.3
Gallia OH 156.89 Fayette AL 214.0
Huntingdon PA 156.63 Itawamba MS 213.7
Mercer wv 155.15 Carroll VA 213.7
Grainger TMN 155.11 Rhea TN 213.5
Wayne WV 154.85 Winston AL 213.4
Hampshire wv 154.74 Prentiss MS 213.1
Polk IN 154.65 Sequatchie IN 213.1
Wetzel wv 154.62 Nicholas wv 212.6
Winston AL 154.35 Pickens AL 212.4
Fannin GA 154.12 Tishomingo MS 212.2
Claiborne TN 154.03 Jackson AL 212.2
Monroe OH 153.63 Lamar AL 211.8
Martin KY 153.44 Vinton OH 211.2
Cumberland TN 153.23 Rowan KY 211.0
Monroe TN 153.18 Logan wv 210.8
Monroe KY 152.89 Macon NC 210.5
Talladega AL 151.90 Boone wv 210.4
Clinton PA 151.83 Tazewell VA 210.4
Rowan KY 151.75 Banks GA 210.2
Marion AL 151.75 Morgan wv 210.0
Noble OH 151.65 Hardy wv 209.9
Calhoun MS. 151.54 Floyd VA 209.9
Hocking OH 151.29 Franklin AL 209.8
Webster MS 151.27 Mitchell NC 209.7
Towns GA 150.65 Highland VA 209.6
Pike KY 150.21 Polk TN 209.3
Bedford PA 149.67 Walker AL 209.2
Randolph AL 149.54 Hampshire wy 208.9
Tyler wv 149.43 Union MS 208.8
Venango PA 149.32 Winston MS 208.7
Meigs TN 149.25 Marion TMN 208.7
Pendleton WV 148.62 Madison GA 208.0
Bledsoe TN 148.60 Clay AL 208.0
Adair KY 148.52 Doddridge wv 207.3
Allegany MD 148.41 Adams OH 207.1
Tippah MS 148.35 Chilton AL 207.0
Athens OH 147.96 Franklin GA 205.8
Belmont OH 147.30 Towns GA 205.3


Public policy, both state and national, has been important in assisting these counties to
achieve a status close to national averages on many indicators of economic health. Most have
benefitted from the development of the interstate and Appalachian highway systems, the shift
of manufacturing southward over the post-war period, the existence of state economic
development and training programs, the construction of educational facilities, the growth of
tourism and destination developments for retired persons, and the provision of additional
infrastructure in the form of water and sewers. In all, the rise of these counties to near equal
status with national norms is a testament to successful national economic development policy
and plans.

The story of the lucky 21 must be tempered, however, with the knowledge that two
counties fell back into distress. In important ways, Doddridge and Pike Counties represent the
status quo experience of distressed counties in Appalachia. Isolation, low levels of education, a
retreat of natural resource-based industries, and a lack of social infrastructure reflect basic
problems facing the distressed county group as a whole. As this report is being written,
Chickasaw County, like Doddridge and Pike, is in danger of falling back to severely distressed
status because several manufacturing firms have closed down or laid off workers. This
experience signals the fragile nature of economic success found in many Appalachian counties.
The recent crisis in Asia has only added to the problems of these counties. Job losses reported
in once booming areas of the South such as Charlotte, NC, must be considered harbingers of

what is yet to come to the ARC region (New York Times, November 10, 1998).

25


Building on Past Experience: Creating a
New Future for Distressed Counties

Amy K. Glasmeier and Kurtis G. Fuellhart
Introduction

This report presents a detailed examination of Appalachian counties that have been
designated as distressed by the Appalachian Regional Commission (ARC). The ARC
designation of distressed is applied to counties that meet critical thresholds of high poverty and
unemployment rates, and low levels of per-capita market income (personal income minus
transfer payments). The Distressed Counties Program created in 1981 as part of the ARC’s
Finish-up Program has been operational since 1982. One of its main purposes is to authorize
funds for a special set-aside to bring water and sewer services to the least advantaged counties
in the ARC region. Since 1988, of the 90 counties designated distressed in 1988, 19 have left
the distressed counties category.

This report on distressed Appalachian counties is divided into five parts. The first part
describes both ARC and broader federal agency attempts to identify areas of highest economic
and social need. The second part examines case studies of the 19 of the 21 counties that left
and stayed out of distressed status over the 1988-1995 period. In the third part, we present a
detailed examination of the entire set of ARC counties based on a descriptive analysis of
statistical data from the last 20 years. We contrast the experiences of distressed and non-
distressed counties. In the fourth part of the report we examine an alternative measure of
economic health that could provide more accuracy in portraying and tracking distressed county

progress over time. Because of the limits of the existing ARC distressed index, tied as it is to


average county-level index and the index weighted by county population were calculated and
plotted by year. The results are shown in Figure 4 (Mean Regional Index, 1980-1995). The
unweighted index is the simple average index score across the 399 ARC counties—in other
words, all counties are treated equally (small and larger counties are treated the same). In
contrast, in the calculation of the weighted index, county scores are weighted by yearly Bureau
of Economic Analysis (BEA) population figures—thus, counties with larger populations are
counted more heavily in the calculation of the region’s overall score. Comparison of these two
different groupings demonstrates the significant differences in the experiences of large and
small counties.

The most immediate result observable in the figure is the significant yearly difference
between the two lines; in particular, the unweighted index is consistently higher (indicating
lower levels of economic health) than the weighted indicator. This difference suggests that
large places (measured by population) have generally Jower index scores and thus experience
better economic health compared with smaller places. In other words, they are better off. This
trend is consistent throughout the period, and in fact seems to have intensified through time.

The overall cyclical trends in both lines are fairly similar. After a rapid decline in
overall economic health into the mid-1980s, a slow improvement occurred into the early 1990s.
However, the unweighted index shows a slower and less pronounced recovery than the
weighted index, again suggesting a difference in kind between large and small places. Further,
when all counties are weighted equally, the recovery never reaches 1980 levels—thus,
measured in this way, smaller counties appear little better off and in some sense could be
considered worse off than they were in 1980. When the index is weighted for population the

40


The origins of student involvement in the distressed counties analysis actually dates back to the
previous year, when Dr. Glasmeier taught the advanced undergraduate analysis and writing
course in the spring of 1997. Fifteen students courageously embarked upon a field course
which included field travel, and pioneered the course design employed in subsequent
experiences with undergraduate research assistants. Their efforts paved the way for future
courses. These students included Joel Baker, Joshua Dziubek, Bob Furmanek, MaryLee
Glassberg, Kevin Grambley, David Haveranek, Jay Hershey, Rebecca Jacobstein, Jennifer
Langer, Ben Mutzabaugh, Mitchell Richter, Eric Storms, Jason Ward, Krista Weider, and
Eric Westendorf.

Because this report rests so heavily on fieldwork, by implication there are numerous people
throughout the region to whom we owe a debt of thanks. The folks in Big Ugly, West
Virginia, hosted us in their wonderful community center. The doctors in Charleston, West
Virginia, helped us begin to understand the problems of children’s health in the region. The
citizens of Lincoln County helped us see what community pride means to a place that has had
a remarkable and heroic history, of which they are proud.

The state representatives to the ARC kindly listened to our presentation quite by accident when
we traveled to Washington, DC, to present our findings to the Commission’s staff. Thanks to
Jesse White, Tom Hunter, and John Cartwright, for their help and support of this project.
Also, thanks are due to several reviewers who read the original report and made helpful
suggestions for its improvement.

As with everything we do, we are indebted to the staff of the Institute for Policy Research and
Evaluation, The Pennsylvania State University, including Michelle Aungst, Lee Carpenter,
Patti Doroschenko, Mary Jane Johnson, Greta O’Toole, and May Zha. Our work is their work
and our work would never get done if it weren’t for the superb persistence and effort they give
to each and every project that goes through the Institute. Thank you.

ix


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34


on poverty is a double-edged sword. On the one hand, counties that should have cycled out of
the designated distressed category status may have retained access to more generous funding
levels simply because of the lag in the poverty variable. On the other hand, counties that
experienced significant economic decline on an intercensal basis might have had difficulty
securing funds in a timely fashion given the original criteria.

An ARC analysis to determine the extent of the distressed designation compared ARC
distressed counties with all counties nationally. The measure proved to be quite conservative
when applied to the nation as a whole. Only 525 of a total 3,140 counties would qualify under
the ARC designation. Comparable counties included those in the Mississippi Delta, the Rio
Grande border counties of Texas, the Mesabi range in Minnesota, as well as scattered areas
associated with American Indian reservations. The majority of these so-called distressed
counties are nonmetropolitan, with slower population and income growth rates than are found
for the rest of the country. About one-quarter of the counties were highly dependent on
manufacturing, while another and much smaller number were either dependent on mining or
dominated by federal land ownership. Recent research by Mark Nord of the Economic
Research Service, U.S. Department of Agriculture, re-examined the larger question of
nationwide county distress and found a general decline in the number of counties in high-level
distress over the 1960-1980 period (Nord, 1998). Based on adjusted income (income minus
transfer payments), our results, emphasizing the 1980s and 1990s, indicate that conditions in
ARC distressed counties diverged strongly from national trends. Income levels in distressed

counties declined over the 1980-1995 period. Nord’s later work confirmed our findings.

13


program targets able to counter or reduce the deeper underlying problems facing these areas.
Distressed program funds have largely emphasized the provision of basic infrastructure.
Relatively little of the set-aside resources have been targeted to the social needs of these
communities. Nor were these funds of the type or magnitude to confront the deeper underlying
problems that precipitated distressed counties’ social needs, including the high level of
absentee land ownership and therefore lack of local assets, the severe environmental
degradation that accompanied countless years of strip mining and poor timber practices which
contributed to health problems, and an elite power structure that restricted residents’ abilities
to seek out and demand access to institutions of community ‘development and social change
(Gaventa, 1980; Whisnant, 1994). To this day, the original program emphasis provides the
backbone of assistance that aids the most economically disadvantaged counties in the region.
1.1.1 Justification for the Distressed Program. On the eve of the birth of the
Distressed Counties Program, the ARC identified 900,000 Appalachians living in some 67
counties who had not appreciably benefitted from the overall program. More than three-fourths
of those residents lived in 1,800 communities and “small settlements of 25 or more homes.”
These communities and literal clusters of homes ranged in size from between 25 to 50 families,
to counties with a total population of 5,000-7,000 residents (Appalachian Regional
Commission, 1981). Too small to benefit from the ARC’s original economic development
principle of “concentrated public investments made in the region in areas where there is the
greatest potential for future growth, and where the expected return on public dollars would be
the greatest” (Hansen, 1972, p. 268), and perhaps too isolated and too insular to request more
from the public sector (Gaventa, 1980; Whisnant, 1994), these deeply distant, rural places

8


Appendix F.

Appendix G.

Appendix H.
Appendix I.

Appendix J.

Table of Contents
(cont.)

Page
Comparison Between ARC Designation and the Index of
Economic Health .............cccccceeeeeeeeeeeee ee eeneeeeeeeeeeeeeeseeeeerennes F-1
Counties with Index Changes > 10% ...........ccccceesseeeeeeeeeeeeeeeunes G-1
Logistic Analysis of ARC Distressed Designation Binary
Dependent Variable ..................ccccccccceee eee e cece cece cece cece sees eee enee H-1
Change in Index Scores (mean 1991-1995 versus mean
1980-1984) 0... eee cece ec eceeeee cece see eeteeeeseeeeeeeseeeeeeseseeareeeeeene es Ll
Regression Diagnostics ..................ccccececeeeeee cesses ee eeeess ees aeeeeee ees J-1

vi


Appendix J. Regression Diagnostics

Overall, the regression model performed very well. A chart plotting actual and
predicted values (following page) graphically illustrates the high levels of association indicated
by the R? value of 0.797.

All independent variables were examined for possible collinearity problems through the
calculation of variance inflation factors. In no instance was there a factor greater than 2.30,
indicating no problems on this front.

Overall, the number of cases for which there is a “bad fit” is relatively small. An
analysis of residuals indicated that only 15 of 387 cases (3.9%) had standardized residuals
greater than |2|, although two counties had standardized residuals greater than |4|. A chart
(also shown below) plotting standardized predicted and standardized residual values reveals no

relationship, indicating that heteroscedacticity is not a problem in the model.

J-l


the percent of the population over 65. We know from national studies that single mothers tend
to have very low incomes, live in or near poverty levels, and require access to support
programs such as child care services and health care to work effectively in the wage-earning
economy. We also know that single female-headed households face special difficulty in
securing employment given the needs of children for parental support and oversight. Similarly,
national studies suggest the elderly tend to experience low levels of income, have problems
with mobility, and must rely on public programs, particularly for health care.

Human capital resources are scarce in distressed counties. Low numbers of the
population with greater than high school educations are thought to signify a lack of local
capacity represented by technically trained individuals capable of undertaking complex, skill-
based jobs. Of considerable importance are the institutional implications behind these results.
Distressed counties lack college-educated citizens. This deficit has implications for both the
supply of and the demand for education. From the supply side, individuals need supportive
institutions to encourage them to pursue higher education. Thus, the extent to which distressed
counties lack effective secondary educational institutions contributes to the reduced number of
citizens able to and interested in attending college. On the demand size, diminished interest in
college is in part the result of a lack of role models that by example demonstrate the ability to
achieve and to benefit from a college education. The complexity of this problem is well-
known. Special efforts are required to bring the two sides of the issue effectively together.

Serious labor force issues reside within the region’s aging population. Appalachia is
facing a serious future labor shortage as the populations in distressed counties age in place

while younger and more mobile residents seek opportunities elsewhere. The implications of

63


Table 1. Case Studies for 14 Counties
(cont.)

Pickett County, Tennessee

Pickett County, Tennessee, demonstrates a slow but steady progression toward a bright economic
future. In 1960, the county’s poverty rate was 32 percent and average per-capita income was one-
third the national level. Pickett, like its neighbors, Overton, Clay, and Fentress, all experienced
substantial improvements over that time. Blessed with rich Cumberland Plateau soil and flat ground,
agriculture was profitable and small settlements were possible. A combination of tourism-related,
education, and transportation infrastructure investments, blended with a small population, have
helped Pickett County develop a stable economy with a strong future. With its own manufacturing
job growth and proximity to adjacent counties enjoying increased numbers of manufacturing and
service jobs, many of Pickett’s residents commute to employment opportunities. This commuting
pays off; the county receives a net 18 percent of income from wages earned outside the area.

Polk County, Tennessee

Polk County, Tennessee, close to Chattanooga, is blessed with a large timber forest and beautiful
rivers, has benefitted from TVA’s dam developments, is serviced by several state and four-lane
highways, and has railroad access. Manufacturing employment has been present since the 1950s and
the county has gained jobs in the tourist industry. Net commuter income has doubled since the
1970s.

Preston County, West Virginia

Preston County, West Virginia, historically has enjoyed a coal-based economy. The county is
mountainous with lush forests. Morgantown, a major university town, is close by (25 miles). The
county is served by an interstate and a state highway, and has rail service in the southern portion.
Preston County has been able to take advantage of its natural environment in attracting tourists. The
county boasts two state forests, one national forest, a ski resort, white-water rafting, and other
outdoor activities.

Pulaski County, Kentucky

Pulaski County, Kentucky, has both mountainous and flat topography. The county has benefitted
from TVA dam projects and is served by a railroad and several state and federal highways. It has
long embraced agricultural production, and moved into manufacturing at the turn of the century.
Land has and continues to be privately held. Population growth over the post-war period has been
strong. The county has a regional medical center.

18



variation in indicator usage across a range of programs, and states within and outside the ARC
region. Based on their study, most states use absolute indicators of eligibility. Thus, incidence
of poverty, population or migration measures, housing conditions, unemployment rates, and a
variety of income measures within separate programs are combined to determine eligibility
based on economic distress. A smaller sub-set of programs use ranked measures which include
unemployment rates, poverty rates, and income levels. Other criteria such as quality of
housing or access to education are used by programs providing aid in these areas. Regardless
of rank or absolute measure, most measures of distress use some combination of employment,
poverty, and income to designate distressed status.

The geographic unit of analysis varies by program area and shows a wide range of
geographic eligibility. While counties are the basic unit of analysis, school districts,
communities of a specific size (less than 10,000), political subdivisions of various sizes and
types, tribal service areas, census tracts, metropolitan areas, states or state subregions, and
housing developments qualify for special designation depending on program design and goals.
Variation in the unit of analysis obviously makes determination of distress somewhat difficult,
given that data on at least some of the indicators used to define local conditions are not readily
available or not available in a timely fashion for most geographic aggregates. Perhaps the
largest discrepancy plaguing most of these programs is use of the poverty level in the
population. Poverty statistics are based on the decennial census. While the U.S. Department of
Commerce, Bureau of the Census, provides intercensal estimates of poverty, these are not
widely available and have certain geographic restrictions. Thus, one of the most important
components of distressed designations, the poverty rate, does not co-vary in time with the

4


outside the region. A second, important characteristic binding these counties together is
population growth based in part on interregional migration, which occurred over the post-war
period. While some of the counties grew only minimally or not at all, only eight posted
negative growth. Thus the majority experienced a positive change in population growth over
the 1930-1995 period. Some posted rather remarkable rates of growth as illustrated by the
standard deviation in population growth of the group, which was 61 percent—three times the
average growth rate of 24 percent. To the extent emigration occurred, counties losing
population may have actually benefitted from this decline in as much as it served to tighten
labor markets and decrease unemployment rates through time.

These counties share other characteristics as well. More than one-third are either
metropolitan (metro), have an urbanized area of at least 20,000, or are adjacent to metro areas.
The benefit of this status is clearly seen in the amount of net income derived from employees
who commute outside their county of residence for jobs. On average, almost 15 percent of the
income circulating in these counties comes from outside the local jurisdiction (REIS Journey to
Work Data 1970-1995). Residents are geographically proximate enough to other labor markets
to find employment. For some counties the importance of this income source is significant.
More than one-quarter of the counties’ residents earn as much as 25 percent of their net income
from outside their county of residence. Commuting times support these findings.

The group is surprisingly homogeneous on other measures. The average rate of poverty
is 20 percent, several percentage points below the distressed group average, and the standard
deviation is only 2 percentage points, suggesting considerable similarity within the group.
Affinity within the group also is demonstrated when examining average per-capita income. The

23


Combined Index Reliability Scores

Standardized
Year Alpha
1980 0.836
1981 0.825
1982 0.812
1983 0.855
1984 0.859
1985 0.881
1986 * 0.883
1988 0.909
1989 0.893
1990 0.894
1991 0.896
1992 0.894
1993 0.894
1994 0.882
1995 0.888

Source: Authors’ calculations on index components. See text for details.

To further confirm that the four county-level measures could appropriately be
combined into one “dimension” and to confirm that no other relevant dimensions in the data
were present, an eigen-analysis of the four individual measures on a yearly basis was
conducted. The tests indicate that a single dimension (i.e., the combined index) accounts for an

average of 73.07% of the variance across the four measures. In no year does a second

D-2


Appendix B. Comparison of Rank-ordered Indicies

(cont.)

PENN STATE INDEX (mean 1980-1994) PICKARD INDEX

County State Index County State Index
Washington TMN 109.93 Knox TMN 122.5
Anderson sc 109.50 Catoosa GA 121.8
Jackson GA 109.48 Washington OH 120.5
Carroll GA 109.17 Boyd KY 119.7
Bath VA 109.04 Muskingum OH 119.5
White GA 108.96 Marion wv 119.3
Holmes OH 108.92 Allegany MD 117.7
Habersham GA 108.73 Hamilton TMN 117.6
Gordon GA 108.54 Cabell wy 116.8
Clark KY 107.84 Mifflin PA 115.5
Lee MS 107.79 Carroll OH 114.9
Murray GA 107.42 Greenville sc 114.9
Dawson GA 107.35 Pike PA 114.5
Floyd VA 107.22 Lackawanna PA 113.7
Surry NC 107.20 Venango PA 113.4
Monroe PA 106.87 Blair PA 112.5
Perry PA 106.80 Washington PA 112.2
Jefferson AL 106.47 Coshocton OH 110.6
Transylvania = NC 106.43 Kanawha wy 109.6
Craig VA 106.20 Elk PA 109.2
Lumpkin GA 105.73 Delaware NY 108.0
Anderson TN 105.61 Madison AL 106.2
Bradley TN 104.74 Allegany NY 105.3
Catoosa GA 103.81 Westmoreland PA 105.1
Centre PA 103.42 Schuyler NY 103.6
Watauga NC 102.91 Anderson TN 103.6
Broome NY 102.80 Jefferson OH 103.0
Allegheny PA 102.74 Clinton PA 102.8
Pike PA 102.59 Otsego NY 102.4
Sullivan TMN 102.43 Ohio wv 102.2
Washington VA 101.92 Columbia PA 102.1
Henderson NC 101.81 Tioga PA 101.9
Tioga NY 101.49 Cattaraugus NY 101.8
Clermont OH 101.34 Crawford PA 101.5
Pickens sc 101.28 Tuscarawas OH 101.5,
Banks GA 100.84 Wood wv 101.5
Spartanburg sc 100.51 Steuben NY 100.7
Hamilton 1TMN 99.81 Warren PA 100.7
Buncombe NC 99.47 Bradford PA 99.2
Caldwell NC 99.41 Clermont OH 98.9
Burke NC 99.22 Lycoming PA 96.7
Paulding GA 98.57 Brooke wy 96.6
Wilkes NC 97.97 Lawrence PA 96.3
Yadkin NC 97.52 Forsyth NC 96.3
Polk NC 97.09 McKean PA 96.2
Madison AL 96.29 Centre PA 94.5
Knox TMN 95.48 Monroe PA 93.4
Hall GA 94.18 Chautauqua NY 92.1

B-8


other indicators such as unemployment rate or levels of income. Given the potential
susceptibility of places designated distressed to fluctuations in the business cycle, which can
have short-term and direct impacts on poverty levels, the lack of timely data presents a serious
problem.

The use of a special designation for areas of economic distress dates back several
decades. USDA’s loan and grant programs and EDA’s basic program framework have used
special designations for more than 25 years. ARC’s program, along with the TVA, was
established at approximately the same time in response to similar congressional mandates.
Given the specificity of these programs it is interesting to note that none of them have been
formally evaluated to determine whether such an action has made any headway in redressing
the economic and social circumstances which led to the initiation designation (Fullenbaum and
McNeill, 1995).

1.1 The Historical Basis of the Distressed County Designation: ARC and the
Finish-up Program

On December 31, 1981, the eve of the new year, the 13 governors of the ARC filed
with Congress, A Report to Congress from the Appalachian Governors, which “gave birth to
the concept of a Distressed County Initiative” (Decker, 1994). At the time of the issuance of
the Finish-up Program, the Administration, the Congress, and the public had come to focus on
the need for a less costly national government and thus sought to reduce the size and number of
domestic development agencies, including the ARC. Like many federal “development agencies”
during the 1980s the ARC began to feel pressure to articulate a “finish-up” strategy designed to

identify within limits a plan to bring the agency’s mission to completion (Appalachia, 1984).


Appendix D. Evaluation of the Combined Score Index

An important consideration in the development of the combined index is the extent to
which the four components (i.e., the four individual indices) are measuring the “same thing.”
That is, the combined scale only has meaning to the extent that the combination of the four
individual indices actually measure the construct of interest, in this case, economic health. To
assess the adequacy of the scale, standardized alpha reliability coefficients were calculated for

each year. The coefficient is calculated as follows (Norusis, 1997: 108; Nunnally, 1967: 223):

nr
oe =————_
1+(n-—I)nt

where n = the number of individual items summed to form the index and r = the average
correlation coefficient between items.

The maximum possible value for the coefficient is 1, indicating the highest reliability.
Reliability scores for the economic health index are listed below. In no year does the reliability
fall below 0.081, and the average over the 15-year time period is 0.873. Overall, the scale

exhibits high reliability throughout the study period.


EIPS
51091
1117
47087
13085
42023
47137
13291
54071
1009
4N175
54031
47015
47141
13311
21137
1007
1043
47063
1133
47049
1079
13187
51045
37189
1073
42027
47041
1077
47093
37043
54037
47051
1103
21207
47155
47105
37059
1029
1107
47133
37149
111s
1071
1083
42093
1033
42019
47011
28057

Appendix G. Counties with Index Changes > 10%

County
Highland
Shelby
Jackson
Dawson
Cameron
Pickett
Union
Pendleton
Blount
Van Buren
Hardy
Cannon
Putnam
White
Lincoln
Bibb
Cullman
Hamblen
Winston
Fentress
Lawrence
Lumpkin
Craig
Watauga
Jefferso
Centre
De Kalb
Lauderdale
Knox
Clay
Jefferson
Franklin
Morgan
Russell
Sevier
Loudon
Davie
Cleburne
Pickens
Overton
Polk
St. Clair
Jackson
Limestone
Montour
Colbert
Butler
Bradley
Ttawamba

121.72
113.99
86.89

147.22
106.17
120.60
107.22
169.38
113.34
105.14
82.66

114.24
158.87
156.50
90.51

110.56
127.79
107.32
113.66
122.79
106.11
98.80

118.15

-.1596
-.1560
-.1558
-.1546
-.1531
-.1503
-.1481
-.1468
-.1467
-.1461
~.1457
-.1456
-.1447
1446
1417
1416
-.1404
1375
1338
-.1335
-.1326
1322
-.1309
-.1301
-.1269
-.1261
-.1227

Change
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive


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PIPOD »
“ASIMIBIO ,0y “(E-O SAPO ayeag) sear ue }odor}aut UT SaMUNOD 4OF 1, PEPOD 4

“ASIMIOIIO ,(,, ‘UOISAI YW ap Jo apa ayy Suoye sorwunod 10J 1, PEPOD ,

OUV Aurump VIN (TexU99 “yINOs ‘YIOU) UOISaIqns Qyy :uoNRDo] AUNOD
vasn Aurump £661 seare ueyjodonaur 0) yusse{pe :uoNed0] AUNOD
vasn Auump £661 eore ueijodonow :uoneso] AuNog

sdew Day Awumnp VIN UOIS91 DYV a) Jo ,o8pa,, ay) Suoye :uoNed0] AND
snsuag “Sn. snonulu0s 0661 saeok LT > UdIp[Yyo ya ‘apurs ‘apeuay uonejndod a8ejwo0I0g
snsuag “sn snonunuod 0661 se18ap Sq WIM uONendod jo adeiusoI0g
snsuap “sn snonurjuo03 0661 +69 pose uoneindod jo advjusorag
susaneg ssouisng Ajunod snonunuos +661 saaxojdura Q] > syuouryst{qeiso Jo aseIUa.IEg
snsus9 “$°n snonupuos 0661 [210 Jo aSejusor0d sv d910 Joge] afeu,]
siaqa snonuruoo 661 40199S JUSWUIOAOS dy} WOI, BWOOUI Jo aSeIUIOINg

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sIau snonuruo0o 661 1o}99S [1e}91 dt] WO IUIOdUT Jo aseIUVOIEg

siau snonunuo0s 1661 Jo}oas SULINIORINUBUT WOIJ SUIODUT Jo aseIUDDIOg
ao.1n0g qualua.inseayy awax aqui,

$ad1nog pure seyqeiieA
{Jopoy] UOIssaAdOY Ivar] *[][ ons

59


argued that the absence of larger employers by default implies that small establishments
represent high levels of self-employment. Second and relatedly, given the major role branch
plant employment has played in the development of the ARC over the last 30 years, by
implication distressed counties have not shared equally in this source of employment growth
(Jensen, 1998). Other research suggests that an economic base dominated by small goods-
producing establishments is negatively correlated with rural employment growth over the
1980s (Economic Research Service, 1996).

Beneath these broader trends is the specific geography of Appalachia. The geography of
distressed counties is distinct and well-known. They tend to be very rural, remote from metro
areas, and on the edges of the region, lacking adjacency to metro areas. These counties also
tend to be concentrated in the central portion of the region, though as previous analysis
indicates, there are important exceptions to this tendency.

Clearly, the description of distressed counties presented here represents a far more
complex set of issues than can be dealt with by a program that largely emphasizes basic water
and sewer services. In addition to the statistical analysis, we also can look back on the case
study research to find important attributes of the region. Distressed counties have many needs,
such as jobs, skills training programs, social programs to care for children (allowing parents to
work), health programs to provide elderly and children with access to needed services, and
geographic access to more developed portions of the region. While the Distressed Counties
Program was never intended to eradicate the underlying problems facing these challenged

areas, the original and far broader ARC program was. Evidence from the case studies of

65


Thus, although precipitated by changes in national emphasis in the realm of welfare and
poverty policies, the origins of the Distressed Counties Program date back much earlier in
ARC history. The selection of identifying criteria for counties in distress was based on ongoing
discussions of the region’s problems inside the Commission. Similar concerns were present in
debates occurring within and among federal and increasingly international agencies attempting
to pare down the focus of development programs. The intent of the distressed measure was to
develop an analytical method by which improvements in the conditions of counties could be
tracked through time. While the specific measures used through time have changed and vary to
some extent across different policy contexts, nonetheless, the overarching goal has been to
establish a baseline that could be used to assess improvements (or declines) in local conditions.

Being created in the early 1980s, in an era in which federal deficits drastically reduced
support for the ARC, the Commission’s Distressed Counties Program was not originally
empowered with the tools and resources to make major inroads towards resolving some of its
communities’ most pressing problems. Indeed, for these isolated communities, the full
complement of ARC programs that emphasized people- and place-based mandates was
desperately needed. The problems identified in the original ARC report still held—child,
human development, and community development; unfortunately, the small amount of annual
funds set aside for the most distressed areas was simply not enough to have a major impact on
the most serious concerns confronting these communities. The comprehensive development
support received by the larger and more urban counties in the region over the late 1980s and
early 1990s was never made available to the most distressed locations. Although basic
infrastructure has been provided in numerous places, nonetheless, by the end of the 1990s,

10


eigenvalue > 1.0 appear (the common cutoff value for a “relevant” dimension). In sum, both
the reliability analysis and the eigenvalue analysis indicate that the four individual measures

can appropriately be combined into a single measure of county-level status.

D-3


have occurred nor can it tell us about those that did not change. Nonetheless, by examining
those counties which did leave the designation, we are able to determine the degree of
similarity in the group’s experience, and identify some of the unique attributes of these
locations.

In the spring of 1997 and the spring of 1998, case studies were conducted for 14 of the
21 counties that had left the severely distressed category between 1988 and 1995.‘ Both
statistical analysis and field research were completed for these counties. Professionals
contacted in the field included local economic development practitioners, local political
officials, and medical health, education, and community development professionals. In
addition, we contacted LDD staff in local offices. The initial statistical analysis was quite
general and covered the period 1970-1990.

We present highly condensed summaries of the 14 counties’ experiences in Table 1.
We separate those counties that have continued to remain undistressed from the two cases
which returned to the distressed category over the 1995-1998 period. The 14 case studies were
supplemented with more detailed statistical analysis of a set of variables that looked further
back in time in an attempt to capture longer-term trends. This secondary analysis considered
all 21 counties.

In addition to the case studies of successful transition counties, in the spring of 1998

we undertook a series of studies of distressed counties with a particular emphasis on children’s

* Two counties, Doddridge and Pike, fell back to distressed status between 1996 and
1998; nonetheless, these two counties’ experiences are reported here.

15


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$661-086T “Xopuy [oAa]-AjunoD uvoy “¢ oundty

44


Concurrently, it can be hypothesized that in situations where there is a relatively small labor
force in comparison with population levels, there will be a greater overall dependence on
government supports in the form of transfer payments of differing types. The county-level

labor force/population ratio index for county c in year j is calculated as follows:

100

1 _| (LFPOP.-LFPOPs)/
/ LEPOPs |

LEPOP.s, =

where LFPOP,, = county labor force divided by total county population in county c in year j;
and, LFPOP,; = U.S. labor force divided by U.S. population in year j. As the labor force to
population ratio gets smaller relative to the nation’s average, the index becomes larger.

The final individual measure of county-level economic health is the ratio of per-capita
transfer payment income to’per-capita earned income. This measure assumes that transfer
payments are made largely to those individuals and entities that do not generate self-sustaining

levels of income. The index is calculated as follows:

1o0[PCTP SoMa

[Poe pcm|

TEPix, =

where PCTP, = county per-capita transfer payments in year j; PCMI, = county per-capita
market income in year j; PCTP,, = USA per-capita transfer payments in year j; and
PCMI,, = USA per-capita market income in year j. As the ratio of transfer payments to earned

income becomes larger than the national average, the value of the index becomes > 100.

C3


the rest of the region’s counties, and to explore the relative conditions of counties through
time. The research was designed to identify underlying qualities/characteristics of persistently
distressed counties with the goal of suggesting program areas that could assist in the future
development of these counties.

We began our analysis of distressed counties through an examination of the ARC’s own
designation of distressed. This is followed by a discussion of an alternative index developed to
overcome some of the temporal and analytical limitations of the ARC’s index. We developed a
surrogate measure of distress that would mimic the ARC definition, but which would not be
constrained by a number of technical problems built into the ARC designation. We
subsequently compared our index results with those of a comparable study of counties based on
the ARC designation. The degree of overall correlation (.8) between the two methods of
analysis was significant. This means the two measures are assessing the same underlying
conditions. Thus, our index accomplishes two goals: it presents results which are consistent
with ARC policy interests while allowing more detailed year-to-year examination of county
conditions.

3.0.1 Examining the ARC Designation. We turn first to a discussion of the existing
system of distress designation. The ARC uses a five-category scale to describe the economic
status of counties under its auspices. This scale, in which 1 describes the “worst” case and 5
describes the “best” case, is based upon three underlying criteria: poverty level, unemployment
rates, and market income. Rankings are applied to member counties based upon (1) their

relative level on each of these distress measures in comparison with U.S. averages; and

27


conditions found in many of these and the remaining distressed parts of the region differ little
from those that described them in 1981.

1.2 Implementation of the ARC’s Distressed Designation

The original ARC distressed designation was based on four measures: income,
unemployment, poverty, and infant mortality rates. These measures were selected from a
larger list of 12 indicators. Used until 1988, the set was pared down by one with the
elimination of the infant mortality rate. This action was taken as the region’s mortality rate
converged with the national average.*

1.3 Benefits from Receipt of the Distressed Designation

Distress benefits represent adjustments made in existing program criteria for project
funding. Those states with the distressed designation can receive 80 percent rather than the
usual 50 percent in matching funds for projects from the ARC. This more generous match
applies to all ARC funds and not just the distressed county set-aside. States also can and
regularly do use their general Area Development allocations in conjunction with distressed
status.

The level of funding available under the distressed county set-aside is determined
annually. The formula for distribution includes “the area and number of qualifying counties in

each state, their population; and the proportionate number of houses without plumbing in the

3 Removal of the infant mortality indicator greatly improved the underlying relationship
between the concept of distress and the ARC’s designation. Mortality statistics are notoriously
problematic, particularly in rural counties where small numbers and a high degree of inter-
temporal variation make these statistics unreliable as predictors of underlying socioeconomic
conditions. Normally, distressed county funds cannot be used for a smaller unit of analysis.

11


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we6L L961 0861

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(9) aways —*e—
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(Ze) 1ONES — —
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(v9) odSNON —¥—
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(cor) ouaN—

adAy, Sua Aq soueunlosz9g Ayunog ‘OT ans

55


Table 1.
Table 2.
Table 3.
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.

Figure 6.

Figure 7.

Figure 8.
Figure 9.
Figure 10.
Figure 11.

Figure 12.

List of Tables and Figures

Page
Case Studies for 14 Counties ...............ccccccececc cece cece eee e cette cece eeesen eee aeaannes 16
Statistics for 21 Counties ..............ccccceeeeecce cece ceee eee eeeeeseeeeeeeeeesauaeeeeeuaens 21
ARC Distressed County Designation Criteria .................0cccceeeeeceeeeeeeeeeeeeeee 29
Economic Status Ratings by Year, 1992-1998 ............cccceeceeseeeecesseeeeeeeenes 30
Distress/Non-Distress Status by Year, 1992-1998 .......... cee ecceeeeeeeeeeeeeenneeenee 31
Average Distress Status, 1992-1997 oo... ccceeceeecc rece eeeeneeenaeeeneeenaees 34
Mean Regional Index, 1980-1995 0.0.0.0... eecceeeceeeeeeeeeceeeceeeeeeeeseeeeeseneeraeee 41
Mean County-level Index, 1980-1995 ..........cceecceeeeceseceeeeeeeeeueeeneeenesenee 44
County-level Improvement or Decline: Mean Index 1991-1995 versus
5): (0) Oo) EES EEEEESOSESOSSSOSSSOOSOOSOOOSOOOOOOOOOOOOOOOON 47
County-level Improvement or Decline > 10%: Mean Index 1991-1995
versus 1980-1984... cece ccc ceecrereeeeeeces sea eeeeeeeeeeeeeessaaeesesenaaeseeeee ee 49

Rank-order Correlation Coefficients ...

County Performance, 1980-1995: Pickard Quartiles ...............ccceeeeeseeeeeeee eee 52
County Performance by ERS Type ..............ccceecseeceesecseeeceu sess eeceeeeeneeeeeee 55
Linear Regression Model: Variables and Sources .................cccceccssesseseeeeeeee 59
Linear Regression Model: Predicting the 1994 County-level Index.................. 61

vii


Appendix C. Alternative Index Specification

Alternative Index Specification. In our index, four separate measures of economic
health are used to obtain a single, average measure of county-level economic health in
comparison with national levels. While each of the four measures is highly related, each also
was designed to capture a slightly different facet of economic status.

Index Calibration. Each of the four individual indices are scaled such that a county-
level value equal to the national average yields an index score of 100. Scores > 100 reflect a
county situation that is worse than national averages (e.g., lower per-capita market income or
higher unemployment rates). Therefore, for a given year, a county that was at national
averages across the four individual measures would have an overall index of 100; counties that
are “better-off” than the national average would have a score < 100; and counties “worse-off”

than national averages would have an overall index > 100.

In equation form, the yearly overall county-level index can be summarized as follows:

PCMliax, + URTin, + LFPOPixx, + TFPitc,

IDX, =
4

where IDX; = county-level index in year j. Below, the rationale for using each of the four
measures and their specific calculation is reviewed.

The Individual Measures. The first county-level measure utilized, per-capita market
income in relation with U.S. averages (PCMI,,,), is a direct measure of income and is simply

an extension of the ARC’s traditional use of market earnings. Market income is defined as

C-1


3.0.2 A First Cut at the ARC Five-category Ranking. Our first effort was designed to
examine in detail both numerically and spatially the distribution of distressed counties using
the ARC definition. This required us to confront the problem that the allocation of counties
was not based on a summary measure derived from continuous or interval data, but rather was
determined by counties meeting certain thresholds. By implication, ARC’s procedure
converted data with considerable variation (actual income levels and unemployment and
decennial poverty rates) into data with much less variation, with each county no longer
analytically distinguishable from other counties in the same category. While each threshold is
individually meaningful because the ARC index is non-additive, taken together it is difficult to
decipher differences within each category. Moreover, the lack of an additive continuous
measure highlights a problem: a very insignificant shift in one of these three variables can
cause a highly significant change in “outcome.” Therefore, if a county is close to a relevant
“cutoff” level for poverty, unemployment, or income, a very slight shift in one of these values
can cause a distress ranking change. In other words, because the ARC measure reduces the
data to categories, change would be more reflective of where the county was along the three
scales rather than the magnitude of any changes themselves.

Related to this problem is the fact that county-level poverty is determined only once
every decade—at the decennial census. Second, because of the influence of the poverty rate,
year-to-year movements in distress status are a function primarily of shifts in market income
and unemployment. And of those two, unemployment is by far the more volatile indicator.
Thus, the year-to-year instability of rankings on the 5-point scale, and the probable lags among
unemployment, poverty, and income, and variables that might underlie such measures, render

32


Appendix E. Index Volatility

Each of the indicators of economic health that are combined to form the yearly index
have different levels of year-to-year volatility. To assess this volatility, we calculated the

standard deviation of each measure over the study period (1980-1995). The results were as

follows:
Economic Health Measure SD
Unemployment 27.16
Transfer payments/market income 12.90
Market income 9.22
Labor force/population ratio 8.33
COMBINED INDEX 10.63

As the table shows, the volatility of the unemployment measure is extreme. However,
our method of combining measures of economic health into a single index does a relatively

good job of smoothing out these deviations, at least to some extent.

E-1


the greatest level of economic hardship. These results also mirror Jerome Pickard’s
comparison of distressed counties in 1959-1960 and 1980-1986, in which he found that:

The axis of “hard core” severely distressed counties extends from Preston Co., WV,

southward through eastern Kentucky to the Tennessee Cumberlands. Most of the

counties which deteriorated to severe distress were west of this axis in Kentucky or to
the southwest in Tennessee, Alabama, or Mississippi, with only a few scattered
counties to the east or southeast. Most of the counties which improved from severe
distress in 1959-1960 were south and east of this axis, with a few farther north in
southern Ohio and Pennsylvania (Pickard, undated).

Our re-coding of counties into a binary designation based upon average distress levels
over time, while ignoring some of the more subtle differences in yearly county status and
status change, does preserve the major trends outlined above. Although by including those
counties with a score < 3 in incorporated areas which were not severely distressed,
experimentation with simply plotting those counties with a score of < 2 did not reveal
demonstrative differences in the general results. The spatial pattern of distress was slightly
more compact, but nonetheless the overall pattern prevailed. As we treated this analysis as
exploratory to define the boundaries of the problem, we chose to draw the circle around
counties facing difficult economic circumstances rather broadly, particularly given the
limitations of the indicators underlying the measure.

Part IV: The Development of an Alternative Distress Index: An Index of Economic Health

4.0 Introduction

In an effort to retain as much information about the experiences of distressed counties
as possible, we developed a surrogate index which we call an index of “economic health,”
which simultaneously captures the underlying characteristics of interest to the ARC

(unemployment, income) and additional characteristics that relate to the health and

35