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Executive Summary

The Appalachian Regional Commission (ARC) has been a proactive leader in advancing the 

region’s well-being for over four decades. Part of its success is rooted in efforts to develop 

sound benchmarks from which to monitor the region’s progress. The ARC’s index of distress 

(composed of poverty, unemployment, and per capita income) has been a valuable tool in years 

past. But new economic realities have accelerated the need to explore the development of a set of 

indicators that are better aligned with the important shifts taking place in the region, nation and 

world. Without doubt, Appalachian communities with sizable numbers of poorly educated 

workers and those experiencing a near-constant outflow of talented youth and skilled workers are 

facing intense struggles in today’s global economy. As a result of these shifting economic 

circumstances, the ARC commissioned a systematic re-examination of its distress index. The 

intent of this project is to identify a new set of core variables that can provide a more meaningful 

and current benchmark of the critical factors needed for long-term socioeconomic progress in the 

region. 

In this executive summary, we provide an overview of the results of our ARC-supported project 

titled, Developing and Assessing Potential Forward-Looking Distress Indicators for the 

Appalachian Region. Funded in December 2008, the project was intended to develop a series of 

new distress indexes and to evaluate their performance vis-à-vis the current ARC index. The 

project team consists of economists and sociologists whose research experience and present 

institutional affiliations encompass Northeastern, North Central, and Southern states that are part 

of the ARC’s geographic boundaries. 

The project has three goals: 

 Provide an intensive evaluation of alternative forward-looking indicators along with other 

indicators; 

 Develop a series of new distress indexes; and, 

 Compare the performance of these new distress indexes along with the current distress 

index used by the ARC. 

To accomplish these goals, the following research tasks were undertaken and results achieved: 

1. Gathering background information from ARC staff and state representatives to inform 

data analysis and evaluation. Personal meetings and webinar sessions were conducted 

that provided useful information about the relevance of a variety of indicators and 

statistical procedures. These multiple insights were incorporated into subsequent 

research and into this final report. 

2. Data collection and estimation of select variables. The project required intensive datacollection from key sources for the 1996 through 2007 time period, as well as estimation 

of select variables. Given the importance of employing the best possible data available 

and the need for timely new indicators, the research team collected data from a variety of 

secondary sources in addition to the decennial Census of Population. 

Executive Summary 
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3. Select and evaluate a series of key indicators for use as candidate variables in new 

distress indexes. We evaluated approximately 40 variables that represent a group of seven 

types of socioeconomic indicators: population growth; educational attainment; income; 

housing and housing change; entrepreneurship and self-employment; labor market 

strength; and poverty. The indicators assessed include a set of forward-looking variables, 

with particular attention given to population growth, education (including college 

graduates), new labor market measures (including use of the employment rate as a 

replacement for the unemployment rate), as well as variables currently employed by the 

ARC. To evaluate these 40 potential candidate variables, we identified five variables (the 

poverty rate for 2007, per capita market income for 2006, total job growth over 1996-

2006, population growth over 1996-2006, and the employment/population ratio for 2007) 

for use as the economic outcomes to help in selecting the candidate variables best 

correlated with future distress. 

Based on our evaluation (through statistical analysis) of the 40 potential candidate 

indicator variables, the following five were determined to be most viable for 

incorporating into a distress index: 

 The employment/population ratio;

 Poverty rate;

 Per-capita market income;

 Percent of the adult population (25 + years old) with at least one-year of college 

education, and/or percent with at least a four-year college degree; and, 

 Ten-year percent change in population. 

4. Use the candidate indicator variables to develop new, alternative distress indexes and 

evaluate their ability to predict current and future distress. Using various combinations 

of the five candidate indicators above, we developed two dozen different distress indexes, 

assigning each indicator an equal weight in each index. We evaluated these new 

alternative distress indexes (measured in the 1996/1997 period) to see how they 

performed in predicting future distress (as indicated by our benchmark indicators, the 

poverty rate for 2007, per capita market income for 2006, total job growth over 1996-

2006, population growth over 1996-2006, and the employment/population ratio for 2007, 

along with the ARC’s current distress index). We then evaluated these new alternative 

distress indexes, measured in the most recent time period, to see how they performed in 

predicting current distress. 

Based on these analyses, six indexes were closely grouped in terms of best-performance. 

Of these, we determined the two best performing indexes to include: (a) the ten-year 

percent change in population, the employment/population ratio, the poverty rate, and the 

percent of the adult population with a college degree; and (b) all five candidate indicator 

variables. As we explain in Section 3 of this report, our recommendation is to adopt the 

candidate distress indexes that include the four-year college degree attainment (Bachelors 

degree) share rather than one-year college attainment.

 
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5. Compare the performance of the six new distress indexes with the current index used by 

the ARC. We focused on the six best-performing indexes and compared them to the 

current ARC Distress Index. A series of maps and tables were generated to help identify 

specific counties that entered or exited the distress category when each of these six new 

alternative indexes were compared with the current ARC indicator. 

Overall, the results of our analysis show that with the use of the new candidate indexes, a greater 

number of ARC counties would be classified as either distressed or at-risk when contrasted with 

the current ARC Index. This pattern is relative to the nation in that we employ the same 

classification scheme used by the ARC. In particular, the lowest 10% of U.S. counties are 

defined as “distressed” and the lowest 10- 25% of counties as “at-risk.” Compared to the 74 

distressed and 88 at-risk that are delineated using the ARC’s current FY 2007 index, between 82-

95 counties are classified as distressed and 87-112 are defined as at-risk using the six new 

candidate indices that we developed. 

In conclusion, the new candidate distress indexes should provide a useful benchmark for charting 

the progress of the Appalachian Region. The indexes are composed of indicator variables that 

should be credible, transparent, and acceptable to the ARC, Congress, and OMB. The indexes 

can be constructed in relatively up-to-date manner. Finally, the indexes perform well in 

identifying the counties that are currently in a distress, as well as in predicting the counties that 

are at greatest risk for falling into future distress. Because ARC policy interventions are 

particularly aimed at reducing future distress, these new candidate indexes should be useful in 

guiding the critical investments needed to sustain the region’s progress in the years ahead. 
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Introduction and Overview

The Appalachian Regional Commission (ARC) has been a proactive leader in advancing the 

region’s well-being for over four decades. Part of its success is rooted in efforts to develop 

sound benchmarks from which to monitor the progress achieved both in the region as a whole 

and in the smaller core of counties designated as economically distressed. The ARC’s current 

index of distress (composed of poverty, unemployment, and per capita income) has been a 

valuable tool in years past. But the region’s new economic realities have accelerated the need to 

identify a more meaningful set of measures for monitoring the region’s long-term social and 

economic progress and vitality. Knowledge-based jobs, for example, are core drivers of 

prosperity in today’s economy and entrepreneurial-friendly communities are a vital part of 

spurring local innovation and creativity. As such, these types of economic shifts should be 

accommodated in any new measure of distress. 

No doubt, these changed economic realities are proving burdensome to many Appalachian 

communities, particularly those with sizable numbers of poorly educated workers, those that 

have faced a near-constant outflow of talented youth and skilled workers, and/or those with a 

limited history of support or commitment for entrepreneurial and knowledge-based development. 

As a result of these shifting economic circumstances, the ARC has launched a systematic reexamination of its economic distress index, the matter that is at the very heart of our project. The 

intent of this project is to explore a new set of core variables that can offer a more contemporary 

examination of the critical elements needed to promote long-term socioeconomic progress in the 

region. 

In this report, we document the results of our ARC-supported project titled, Developing and 

Assessing Potential Forward-Looking Distress Indicators for the Appalachian Region. Funded 

in December 2008, this initiative is intended to develop new alternative distress indices and to 

evaluate their performance vis-à-vis the current ARC measures. The project team consists of 

economists and sociologists specializing in community and regional well-being and spatial 

analysis. The team’s research experience and present institutional affiliations encompass the 

three regions (Northeast, North Central, and Southern) having states that are part of the ARC’s 

geographic boundaries. The project builds on the research team’s past work for the ARC, as 

documented in the report An Assessment of Alternative Measures for Determining Economically 

Distressed Counties and Areas in the Appalachian Region (April 2008). In this earlier report, we 

outlined the strengths and limitations associated with ARC’s current indicators. Moreover, we 

delineated new indicators, including “forward-looking” measures -- such as educational 

attainment, housing change, and entrepreneurship – that we believed would perform better than 

current measures in guiding the type of critical investments that will be needed by the ARC in 

the region. 

Section 1 
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The project has three goals: 

 Provide an intensive evaluation of alternative forward-looking indicators along with other 

indicators; 

 Develop a series of new distress indexes; and, 

 Compare the performance of these new distress indexes along with the current distress 

index used by the ARC. 

This report describes our project’s accomplishments in meeting these three goals. In this 

introductory section, we provide a brief overview of tasks completed and the results of our 

research. The subsequent sections of this report provide a more detailed discussion of the 

research results associated with each of the project three goals. 

Overview of Project Accomplishments: 

We first offer a snapshot of the important activities that we have accomplished over the course of 

this project. In sum, we completed the following: 

 

Gather information from ARC to inform data analysis. Since the start of the project, the research 

team has held conference calls, undertaken email discussions, and met on a face-to-face basis 

with ARC staff/representatives to gather information about specific needs and to solicit 

comments about the usefulness of particular indicators and statistical procedures. On February 

4, 2009, the team conducted a webinar from the ARC headquarters in Washington to describe 

our research plans and procedures to the ARC State Representatives and ARC staff. On June 8, 

2009, the team provided a preliminary report and conducted a webinar from the ARC 

headquarters to explain the results to the ARC State Representative and ARC staff. These inputs 

provided useful information about the relevance of different indicators and statistical procedures. 

These multiple insights have been incorporated into our subsequent research endeavors and into 

this final report. 

Collect data and undertake estimation of select variables. This project required intensive datacollection, data-base management, and estimation of variables. In order to evaluate the future 

performance of indicators, it is important that data for each specific indicator be available for a 

span of at least one decade. As such, our team retrieved data from key sources for the 1996 

through 2007 time period. Given the importance of employing the best possible data available, 

coupled with our desire to examine a variety of new indicators, the research team had to collect 

data from a variety of secondary sources in addition to the decennial Census of Population, 

factors that added complexity to the programming and data-base management components. 

Finally, as we explain below, some variables were not available for all counties for the years 

required and as such, they had to be estimated statistically. 

Select and evaluate a series of indicators that can be used as candidates for a new distress index. 

We evaluated approximately 40 variables that represent a group of seven types of indicators: 

population growth; educational attainment; income; housing and housing change; 

entrepreneurship and self-employment; labor market strength; and poverty. The indicators we 
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assessed include variables currently being used by the ARC and a set of forward-looking 

measures, with particular attention given to population growth, education (including college 

graduates), and new labor market measures (including use of the employment rate as a 

replacement for the unemployment rate). To evaluate these 40 potential candidate indicators, we 

identified five indicators (the poverty rate for 2007, per capita market income for 2006, total job 

growth over 1996-2006, population growth over 1996-2006, and the employment/population 

ratio for 2007) for use as benchmark indicators in selecting the candidate variables that best 

correlated with future distress. Based on our evaluation (through statistical analysis) of the 40 

potential candidate indicators, the following five were determined to be most viable for 

incorporating into a distress index: 

 The employment/population ratio; 

 Poverty rate; 

 Per-capita market income; 

 Percent of the adult population (25 + years old) with at least one-year of college 

education, and/or percent with at least a four-year college degree; and, 

 Ten-year percent change in population. 

Use selected indicators to develop new alternative distress indexes. Using the five candidate 

indicators above, we developed approximately 25 different indices (by combining various 

indicators and assigning each indicator an equal weight in each index). We evaluated these new 

alternative distress indexes (measured in the 1996/1997 period) to see how they performed in 

predicting future distress (as indicated by our benchmark indicators, the poverty rate for 2007, 

per capita market income for 2006, total job growth over 1996-2006, population growth over 

1996-2006, and the employment/population ratio for 2007). As we explain below, based on this 

analysis, six indexes were closely clustered in terms of best-performance. Of these, the two top 

performing indexes were composed of: (a) all five candidate indicator variables; and (b) the 

employment/population ratio; the poverty rate; college educational attainment levels; and the 

ten-year percent change in population. 

Compare the performance of the new distress indexes with the current index used by the ARC. 

We used the six best-performing indexes and compared them to the current ARC distress 

indicator. Maps identify specific counties that enter or exit the distress category when each of 

these six new alternative indexes is compared with the current ARC indicator. In particular, in 

comparison to the 74 distressed and 88 at-risk counties identified using the current ARC FY 

2007 index, between 82-95 counties are classified as distressed and 87-112 are noted as at-risk 

using our six new candidate indexes. 

In the following sections of this report, we provide more detailed documentation of the results 

for each of the three goals of the project: (1) Evaluate and select candidate indicators; (2) 

Develop a series of new distress indexes; and (3) Compare the performance of these new distress 

indexes along with the current distress index used by the ARC. 
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The Candidate Indicators: General Issues in Selection and Evaluation

The methodology for selecting the candidate indicators follows the principles delineated in the 

team’s April 2008 report. These principles can be summarized as follows: 

 Selection of indicator variables should be based on credibility, transparency, and 

acceptability to the ARC, Congress, and OMB. Moreover, we follow the current ARC 

practice of ranking the variables relative to the rest of the country when determining the 

particular category of distress, an approach is that likely to prove more defensible in the 

eyes of external observers. 

 The component indicator variables selected for each index should be as up-to-date or 

produced with as little time lag as possible. 

 The subsequent index constructed from the indicators variables should be accurate in terms 

of capturing economic distress in the ARC counties. 

Based on the objectives outlined in the project proposal, our first priority is to consider forwardlooking measures (such as population growth and educational attainment). Another priority is the 

inclusion of new labor market measures, such as the employment-to-population rate, as potential 

replacements for the unemployment rate. As a secondary priority, we consider other measures 

outlined in our April 2008 report as being potentially useful. For example, we examine how 

measures of building activity and expansion of bank branch offices can be tapped as potential 

forward-looking measures. Overall, as we explain below, we select and evaluate a total of 40 

different indicators within seven specific groupings, as prospective variables that could be used 

to construct a new distress index. 

Sources of Data and Issues in Constructing Indicators 

One of the traditional critiques of the ARC distress indicators is that some of the data are 

typically drawn from the decennial Census, which means that these variables become rather 

dated as the end of a decade draws near. This major shortcoming is expected to be overcome 

with the planned full implementation of the American Community Survey (ACS). Officially, the 

Census Bureau still maintains that by late 2010, the ACS will produce annual data that will 

capture all of the elements of the decennial Census long-form at a disaggregated geographical 

level including county, census tract, five-digit zip code, and census block group (though there are 

some restrictions for small samples).1

 Because the ACS data are derived using sampling, the 

accuracy will be reduced for less-populated counties. Likewise, the data will not be perfectly upto-date since a five-year moving average will be used for less-populated geographical 

locations—though that has the offsetting advantage of averaging out year-to-year fluctuations (as 

is currently the case in the housing market) that do not capture long-term distress. However, we 

                                                           

1

For more details of current Census plans for the ACS, see U.S. Census Bureau (2009) and Office of Management 

and Budget (2009). 

Section 2 
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believe that the ACS will produce reasonably accurate estimates based on its sampling 

procedures. Thus, despite the shortcomings of the ACS, we view the annual availability of data 

for less-populous counties to be a significant improvement for calculating future ARC distress 

indicators. 

When deriving the candidate distress indices, we examine how candidate variables measured in 

circa 1996/1997 are associated with current distress measured in (or as close as possible to) 

2006/2007. However, as described in more detail in a latter section of this report, some of our 

variables are estimated due to limitations in the availability of current data. For example, the 

2007 employment/population ratio and the 2007 labor-force participation rate are estimated 

because the denominator—population 16 years and over—is not reported annually by the Census 

Bureau. Instead, the Census annually produces estimates of the county population 18 and over, 

as well as the estimated county population between 14 and 17 years of age. For 1996/1997, data 

to estimate the employment/population ratio or the labor force participation rate do not exist. 

Therefore, we substitute their respective rates from the 2000 Census. Fortunately, such 

interpolation will not be necessary with the ACS because it will directly report the necessary 

data. Thus, we believe that when ACS data is fully available, the employment/population and 

labor force participation variables will be even more strongly linked to distress than the numbers 

being used here. 

Another issue in the choice of indicators involves the time-period required to capture distress. 

We use ten year measures of job and population growth because they capture the persistent 

nature of distress that we are seeking to measure. For example, short-term changes in job or 

population growth may reflect a temporary, cyclical event such as a plant opening or closing, or 

a transitory shock to the housing market. Whether such changes are indicative of a major change 

in the county’s underlying prosperity would depend on whether the (say) newly laid off workers 

find work locally, or even outside of the county through commuting. Only after some period of 

time has elapsed would true economic distress reveal itself through these offsetting adjustments. 

Indeed, as the “New Economic Geography” literature, the economic impact literature, and the 

military-base closing literature indicate, communities that face adverse events such as a major 

military-base closing, natural disaster, or even an intensive military bombing exercise, generally 

return to their long-term growth paths within a period of about ten years (Edmiston, 2004; 

Poppert and Herzog, 2003; Kilkenny and Partridge, forthcoming). 

It is important to note that five-year measures of job and population growth are likely to be 

inadequate measures of long-term distress. This conclusion is generally confirmed by Partridge 

et al. (2008, Chapter 6) when they found that replacing the unemployment rate by the five-year 

change in population would result in a relatively small number of changes in the number of 

counties that would be classified as being in distress compared to the current ARC distress index. 

Thus, we view ten-year changes in population and job growth as good measures of distress as 

they are long enough to balance out short-term events, but not so long as to capture trends that 

are not germane. 

We do have specific concerns with regard to the use of poverty rate and educational attainment 

in any index. These variables are not currently available on annual basis for all counties. One of 
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the key measures that would likely be included in any distress index is the current poverty rate. 

Of course, a key shortcoming of using county poverty rates is that the most accurate poverty 

estimates are obtained through the decennial Census. Not only is this a problem for calculating 

annual measures of distress, but it presents problems for our study because we would prefer to 

have annual measures. Specifically, much of our analysis will use data from the circa 1996/1997 

and the circa 2006/2007 periods. To avoid this data-availability shortcoming, we adopted the 

U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) as our annual estimate 

of poverty. One weakness is that the SAIPE does not produce direct estimates of county poverty, 

instead relying on estimates derived from personal income and tax data. To examine whether the 

SAIPE data are accurate enough for our purposes, we calculated the simple correlation 

coefficient between the 1999 county SAIPE poverty rate and the 1999 poverty rate derived from 

the 2000 Census (with the 2000 Census estimate viewed as being quite accurate). The correlation 

was equal to 0.94. The corresponding correlation using 1989 data equaled 0.95. We view this 

correlation as high enough to move forward with the SAIPE estimates in our study. Again, this 

would imply that using the ACS data would lead to even stronger predictors of future distress 

than the SAIPE poverty data used in this report—i.e., the ACS would present direct measures of 

poverty, not the estimates used to derive the SAIPE figures. 

A final key variable is educational attainment. Unfortunately, accurate county-level annual data 

on educational attainment are not available between the Census decades. Thus, we are forced to 

rely on the 2000 decennial Census educational attainment data. Again, we expect that the annual 

ACS data will allow for more accurate future predictors of distress because the ACS will 

produce measures of educational attainment. 

The Set of Indicator Variables Evaluated 

We select and evaluate a total of 40 different indicators, within seven specific groupings that are 

described in our earlier report, as potential variables that could be used to construct a new 

distress index. These variable groupings and the specific variables within each group are 

described in the remainder of this section. 

1. Population growth: This indicator, including its key component of net population 

migration, measures an important response for dealing with economic decline in a 

community; residents, in effect, vote with their feet by moving away if they believe that a 

place offers only a bleak economic future. As such, population growth needs to be 

considered carefully in any redesign of the distress index. Net population loss is a real 

measure of economic deterioration, whereas population growth may occur either in response 

to economic expansion or to features such as attractive natural amenities (as in retirement 

migration, for example). Over longer periods, population growth also serves as a close 

proxy for employment change, another key economic component that is essential to capture. 

Adjustment assistance to counties experiencing significant population loss can be motivated 

with two key arguments: (1) those staying behind have to deal with the very real negative 

fiscal and other consequences of a smaller population base; and (2) assistance could stem or 

even reverse the net out-migration. A third, more indirect argument is that congested or 
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sprawling metro areas receive a public benefit when in-migration from declining areas is 

held in check. 

We analyze the percent population growth between 1986-1996 and 1996-2006 using 

population estimates from the US Census Bureau (www.census.gov/popest/counties/). As 

elaborated above, we use the most current data available at the time of this study and we 

choose ten-year periods so as to even out short-term fluctuations and to focus on longerterm trends. 

In addition to actual population counts, we consider the percent change in the population 20 

years of age or younger (data were also obtained from the Census Bureau). The reason for 

singling out this age group is that it represents, in many ways, “the future” -- both 

metaphorically and as the concrete workforce of a community. Furthermore, research 

suggests that once these individuals leave, it is difficult to attract them back to the 

community. 

2. Educational Attainment: Education is the prime measure of human capital and serves as an 

underlying determinant of an individual’s current and future earnings capacity. Returns to 

education (or spillovers) are enhanced when those with a college degree can locate near 

other workers who also hold college degrees. Moreover, education is highly correlated with 

poverty and it is associated with an individual’s ability to adjust to economic change, take 

advantage of new opportunities, and succeed more generally in the knowledge economy. 

For these reasons, education is connected to current distress and it is a forward-looking 

measure of future distress. 

Indeed, adding high school educational attainment as an indicator may be more important 

than adding population growth in terms of affecting county exit/entry into/from distress (see 

Section 6 of our April 2008 report). We consider a full set of human capital measures, 

including the share of the adult population (25 + years old) in 2000 with: less than a high 

school diploma (nursery school through grade 12 but no diploma); only a high school degree 

(34.7% of the total); an associate’s degree or more, including BA, MA, professional, and 

Ph.D. degrees (22.2%); at least four years of college with degree completion (16.5%); at 

least one year of college (64.7%); and the percent with less than one year of college 

(35.3%). These data are drawn from the US Census Bureau 

(www.census.gov/population/www/socdemo/educ-attn.html), and they allow us to 

determine whether a community lacks sufficient human capital to compete in the knowledge 

economy. 

3. Income: Per capita market income is another important measure of economic well-being or 

distress, and as such, we consider it carefully as a candidate in the new index (as it is 

currently also used in the ARC’s Distress Index). Yet, as noted in our April 2008 report, 

lower per-capita income can reflect a higher level of amenities, lower cost of living, and 

other factors that are not necessarily related to distress. For these reasons, we also evaluate 

other measures of income and earnings, such as changes in per capita income between 
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1986-1996 as well as 1996-2006. Income data are from the Bureau of Economic Analysis, 

Regional Economic Information System (www.bea.gov/regional/index.htm). 

As the availability of natural amenities (including a favorable climate) tends not to change 

over time, we can conclude that any shifts in income observed over time are due to changes 

in factors other than natural amenities – such as deteriorating local economic conditions. In 

other words, a community with low or even negative income growth rates is truly 

experiencing economic difficulties, and not just attracting new residents who are willing to 

give up income for better amenities. 

4. Housing or Housing Change: Housing construction activity, coupled with changes in 

property values, reflects the forward-looking economic outlook of each individual county. 

Housing is not only a fundamental human need but has been, until just recently, viewed as 

an important – if not the key -- vehicle for wealth creation. Homeownership may also be a 

key source of social stability and social cohesion in communities. 

Changing property values denote local on-the-ground assessments of the future direction of 

the community by private businesses or entrepreneurs (the market). Also, changes in new 

home construction can provide good forecasts of future population shifts. In fact, the 

University of Michigan includes building permits issued as a key indicator in its national 

indicator of leading economic activity. Thus, innovative measures of housing conditions 

can be useful additions to a forward-looking index. 

We examine building permits data (both permits issued and values of the permits) from the 

Census Bureau (www.census.gov/const/www/permitsindex.html). In addition, we identify 

an equally current data series from the same source on the number of housing units in each 

county, for the ten-year intervals corresponding to those used for population and 

employment changes. Since permits reflect intended rather than actual starts, and given the 

upheaval in the housing market associated with the present economic collapse and concerns 

about the completeness of the permitting process in some ARC states, we include only the 

actual number of housing units (from the Census Bureau) for the periods 1990-2000 and 

2000-2007 in our analysis. In fact, the housing unit measure outperforms the building 

permits measure on virtually all counts. The data source is censtats.census.gov/cgibin/usac/usatable.pl. 

In addition, we examined changes over time in the number of bank branch offices as 

supplemental predictors of subsequent economic growth. Banks are key sources of funds 

for homebuyers and other local businesses wishing to expand. Bank branch numbers are 

based on official counts and as such do not suffer from potential under-reporting problems 

(as may occur with residential building permits). However, our analysis shows that growth 

in bank branch offices tends to follow population expansion, rather than the other way 

around. Thus, it is more of a lagging indicator and thus, we choose to rely on population 

growth. 
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5. Entrepreneurship and Self-Employment: A forward-looking measure of the local 

community’s ability to compete is the presence of strong entrepreneurial capacity. Though 

“entrepreneurial capacity” cannot be directly measured from any federal data source, a 

plausible proxy is self-employment in the form of small business ownership. Because new 

business formation is motivated by a host of favorable (e.g., a new idea or business 

opportunity) and unfavorable (e.g., a factory lays off workers) reasons, we attempt to sort 

out entrepreneurship of “necessity” from entrepreneurship of “opportunity” in the region. As 

described (section 5.2.5) of our April 2008 report, one clue about the extent to which selfemployment growth in an ARC county is a response to opportunity as opposed to necessity 

may be found in the returns to self-employment, measured as average earnings per selfemployed worker. This distinction between different forms of entrepreneurship may be an 

important measure of a county’s ability to adjust to new employment realities. 

We obtain data on both self-employment numbers and earnings from self-employment from 

the Regional Economic Information System of the U.S. Bureau of Economic Analysis, for 

1996 and 2006 (www.bea.gov/regional/index.htm). In order to compare counties of various 

sizes, we express these numbers either as a percent of the total workforce (in the case of 

jobs) or as a percent of total earnings (in the case of incomes). We also examine the 

earnings per self-employed worker in order to sort out opportunity entrepreneurship (higher 

earnings) from entrepreneurship of necessity (indicated by lower earnings). After careful 

analysis, we conclude that self-employment, while correlated with local economic strength, 

does not provide sufficient additional information beyond that already contained in job 

changes over time to warrant inclusion of this measure in a distress index. 

6. Labor Market Strength: We consider several measures of labor market conditions as 

candidate variables for the distress indexes. As noted in our April 2008 report, the 

unemployment rate fails, in general, to adequately capture labor market conditions. The 

employment rate (employment rate divided by the population that is sixteen years old and 

over), in conjunction with annual employment growth, are better indicators of overall labor 

market strength. The employment rate directly captures labor-force participation, 

unemployment and discouraged-worker effects. It proves more informative than the 

conventional unemployment rate measure. Specifically, an ARC county may have low 

employment rates due to a combination of high unemployment rates (i.e., those nonemployed individuals who are actively seeking work) and low labor force participation 

because large numbers of non-employed individuals are not working or not actively seeking 

work. 

In particular, we calculate the employment to population (16 years and older) ratio for 2000 

and 2007 from Census Bureau data [see discussion above on data collection issues for intercensus years], the labor force participation rate in 2000 and 2007 (workers who are in the 

labor force relative to all those eligible) and the 1997 unemployment rate, for comparative 

purposes. 

7. Poverty Rate: We maintain that the poverty rate should remain one of the core variables of 

the ARC distress index. Our investigation considers the overlap of poverty with the other 
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indicators included in the complete distress index, as is described in more detail further 

below in this report. Data are from www.census.gov/hhes/www/poverty/poverty.html. 

To summarize, along with the current variables used by the ARC, our first priority is to consider 

forward-looking measures, such as population migration and educational attainment (e.g., 

college and high school graduates). Another priority includes the new labor market measures, 

such as the employment-to-population rate, as a potential replacement for the unemployment 

rate. In this construction, we also explore using the U.S. Census Bureau’s Longitudinal 

Employer-Household Dynamics (LEHD) database to derive innovative measures of employment 

activity and job quality. However, as this data collection effort is still not complete for all of the 

ARC states, we are unable to conduct the same comprehensive analysis covering all geographies 

as we have done with the other variables. The major shortcoming of LEHD data is that it does 

not go back very far in time, making historical analysis impossible for our assessment. Thus, 

while the LEHD is a treasure trove of data, it is not useful for the historical analysis that is 

necessary to derive new distress indices. 

As a secondary priority, we consider the other measures described above. In particular, for 

housing, we examine how measures of building activity and expansion of bank branch offices 

could be used in a potential forward-looking index. Here, we determine that population growth 

more effectively captures local conditions and that bank offices are more of a lagging than a 

leading indicator (in effect, population growth drives these other measures). Likewise, we 

consider measures of entrepreneurial activity as another forward-looking measure. 

Geographical Patterns in the 2006/2007 County Patterns of Indicator Variables 

Figures 2.1 to 2.8 display maps for eight indicator variables that we are evaluating. The first set 

of maps (labeled with an “a”) is for the U.S. and the second set of maps (labeled with a “b”) is 

specifically for the ARC region only. The maps display the variables in standardized or Z-score 

form. As we explain in more detail below in Section 3, the Z-score is a simple numeric 

transformation that makes it easier to directly compare the distributions of two different 

variables. For example, it is difficult to directly compare the poverty rate, which is measured as 

a percentage, with per capita market income, which is measured in dollars per person. With the 

Z-score transformation, variables that inherently vary a great deal (or statistically those with a 

large standard deviation), are scaled so as to make them more comparable with variables that do 

not vary as much from one county to the next. For example, we can directly compare 

unemployment rates, which may vary nationally from 4% to 18%, to poverty rates that may vary 

from 5% to 50%. 

Figures 2.1a and 2.1b show Z-scores for the rate of population growth between 1996 and 2006. 

While the center of the nation experienced remarkable population loss, so did a core set of ARC 

counties that straddle the Kentucky, West Virginia and Virginia borders. A closer look at Figure 

2.1b shows that four of these counties are in southwest West Virginia, and one each is located in 

Kentucky and Virginia. The important role of commuting and attendant urban sprawl is evident 

from the significant population expansion in eastern Pennsylvania and eastern West Virginia, as 

well as around the Atlanta metropolitan area. In the national map (Figure 2.1a), significant 
15 | Page

population growth stands out in Texas in the Dallas-Ft. Worth mega-region (note the ring around 

the urban core), San Antonio, and Houston, as well as in selected counties of Colorado, Arizona 

and Nevada. 

The Z-score map for the 2000 adult population share with at least one year of college (Figure 

2.2a) shows considerable concentration, with high shares especially in Colorado and in a handful 

of counties in Wyoming, Montana and Idaho. In the eastern half of the nation, high 

concentrations of this variable tend to be found in or around major cities. In Figure 2.2b, for the 

ARC counties, higher shares of adults with at least one year of college are found in large cities or 

college towns (such as Centre County, PA, home to Pennsylvania State University). 

Figures 2.3a and 2.3b, showing z-score maps of the proportion of adults who have completed a 

four-year college degree, are similar to the immediately preceding maps, and yet there are subtle 

and important differences. In particular, Figures 2.3a and 2.3b appear to be lighter in color, 

suggesting that the relative concentrations of adults with four years of college in certain counties 

tends to be greater than is the case for adults with just one year of college. Especially in Figure 

2.3b, the relatively small share of adults having completed four years of college in Central 

Appalachia (and especially southwest West Virginia) is noteworthy. 

Relatively high levels of 2006 per capita market income are evident in Figure 2.3a along the 

Northeast Seaboard, southern Florida, coastal California and in Wyoming. In the latter states, 

the high income could reflect natural resource-based activities or the high incomes of transplants 

who earned their wealth elsewhere – such as on Wall Street or in Hollywood. In Figure 2.4b, per 

capita market income tends to be high around major cities (Pittsburgh, PA and Montgomery, 

AL). The contiguous counties in Kentucky with low incomes stand out, and relatively strong 

spillover effects from Lexington (Fayette County) into the adjoining ARC counties are also 

evident. 

A more dispersed pattern of high and low Z-scores appears in the maps showing 2006 selfemployment shares of total county employment (Figure 2.5a). It should be noted that this 

variable captures non-farm self-employment or proprietorships. A few counties stand out with 

high rates of self employment, particularly in the central part of the nation – this is also the 

region with large population losses shown earlier in Figure 2.1a. For the ACR region, Figure 

2.5b reveals relatively low self-employment shares in southwest West Virginia. 

Another key variable, the 2007 employment-to-population ratio (Figure 2.6b), shows high rates 

in some of the Rocky Mountain states and relatively widespread occurrences of very low rates in 

the ARC region (as well as other southeast states). This ratio is a powerful predictor of 

economic well-being and, indeed, some overlap between Figures 2.6a and 2.4a (for per capita 

market income) is evident. In Figure 2.6b, the low employment/population ratios in some of the 

eastern Kentucky counties are quite noticeable. 

For the unemployment rate (Figure 2.7a), a substantially different pattern emerges as compared 

to the employment-to-population ratio (shown previously in Figure 2.5a). This different pattern 

confirms the importance of evaluating the employment/population ratio as a potentially more 
16 | Page

reliable alternative to the unemployment rate as a measure of underlying economic distress. 

Note especially the concentration of low unemployment rates in the northern Great Plains region 

and the Rocky Mountain states, and the high unemployment rates in California, which entered 

the current recession at a relatively early date. Michigan, South Carolina and the Mississippi 

Delta region also have high unemployment rates. In contrast, unemployment rates are very low 

in Virginia and parts of north central Alabama. In Figure 2.7b, a seemingly sharp divide along 

state lines appears between Kentucky and Virginia on the one hand, and between Alabama and 

Mississippi on the other. The counties surrounding metropolitan Atlanta also enjoy 

comparatively low unemployment rates. 

Turning to the last set of maps, Figure 2.8a shows Z-scores for poverty rates across the nation. 

Again, the central core of the ARC region stands out as does the Mississippi Delta region, 

southwest Texas and portions of South Dakota. Quite remarkably, there is also a band of very 

high poverty counties just outside the ARC boundary in Alabama. The map displayed in Figure 

2.8b for the ARC region tends to confirm this pattern, but also shows a very high poverty county 

within the ARC border in Alabama. Also evident are counties with relatively high poverty rates 

in eastern Kentucky, southwest West Virginia, and Ohio. 
17 | Page

Figure 2.1a: County Distribution of Z-scores for Population Growth, 1996-2006

United States, Z-score of Popu

Figures 2.1 – 
18 | Page

Figure 2.1b: County Distribution of Z-scores for Population Growth in ARC Counties, 1996-2006 

ARC Counties, Z-score of 
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Figure 2.2a: County Distribution of Z-scores for 2000 Share of Adults with at least One Year of College Educat
20 | Page

Figure 2.2b: County Distribution of Z-scores for 2000 Share of Adults with at least One Year of College Educat
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Figure 2.3a: County Distribution of Z-scores for 2000 Share of Adults with a Four-Year College Degree 
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Figure 2.3b: County Distribution of Z-scores for 2000 Share of Adults with a Four-Year College Degree, ARC C
23 | Page

Figure 2.4a: County Distribution of Z-scores for 2006 Per-Capita Market Income
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Figure 2.4b: County Distribution of Z-scores for 2006 Per-Capita Market Income, ARC Counties 
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Figure 2.5a: County Distribution of Z-scores for 2006 Nonfarm Self-Employment Share of Total Employment
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Figure 2.5b: County Distribution of Z-scores for 2006 Nonfarm Self-Employment Share of Total Employment, 
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Figure 2.6a: County Distribution of Z-scores for 2007 Employment/Population Ratio
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Figure 2.6b: County Distribution of Z-scores for 2007 Employment/Population Ratio, ARC Counties
29 | Page

Figure 2.7a: County Distribution of Z-scores for 2007 Unemployment Rate
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Figure 2.7b: County Distribution of Z-scores for 2007 Unemployment Rate, ARC Counties
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Figure 2.8a: County Distribution of Z-scores for 2007 Poverty Rate 
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Figure 2.8b: County Distribution of Z-scores for 2007 Poverty Rate, ARC Counties
33 | Page

Methodology: Identifying Specific Candidate Variables 

and Building Distress Indexes 

In this technical section, we describe the methodology used to select the candidate distress 

indicator variables and to build the distress indices presented in Section 4. To improve 

readability, the longer statistical tables can be accessed in the Appendix. For those with less 

interest in the technical details of our work, we invite you to skip directly to Section 4 where we 

present our results. 

In deriving our candidate distress indexes, we generally follow the process described during the 

February and June 2009 webinars organized at the ARC headquarters and in the April 2008 

report we submitted to the ARC. Our methodology involves (1) identifying individual 

“Candidate Variables” that are associated with contemporaneous and future distress as described 

in Section 2 above, and (2) using the Candidate Variables to construct six top “Candidate 

Distress Indexes” presented to ARC for further examination. The six top Candidate Distress 

Indexes are constructed using a variety of combinations of the Candidate Variables. 

As described in Section 2, we consider 40 Candidate Variables that are linked to future distress 

and local economic growth. These measures are associated with labor market conditions, 

income, poverty, housing and self employment, among other factors. A priori, these 40 

Candidate Variables are perceived as good measures of contemporaneous distress. To assess 

whether they are also suitable forward-looking variables for determining future distress, we 

collect data on the same variables dating back to the mid-1990s. 

Selecting Specific Candidate Distress Indicator Variables 

We considered a variety of methodologies to select our distress indicators. Sophisticated 

regression strategies were initially considered which have the advantage of being considerably 

less data intensive than the approach we eventually selected. Yet, we decided to forego this 

methodology for two reasons. First, there is no clear, single dependent variable for distress—so 

pursuing statistical analysis without a clear dependent variable is problematic. Second, ARC 

stakeholders desire an end-product that is transparent to non-statisticians. As a result, we employ 

a multiple-step approach that selects the variables associated with both current and future distress 

in a clear and straightforward manner. The desired outcome is to develop Candidate Distress 

Indexes that could help target funding to counties that are currently in distress and at the greatest 

risk of experiencing future distress. Because ARC policy intervention is usually aimed at 

reducing future distress, our empirical goals seem particularly appealing. 

Our methodology for selecting Candidate Variables is explained in the following steps. The first 

step is to consider whether our Candidate Variables are strongly associated with future distress. 

As noted previously, our interest is to address future distress in a manner that is consistent with 

the ARC tradition of addressing long periods of persistent distress—rather than short-term 

cyclical downturns such as a plant closing. As such, we consider whether the Candidate 

Variables are associated with future distress (or economic outcomes) about ten years in the 

Section 3 
34 | Page

future. Thus, we take the Candidate Variables measured in circa 1996/1997 (or as close as 

possible due to data availability) and examine their correlation with key economic outcomes 

around the current or 2006/2007 time period. 

While numerous economic outcomes in 2006/2007are considered in this analysis, we focus on 

key recognizable outcomes. Specifically, Table 3.1 outlines the five measures of 

“contemporaneous” distress that we believe are the most important and most easily defensible to 

ARC stakeholders and external observers. These five key indicators provide a general picture of 

overall economic prosperity and help capture the broad dimensions of community distress, 

without examining so many outcomes as to become too unwieldy. We view high correlations 

with these five contemporaneous economic measures as a good reflection that a Candidate 

Variable measured in the mid 1990s would be closely associated with future distress. 

Table 3.1: Five Key Indicators of Current Distress 

As noted in the previous section, the ten-year measures of employment and population 

growth reflect the persistent nature of the distress that we are trying to capture here. This 

persistence would not be well-represented in measures calculated over shorter periods of 

time, such as two or five years. 

 

The predictive performance of these Candidate Variables for the ARC counties is demonstrated 

in Appendix Table A.1. This table shows the correlations for Candidate Variables measured in 

the mid-1990s (down the rows) with outcomes measured around 2006/2007 (columns across the 

top). For comparison, the very top row shows the 1997 ARC distress index measured on a 1 to 5 

scale with one being categorized as in attainment and five being classified as distressed. The 

correlation of the percent change in population between 1986 and 1996 with the 2007 poverty 

rate is -0.41, which is of moderate predictive power. 

The predictive performance of the same Candidate Variables for all U.S. counties is 

demonstrated by the correlations shown in Appendix Table A.2. U.S. aggregate correlations are 

used as a way to appraise the reliability of the ARC county results. For example, for all U.S. 

counties, the correlation of the 1986-1996 percent change in population with the 2007 poverty 

rate is only -0.22. In other words, population growth is related to a much lesser degree to poverty 

in the overall U.S. than in the ARC region. 

We provide many of the correlations for the 2006/2007 variables shown in Tables A.1 and A.2 

for comparison purposes. Evaluating the size of all these correlations, the five variables 

identified in Table 3.1 are the most informative variables in terms of whether a Candidate 

Variable measured in 1996/1997 also is associated with distress in 2006/2007. 

1 2007 poverty rate 

2 2006 per-capita market income 

3 1996-2006 total job growth (including proprietors) 

4 1996-2006 population growth 

5 2007 employment/population ratio 
35 | Page

Based on discussions with ARC stakeholders, and after reviewing the distress indicator literature, 

we view the correlations with the 2007 poverty rate as particularly important in indicating 

distress. Thus, we require some Candidate Variables (measured in the 1996/1997 period) to be 

highly correlated with the 2007 poverty rate. Not surprising, the 1997 poverty rate is highly 

correlated with the 2007 rate (0.92), while the 2000 employment/population ratio and the 1996 

per-capita income also have correlations in the -0.74 or -0.77 range. Given the persistence of the 

poverty rate, we include the poverty rate as a good Candidate Variable in further assessment. 

For illustration purposes, Figures 3.1 to 3.3 report scatter plots of the 1996 poverty rate with the 

2007 poverty rate, 2006 per capita market income, and the 2007 employment/population rate. 

These figures show the strong association between the 1997 poverty rate and future economic 

outcomes. 

Next, we examine several groupings of variables to select additional Candidate Variables to be 

used in constructing Candidate Distress Indexes. First, the 1986-1996 population growth variable 

consistently dominates the 1986-1996 job growth variable across the board—even being more 

highly associated with 1996-2006 job growth. Second, 1996 per-capita market income is also 

highly associated with most of the key variables, though it is only moderately associated with 

1996-2006 population and job growth. Third, the 2000 employment/population rate is more 

strongly correlated with the five key 2006/2007 measures than the labor-force participation rate. 

Conversely, the 1996 unemployment rate is much less strongly related to 2006/2007 outcomes 

than either the employment population ratio or the labor force participation rate. The weak 

performance of the unemployment rate is consistent with the general limitations of the 

unemployment rate recognized by ARC stakeholders and within the academic literature 

(Partridge et al., 2008). 

Among the education variables, we focus on measures that reflect at least some college 

attainment. This follows from the February 2009 ARC webinar in which stakeholders strongly 

voiced this preference. College-educated workers better reflect the shift to the knowledge 

economy. (We report the correlations for high school dropouts and high school graduates for 

comparison.) The percent of the adult population with at least one year of college education 

generally outperforms the percent of the adult population with at least an associate degree and 

the percent of the adult population with at least a bachelor’s degree. Yet, given the focus on the 

knowledge economy in the literature and among ARC stakeholders, we retain the bachelors 

degree for subsequent analysis because the differences in correlations among the educational 

attainment groups are not that sizable. Overall, the five (six including the bachelor’s degree 

variable) Candidate Variables selected for further analysis are shown in Table 3.2. 

Table 3.2: Candidate Indicator Variables Selected for Further Analysis for Inclusion into 

Distress Indexes 

1 Employment/population ratio 

2 Poverty rate 

3 Per-capita market income 

4 Percent of the adult population with at least one year of college education (also at least a four-year 

Bachelors degree) 

5 10 -year percent change in population 
36 | Page

It should be noted that other variables are not as highly associated with the five key 2006/2007 

economic outcomes. For example, the 1986-1996 percent change in per-capita income, the 1996 

self employment share of total employment, and 1996 building permits per capita are not as 

highly correlated with these five key outcomes. 

The results of our selection of the candidate indicator variables are also supported by the 

correlation analysis for all U.S. counties. As noted previously, Table A.2 shows the 

corresponding correlations with 2006/2007 outcomes for all U.S. counties. Comparisons to the 

entire U.S. represent a valuable way to examine the validity of the results. One of the key 

patterns is that the ARC regional correlations for the key economic outcome variables are greater 

than the corresponding correlations for all U.S. counties. However, our basic conclusions remain 

unchanged in that the best Candidate Distress Variables shown in Table 3.2 for the ARC region 

appear to be the same for the U.S. as a whole. This correspondence with the U.S. adds credibility 

to our findings, which would help make a compelling argument when proposing a new economic 

distress index to OMB and Congress. 

The five Candidate Variables displayed in Table 3.2 appear to be solid forward-looking 

indicators associated with economic outcomes 10 years in the future (six when including the 

four-year college graduate share). Of course, these Candidates are selected on the basis of their 

characteristics in 1996/1997. Thus, one outstanding question is whether the Candidate Variables 

listed in Table 3.2 are linked to contemporaneous outcomes measured in 2006/2007. In this 

fashion, Table 3.3 presents the correlations of the six Candidate Variables (including the fouryear college graduate share) measured in (or as close as possible to) 2006/2007 with the five key 

economic outcomes measured in 2006/2007. We also show the respective correlations for the 

2007 unemployment rate because it is currently being used by ARC in its distress index. 

Table 3.3: Correlations between 2006/2007 Candidate Variables, the Unemployment Rate and 

 2006/2007 Economic Outcomes, ARC Counties 

Poverty 

Rate 

2007 

Per-capita 

Market 

 Income 2006 

% Job 

Growth 

1996-06 

% 

Population 

Growth 

1996-06 

EmploymentPopulation Ratio 

2007 

Employment-population ratio 2007 -0.72 0.71 0.45 0.42 1.00 

Unemployment rate 2007 0.61 -0.61 -0.37 -0.29 -0.62 

Poverty Rate 2007 1.00 -0.74 -0.32 -0.33 -0.72 

Per-capita market income 2006 -0.74 1.00 0.36 0.32 0.71 

Percent share of adult population with a 4-yr college 

degree or more (BA+MA+professional+PhD), 2000 -0.37 0.72 0.26 0.26 0.47 

Percent share of adult population with more than one 

year of college (more than one year at some 

college+associates+BA+MA+professional +PhD), 2000 

-0.47 0.77 0.26 0.28 0.53 

Population percent change 1996-06 -0.33 0.32 0.73 1.00 0.42 
37 | Page

The unemployment rate consistently underperforms relative to the 

employment-population ratio as shown by the size of correlations for both 

variables. The results clearly demonstrate that not only are the six 

Candidate Variables (including the four-year bachelors degree share) 

strong forward-looking indicators of future distress, but they also capture 

contemporaneous distress as well. 

Methodology to Construct the Distress Indexes Using 

the Candidate Variables 

The first step in the process of building actual Candidate Distress Indexes 

is to create Z-scores for each of the five Candidate Variables described 

above. Z-scores are commonly used by social scientists to compare 

variables that are measured using different metrics (such as income in 

dollar figures and poverty in percentages) and having different 

distributions. In effect, Z-scores allow for comparisons between variables 

and without it, one would be attempting to compare “apples” and 

“oranges.” For example, by using Z-scores, we can directly compare 

unemployment rates, which may nationally vary from 4% to 18%, to 

poverty rates that may vary from 5% to 50%. 

A Z-score is created through a statistical transformation of a variable that 

sets its mean equal to zero and its standard deviation equal to 1. Thus, for 

each of the 5 Candidate Variables, we create a transformed variable that 

reflects whether a county’s value for the variable is (say) 0.5 standard 

deviations above the mean. For example, if the mean 1986-1996 

population growth rate for all counties is 12% and the standard deviation 

is 10%, then a county that experienced a 17% population growth rate 

would be 0.5 standard deviations above the mean and be given a 

population-change Z-score of 0.5. Likewise, a county that experienced a 

population growth rate of only 2% over the period would be given a Zscore of -1.0 to correspond to its falling 1.0 standard deviation below the 

mean population growth rate of 12%. 

For every Candidate Variable except the poverty rate, a positive Z-score is 

associated with a more favorable outcome. In order to convert the poverty 

rate Z-score into the same basis, we take the negative of the poverty rate 

Z-score-- i.e., – Z-scorepovrt. Thus, if the mean poverty rate is 15% with a 

standard deviation of 10%, a county with a poverty rate of 20% would be 

assigned a Z-score of -0.5 -- i.e., negative Z-scores would then reflect 

outcomes below the national mean for all of the Candidate Variables. 

As we noted above, converting the variables into Z-scores is important in 

constructing a distress index because it places all of the Candidate 

Variables on an even basis regardless of their variability. For example, the 

Side Box 3.1: 

The sensitivity of changing to an 

index based on Z-scores can be 

examined using the current ARC 

Distress Index. Using the process 

described in this section, we 

calculated the FY2007 ARC 

Distress Index based on Z-scores 

rather than the current procedure. 

Figures 3.1a and 3.1b show how 

the ARC distress categorization 

would change if it were based on 

Z-scores. 

Red indicates counties that would 

be categorized as distressed under 

both indexes—or there would be 

no change. 

Blue indicates counties that would 

be categorized as distressed with 

the ARC Index, but would not be 

categorized as distressed using the 

Z-score index. 

Green indicates counties that 

would be categorized as distressed 

using the Z-score index, but not 

under the current ARC Index. 

White indicates counties that 

would not be categorized as 

distressed using either index. 

The results show that 33 counties 

would change position, mostly in 

Central and Southern Appalachia. 

Sixteen counties not categorized by 

the current ARC method as distress 

would now fall into the distressed 

category when using the Z-score 

method (mostly in Ohio and 

Mississippi). On the other hand, 17 

counties classified as distressed 

using the current method would no 

longer fall into the distress 

category with the Z-score method, 

with a large number of these being 

located in West Virginia. Thus, 

there is very little net change in the 

number of distressed counties 

using z-scores. 
38 | Page

current ARC Distress Index gives a greater weight to the poverty rate by default--simply because 

the poverty rate has greater variability than the unemployment rate or the per-capita market 

income, as we demonstrated in our 2008 report (Partridge et al., 2008). Giving the poverty rate a 

higher weight may be appropriate, but this characteristic is an artifact of the poverty rate having 

a larger standard deviation, not because of a formal process of selecting weights by the ARC 

staff and stakeholders. With the Z-score transformation, this problem of different weighting 

among the variables comprising a distress index is avoided. 

The current ARC Distress Index is composed of three variables. One potential shortcoming of 

this index is that three variables may be inadequate to capture all the dimensions of distress. 

Another shortcoming is that a distress index composed of only three variables may be prone to 

large year-to-year fluctuations when one variable changes, which may create transitional funding 

issues. Thus, considering up to five Candidate Variables mitigates these concerns without 

creating an index that is too unwieldy because it contains too many variables. 

Using the five Candidate Distress Variables, we create 24 different permutations (combinations 

of different Candidate Variables) that are used to identify potential Candidate Distress Indexes. 

The basic strategy is to construct several permutations composed of 3 of the 5 Candidate 

variables, then several permutations composed of 4 of the 5 Candidate Variables, and finally one 

composed of all five Candidate Variables. Based on these permutations, once we identify the top 

potential Candidate Distress Indexes, we create additional Candidate Distress Indexes by 

substituting the percent of the population with at least one-year of college education with the 

percent with at least a bachelor’s degree. We substituted different variables for education 

because the ARC States and staff suggested that differing measures of years of college 

completion might have varying effects in the region. Thus, our substitution considers whether 

using fewer years of college attainment (one year) has different effects than using a four-year 

college degree. 

Then, for each of the two dozen permutations of Candidate Distress Indexes, the respective Zscores for the corresponding Candidate Variables (including the negative of the poverty rate Zscore) are summed to obtain an overall measure of relative distress. For a given year, equation 

3.1 illustrates a Candidate Distress Index (D) based on using population growth, the percent of 

the adult population with at least one-year of college education, and the poverty rate: 

 

(3.1) Dpop_col_pov = Zpop + Zcol + Zpov.

Given our construction of the Z-scores, more positive values of the Candidate Distress Index 

indicate a more vibrant environment, while a more negative value indicates a county that is 

falling more into distress. We repeat this calculation for each of the 25 permutations and 

combinations to obtain the various Z-score based Candidate Distress Indexes. 

The current ARC ranking procedure is then used to categorize each county into the five 

classifications running from distressed to attainment. Specifically, for each of the two dozen 

Candidate Distress Indexes measured in 1996/1997, we rank all of the approximately 3,100 U.S. 

counties into the five ARC attainment categories: the lowest 10% Z-scores are considered 
39 | Page

Distressed; 10%-25% Z-scores are categorized as At-Risk; 25%-75% of Z-scores are classified as 

Transitional; 75%-90% of Z-scores are denoted as Competitive; 90%-100% of Z-scores are 

considered in Attainment. Each ARC region county is then assigned to the category into which 

they fall for the entire nation. 

Ranking the Candidate Distress Indexes 

To assess whether the two dozen 1996/1997 Candidate Distress Indexes are forward-looking 

measures of distress, we calculate their correlation with the five key contemporaneous 

(2006/2007) measures of economic outcomes shown in Table 3.1, as well as the other ancillary 

measures used in Tables A.1 and A.2.2

 The resulting correlations for the ARC region are shown 

in Appendix Table A.3 and the corresponding correlations across all U.S. counties are reported 

in Appendix Table A.4. The last column of these two tables shows the average correlations for 

the five key economic outcome variables. We rank the top Candidate Distress Indexes based on 

this average, which implicitly gives each of the five key outcomes an equal weight in appraising 

the overall economic outcomes. In cases in which there are ties in the average correlation, 

Candidate Distress Indexes that use more Candidate Variables are ranked higher because that 

would better reflect broader dimensions of economic distress. Indexes based on more indicators 

would also have the advantage of experiencing less-year-to-year fluctuation. We also rank more 

highly the Candidate Distress Indexes that include the poverty rate due to poverty’s longstanding significance as an economic benchmark in the ARC region. 

Based on the criteria above, we report the top six-ranked indexes in upper six rows of Table A.3. 

The highest-ranked Candidate Distress Index has an average correlation of 0.71. It is composed 

of the ten-year population growth, employment/population rate, percent of the population with at 

least one-year of college education, and the poverty rate. The second ranked index—also with an 

average correlation of 0.71—includes the same set of Candidate Variables but does not include 

the percentage of the population with at least one-year of college education. Finally, the sixth 

highest ranked Candidate Distress Index includes the percent of the population with at least oneyear of college education, per-capita market income, ten-year population growth, and the poverty 

rate (with an average correlation of 0.70). 

For the top-six ranked (circa) 1996/1997 indexes, there is relatively little difference in terms of 

their average correlation with 2006/2007 economic outcomes. As one moves further down the 

list, the forward-looking correlations tend to decline more rapidly. Thus, at the very top, there is 

less to differentiate the indexes from one another, which would suggest that the Commission 

would not be making a major mistake were it to choose one index over another—at least on an a 

priori basis. However, different sets of counties would be affected by the choice of a specific 

index. 

As explained earlier, the percent of the population with a four-year college degree will be given 

further attention since it is perceived as more strongly linked to a county’s potential to participate 

                                                           

2

The values assigned to the Distress Indexes are a value of 5 for a distressed county, a value of 4 for a county 

classified as at-risk, and so forth, to a value of 1 when the county is classified as in attainment. 
40 | Page

in the emerging knowledge economy. Of the top-six Candidate Distress Indexes presented in the 

top tier of Table A.3, four include the percent of the population with at least one year of college 

education. For these four Candidate Distress Indexes, we replace the percent of the adult 

population with at least one year of college education with the percent with at least a bachelor’s 

degree. We then create four additional Candidate Distress Indexes. These four alternative distress 

indexes are reported in the bottom rows of Table A.3 (see rows 1b-4b). 

The average correlations in the final column of Table A.3 illustrates that using the college 

graduate share results in no measurable change in the predictive capacity of the indexes. 

Moreover, the highest-ranked Candidate Distress Index using four-year college graduate includes 

the same variables as the highest-rated Candidate using the percent with at least one-year of 

college education (with the only difference being the college attainment variable). Likewise, the 

second-highest Candidate Distress Index using the bachelor’s degree is the one that includes all 5 

Candidate Variables—which is also the second-highest Candidate Distress Index when using the 

one-year college attainment share. The overall conclusion is that replacing the one-year college 

attainment share with the bachelor’s attainment share does not make a perceptible difference as a 

forward-looking indicator of economic distress. Given that having at least a bachelor’s degree is 

more closely linked to the emerging knowledge economy, we have a slight preference for 

Candidate Distress Indexes that include the bachelor’s degree share, which is in line with the 

preferences of ARC stakeholders. 

Table A.4 reports the corresponding Candidate Distress Index correlations using all 3,100 U.S. 

counties rather than the group of ARC counties. The table is ordered in the same manner as 

Table A.3 with the top indicators using the one-year college attainment share in the upper portion 

of the table and four corresponding measures using bachelor’s degree attainment in the bottom 

tier. Among the ones using bachelor’s degree attainment, the Candidate Index with the highest 

correlation (0.63) is the one that uses ten-year population growth, employment/population rate, 

percent of the adult population with at least a bachelor’s degree, and the poverty rate. The second 

highest-ranked index includes all five Candidate Variables, while the one ranking third-highest 

includes all the variables with the exception of the poverty rate. 

For the Candidate Distress Indexes using at least one-year college attainment, the top-three 

ranked indexes use the same variables as the top-three ranked Candidate Distress Indexes using 

bachelor’s degree attainment. The only difference is that a modest change occurs in the ordering 

of the indexes. In addition, the rankings using all U.S. counties tend to be aligned with the results 

of the rankings of the ARC counties. 

A general finding is that the top-ranked choices in terms of average correlation are very closely 

clustered—with a more rapid decline when moving further down the list. But, in order to 

winnow the list to a more manageable size, we decide to move forward with Candidate Distress 

Indexes that include at least four Candidate Variables. We do so for at least three reasons: (1) 

indexes that include at least four Candidate Variables represent the largest change from past 

ARC practice; (2) there is less year-to-year variability when including a greater number of 

variables; and (3) they capture more dimensions of distress. Thus, we no longer consider the 

Candidate Distress Index composed only of ten-year population growth, employment/population 
41 | Page

ratio, and per-capita income, nor the Candidate Distress Index composed of ten-year population 

growth, the employment/population ratio, and the poverty rate. 

This leaves six Candidate Distress Indexes for further consideration, three that include at least 

one-year college attainment variable and the other substituting this measure with the bachelor’s 

degree attainment share. In Table 3.4, we report the six indexes we recommend for further 

consideration by the ARC: section 3.4a lists the variables and section 3.4b presents them in chart 

form. Appendix Table A5 presents descriptive statistics for each of the six Candidate Distress 

Indexes for (1) the ARC region and (2) for the remaining U.S. counties. Appendix Table A6 

presents the underlying Z-score values for the six Candidates for each ARC county, as well as 

comparisons to the FY2007 ARC Distress Index. 

Our recommendation is to rank more highly the Candidate Distress Indexes that include the 

bachelor’s attainment share rather than one-year college attainment. Among those, our two 

highest-ranked Candidate Distress Indexes use all five Candidate Variables (with bachelor’s 

degree attainment) or uses population growth, employment/population rate, bachelor’s degree 

share; and poverty rate. Though we are not strongly recommending Candidate Distress Indexes 

that use the one-year college attainment share, they do provide an excellent comparison to 

examine the sensitivity of the precise counties that fall into the distress category (see Section 4). 

The correlations of these Candidate Distress Indexes with 2006/2007 outcomes are reported in 

Table 3.5. In addition, the last two columns of Table 3.5 include the ARC Distress Index 

measured in 2007 and 2009. 

Table 3.4: The Six Indexes Recommended for Further Consideration to the ARC 

Panel a. The Six Indexes 

1 Population growth, Employment/population ratio, Percent of adults with a four-year college degree, Poverty Rate 

2 Population growth, Employment/population ratio, Percent of adults with a four-year college degree, Per-capita market income, 

Poverty Rate 

3 Population growth, Employment/population ratio, Percent of adults with a four-year college degree, Per-capita market income 

4 Population growth, Employment/population ratio, Percent of adults with at least one year of college education, Poverty Rate 

5 Population growth, Employment/population ratio, Percent of adults with at least one year of college education, Per-capita 

market income, Poverty Rate 

6 Population growth, employment/population ratio, Percent of adults with at least one year of college education, Per-capita 

market income 
42 | Page

Panel b. Specific Candidate Variables Comprising Each Index 

Poverty 

Rate 

2007 

Population 

growth 

1996-2006 

Employment/ 

population ratio 

2007 

% of 

adults 

with a 

four-year 

college 

degree 

2000 

% of adults 

with at least 

one year of 

college 

education 

2000 

Per-capita 

market 

income 

2006 

Index 1 X X X X 

Index 2 X X X X X 

Index 3 X X X X 

Index 4 X X X X X 

Index 5 X X X X 

Index 6 X X X X 

Table 3.5: Correlations between the Lagged Candidate Distress Indexes and 2006/2007 Key 

 Economic Outcomes, ARC Counties 

Lagged Candidate Distress Indexes 

%Pop. 

Growth 

1996-06 

Poverty 

Rate 2007

Per-capita 

Market 

Income 

2006 

%Job 

Growth 

1996-06 

Employment/

Population 

ratio 2007 

ARC 

Distress 

Index 

2007 

ARC 

Distress 

Index 

2009 

Population growth 86-96, Employment /population ratio 

00, Percent of adults with a four-year college degree 00, 

Poverty Rate 97 

0.65 -0.78 0.80 0.52 0.80 -0.83 -0.79 

Population growth 86-96, Employment /population ratio 

00, Percent of adults with a four-year college degree 00, 

Poverty Rate 97, Per-capita market income 96 

0.62 -0.79 0.85 0.49 0.80 -0.85 -0.82 

Population growth 86-96, Employment /population ratio 

00, Percent of adults with a four-year college degree 00, 

Per-capita market income 96 

0.67 -0.70 0.83 0.53 0.78 -0.80 -0.77 

Population growth 86-96, Employment /population ratio 

00, Percent of adults with at least one year of college 00, 

Poverty Rate 97, Per-capita market income 96 

0.55 -0.86 0.85 0.45 0.80 -0.87 -0.84 

Population growth 86-96, Employment /population ratio 

00, Percent of adults with at least one year of college 00, 

Poverty Rate 97 

0.64 -0.79 0.81 0.51 0.80 -0.83 -0.80 

Population growth 86-96, Employment /population ratio 

00, Percent of adults with at least one year of college 00, 

Per-capita market income 96 

0.67 -0.72 0.83 0.52 0.78 -0.81 -0.78 
43 | Page

Evaluating How the Top Candidate Distress Indexes Correlate 

with Contemporaneous Distress 

The top-six Candidate Distress Indexes reported in Table 3.4 appear to do an admirable job as 

forward-looking indicators of future distress. Next, we briefly confirm that they are also highly 

linked to contemporaneous measures of economic outcomes. To do this, for the top-six 

Candidate Distress Indexes shown in Table 3.4, we now derive their values based on circa 

2006/2007 data (rather than circa 1996/1997 data used in the previous sub-section). The 

correlations of these circa 2006/2007 Candidate Distress Indexes with the five key 2006/2007 

economic outcomes (listed in Table 3.1) are reported in Table 3.6. The results indicate that there 

is a very high correlation with contemporaneous measures of economic distress, often in the 0.8 

range. Thus, we conclude that the top-six Candidate Distress Indexes are strong indicators of 

both current and future economic distress. 

The last two columns of Table 3.6 show that the correlations of the top-six 2006/2007 Candidate 

Distress Indexes with the current 2007/2009 ARC Distress Index are also in the range of 0.77~ 

0.84. Thus, because the correlations are not near 1.0, the Candidate Indexes do not merely 

represent incremental change from the current ARC Index. Yet, the relatively high correlation 

also means that they would not constitute a radical change. The question of which particular 

counties are affected when using these Candidate Distress Indexes versus the current ARC Index 

is discussed next in Section 4. 

Table 3.6: Correlations between the Candidate Distress Indexes and 2006/2007 Key Economic 

Outcomes 

Candidate Distress Indexes

Population 

growth 

 96-06

Poverty 

Rate 07

Per-capita 

market 

income 

2006

Job growth 

1996-06 

Employment

/population 

ratio 2007 

ARC 

Distress 

Index 

2007 

ARC Distress 

Index 2009 

1) Population growth 96-06, Employment/population 

ratio 07, Percent of adults with a four-year college 

degree 00, Poverty Rate 07 

0.70 -0.81 0.81 0.60 0.84 -0.83 -0.81 

2) Population growth 96-06, Employment/population 

ratio 07, Percent of adults with a four-year college 

degree 00, Per-capita market income 06, Poverty 

Rate 07 

0.64 -0.82 0.87 0.57 0.83 -0.86 -0.84 

3) Population growth 96-06, Employment/population 

ratio 07, Percent of adults with a four-year college 

degree 00, Per-capita market income 06 

0.70 -0.66 0.84 0.62 0.80 -0.78 -0.76 

4) Population growth 96-06, Employment/population 

ratio 07, Percent of adults with at least one year of 

college education 00, Per-capita market income 06, 

Poverty Rate 07 

0.64 -0.83 0.87 0.56 0.83 -0.86 -0.84 

5) Population growth 96-06, Employment/population 

ratio 07, Percent of adults with at least one year of 

college education 00, Poverty Rate 07 

0.69 -0.82 0.81 0.59 0.84 -0.84 -0.81 

6) Population growth 96-06, employment/population 

ratio 07, Percent of adults with at least one year of 

college education 00, Per-capita market income 06 

0.70 -0.68 0.84 0.61 0.81 -0.80 -0.77 
44 | Page

Figures 3

Figure 3.1a: Index constructed using proposed equal-weight Z-score method--using current ARC variables (Un

Rate, Poverty Rate, Per-Capita Market Income)
45 | Page

Figure 3.1b: Index constructed using proposed equal-weight Z-score method--using current ARC variables (Un

 Poverty Rate, Per-Capita Market Income), ARC Counties 
46 | Page

 Figure 3.2: Correlation between 2007 Poverty Rate and 1997 Poverty Rate, ARC Counties
47 | Page

Figure 3.3: Correlation between 2006 Per-Capita Market Income and 1997 Poverty Rate, ARC Counties 
48 | Page

 Figure 3.4: Correlation between 2007 Employment/Population Ratio and 1997 Poverty Rate, ARC Counties 

y = -0.9373x + 69

R2 = 0.5112

2030405060708090

100

0 10 20 30 40 

Poverty rate, 1997

Employment-population ratio 2007
49 | Page

Comparing the Candidate Distress Indexes to the ARC Distress Index

In the previous section, we identified our six preferred Candidate Distress Indexes for further 

analysis (see Table 3.4). This section demonstrates how each of the six Candidate Distress 

Indexes would affect the distress categorization of each ARC county. Because the “weakest” 

10% of U.S. counties are categorized as being in distress, the total number of distressed counties 

in the U.S. does not change using our Candidate Distress Indexes compared to the ARC index. 

However, the geographical distribution of the counties inside and outside of the ARC region 

categorized as being in distress can change due to the differing implicit weighting in the current 

ARC Distress Index and the different variables used in the construction of each Candidate Index. 

In other words, our more refined analysis may find that a greater number of ARC counties are in 

fact distressed relative to the nation when contrasted with ARC’s current distress indicator. 

To show the categorization of each of the Candidate Distress Indexes, we follow the approach 

outlined in our April 2008 ARC report (Partridge et al., 2008). Specifically, we report four sets 

of results for the Candidate Distress Indexes using 2007 data. 

 First, we present a map for the entire U.S. that shows how the distress categorization 

would change if the respective 2006/2007 Candidate Distress Index replaced the FY2007 

ARC Distress Index. The U.S. comparison is primarily used to show how the Candidate 

Distress Index benchmarks to the entire nation. 

 Second, we present the corresponding map of how the distress categorization changes for 

the ARC region, which represents the most important result of our analysis. This is the 

same comparison to the FY2007 ARC Distress Index as the U.S. map, but we “zoom in” 

to the ARC region only. 

 Third, we report a table that summarizes the changes across each of the five ARC 

economic categories by state that would occur if the alternative candidate variables were 

used compared to the FY2007 ARC Index. Appendix Table A7 presents a brief summary 

solely of the number of distressed counties for each Candidate Index by state (as well as 

for the ARC Indexes for FY2007 and FY2010). 

 Fourth, for each of the six Candidate Distress Indexes, we show a map that illustrates 

how the Candidate Indexes would classify the ARC counties into the five ARC economic 

categories ranging from distress, at-risk, all the way to attainment. The general order of 

our discussion of the six Candidate Distress Indexes follows our minor preference in 

ranking each Candidate Index. 

To preview the results, the general pattern is that the Candidate Indexes suggest that a greater 

number of ARC counties would be classified as either distressed or at-risk compared to the 

current ARC Distress Index. Specifically, compared to the 74 distressed and 88 at-risk ARC 

counties delineated using the current index (in FY2007), there would be between 82-95 counties 

classified as distressed and 87-112 classified as at-risk using the 6 Candidate Indexes. Note that 

Section 4 
50 | Page

this is relative to the nation in that the Candidate Indexes also use the lowest 10% of U.S. 

counties to be classified as distressed and the lowest 10 to 25% to be classified as at-risk. 

Candidate Index 1: Population Growth, Employment/Population Rate, Four-year College 

Attainment, and Poverty Rate 

The four sets of results are presented graphically in Figures 4.1a-4.1c and summarized in Table 

4.0 (which outlines changes taking place in the distress status of ARC counties when various 

indexes are employed). Figure 4.1a shows how the distress categorization would change 

nationally if the Candidate Index replaced the current ARC Index. We use the following colors 

to represent the changes. Red indicates counties that would be categorized as distressed under 

both indexes – in other words, both measures produce the same result. Blue represents counties 

that are categorized as distressed with the ARC index, but that would not be categorized as 

distressed using the Candidate Index. Green refers to counties that would be categorized as 

distressed using the Candidate Index, but not under the current ARC index. White indicates 

counties that are not categorized as distressed using either index. 

Many counties in Central Appalachia turn green when this Candidate Index (containing 

population growth, employment-to-population ratio, percent of adults with four years of college, 

and the poverty rate) is used. Outside of the ARC region, the Candidate Index indicates more 

distressed (green) counties in the fringes of the Mississippi Delta and the Historic Cotton Belt, as 

well as in several counties in Georgia and Alabama and fewer distressed (blue) counties along 

the Rio Grande and in the Great Plains Reservations. 

Figure 4.1b shows the same distress categorization specifically for the ARC region. The new 

Candidate Index reveals a scattering of green (newly distressed) counties throughout the Central 

Appalachian area, and an arc of blue (no longer distressed) counties stretching from Kentucky 

into Tennessee. The regions of Mississippi and Alabama that are close to the southern ARC 

border also have a total of five newly distressed counties (i.e., the southwest part of the ARC 

region near the Delta and the Cotton Belt) according to this Candidate Index, offsetting some of 

the blue counties that would fall out of the distress category (including one county in 

Pennsylvania). 

Table 4.0 indicates that overall, 34 counties change distress status (for comparison, 74 ARC 

counties were classified as distressed in FY2007 using the ARC Distress Index). Specifically 21 

counties move into the distress category and 13 counties move out of the distress category, for a 

net addition of 8 more counties categorized as being in distress using this Candidate Index. Table 

4.1 confirms using this Candidate Index that 8 more counties are in distress using this Candidate 

Index (82 total in distress) compared to the current ARC Index measured in FY2007 (74 in 

distress). In particular, 11 more counties are classified as being in distress with the new 

Candidate Index, and 3 counties drop out of distress status (for a net change of 8). In terms of 

details, the changes break out as follows: Five states have additional distressed counties (AL, +2; 

MS, +2; OH, +2; VA, +4; and WV, +1) and two states have fewer distressed counties (KY, −2; 

and PA, −1). Furthermore, there is a net decrease of 1 county considered at-risk with this 

Candidate Index (with 12 becoming at-risk and 13 falling out of at-risk status), and the number 
51 | Page

of transitional counties falls by 18 (from 225 to 207 counties). The number of competitive 

counties increases by 6, and the number of in attainment counties rises by 5 with this Candidate 

Index. Details as to where these counties are found, specifically by state, are reported in Table 

4.1. For example, most of the counties (4) that are in attainment under this Candidate Index are 

located in Georgia. 

Finally, Figure 4.1c shows the full range of ARC economic categories using the Candidate 

Index. Clearly, the largest concentration of red counties is observed across Central Appalachia 

(Kentucky and West Virginia especially) but the very southern reaches of the region in 

Mississippi and Alabama are also shaded in dark red. Light blue counties (competitive) are 

scattered throughout the region, with a notable concentration of dark blue (attainment) counties 

around the Atlanta, GA metropolitan area, and a single dark blue county in New York. 

Candidate Index 2: Population Growth, Employment/Population Rate, Four-year College 

Attainment, Poverty Rate, and Per-capita Market Income 

Figures 4.2a and 4.2b show a heavy concentration of red counties in Kentucky and West Virginia 

(indicating that both the current ARC Index and this Candidate Index classify the counties in the 

same manner, i.e., as distressed). In addition, blue counties that are no longer classified as being 

distressed (using Candidate 2) and green counties (that Candidate 2 now classifies as being 

distressed) are interspersed with the red ones. Further, while the counties in the southwestern 

ARC are mostly red, a few green counties and one blue county can be found in this region. 

Figure 4.2c shows how the counties compare with one another when we apply the ARC Region 

County Economic Levels. 

With this indicator the number of distressed counties (also) rises to 82, while the number of 

counties at-risk increases to 94 (see Table 4.2). Virginia adds 4 distressed counties compared to 

the current ARC distress index, while Mississippi, Ohio and Tennessee each add two distressed 

counties, and Alabama adds one. In contrast, Kentucky and West Virginia each have one less 

distressed county. In terms of the at-risk category, Kentucky (1 added county), Tennessee (2) 

and West Virginia (6) all have more at-risk counties as compared to the current ARC index. 

There also are fewer transitional counties (199 compared to 225), eleven additional competitive 

counties and one more county overall that is in attainment. In fact, Georgia and Alabama each 

add one county in attainment while Virginia loses its one county that is in attainment under the 

current ARC index. 

Candidate Index 3: Population Growth, Employment/Population Rate, Four-year College 

Attainment, and Per-capita Market Income 

Figures 4.3a and 4.3b show a concentrated, if not sustained, cluster of red counties in the border 

area between Kentucky and West Virginia but there are also a handful of green counties (i.e., our 

method suggests distress whereas ARC’s current index does not). The southwestern portion of 

the ARC region shows a somewhat balanced mosaic of blue, green and red counties. Figure 4.3c 

shows the distribution of ARC Region Economic Levels when our indicator is applied. Clearly, 

a distinct core of dark red counties in the central Appalachian Region remains, and the darker 

blue (in attainment) counties around the Atlanta metro area are noteworthy. 
52 | Page

With this third candidate index, the number of distressed counties rises yet further (Table 4.3), 

when compared to the FY2007 ARC Index (to 90 from 74). Many of these added distressed 

counties (8) are found in Virginia, perhaps surprisingly, where the number in distress skyrockets 

from 1 to 9. However, West Virginia (plus 6 counties) and Ohio (plus 3) also see increases while 

Kentucky (minus 3, from 34 to 31) experiences a reduction in the number of distressed counties, 

as do Georgia and Pennsylvania. The number of at-risk counties rises from 88 using the ARC 

Index to 100 with this third candidate index. Pennsylvania experiences the greatest increase, 

from 1 to 13 counties, while the largest drop occurs in North Carolina (from 7 to only 3 

counties). 

Next, we turn to the same analysis with the only exception that we use at least one year of 

college attainment rather than a bachelor’s degree. The first measure includes both the poverty 

rate and per capita market income (note that this does not perfectly match index 4.1 as that one 

did not include per capita market income). 

Candidate Index 4: Population Growth, Employment/Population Rate, At Least One Year 

College Attainment; Poverty Rate, and Per-capita Market Income 

Figures 4.4a and 4.4b compare county distress status for our fourth candidate index with the 

status obtained using the ARC Index. As noted, the major modification here is in terms of the 

number of years of college attended by the adult population. Again a cluster of red counties 

appears in the central ARC region, along with a smattering of mostly green counties – indicating 

that our measure picks up distress where the current ARC measure does not. Figure 4.4c 

demonstrates the shifts in the ARC Region County Economic Levels under this alternative index. 

With Candidate Index 4, there are 87 distressed counties or 13 more than is the case with the 

current ARC Index. Here most of the increases (4) are observed in Tennessee, where the number 

of counties in distress rises from 7 to 11, and in Virginia (where the increase is from 1 to 5 

counties, also for a net addition of 4). The number of at risk counties is 99 and there are 193 

transitional counties with this fourth Candidate Measure. There are more competitive counties 

with this indicator (33 vs. 26) and one more county is in attainment when compared to the 

current ARC measure. The number of competitive counties in Georgia doubles, from six to 

twelve when we switch from the ARC Index to the Candidate Index constructed here. 

Candidate Index 5: Population Growth, Employment/Population Rate, At Least One Year 

College Attainment, and Poverty Rate 

In Figures 4.5a and 4.5b, the now familiar pattern of red counties in the core ARC region again 

emerges, along with a notable number of green counties and a few blue counties. In the 

southwestern portion of the region, more counties are red, and three each are blue and green. 

Figure 4.5c shows the ARC County Economic Levels based on this index. 

With this candidate index, 88 counties are in distress with Tennessee, Virginia and West Virginia 

showing the largest increases, respectively adding 6, 4, and 4 more counties. Alabama, 

Kentucky, and Mississippi retain the same total number of distressed counties (34) under either 

scenario. In terms of the at-risk classification, we see an increase from 88 to 104 for the total 

count (or a net addition of 16 counties at-risk). The number of transitional counties is 187, 
53 | Page

compared with 225 using the ARC index, with the biggest differences occurring in Georgia (a 

drop of ten counties, from 26 to 16), West Virginia (a drop from 21 to 12) and Pennsylvania (a 

drop from 45 to 37). With this measure, 30 counties are competitive (up from 26 under the ARC 

Index) and 11 are in attainment (up from 7). 

Candidate Index 6: Population Growth, Employment/Population Rate, At Least One Year 

College Attainment, and Per-capita Market Income 

Turning to our last candidate index, the results in Figures 4.6a and 4.6b show that the core ARC 

region appears in red, along with a number of green counties, but with relatively fewer blue 

counties. In the southwestern corner of the region, a few scattered blue and green counties 

appear, along with five red counties clustered into two groups. In the national map, a number of 

blue counties appear in the West, along with nearly a dozen along the Rio Grande River region 

(Figure 4.6a). Figure 4.6c portrays the ARC region using County Economic Levels, again with 

the familiar core set of counties stretching through Kentucky and West Virginia. The dark red 

counties are often, but not always, near or surrounded by pink counties, suggesting that the 

highest amount of distress is concentrated in the core region, tapering off as one moves away 

from this core, especially towards the northeast (Pennsylvania). 

Based on this sixth index, 95 counties are distressed, compared to 74 with the ARC Index. 

Major changes in county classifications occur in Ohio (3 counties are added), Tennessee (4 are 

added), Virginia (an increase of 8, from 1 to 9 counties), and in West Virginia, where the 

increase is 7, from 16 to 23 counties. Kentucky actually has one less county in distress under this 

scenario (33 rather than 34 using the ARC Index). In Pennsylvania the number of distressed 

counties drops from 1 under the FY2007 ARC Index to zero if this sixth candidate index is used. 

The number of at-risk counties jumps by 24, from 88 to 112: more than two-thirds of this 

increase occurs in Pennsylvania, where the number skyrockets from only one to 18. The number 

of transitional counties drops from 225 to 168, with significant declines occurring in 

Pennsylvania (from 45 to 29) and in West Virginia (from 21 to 10 counties). Eleven more 

counties are competitive using this indicator compared to the FY2007 ARC Index (with most of 

this change occurring in Georgia with a doubling of counties from 6 to 12). Eight counties are 

now found to be in attainment, which is virtually unchanged from the ARC case. 

Summary of Results 

Overall, the new sets of candidate variables included in our comprehensive analysis give us 

greater confidence in the final county distress designations. Of equal importance, these variables 

are good indicators both of current distress conditions and the conditions that are likely to exist 

in the future. One striking result is the strong persistence of a core set of ARC counties across 

Kentucky and West Virginia that remain in distress even after other measures are included, 

especially the employment-to-population ratio, educational attainment and population growth. 

Yet, there are also many changes in the counties that are classified as distressed, with between 

28-47 counties changing status using the Candidate Indexes versus the current ARC Index (74 

counties are classified as distressed with the current ARC Index). Remarkably, our analysis 

reveals a strong and immutable association between poverty and people being in the workforce. 
54 | Page

In other words, employment remains one of the most powerful tools available to move people 

out of poverty, but in turn, employment depends on levels of education. 

Our analysis then leads us to classify a greater number of ARC counties as being in distress 

(compared to the U.S. benchmark), with these counties most generally in Alabama, Tennessee, 

Virginia, and West Virginia. Not a single county in New York or Pennsylvania is identified as 

distressed as a product of our investigation, although Pennsylvania usually adds several more 

counties designated as at-risk. Figure 4.7 shows the general geographical pattern applies even 

after increasing the weight on the population growth variable in the overall index fourfold to 

assess the sensitivity of the results.3 As shown in Figure 4.7, the distribution of distressed 

counties is fairly similar to the six Candidate Indexes, even after heavily weighting population 

growth.

                                                           

3

We strongly weight population growth because distress appears to more greatly manifest itself along that dimension 

in Pennsylvania and New York (see Partridge et al., 2008). 
55 | Page

Figure 4.1a: Candidate Index 1: Population Growth, Employment/Population Rate, Four-Year College Attai

Figure
56 | Page

Figure 4.1b: Candidate Index 1: Population Growth, Employment/Population Rate, Four-Year College Attai
57 | Page

Figure 4.1c: County Economic Status, Candidate Index 1: Population Growth, Employment/Population Rate

Attainment, Poverty Rate 
58 | Page

Figure 4.2a: Candidate Index 2: Population Growth, Employment/Population Rate, Four-Year College Attai

Per-Capita Market Income 
59 | Page

Figure 4.2b: Candidate Index 2: Population Growth, Employment/Population Rate, Four-Year College Attainme

Per-Capita Market Income 
60 | Page

Figure 4.2c: County Economic Status, Candidate Index 2: Population Growth, Employment/Population Rate, 

Attainment, Poverty Rate, Per-Capita Market Income 
61 | Page

Figure 4.3a: Candidate Index 3: Population Growth, Employment/Population Rate, Four-Year College Attainme

Market Income 
62 | Page

Figure 4.3b: Candidate Index 3: Population Growth, Employment/Population Rate, Four-Year College Attainm

Market Income 
63 | Page

Figure 4.3c: County Economic Status, Candidate Index 3: Population Growth, Employment/Population Rate

Attainment, Per-Capita Market Income 
64 | Page

Figure 4.4a: Candidate Index 4: Population Growth, Employment/Population Rate, One-Year College Attainme Per-Capita Market Income 
65 | Page

Figure 4.4b: Candidate Index 4: Population Growth, Employment/Population Rate, One-Year College Attainme Per-Capita Market Income 
66 | Page

 Figure 4.4c: County Economic Status, Candidate Index 4: Population Growth, Employment/Population Rate, O

Attainment, Poverty Rate, Per-Capita Market Income 
67 | Page

Figure 4.5a: Candidate Index 5: Population Growth, Employment/Population Rate, One-Year College Attainme
68 | Page

Figure 4.5b: Candidate Index 5: Population Growth, Employment/Population Rate, One-Year College Attainme
69 | Page

Figure 4.5c: County Economic Status, Candidate Index 5: Population Growth, Employment/Population Rate, O

Attainment, Poverty Rate 
70 | Page

Figure 4.6a: Candidate Index 6: Population Growth, Employment/Population Rate, One-Year College Attainme

Market Income 
71 | Page

Figure 4.6b: Candidate Index 6: Population Growth, Employment/Population Rate, One-Year College Attainme Market Income 
72 | Page

Figure 4.6c: County Economic Status, Candidate Index 6: Population Growth, Employment/Population Rate, On

Attainment, Per-Capita Market Income 
73 | Page

Figure 4.7: Weighted Version of Candidate Index 1: Population Growth (weighted by a multiple of 4), Employm Four-Year College Attainment, Poverty Rate 
74 | Page

Table 4.0: Change in County Economic Distress Status When Switching from Current FY2007 ARC Index to P

Candidate Index 1 Candidate Index 2 

Population Growth, Employment/ 

Population Rate, Four-Year College 

Attainment, Poverty Rate 

Population Growth, Employment/Population 

 Rate, Four-Year College Attainment, 

Poverty Rate, Per-Capita Market Income 

Population 

Rate, F

Pe

Distressed using ARC 

method but not ours 

Distressed using our 

method but not ARC 

Distressed using ARC 

method but not ours 

Distressed using our 

method but not ARC 

Distressed us

method but 

13 21 10 18 15 

Total counties that 

change distress status 

34 28 

Net counties moving 

into distress 

8 8 

Candidate Index 4 Candidate Index 5 

Population Growth, Employment/Population 

Rate, One-Year College Attainment, 

Poverty Rate, Per-capita Market Income 

Population Growth, Employment/Population 

Rate, One-Year College Attainment, 

Poverty Rate 

Population 

Rate, O

Pe

Distressed using ARC 

method but not ours 

Distressed using our 

method but not ARC 

Distressed using ARC 

method but not ours 

Distressed using our 

method but not ARC 

Distressed us

method but 

8 21 9 23 13 

Total counties that 

change distress status 

29 32 

Net counties moving 

into distress 

13 14 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007 the 

410 counties. However, for comparable geography, the ARC Index shown in the table classifies the 420 counties that currently comp

Table
75 | Page

Table 4.1: Number of Counties in Each Economic Status Category by State Using the Current FY2007ARC Inde

Index 1 (Population Growth, Employment/Population Rate, Four-Year College Attainment, Poverty R

States 

Distressed At-risk Transitional Competitive 

ARC 

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty Rate 07 

ARC

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty Rate 07

ARC

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty Rate 07 

ARC

Population growth 96-06

Employment/population 

ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty Rate 0

Alabama 3 5 9 11 23 18 1 1 Georgia 0 0 0 1 26 18 6 9 Kentucky 34 32 13 13 7 9 0 0 Maryland 0 0 0 0 2 3 1 0 Mississippi 9 11 11 7 4 6 0 0 New York 0 0 0 0 14 13 0 0 North Carolina 0 0 7 0 18 22 4 7 Ohio 3 5 10 9 18 17 1 1 Pennsylvania 1 0 1 3 45 45 5 4 South Carolina 0 0 1 0 4 5 1 1 Tennessee 7 7 14 15 27 26 4 4 Virginia 1 5 6 8 16 11 1 1 West Virginia 16 17 16 20 21 14 2 4 Total 74 82 88 87 225 207 26 32 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007, the 

410 counties. However, for comparable geography, the ARC index shown in the table classifies the 420 counties that currently comp
76 | Page

Table 4.2: Number of Counties in Each Economic Status Category by State Using the Current FY2007ARC Ind

Index 2 (Population Growth, Employment/Population Rate, Four-Year College Attainment, Poverty RMarket Income)

States 

Distressed At-risk Transitional Competitive 

ARC 

Population growth 96-06, 

Employment/population 

 ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty 

Rate 07, Per-capita market 

income 06 

ARC 

Population growth 96-06, 

Employment/population 

 ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty 

rate 07, Per-capita market 

income 06 

ARC

Population growth 96-06, 

Employment/population 

 ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty 

rate 07, Per-capita market 

income 06 

ARC

Population growth 96-06, 

Employment/population 

 ratio 07, Percent of adults 

with a four-year college 

degree 00, Poverty 

rate 07, Per-capita market 

income 06 

Alabama 3 4 9 10 23 20 1 1 Georgia 0 0 0 1 26 18 6 12 Kentucky 34 33 13 14 7 7 0 0 Maryland 0 0 0 0 2 3 1 0 Mississippi 9 11 11 7 4 6 0 0 New York 0 0 0 0 14 13 0 1 North Carolina 0 0 7 1 18 21 4 7 Ohio 3 5 10 11 18 15 1 1 Pennsylvania 1 0 1 3 45 44 5 5 South Carolina 0 0 1 0 4 5 1 1 Tennessee 7 9 14 16 27 23 4 4 Virginia 1 5 6 9 16 10 1 1 West Virginia 16 15 16 22 21 14 2 4 Total 74 82 88 94 225 199 26 37 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007, the 

410 counties. However, for comparable geography, the ARC index shown in the table classifies the 420 counties that currently comp
77 | Page

Table 4.3: Number of Counties in Each Economic Status Category by State Using the Current FY2007ARC Inde

Index 3 (Population Growth, Employ./Pop. Rate, Four-Year College Attainment, Per-Capita Market In

States 

Distressed At-risk Transitional Competitive 

ARC 

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with a four-year college 

degree 00, Per-capita 

market income 06 

ARC 

Population growth 96-06, 

Employment/population 

ratio 07, Percent of 

 adults with a four-year 

college degree 00, Percapita market income 06 

ARC

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with a four-year college 

degree 00, Per-capita 

market income 06 

ARC

Population growth 96-06, 

Employment/population 

ratio 07, Percent of 

adults with a four-year 

college degree 00, Percapita market income 06 

A

Alabama 3 4 9 11 23 18 1 2 Georgia 0 1 0 0 26 17 6 13 Kentucky 34 31 13 13 7 9 0 1 Maryland 0 0 0 1 2 2 1 0 Mississippi 9 9 11 10 4 4 0 1 New York 0 0 0 0 14 13 0 0 North Carolina 0 0 7 3 18 19 4 7 Ohio 3 6 10 11 18 14 1 1 Pennsylvania 1 0 1 13 45 34 5 5 South Carolina 0 0 1 0 4 5 1 1 Tennessee 7 8 14 15 27 24 4 5 Virginia 1 9 6 6 16 8 1 2 West Virginia 16 22 16 17 21 13 2 3 Total 74 90 88 100 225 180 26 41 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007, the 

410 counties. However, for comparable geography, the ARC index shown in the table classifies the 420 counties that currently comp
78 | Page

Table 4.4: Number of Counties in Each Economic Status Category by State Using the Current FY2007 ARC Index 

 Index 4 (Population Growth, Employment/Pop. Rate, One Year College Attainment, Poverty Rate, Per-c

States 

Distressed At-risk Transitional Competitive 

ARC 

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least one year of 

college education 00, 

Poverty Rate 07, 

Per-capita market 

income 06 

ARC 

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least one year of 

college education 00, 

Poverty Rate 07, 

Per-capita market 

income 06 

ARC

Population growth 96-06, 

Employment/population ratio 

07, Percent of adults with at 

least one year of college 

education 00, 

Poverty Rate 07, 

Per-capita market 

income 06 

ARC

Population growth 96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least one year of 

college education 00, 

Poverty Rate 07, 

Per-capita market 

income 06 

Alabama 3 4 9 11 23 19 1 1 Georgia 0 0 0 3 26 16 6 12 Kentucky 34 36 13 11 7 7 0 0 Maryland 0 0 0 0 2 3 1 0 Mississippi 9 9 11 9 4 6 0 0 New York 0 0 0 0 14 13 0 1 North Carolina 0 0 7 2 18 21 4 6 Ohio 3 4 10 12 18 15 1 1 Pennsylvania 1 0 1 8 45 40 5 4 South Carolina 0 0 1 0 4 5 1 1 Tennessee 7 11 14 14 27 23 4 4 Virginia 1 5 6 8 16 11 1 1 West Virginia 16 18 16 21 21 14 2 2 Total 74 87 88 99 225 193 26 33 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007, the 

410 counties. However, for comparable geography, the ARC index shown in the table classifies the 420 counties that currently comp
79 | Page

Table 4.5: Number of Counties in Each Economic Status Category by State Using the Current FY2007 ARC Index 

Index 5 (Population Growth, Employment/Population Rate, One Year College Attainment, Poverty Rat

States 

Distressed At-risk Transitional Competitive 

ARC 

Population growth 

96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least 

one year of college 

education 00, 

Poverty Rate 07 

ARC 

Population growth 

96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least 

one year of college 

education 00, 

Poverty Rate 07 

ARC 

Population growth 

96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least 

one year of college 

education 00, 

Poverty Rate 07 

ARC

Population growth 

96-06, 

Employment/population 

ratio 07, Percent of adults 

with at least 

one year of college 

education 00, 

Poverty Rate 07 

Alabama 3 3 9 13 23 18 1 1 Georgia 0 0 0 3 26 16 6 9 Kentucky 34 34 13 13 7 7 0 0 Maryland 0 0 0 0 2 3 1 0 Mississippi 9 9 11 10 4 5 0 0 New York 0 0 0 0 14 13 0 1 North Carolina 0 0 7 0 18 22 4 7 Ohio 3 4 10 11 18 16 1 1 Pennsylvania 1 0 1 12 45 37 5 3 South Carolina 0 0 1 0 4 5 1 1 Tennessee 7 13 14 13 27 22 4 4 Virginia 1 5 6 8 16 11 1 1 West Virginia 16 20 16 21 21 12 2 2 Total 74 88 88 104 225 187 26 30 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007, the 

410 counties. However, for comparable geography, the ARC index shown in the table classifies the 420 counties that currently comp
80 | Page

Table 4.6: Number of Counties in Each Economic Status Category by State Using the Current FY2007 ARC Index 

Index 6 (Population Growth, Employment/Population Rate, One Year College Attainment, Per-Capita M

States 

Distressed At-risk Transitional Competitive 

ARC 

Population growth 96-06, 

employment/population 

ratio 07, Percent of 

adults with at least one 

year of college education 

00, Per-capita market 

income 06 

ARC 

Population growth 96-06, 

employment/population 

ratio 07, Percent of 

adults with at least one 

year of college education 

00, Per-capita market 

income 06 

ARC

Population growth 96-06, 

employment/population 

ratio 07, Percent of 

adults with at least one 

year of college education 

00, Per-capita market 

income 06 

ARC

Population growth 96-06, 

employment/population 

ratio 07, Percent of 

adults with at least one year 

of college education 00, 

Per-capita market income 

06 

Alabama 3 3 9 13 23 17 1 2 Georgia 0 1 0 2 26 16 6 12 Kentucky 34 33 13 13 7 8 0 0 Maryland 0 0 0 0 2 3 1 0 Mississippi 9 9 11 10 4 5 0 0 New York 0 0 0 0 14 13 0 1 North Carolina 0 0 7 3 18 19 4 7 Ohio 3 6 10 14 18 11 1 1 Pennsylvania 1 0 1 18 45 29 5 5 South Carolina 0 0 1 0 4 5 1 1 Tennessee 7 11 14 14 27 23 4 4 Virginia 1 9 6 6 16 9 1 1 West Virginia 16 23 16 19 21 10 2 3 Total 74 95 88 112 225 168 26 37 

Notes: In FY 2007, there were four ARC counties “grandfathered” as distressed even though they were ranked as “at risk” using thresults do not classify these four “grandfathered” counties as distressed to be comparable with our Candidate Index. In FY2007, the 

410 counties. However, for comparable geography, the ARC index shown in the table classifies the 420 counties that currently comp
81 | Page

Concluding Comments 

Like many pockets of rural America, the ARC region is facing a host of challenges as a 

result of the economic restructuring that is occurring at the regional, national, and global 

levels. Adding further complexity is the demographic realities that are playing out in 

scores of rural communities across the region – places that are losing young people and 

individuals of prime working age, leaving behind an aging population to tackle the 

myriad of issues that will shape the long-term fate of these communities. At the same 

time, emerging opportunities are surfacing in such diverse areas as bio-energy, 

entrepreneurship, creative activities, e-commerce/information technology, and valueadded agriculture, to name a few. Of central issue is how well local leaders, residents, 

and organizations will be able to muster the energy and resources to respond to these 

important possibilities. No doubt, history suggests that counties and communities that are 

the most socially and economically distressed will find it tough to build for the future 

UNLESS investments are made to strengthen their capacity to effectively act on these 

emerging opportunities. 

In many respects, the ARC investments that have been targeted to the most distressed 

counties in the region are designed to even the playing field – to offer these counties an 

opportunity to plant and nurture the seeds that can advance their long-term viability. As 

such, it is crucial that the process embraced for identifying these distressed counties be 

pursued in a manner that is scientifically sound, methodologically rigorous, and aligned 

with the new economic, demographic, and social realities of the region, nation, and 

world. The decision by the ARC to invest in this important research study is reflective of 

its commitment to ensure that the best approach is in place for to delineating the levels of 

economic distress being felt by counties in the region today (and/or years to come). 

As we have stated at various places in this report, the current ARC index -- composed of 

the poverty rate, the unemployment rate, and per capita income – has been an important 

tool for identifying counties that have experienced distress. But, current and emerging 

economic realities have accelerated the need to explore a new mix of variables that might 

further improve ARC’s ability to define distress in these changing times. The intent of 

this project has been to identify a core set of variables and respective indexes that can 

monitor the region’s progress over time, and evaluate the long-term vitality and 

sustainability of ARC member counties. 

Careful attention and consideration has been given to approximately 40 variables during 

the course of our study, measures that capture key components of the socioeconomic 

vitality of ARC counties. After our in-depth analyses, we have honed in on a handful of 

that constitute the most viable candidate measures. In particular, we recommend that 

four of the following Candidate Variables be incorporated into a new ARC index of 

distress: 

 The employment/population (16 and over) ratio;

 Poverty rate;

 Per-capita market income;

Section 5 
82 | Page

 Percent of population (25+ years old) with at least a four-year college 

degree; and, 

 Ten-year percent change in population. 

In particular, we found three new Candidate Indexes composed of combinations of four 

or five these variables to be more powerful in discerning county-level distress than the 

metrics currently in use by the ARC (specifically the first three indexes in Table 3.4). 

We also identified three additional new Candidate Indexes that use at least one-year of 

college educational attainment to be more powerful than the current ARC Distress Index 

(Indexes 4 through 6 in Table 3.4). With the use of the six new Candidate Indexes, a 

greater number of ARC counties are classified as either distressed or at-risk when 

compared to the current ARC Index (FY2007). In light of the theoretical and empirical 

strengths associated with these alternative indexes, we suggest that these indices translate 

into a more accurate set of benchmarks for charting the future progress of the 

Appalachian Region. 

Although the research team engaged in this important research has provided its 

recommendations to the ARC, we recognize that these indexes must be corroborated with 

the “on the ground” knowledge and experiences of ARC state partners and county 

leaders. It is when these additional and vital pieces of information are in place that the 

ARC will be in a strong and defensible position to propose adoption of a new strategy for 

defining economic distress in the region, if not for the U.S. as a whole. 
83 | Page

Edmiston, Kelly D. 2004. “The Net Effects of Large Plant Locations and Expansions of 

County Employment.” Journal of Regional Science 44: 289-319. 

Kilkenny, Maureen and Mark D. Partridge. forthcoming. “Export Sectors and Rural 

Development.” American Journal of Agricultural Economics. 

Partridge, Mark D., Linda Lobao, Wilner Jeanty, Lionel J. “Bo” Beaulieu, and Stephan 

Goetz. 2008. An Assessment of Alternative Measures for Determining Economically 

Distressed Counties and Areas in the Appalachian Region. Report prepared for the 

Appalachian Regional Commission (March 31), 104 pages. Available at: 

http://www.arc.gov/images/reports/2008/states/ARC_measures_counties_full.pdf

(accessed on September 5, 2008). 

Poppert, Patrick E. and H. W. Herzog Jr. 2003. “Force Reduction, Base Closure, and the 

Indirect Effects of Military Installations on Local Employment Growth.” Journal of 

Regional Science (43): 459-481. 

U.S. Census Bureau. 2009. “Census Bureau’s Proposal For American Community Survey 

(ACS) 5-Year Data Products.” Available at: 

http://www.census.gov/acs/www/Downloads/proposal_acs5yearproducts.pdf (accessed 

on April, 18, 2009). 

U.S. Office of Management and Budget. 2009 Federal Register, Vol. 74, No. 43 / Friday, 

March 6, 2009. Available at:

http://www.census.gov/acs/www/Downloads/FedRegNotice_E9-4803.pdf

References 

Appedix Table
84 | Page

Table A.1: Correlations between 1996/1997 Candidate Variables and 2006/2007 Economic Outcome, ARC Coun

Candidate Variables 

Poverty Rate 

2007 ∆

Poverty Rate 

1997-07 

Per-capita Market 

Income 2006 %∆

Per-capita Market 

Income 1996-06 

% Job Growth 

1996-06 

% Population 

Growth 1996-06 

EmploymentPop ratio 2007 

Labor Force Part. 

rate 2007 

Housing Units 2007 

Nonfarm Self 

Employ. share of 

Total Empl., 2006 %∆

ARC Distress Index 1997 0.73 -0.08 -0.71 0.19 -0.27 -0.35 -0.64 -0.63 -0.35 -0.02 -%Population Change 1986-96 -0.41 0.04 0.40 -0.17 0.70 0.89 0.50 0.49 0.12 0.25 0%Job Growth 1986-96 -0.25 0.07 0.32 -0.15 0.49 0.68 0.36 0.35 0.12 0.16 0Per-capita Market Income 1996 -0.77 0.09 0.93 -0.16 0.31 0.41 0.71 0.69 0.53 -0.11 0%Growth Per-capita Market Income 1986-96 -0.08 0.12 0.16 -0.16 0.12 0.22 0.20 0.21 0.02 0.13 0% share of adult population with less than a high school degree, 2000 0.70 0.00 -0.73 -0.05 -0.20 -0.10 -0.55 -0.53 -0.41 0.11 -% share of adult population with only high school degree, 2000 -0.27 -0.17 -0.10 0.01 -0.11 -0.27 -0.02 -0.01 -0.16 0.17 -% share of adult population with an associates degree or more (associates+BA+MA+professional+PhD), 2000 -0.44 0.12 0.75 0.09 0.23 0.22 0.51 0.48 0.52 -0.25 0% share of adult population with a 4-yr college degree or more (BA+MA+professional+PhD), 2000 -0.37 0.15 0.72 0.11 0.26 0.26 0.47 0.45 0.51 -0.25 0% share of adult pop. with more than one year of college (> 1 year at some college +associates+BA+ MA+professional+PhD), 2000 -0.47 0.13 0.77 0.06 0.26 0.28 0.53 0.51 0.51 -0.24 0% share of adult population with less than a year of college, 2000 -0.48 -0.08 0.44 -0.06 0.21 0.25 0.40 0.38 0.18 0.04 0Employment-Population ratio 2000 -0.74 0.10 0.69 -0.21 0.40 0.56 0.84 0.84 0.28 0.09 0Labor Force Participation rate 2000 -0.73 0.11 0.68 -0.21 0.38 0.55 0.83 0.84 0.27 0.08 0

Appendix Tables A.
85 | Page

Poverty Rate 1997 0.92 -0.13 -0.77 0.17 -0.28 -0.34 -0.73 -0.72 -0.31 -0.06 -Unemployment Rate 1997 0.45 -0.13 -0.56 0.09 -0.25 -0.34 -0.47 -0.44 -0.27 0.05 -Nonfarm Proprietor Income as a share of Total Personal Income 1997 -0.09 -0.01 0.04 -0.03 0.04 -0.01 0.11 0.12 0.05 0.23 -Average Nonfarm Proprietor Income to Wage-and-Salary Income ratio 1997 0.03 0.04 0.03 -0.05 -0.03 -0.07 0.03 0.04 0.14 -0.08 -Population Under 20 yrs, 2000 -0.25 0.04 0.52 0.06 0.04 0.05 0.27 0.26 0.92 -0.22 0Housing Units 2000 -0.26 0.05 0.55 0.07 0.02 0.01 0.27 0.26 0.99 -0.22 0Building Permits 2000 -0.10 -0.02 0.13 -0.02 0.10 0.07 0.14 0.14 0.07 0.00 0Nonfarm Self Employment as a Share of Total Employment, 1996 -0.11 -0.16 -0.09 0.04 0.40 0.28 0.07 0.07 -0.21 0.75 0% Change in Housing Units from 1980-90 -0.34 -0.01 0.36 -0.14 0.61 0.78 0.42 0.41 0.16 0.20 0% Change in Housing Units from 1990-2000 -0.16 0.01 0.14 -0.15 0.62 0.85 0.32 0.32 -0.06 0.31 0Non-farm self employment returns (total non farm proprietor income/total non farm proprietor employment), 1997 -0.14 0.04 0.28 -0.01 -0.07 -0.13 0.09 0.08 0.40 -0.26 -Percent Change in Housing Units from 1990-00 -0.16 0.01 0.14 -0.15 0.62 0.85 0.32 0.32 -0.06 0.31 0Average value of Building Permits during period 96-07 0.03 -0.11 0.03 0.01 0.15 0.11 0.04 0.05 0.06 0.07 0Building permits per capita, 1996 0.02 0.00 -0.05 -0.09 0.02 0.03 0.04 0.04 -0.03 0.01 0Average value of Building Permits during period 86-96 -0.03 -0.12 0.10 -0.04 0.17 0.14 0.09 0.10 0.15 0.01 0
86 | Page

Table A.2: Correlations between 1996/1997 Candidate Variables and 2006/2007 Economic Outcomes, All U.S. 

Candidate Variables 

Poverty Rate 

2007 

∆ Poverty Rate 

1997-07 

Per-capita Market 

Income 2006 %∆

Per-capita Market 

Income 1996-06 

%Job Growth 

1996-06 

%Population 

Growth 1996-06 

EmploymentPop ratio 2007 

Labor Force Part. 

rate 2007 

Housing Units 

2007 

Nonfarm Self 

Employ. share of 

Total Empl., 2006 %∆

in Pop. Under 20 

%Population 1986-96 -0.22 0.00 0.26 0.04 0.61 0.82 0.06 0.06 0.15 0.12 0.61 %Job Growth 1986-96 -0.18 0.02 0.19 0.00 0.36 0.54 0.12 0.12 0.04 0.01 0.38 Per-capita Market Income 1996 -0.65 0.11 0.91 -0.03 0.20 0.25 0.52 0.51 0.35 -0.07 0.23 Per-capita Market Income Percent Change 1986-96 -0.03 0.14 0.18 -0.23 0.06 0.12 0.07 0.08 -0.01 0.00 0.11 % share of adult population with lea high school degree, 2000 0.71 -0.05 -0.59 -0.10 -0.20 -0.13 -0.57 -0.56 -0.14 -0.05 -0.04 % share of adult population with only high school degree, 2000 -0.04 0.09 -0.41 -0.18 -0.23 -0.26 -0.13 -0.11 -0.31 0.13 -0.26 % share of adult population with an associates degree or more (associates+BA+MA+professional+PhD), 2000 

-0.49 0.07 0.75 0.17 0.28 0.27 0.48 0.46 0.33 -0.08 0.23 

% share of adult population with a 4-yr college degree or more (BA+MA+professional+PhD), 2000 

-0.43 0.07 0.75 0.18 0.29 0.29 0.44 0.43 0.34 -0.07 0.25 

% share of adult population with more than 1 year of college (> 1 year at a college+associates +BA+MA+professional+PhD), 2000 

-0.51 0.02 0.71 0.18 0.29 0.26 0.49 0.48 0.30 -0.04 0.19 

% share of adult population with less than a year of college, 2000 -0.37 -0.10 0.09 0.00 0.04 0.01 0.26 0.25 -0.05 0.11 -0.06 Employment-Population ratio 2000 -0.70 0.09 0.57 0.05 0.23 0.24 0.84 0.84 0.12 0.03 0.16 Labor Force Participation rate 2000 -0.68 0.09 0.56 0.05 0.22 0.24 0.83 0.84 0.12 0.03 0.15 
87 | Page

Poverty Rate 1997 0.91 -0.25 -0.60 0.01 -0.20 -0.22 -0.58 -0.58 -0.10 -0.09 -0.15 Unemployment Rate 1997 0.53 -0.15 -0.38 0.01 -0.11 -0.10 -0.47 -0.43 -0.06 0.00 -0.09 Nonfarm Proprietor Income as a share of Total Personal Income 1997 

-0.09 -0.17 0.12 0.19 -0.02 -0.12 0.20 0.19 0.06 0.26 -0.15 

Average Nonfarm Proprietor Income to Wage-and-Salary Income ratio 1997 

0.16 -0.01 0.00 -0.07 -0.08 -0.13 -0.02 -0.02 0.10 -0.04 -0.07 

Population Under 20 yrs, 2000 -0.09 -0.03 0.30 0.06 0.06 0.12 0.05 0.06 0.95 -0.11 0.18 Housing Units 2000 -0.10 -0.02 0.36 0.08 0.07 0.13 0.07 0.07 0.99 -0.13 0.19 Building Permits 2000 -0.02 0.01 0.04 0.00 0.05 0.05 0.03 0.03 0.08 -0.03 0.05 Nonfarm Self Employment as a share of Total Employment, 1996 -0.25 -0.25 0.04 0.11 0.29 0.15 0.12 0.11 -0.15 0.71 -0.06 % Change in Housing Units from 1980-90 -0.19 -0.09 0.29 0.14 0.56 0.67 0.07 0.07 0.17 0.11 0.51 % Change in Housing Units from 1990-2000 -0.10 0.02 0.15 0.05 0.61 0.82 0.03 0.03 0.06 0.12 0.60 Non-farm self employment returns (total non farm proprietor income/total non farm proprietor employment), 1997 

-0.02 0.04 0.39 0.01 -0.03 0.01 0.04 0.05 0.40 -0.21 0.11 

Percent Change in Housing Units from 1990-00 -0.10 0.02 0.15 0.05 0.61 0.82 0.03 0.03 0.06 0.12 0.60 Building permits per capita 96 0.03 -0.03 -0.03 0.01 -0.01 -0.06 0.00 0.00 -0.03 0.05 -0.05 Average value of building permits during period 86-96 -0.13 0.02 0.15 -0.03 0.10 0.10 0.10 0.09 0.04 -0.02 0.07 
88 | Page

Table A.3: Correlations between Candidate Distress Indexes and 2006/2007 Economic Outcomes, ARC Countie

ARC 

ranking Candidate Distress Indexes 

ARC Distress Indic. 2007 

ARC Distress Indic. 2009 

Poverty Rate 2007 

∆ in Poverty Rate 1997-07 

Per-capita Market Income 2006 %∆ Per-capita Market Income 

1996-06 

%Job Growth 1996-06 

%Pop. Growth 1996-06 

Employ/Pop ratio 2007 

Labor Force Part. rate 2007 

Housing Units 2007 

% 

Nonfarm Self Employ of Total 

Employ. 

1 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00, Poverty Rate 97 

-0.83 -0.80 -0.79 0.12 0.81 -0.16 0.51 0.64 0.80 0.80 0.37 0.06 

2 

Population growth 86-96, Employment /population ratio 00, Poverty Rate 97 -0.81 -0.77 -0.80 0.10 0.72 -0.21 0.53 0.69 0.80 0.80 0.27 0.15 

3 

Population growth 86-96, Employment /population ratio 00, Per-capita market income 96 

-0.80 -0.76 -0.73 0.09 0.76 -0.21 0.55 0.73 0.79 0.78 0.34 0.10 

4 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00, Poverty Rate 97, Per-capita market income 96 

-0.87 -0.84 -0.86 0.08 0.85 -0.15 0.45 0.55 0.80 0.79 0.39 0.04 

5 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00, Per-capita market income 96 

-0.81 -0.78 -0.72 0.11 0.83 -0.15 0.52 0.67 0.78 0.77 0.42 0.01 

6 

Percent of adults with at least one year of college 

00, Per-capita market income 96, Population growth 86-96, Poverty Rate 97 

-0.84 -0.82 -0.79 0.12 0.86 -0.14 0.49 0.60 0.75 0.74 0.44 0.00 

7 

Population growth 86-96, Percent of adults with at least one year of college 00, Poverty Rate 97 -0.82 -0.79 -0.76 0.12 0.81 -0.13 0.53 0.64 0.74 0.73 0.39 0.04 

8 Population growth 86-96, Poverty Rate 97 -0.79 -0.74 -0.79 0.10 0.69 -0.20 0.58 0.72 0.73 0.72 0.25 0.18 

9 

Population growth 86-96, Per-capita market income 96 -0.76 -0.71 -0.66 0.07 0.73 -0.20 0.61 0.79 0.69 0.67 0.35 0.11 
89 | Page

10 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00 

-0.76 -0.73 -0.67 0.11 0.75 -0.14 0.56 0.72 0.77 0.76 0.36 0.05 

11 

Population growth 86-96, Employment /population ratio 00 -0.71 -0.65 -0.64 0.08 0.61 -0.21 0.60 0.81 0.74 0.74 0.22 0.18 

12 

Employment /population ratio 00, Per-capita market income 96 -0.85 -0.82 -0.80 0.10 0.85 -0.20 0.38 0.53 0.83 0.82 0.41 0.00 

13 

Employment /population ratio 00, Percent of adults with at least one year of college 00, Percapita market income 96, Poverty Rate 97 -0.86 -0.85 -0.83 0.13 0.89 -0.14 0.36 0.45 0.81 0.79 0.45 -0.05 

15 

Population growth 86-96, Percent of adults with at least one year of college 00, Per-capita market income 96 

-0.77 -0.75 -0.65 0.10 0.83 -0.11 0.53 0.66 0.69 0.68 0.45 -0.02 

16 

Employment /population ratio 00, Percent of adults with at least one year of college 00, 

Poverty Rate 97 

-0.85 -0.84 -0.83 0.13 0.86 -0.13 0.36 0.46 0.82 0.81 0.42 -0.03 

17 Employment /pop. ratio 00, Poverty Rate 97 -0.84 -0.82 -0.87 0.12 0.77 -0.20 0.36 0.47 0.83 0.82 0.31 0.07 18 

Employment /population ratio 00, Percent of adults with at least one year of college 00, Percapita market income 96 -0.83 -0.82 -0.74 0.12 0.89 -0.12 0.37 0.48 0.79 0.78 0.49 -0.09 

19 Per-capita market income 96, Poverty Rate 97 -0.87 -0.87 -0.89 0.12 0.88 -0.17 0.31 0.39 0.75 0.74 0.42 -0.02 20 Employment /population ratio 00, Percent of adults with at least one year of college 00 -0.79 -0.77 -0.70 0.13 0.84 -0.10 0.38 0.49 0.80 0.79 0.45 -0.08 21 Population growth 86-96, Percent of adults with at least one year of college 00 -0.67 -0.63 -0.53 0.10 0.70 -0.08 0.60 0.73 0.62 0.61 0.37 0.02 22 % adults with at least one year of college 00, Per-capita market income 96, Poverty Rate 97 -0.85 -0.85 -0.81 0.13 0.91 -0.10 0.32 0.38 0.74 0.72 0.49 -0.10 23 % of adults with at least one year of college 00, Poverty Rate 97 -0.82 -0.83 -0.81 0.15 0.88 -0.07 0.31 0.35 0.73 0.71 0.46 -0.09 24 % of adults with at least one year of college 00, Per-capita market income 96 -0.76 -0.77 -0.65 0.12 0.90 -0.05 0.31 0.36 0.66 0.63 0.56 -0.19 1b 

Population growth 86-96, Employment /population ratio 00, Percent of adults with a four-year college degree 00, Poverty Rate 97 

-0.83 -0.79 -0.78 0.13 0.80 -0.15 0.52 0.65 0.80 0.79 0.37 0.06 
90 | Page

2b 

Population growth 86-96, Employment /population ratio 00, Percent of adults with a four-year college degree 00, Poverty Rate 97, Per-capita market income 96 

-0.85 -0.82 -0.79 0.12 0.85 -0.15 0.49 0.62 0.80 0.79 0.41 0.03 

3b 

Population growth 86-96, Employment /population ratio 00, Percent of adults with a four-year college degree 00, Per-capita market income 96 

-0.80 -0.77 -0.70 0.11 0.83 -0.14 0.53 0.67 0.78 0.77 0.42 0.01 

4b 

% of adults with a four-year college degree 00, Per-capita market income 96, Population growth 86-96 , Poverty Rate 97 

-0.84 -0.82 -0.77 0.13 0.86 -0.13 0.50 0.61 0.75 0.73 0.44 0.00 

*Notes: Average in the final column refers the average of the five variables in grey: 2007 poverty rate, 2006 per-capita market incomgrowth including proprietors, 1996-2006 population growth, and 2007 employment/population ratio. See sections 2 and 3 for more d1b-4b at the bottom of the table are Candidate Distress Indexes 1, 4, 5 and 6 replacing at least one-year of college education with at bachelor’s degree.
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Table A.4: Correlations between Candidate Distress Indexes and 2006/2007 Economic Outcomes, All U.S. Cou

ARC 

ranking 

U.S. 

ranking Candidate Distress Indexes

Poverty Rate 2007 ∆ in Poverty Rate 

1997-07 

Per-capita Market 

Income 2006 %∆ Per-capita 

Market Income 

1996-06 

%Job Growth 1996-

06 

%Pop. Growth 

1996-06 

Employ/Pop ratio 

2007 

Labor Force Part. 

rate 2007 

Housing Units 2007 %

Nonfarm Self 

Employ of Total 

Employ

% Under 20 yr. old 

Ch2000

1 1 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00, Poverty Rate 97 

-0.78 0.12 0.72 0.08 0.44 0.52 0.66 0.65 0.22 0.07 0

2 4 

Population growth 86-96, Employment /population ratio 00, Poverty Rate 97 

-0.80 0.15 0.63 0.03 0.45 0.56 0.64 0.64 0.16 0.11 0

3 2 

Population growth 86-96, Employment /population ratio 00, Per-capita market income 96 

-0.69 0.09 0.77 0.02 0.46 0.58 0.62 0.62 0.27 0.04 0

4 3 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00, Poverty Rate 97, Per-capita market income 96 

-0.83 0.13 0.77 0.04 0.37 0.44 0.67 0.67 0.22 0.06 0

5 5 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00, Per-capita market income 96 

-0.68 0.07 0.81 0.07 0.44 0.52 0.63 0.62 0.30 0.02 0

6 6 

Percent of adults with at least one year of college 00, Per-capita market income 96, Population growth 86-96 , Poverty Rate 97 

-0.75 0.12 0.81 0.06 0.42 0.51 0.54 0.54 0.29 0.03 0

7 12 

Population growth 86-96, Poverty Rate 97 -0.72 0.16 0.55 0.02 0.52 0.67 0.41 0.41 0.16 0.13 0

8 8 

Population growth 86-96, Percent of adults with at least one year of college 00, Poverty Rate 97

-0.73 0.12 0.70 0.09 0.49 0.58 0.51 0.50 0.24 0.07 0
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9 14 

Population growth 86-96, Per-capita market income 96 -0.55 0.07 0.74 0.00 0.51 0.67 0.37 0.36 0.31 0.03 0

10 7 

Population growth 86-96, Employment /population ratio 00, Percent of adults with at least one year of college 00

-0.63 0.05 0.69 0.12 0.50 0.59 0.62 0.61 0.25 0.05 0

11 9 

Population growth 86-96, Employment /population ratio 00 -0.60 0.06 0.54 0.05 0.54 0.69 0.58 0.58 0.18 0.10 0

12 16 

Employment /population ratio 00, Per-capita market income 96 -0.75 0.11 0.82 0.01 0.24 0.27 0.75 0.75 0.26 -0.02 0

13 10 

Employment /population ratio 00, Percent of adults with at least one year of college 00, Per-capita market income 96, Poverty Rate 97

-0.81 0.14 0.82 0.05 0.27 0.29 0.71 0.71 0.25 0.00 0

14 11 

Population growth 86-96, Percent of adults with at least one year of college 00, Per-capita market income 96

-0.59 0.05 0.80 0.08 0.47 0.57 0.46 0.45 0.34 0.00 0

15 13 

Employment /population ratio 00, Percent of adults with at least one year of college 00, Poverty Rate 97

-0.82 0.14 0.74 0.09 0.28 0.28 0.75 0.74 0.20 0.03 0

16 21 

Employment /population ratio 00, Poverty Rate 97 -0.86 0.18 0.63 0.02 0.23 0.25 0.76 0.76 0.12 0.06 0

17 15 

Employment /population ratio 00, Percent of adults with at least one year of college 00, Per-capita market income 96

-0.72 0.08 0.84 0.07 0.28 0.29 0.71 0.70 0.29 -0.03 0

18 20 

Per-capita market income 96, Poverty Rate 97 -0.85 0.19 0.82 -0.02 0.22 0.26 0.60 0.59 0.24 0.01 0

19 18 

Employment /population ratio 00, Percent of adults with at least one year of college 00

-0.68 0.06 0.73 0.13 0.29 0.28 0.76 0.75 0.24 0.00 0

20 22 

Population growth 86-96, Percent of adults with at least one year of college 00

-0.45 0.01 0.60 0.13 0.56 0.67 0.34 0.33 0.28 0.05 0
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21 17 

% of adults with at least one year of college 00, Per-capita market income 96, Poverty Rate 97

-0.80 0.15 0.85 0.05 0.26 0.28 0.61 0.60 0.28 -0.01 0

22 19 

% of adults with at least one year of college 00, Poverty Rate 97 -0.82 0.16 0.75 0.10 0.28 0.28 0.62 0.61 0.23 0.03 0

23 23 

% of adults with at least one year of college 00, Per-capita market income 96

-0.63 0.07 0.88 0.08 0.26 0.28 0.55 0.54 0.35 -0.06 0

1b 1b 

Population growth 86-96, Employment /population ratio 00, Percent of adults with a four-year college degree 00, Poverty Rate 97 

-0.76 0.14 0.74 0.08 0.45 0.53 0.65 0.64 0.24 0.06 0

2b 2b 

Population growth 86-96, Employment /population ratio 00, Percent of adults with a four-year college degree 00, Poverty Rate 97, Per-capita market income 96 

-0.77 0.14 0.82 0.06 0.40 0.48 0.65 0.64 0.28 0.03 0

3b 3b 

Population growth 86-96, Employment /population ratio 00, Percent of adults with a four-year college degree 00, Per-capita market income 96 

-0.66 0.09 0.82 0.07 0.44 0.53 0.61 0.61 0.31 0.01 0

4b 4b 

% of adults with a four-year college degree 00, Per-capita market income 96, Population growth 86-96, Poverty Rate 97 

-0.73 0.14 0.83 0.05 0.43 0.52 0.53 0.52 0.31 0.02 0

*Notes: Average in the final column refers the average of the five variables in grey: 2007 poverty rate, 2006 per-capita market incomgrowth including proprietors, 1996-2006 population growth, and 2007 employment/population ratio. See sections 2 and 3 for more d1b-4b at the bottom of the table are Candidate Distress Indexes 1, 4, 5 and 6 replacing at least one-year of college education with at bachelor’s degree. 
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Table A.5: Summary Statistics of Candidate Indexes 

Candidate 

Index 1 

Candidate 

Index 2 

Candidate 

Index 3 

Candidate 

Index 4 

Mean of Z-score for US Counties excluding ARC Counties 0.20 0.27 0.21 0.31 Standard deviation of Z-score for US counties excluding ARC counties 2.79 3.60 2.91 3.59 Mean of Z-score for only ARC Counties -1.31 -1.77 -1.38 -2.01 Standard deviation of Z-score only ARC counties 2.51 3.01 2.31 3.08 
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 Table A.6: Z-scores and Rankings of ARC Counties Relative to the ARC Region (n=420) and the U.S. (n=3,102

Candidate Index 1 Candidate Index 2 Candidate Index 3 Candidate Index 4 Candidate Index 5 Candi

County Zscore US rank ARC rank Z- score US rank ARC rank Z- score US rank ARC rank Z- score US rank ARC rank Z- score US rank ARC rank Z- score 

 Alabama 

 Bibb County, Alabama -2.13 2499 274 -2.96 2544 279 -2.46 2590 282 -2.97 2523 270 -2.14 2485 263 -2.47  Blount County, Alabama 0.48 1188 77 0.03 1396 93 -0.29 1407 100 0.20 1377 78 0.64 1164 62 -0.13  Calhoun County, Alabama -1.15 2069 189 -1.18 1903 166 -0.86 1753 147 -1.02 1861 142 -1.00 2010 163 -0.70  Chambers County, Alabama -2.35 2567 288 -3.00 2555 281 -2.92 2748 323 -3.07 2553 277 -2.42 2568 279 -2.99  Cherokee County, Alabama -1.39 2198 219 -1.95 2235 227 -1.34 2033 188 -2.23 2323 231 -1.67 2309 228 -1.62  Chilton County, Alabama -0.68 1823 151 -1.09 1871 160 -0.84 1739 145 -1.27 1973 163 -0.86 1952 152 -1.01  Clay County, Alabama -2.30 2550 282 -2.83 2502 267 -2.77 2709 315 -2.89 2504 265 -2.36 2549 274 -2.83  Cleburne County, Alabama -1.33 2164 213 -1.72 2142 210 -1.67 2222 209 -1.95 2224 209 -1.56 2261 217 -1.90  Colbert County, Alabama -1.23 2115 204 -1.65 2111 202 -1.43 2080 194 -1.53 2071 175 -1.11 2063 170 -1.31  Coosa County, Alabama -2.87 2703 320 -3.65 2713 318 -3.65 2945 365 -3.76 2698 309 -2.99 2697 311 -3.77  Cullman County, Alabama -0.34 1628 123 -0.55 1649 123 -0.70 1656 129 -0.35 1602 105 -0.13 1554 99 -0.49  DeKalb County, Alabama -1.54 2251 228 -2.01 2263 231 -1.48 2116 200 -2.03 2268 220 -1.55 2255 215 -1.50  Elmore County, Alabama 1.84 685 41 1.88 787 46 1.27 819 53 1.93 803 43 1.89 704 38 1.31  Etowah County, Alabama -1.75 2358 257 -2.06 2282 236 -1.69 2233 211 -1.67 2111 185 -1.37 2166 191 -1.31  Fayette County, Alabama -2.78 2684 316 -3.37 2648 305 -3.04 2786 328 -3.44 2638 292 -2.84 2660 302 -3.10  Franklin County, Alabama -2.39 2583 294 -3.01 2561 283 -2.21 2498 261 -3.09 2559 278 -2.47 2578 281 -2.29 
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 Hale County, Alabama -3.33 2812 343 -4.27 2822 344 -2.84 2728 319 -4.29 2806 338 -3.36 2784 330 -2.87  Jackson County, Alabama -1.32 2154 211 -1.66 2121 205 -1.27 1990 182 -1.75 2137 191 -1.40 2182 199 -1.35  Jefferson County, Alabama 0.59 1132 69 2.11 728 44 2.11 595 37 2.11 761 40 0.59 1196 65 2.11  Lamar County, Alabama -3.78 2900 369 -4.59 2870 357 -4.05 3013 382 -4.75 2873 352 -3.94 2887 357 -4.20  Lauderdale County, Alabama -0.63 1794 147 -0.79 1738 132 -0.31 1419 102 -0.99 1845 137 -0.83 1936 149 -0.51  Lawrence County, Alabama -1.83 2397 262 -2.16 2314 242 -2.18 2483 259 -2.37 2375 244 -2.04 2453 258 -2.39  Limestone County, Alabama 1.36 842 49 1.43 892 50 1.02 871 56 1.36 962 48 1.29 904 46 0.96  Macon County, Alabama -4.05 2937 377 -5.08 2929 375 -2.75 2703 312 -5.05 2907 364 -4.02 2908 365 -2.72  Madison County, Alabama 4.11 219 8 5.29 234 7 4.67 204 7 4.97 263 7 3.80 247 10 4.36  Marion County, Alabama -3.39 2824 345 -3.91 2754 331 -3.17 2822 338 -3.90 2724 316 -3.38 2790 332 -3.16  Marshall County, Alabama -0.94 1968 173 -0.99 1817 152 -0.27 1390 99 -0.93 1823 134 -0.89 1969 155 -0.21  Morgan County, Alabama 0.50 1176 75 1.02 1012 59 0.87 913 59 1.06 1051 53 0.54 1218 67 0.91  Pickens County, Alabama -3.73 2891 365 -4.39 2847 350 -3.25 2850 344 -4.30 2808 339 -3.64 2845 346 -3.16  Randolph County, Alabama -2.66 2652 309 -3.46 2669 309 -2.53 2622 291 -3.66 2683 305 -2.86 2664 303 -2.74  Shelby County, Alabama 7.18 43 5 9.15 52 2 7.83 53 2 8.61 54 2 6.64 54 5 7.29  St. Clair County, Alabama 0.85 1018 58 0.91 1043 63 0.35 1105 71 1.05 1054 54 0.99 1016 52 0.49  Talladega County, Alabama -1.46 2223 224 -1.59 2086 198 -1.13 1906 169 -1.53 2067 173 -1.39 2179 197 -1.06  Tallapoosa County, Alabama -1.63 2295 240 -2.05 2279 234 -1.73 2255 216 -2.17 2308 229 -1.75 2342 231 -1.85  Tuscaloosa County, Alabama 0.82 1030 60 1.29 933 52 1.85 642 43 1.15 1027 51 0.67 1157 60 1.71  Walker County, Alabama -2.75 2675 314 -3.06 2572 286 -2.42 2579 280 -2.92 2510 267 -2.60 2613 286 -2.28  Winston County, Alabama -2.46 2597 295 -3.19 2605 293 -3.05 2789 329 -3.37 2622 288 -2.63 2622 290 -3.23 
97 | Page

 Georgia 

 Banks County, Georgia 2.69 451 25 2.74 587 32 2.52 496 24 2.46 668 31 2.40 536 30 2.23  Barrow County, Georgia 3.97 239 10 3.80 381 13 3.17 374 14 4.00 360 10 4.17 203 7 3.37  Bartow County, Georgia 2.69 452 26 2.77 577 29 2.21 565 35 2.63 618 26 2.55 500 24 2.07  Carroll County, Georgia 1.52 787 45 1.34 920 51 1.69 697 44 0.91 1091 57 1.08 986 50 1.26  Catoosa County, Georgia 2.75 433 24 2.66 610 33 2.12 590 36 2.77 591 24 2.86 417 15 2.24  Chattooga County, Georgia -2.63 2646 307 -3.64 2711 317 -3.18 2825 339 -3.87 2718 314 -2.86 2665 304 -3.41  Cherokee County, Georgia 7.46 41 3 8.50 65 3 7.03 77 4 8.48 60 3 7.43 31 2 7.01  Dade County, Georgia 0.51 1172 74 0.14 1344 87 -0.14 1330 92 0.31 1332 75 0.68 1155 59 0.03  Dawson County, Georgia 5.29 118 6 5.80 190 6 4.84 192 6 6.02 175 6 5.50 95 6 5.05  Douglas County, Georgia 4.03 230 9 4.15 336 9 3.40 342 11 4.13 347 9 4.02 217 8 3.39  Elbert County, Georgia -1.60 2281 236 -2.14 2303 241 -1.85 2312 227 -2.51 2402 248 -1.97 2429 254 -2.23  Fannin County, Georgia 0.13 1371 97 -0.45 1605 120 -0.42 1477 108 -0.58 1676 116 0.01 1489 90 -0.55  Floyd County, Georgia 0.23 1321 87 0.37 1238 78 0.61 1002 63 0.08 1427 83 -0.06 1532 96 0.31  Forsyth County, Georgia 12.13 4 1 13.44 13 1 11.84 13 1 12.99 8 1 11.69 4 1 11.40  Franklin County, Georgia -0.44 1683 135 -0.95 1795 145 -0.82 1728 141 -1.14 1916 150 -0.62 1832 136 -1.01  Gilmer County, Georgia 2.92 388 17 2.47 654 38 2.40 516 26 2.09 773 41 2.54 504 26 2.02  Gordon County, Georgia 1.32 855 50 1.05 999 56 0.92 898 58 0.84 1111 61 1.11 973 49 0.71  Gwinnett County, Georgia 7.54 39 2 8.45 66 4 7.37 66 3 8.31 65 4 7.39 35 4 7.23  Habersham County, Georgia 1.24 876 52 0.87 1054 64 0.75 952 60 0.51 1244 71 0.88 1062 54 0.39  Hall County, Georgia 3.88 253 11 4.06 350 11 3.56 320 10 3.69 412 13 3.50 297 11 3.19 
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 Haralson County, Georgia -0.58 1754 143 -0.96 1805 149 -0.84 1740 146 -1.28 1978 164 -0.90 1972 156 -1.16  Hart County, Georgia -1.07 2029 182 -1.81 2187 218 -1.38 2053 190 -2.10 2291 226 -1.35 2156 190 -1.67  Heard County, Georgia -1.27 2135 209 -2.05 2280 235 -1.84 2310 226 -2.54 2409 249 -1.77 2346 233 -2.34  Jackson County, Georgia 2.83 408 20 2.82 561 27 2.35 526 28 2.59 631 28 2.60 491 22 2.12  Lumpkin County, Georgia 3.11 361 16 2.94 537 26 2.65 468 22 2.77 594 25 2.94 401 14 2.47  Madison County, Georgia 1.24 875 51 1.03 1003 57 0.56 1022 65 0.68 1181 63 0.88 1060 53 0.21  Murray County, Georgia 0.86 1012 57 0.36 1248 79 0.43 1074 68 0.09 1419 81 0.60 1192 64 0.17  Paulding County, Georgia 7.40 42 4 7.58 93 5 6.26 105 5 7.58 90 5 7.40 34 3 6.26  Pickens County, Georgia 4.26 208 7 4.78 279 8 4.17 263 9 4.49 302 8 3.96 222 9 3.87  Polk County, Georgia -0.66 1808 149 -1.42 2011 183 -1.02 1855 164 -1.59 2089 179 -0.83 1937 150 -1.19  Rabun County, Georgia 0.38 1236 79 0.19 1319 84 0.01 1255 84 -0.13 1514 96 0.06 1463 89 -0.31  Stephens County, Georgia -0.71 1836 153 -1.15 1895 164 -0.68 1639 124 -1.46 2042 169 -1.02 2021 165 -0.98  Towns County, Georgia 2.67 455 27 2.48 652 37 2.03 617 40 2.35 696 35 2.54 502 25 1.89  Union County, Georgia 2.49 497 31 2.18 717 43 1.62 713 45 2.31 709 37 2.62 485 21 1.74  Walker County, Georgia -0.51 1719 137 -1.03 1837 155 -0.88 1765 149 -1.15 1925 152 -0.63 1836 137 -1.00  White County, Georgia 2.88 395 18 2.42 666 39 2.34 531 29 2.36 692 34 2.82 430 16 2.27  Whitfield County, Georgia 0.52 1167 73 0.84 1061 65 1.06 864 55 0.56 1227 68 0.24 1368 82 0.78 

 Kentucky 

 Adair County, Kentucky -1.82 2391 259 -2.88 2518 275 -1.87 2318 230 -3.33 2610 285 -2.27 2520 269 -2.32  Bath County, Kentucky -2.47 2601 298 -3.48 2671 310 -2.07 2424 250 -4.12 2767 326 -3.11 2718 316 -2.71  Bell County, Kentucky -6.01 3061 408 -7.35 3065 408 -5.16 3082 408 -7.84 3070 408 -6.49 3068 408 -5.64 
99 | Page

 Boyd County, Kentucky -1.51 2240 225 -1.65 2114 203 -1.44 2090 196 -1.41 2029 168 -1.28 2122 180 -1.21  Breathitt County, Kentucky -5.49 3043 400 -6.85 3051 403 -4.46 3055 395 -7.22 3057 404 -5.86 3050 400 -4.83  Carter County, Kentucky -1.83 2396 261 -2.98 2552 280 -2.31 2530 268 -3.34 2616 287 -2.19 2491 264 -2.67  Casey County, Kentucky -2.89 2708 321 -4.05 2782 336 -2.69 2677 302 -4.56 2837 346 -3.39 2793 333 -3.19  Clark County, Kentucky 0.20 1334 91 0.43 1210 72 0.38 1097 70 0.31 1330 74 0.09 1444 87 0.26  Clay County, Kentucky -8.34 3096 418 -10.00 3097 418 -5.70 3096 417 -10.61 3099 418 -8.95 3098 418 -6.31  Clinton County, Kentucky -3.00 2743 327 -4.19 2814 343 -2.75 2701 311 -4.74 2871 351 -3.54 2826 342 -3.29  Cumberland County, Kentucky -4.04 2934 376 -5.55 2979 384 -4.12 3021 385 -6.09 2999 385 -4.58 2962 380 -4.67  Edmonson County, Kentucky -2.33 2560 285 -3.31 2632 301 -2.96 2763 326 -3.63 2677 302 -2.65 2623 291 -3.28  Elliott County, Kentucky -4.13 2947 379 -5.75 2994 387 -3.78 2969 374 -6.45 3018 389 -4.82 2986 385 -4.47  Estill County, Kentucky -4.64 2994 388 -5.97 3007 389 -4.17 3027 388 -6.53 3022 391 -5.20 3013 390 -4.73  Fleming County, Kentucky -2.05 2470 270 -3.11 2584 290 -2.43 2581 281 -3.32 2605 283 -2.25 2514 268 -2.63  Floyd County, Kentucky -5.99 3060 407 -7.03 3056 405 -4.17 3026 387 -7.13 3055 402 -6.10 3059 403 -4.28  Garrard County, Kentucky -0.38 1653 128 -1.02 1832 154 -0.75 1692 136 -1.04 1865 144 -0.40 1707 114 -0.76  Green County, Kentucky -1.67 2319 247 -2.87 2513 273 -2.53 2624 292 -3.33 2611 286 -2.14 2483 262 -3.00  Greenup County, Kentucky -1.55 2255 229 -1.87 2206 222 -1.86 2315 228 -1.78 2146 192 -1.45 2207 205 -1.77  Harlan County, Kentucky -6.46 3071 410 -7.81 3074 410 -5.54 3092 415 -8.22 3076 409 -6.87 3074 410 -5.94  Hart County, Kentucky -2.81 2689 317 -4.07 2790 337 -2.76 2708 314 -4.49 2828 345 -3.23 2755 322 -3.17  Jackson County, Kentucky -6.81 3081 415 -8.52 3089 416 -5.47 3091 414 -9.14 3091 416 -7.43 3087 415 -6.09  Johnson County, Kentucky -3.91 2919 372 -4.93 2915 373 -3.44 2907 356 -5.05 2909 365 -4.03 2911 367 -3.56  Knott County, Kentucky -5.86 3056 405 -7.07 3059 406 -4.40 3048 394 -7.47 3063 406 -6.26 3062 405 -4.80 
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 Knox County, Kentucky -4.92 3014 394 -6.03 3015 393 -3.47 2912 357 -6.66 3034 395 -5.55 3031 395 -4.10  Laurel County, Kentucky -1.56 2266 233 -2.34 2368 252 -1.23 1964 175 -2.63 2433 254 -1.85 2378 244 -1.52  Lawrence County, Kentucky -4.68 2996 390 -6.00 3012 390 -4.36 3044 392 -6.17 3001 386 -4.86 2988 386 -4.53  Lee County, Kentucky -6.79 3080 414 -8.25 3081 413 -5.08 3081 407 -8.78 3085 412 -7.31 3084 414 -5.60  Leslie County, Kentucky -7.13 3086 416 -8.46 3086 414 -5.91 3099 419 -8.84 3089 414 -7.51 3088 416 -6.29  Letcher County, Kentucky -5.64 3050 403 -6.68 3047 402 -4.77 3071 402 -6.90 3043 397 -5.86 3051 401 -4.99  Lewis County, Kentucky -5.00 3020 396 -6.57 3045 400 -4.36 3043 391 -6.93 3044 398 -5.36 3024 394 -4.71  Lincoln County, Kentucky -2.22 2528 277 -3.25 2622 298 -2.42 2576 279 -3.73 2693 307 -2.71 2640 295 -2.90  Madison County, Kentucky 1.50 791 46 1.02 1010 58 1.60 719 46 0.79 1143 62 1.27 911 47 1.36  Magoffin County, Kentucky -6.51 3073 411 -7.91 3076 411 -5.26 3085 411 -8.37 3079 411 -6.97 3076 411 -5.73  Martin County, Kentucky -7.66 3093 417 -8.91 3092 417 -5.58 3094 416 -9.46 3093 417 -8.21 3093 417 -6.14  McCreary County, Kentucky -6.69 3078 413 -8.46 3087 415 -5.19 3083 409 -8.82 3088 413 -7.05 3078 412 -5.55  Menifee County, Kentucky -3.75 2893 367 -5.33 2962 379 -3.39 2894 352 -6.26 3008 387 -4.67 2970 383 -4.32  Metcalfe County, Kentucky -3.41 2828 346 -4.62 2877 360 -3.23 2841 343 -5.15 2919 369 -3.94 2889 359 -3.76  Monroe County, Kentucky -4.19 2949 380 -5.38 2972 381 -3.77 2964 372 -5.84 2984 382 -4.65 2969 382 -4.22  Montgomery County, Kentucky -0.41 1664 131 -0.93 1788 144 -0.26 1388 98 -1.48 2047 171 -0.95 1992 159 -0.80  Morgan County, Kentucky -5.09 3026 397 -6.53 3043 399 -4.61 3062 399 -7.09 3052 400 -5.65 3037 396 -5.16  Nicholas County, Kentucky -2.89 2712 324 -3.62 2706 316 -2.94 2753 325 -3.85 2714 313 -3.13 2724 318 -3.17  Owsley County, Kentucky -8.80 3099 419 -10.45 3100 419 -5.74 3098 418 -11.00 3100 419 -9.35 3101 419 -6.30  Perry County, Kentucky -5.51 3046 401 -6.28 3032 395 -3.66 2946 366 -6.59 3028 393 -5.82 3046 399 -3.98  Pike County, Kentucky -4.11 2945 378 -4.64 2884 362 -3.73 2958 368 -5.01 2905 362 -4.48 2954 378 -4.09 
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 Powell County, Kentucky -3.71 2885 362 -4.79 2900 368 -3.11 2794 331 -5.33 2935 374 -4.25 2931 373 -3.65  Pulaski County, Kentucky -2.19 2521 276 -2.87 2514 274 -1.81 2293 221 -3.06 2552 276 -2.39 2557 276 -2.01  Robertson County, Kentucky -2.61 2642 305 -3.85 2744 327 -2.69 2679 304 -4.35 2813 340 -3.11 2719 317 -3.19  Rockcastle County, Kentucky -3.76 2895 368 -5.02 2924 374 -3.15 2813 336 -5.72 2970 379 -4.46 2951 377 -3.85  Rowan County, Kentucky -0.64 1797 148 -1.52 2062 192 -0.02 1272 88 -2.06 2278 222 -1.18 2084 172 -0.56  Russell County, Kentucky -2.50 2606 299 -3.57 2692 314 -2.41 2574 277 -3.94 2733 317 -2.88 2670 307 -2.79  Wayne County, Kentucky -3.95 2926 373 -5.17 2944 377 -3.26 2854 345 -5.61 2962 377 -4.40 2949 376 -3.70  Whitley County, Kentucky -3.51 2851 354 -4.64 2885 363 -2.62 2651 295 -5.12 2915 368 -3.99 2899 362 -3.10  Wolfe County, Kentucky -6.53 3074 412 -8.20 3080 412 -4.74 3069 401 -8.85 3090 415 -7.18 3082 413 -5.39 

 Maryland 

 Allegany County, Maryland -1.23 2114 203 -1.86 2200 219 -2.00 2386 238 -1.92 2214 206 -1.30 2138 183 -2.07  Garrett County, Maryland 0.29 1282 82 0.18 1325 86 -0.17 1344 93 -0.11 1508 95 0.00 1503 91 -0.47  Washington County, Maryland 0.52 1166 72 0.97 1025 60 0.23 1160 76 0.86 1108 60 0.40 1291 73 0.11 

 Mississippi 

 Alcorn County, Mississippi -1.80 2378 258 -2.54 2430 261 -2.13 2455 256 -2.61 2423 252 -1.86 2382 245 -2.19  Benton County, Mississippi -4.22 2951 382 -5.63 2984 385 -4.57 3060 398 -5.76 2975 380 -4.35 2944 375 -4.70  Calhoun County, Mississippi -3.73 2889 363 -4.42 2850 352 -3.11 2796 332 -4.59 2846 347 -3.90 2884 355 -3.28  Chickasaw County, Mississippi -3.29 2804 342 -4.18 2812 342 -3.38 2890 350 -4.19 2789 333 -3.30 2776 326 -3.39  Choctaw County, Mississippi -4.32 2964 386 -5.52 2978 383 -3.89 2994 376 -5.71 2969 378 -4.51 2955 379 -4.08  Clay County, Mississippi -3.99 2931 374 -4.47 2853 353 -2.86 2735 321 -4.64 2855 348 -4.16 2923 370 -3.04  Itawamba County, Mississippi -1.67 2316 245 -2.18 2317 243 -2.28 2519 267 -1.89 2201 204 -1.39 2174 195 -1.99 
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 Kemper County, Mississippi -4.25 2954 384 -5.47 2974 382 -3.73 2959 369 -5.47 2944 375 -4.25 2929 372 -3.74  Lee County, Mississippi 0.23 1325 88 0.50 1188 70 0.54 1030 66 0.65 1193 64 0.38 1307 75 0.69  Lowndes County, Mississippi -1.61 2286 238 -1.71 2137 209 -0.72 1671 132 -1.83 2179 198 -1.74 2335 229 -0.84  Marshall County, Mississippi -2.89 2710 323 -3.58 2698 315 -2.39 2565 273 -3.78 2702 310 -3.10 2713 314 -2.60  Monroe County, Mississippi -2.39 2582 293 -2.94 2536 278 -2.41 2575 278 -2.95 2518 268 -2.40 2561 277 -2.42 

Montgomery County, Mississippi -4.01 2933 375 -4.90 2911 371 -3.65 2944 364 -5.10 2912 367 -4.21 2927 371 -3.85 

 Noxubee County, Mississippi -5.87 3058 406 -7.17 3062 407 -4.33 3042 390 -7.59 3068 407 -6.29 3063 406 -4.75  Oktibbeha County, Mississippi -0.91 1949 169 -1.08 1864 158 1.53 739 48 -1.83 2175 195 -1.65 2299 227 0.79  Panola County, Mississippi -3.42 2830 347 -4.18 2811 341 -2.03 2400 244 -4.09 2762 324 -3.33 2780 329 -1.94  Pontotoc County, Mississippi -0.53 1729 139 -1.06 1848 157 -0.88 1771 151 -1.05 1871 146 -0.52 1779 127 -0.87  Prentiss County, Mississippi -2.25 2534 279 -3.34 2635 303 -2.89 2742 322 -3.16 2572 280 -2.07 2462 261 -2.72  Tippah County, Mississippi -2.55 2626 301 -3.45 2665 308 -2.74 2696 309 -3.40 2628 290 -2.51 2590 283 -2.70 

Tishomingo County, Mississippi -2.67 2657 312 -3.77 2732 323 -3.34 2878 348 -3.88 2719 315 -2.78 2653 301 -3.45 

 Union County, Mississippi -0.34 1629 124 -0.88 1773 141 -1.00 1837 161 -0.99 1844 136 -0.44 1729 117 -1.10  Webster County, Mississippi -3.69 2884 361 -4.93 2914 372 -4.18 3028 389 -5.20 2922 371 -3.96 2892 361 -4.44  Winston County, Mississippi -2.97 2733 326 -3.78 2734 324 -2.76 2705 313 -4.01 2746 319 -3.21 2745 320 -2.99  Yalobusha County, Mississippi -3.42 2831 348 -4.36 2840 348 -2.80 2719 316 -4.41 2821 342 -3.48 2812 340 -2.85 

 North Carolina 

Alexander County, North Carolina 0.21 1330 90 0.11 1352 89 -0.42 1478 109 0.19 1381 79 0.30 1343 79 -0.34 
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Alleghany County, North Carolina -1.56 2262 231 -1.93 2228 226 -1.68 2224 210 -1.95 2228 210 -1.58 2268 219 -1.70 

 Ashe County, North Carolina -0.42 1673 133 -0.84 1750 135 -0.82 1729 142 -0.96 1835 135 -0.54 1788 130 -0.95  Avery County, North Carolina -0.76 1863 157 -1.31 1964 175 -0.91 1790 154 -1.26 1970 162 -0.71 1876 142 -0.86 

Buncombe County, North Carolina 2.14 609 40 2.49 649 35 2.31 542 33 2.42 681 33 2.07 639 35 2.24 

 Burke County, North Carolina -1.26 2131 208 -1.43 2013 184 -1.29 2001 184 -1.35 2005 166 -1.18 2086 173 -1.21 

Caldwell County, North Carolina -0.84 1903 163 -1.02 1829 153 -1.26 1986 181 -1.00 1853 141 -0.83 1933 148 -1.24 Cherokee County, North Carolina -1.68 2327 251 -2.73 2481 265 -2.36 2549 270 -2.42 2384 246 -1.38 2169 192 -2.06 

 Clay County, North Carolina 0.95 982 56 0.25 1294 81 0.10 1224 82 0.34 1324 73 1.04 1001 51 0.19  Davie County, North Carolina 2.58 472 29 3.27 477 22 2.33 533 30 3.28 490 18 2.59 496 23 2.34  Forsyth County, North Carolina 2.47 504 33 3.55 418 17 3.39 346 12 3.23 496 19 2.15 618 34 3.07  Graham County, North Carolina -1.06 2023 179 -1.87 2205 221 -0.73 1682 134 -1.98 2237 212 -1.17 2081 171 -0.84 

Haywood County, North Carolina 0.23 1320 86 -0.02 1420 97 -0.30 1410 101 0.38 1299 72 0.63 1172 63 0.10 Henderson County, North Carolina 2.39 533 35 2.77 578 30 2.09 601 39 2.81 581 23 2.44 530 29 2.13 

 Jackson County, North Carolina 2.49 499 32 2.25 703 40 2.81 435 20 2.20 737 38 2.44 526 28 2.76  Macon County, North Carolina 0.98 973 55 0.65 1134 67 0.58 1013 64 0.90 1093 58 1.23 932 48 0.83 

Madison County, North Carolina -0.23 1563 115 -0.85 1759 138 -0.68 1641 125 -1.16 1927 153 -0.53 1787 129 -0.98 McDowell County, North Carolina -0.73 1843 154 -1.39 1998 181 -1.46 2096 198 -1.17 1935 155 -0.51 1770 122 -1.24 
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Mitchell County, North Carolina -1.39 2191 217 -2.20 2325 244 -2.09 2435 251 -2.19 2317 230 -1.38 2170 193 -2.08 

 Polk County, North Carolina 2.17 599 39 3.23 482 23 2.65 469 23 2.99 542 21 1.92 691 37 2.40 

Rutherford County, North Carolina -1.08 2035 184 -1.57 2077 196 -1.36 2042 189 -1.50 2054 172 -1.01 2015 164 -1.30 

 Stokes County, North Carolina 0.14 1367 96 -0.06 1437 99 -0.72 1674 133 0.07 1432 84 0.27 1357 81 -0.59  Surry County, North Carolina -0.95 1970 174 -1.19 1908 167 -0.81 1722 140 -1.19 1940 156 -0.94 1987 158 -0.80  Swain County, North Carolina 0.07 1405 102 -0.75 1721 131 -0.40 1464 106 -0.49 1651 112 0.33 1327 78 -0.14 

Transylvania County, North Carolina 0.68 1091 67 0.62 1146 68 0.26 1145 75 0.60 1216 66 0.66 1161 61 0.24 Watauga County, North Carolina 2.27 567 37 2.50 648 34 3.06 392 15 1.96 793 42 1.73 755 41 2.52 

 Wilkes County, North Carolina -0.94 1962 172 -0.89 1779 143 -0.89 1779 152 -0.99 1849 139 -1.04 2029 167 -0.99  Yadkin County, North Carolina -0.42 1672 132 -0.63 1681 127 -0.57 1570 115 -0.55 1670 114 -0.34 1670 110 -0.49  Yancey County, North Carolina -1.12 2050 186 -2.08 2288 238 -2.02 2396 241 -2.29 2346 239 -1.33 2149 188 -2.23 

 New York 

 Allegany County, New York -1.16 2073 191 -1.95 2237 228 -1.69 2234 212 -1.81 2166 193 -1.03 2024 166 -1.56  Broome County, New York 0.06 1407 103 0.11 1354 90 0.01 1259 85 0.12 1401 80 0.07 1454 88 0.02  Cattaraugus County, New York -0.93 1955 170 -1.14 1889 162 -1.03 1862 165 -1.09 1894 147 -0.87 1962 153 -0.98  Chautauqua County, New York -0.80 1881 159 -1.25 1934 169 -1.25 1974 177 -1.11 1903 148 -0.66 1858 140 -1.11  Chemung County, New York -1.02 1994 177 -1.09 1870 159 -0.95 1810 158 -1.04 1870 145 -0.97 1998 160 -0.90  Chenango County, New York -0.70 1831 152 -0.97 1809 150 -1.41 2061 191 -0.86 1800 133 -0.59 1813 134 -1.29  Cortland County, New York -0.31 1610 118 -0.64 1683 128 -0.69 1646 128 -0.49 1650 111 -0.16 1570 100 -0.54 
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 Delaware County, New York -0.63 1789 146 -0.82 1745 133 -0.97 1820 159 -0.73 1747 125 -0.54 1791 132 -0.88  Otsego County, New York 0.17 1352 93 0.02 1399 94 0.12 1216 80 -0.04 1476 89 0.11 1436 86 0.06  Schoharie County, New York -0.19 1539 113 -0.23 1508 110 -0.62 1603 119 -0.13 1515 97 -0.09 1542 98 -0.52  Schuyler County, New York 0.18 1348 92 -0.09 1452 100 -0.68 1644 127 -0.03 1473 88 0.24 1374 83 -0.62  Steuben County, New York -0.83 1895 162 -0.16 1480 105 -0.18 1347 94 -0.05 1481 90 -0.71 1877 143 -0.06  Tioga County, New York 0.83 1025 59 0.94 1036 62 0.17 1184 78 0.97 1072 56 0.86 1082 55 0.20  Tompkins County, New York 3.53 293 12 3.89 371 12 4.55 217 8 2.61 627 27 2.24 591 32 3.26 

 Ohio 

 Adams County, Ohio -2.39 2579 292 -3.22 2611 295 -2.50 2606 287 -3.57 2660 300 -2.73 2642 297 -2.85  Ashtabula County, Ohio -1.38 2186 216 -1.78 2173 215 -1.72 2249 214 -1.88 2193 201 -1.47 2223 208 -1.81  Athens County, Ohio -1.75 2357 256 -2.49 2410 257 -0.20 1360 95 -2.98 2527 271 -2.24 2507 267 -0.69  Belmont County, Ohio -1.85 2403 263 -2.29 2353 250 -2.26 2512 265 -2.33 2361 241 -1.89 2393 246 -2.30  Brown County, Ohio -0.33 1620 122 -0.58 1666 125 -0.83 1732 143 -0.75 1758 127 -0.50 1758 120 -1.00  Carroll County, Ohio -0.83 1894 161 -1.24 1931 168 -1.82 2297 223 -1.61 2091 180 -1.20 2093 174 -2.19  Clermont County, Ohio 2.87 402 19 3.77 388 14 2.79 447 21 3.59 432 14 2.70 462 19 2.61  Columbiana County, Ohio -1.53 2248 227 -2.03 2269 233 -2.03 2404 245 -2.10 2295 227 -1.61 2283 223 -2.11  Coshocton County, Ohio -1.13 2053 187 -1.54 2067 193 -1.91 2332 231 -1.94 2221 208 -1.53 2248 213 -2.31  Gallia County, Ohio -2.77 2683 315 -3.15 2596 292 -1.87 2317 229 -3.37 2624 289 -3.00 2699 312 -2.09  Guernsey County, Ohio -1.42 2209 221 -2.11 2294 239 -2.05 2412 247 -2.23 2324 232 -1.54 2251 214 -2.16  Harrison County, Ohio -2.26 2538 280 -2.93 2529 276 -2.63 2657 297 -2.96 2522 269 -2.30 2527 272 -2.66  Highland County, Ohio -0.38 1652 127 -0.66 1694 130 -0.83 1733 144 -0.80 1775 130 -0.52 1776 126 -0.96 
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 Hocking County, Ohio -1.36 2176 214 -1.89 2215 223 -1.75 2264 217 -2.00 2252 216 -1.47 2218 207 -1.86  Holmes County, Ohio 0.55 1153 71 0.13 1346 88 -0.58 1577 117 -0.67 1725 123 -0.25 1623 106 -1.38  Jackson County, Ohio -1.58 2272 235 -2.34 2370 253 -2.01 2393 240 -2.66 2446 258 -1.90 2399 247 -2.33  Jefferson County, Ohio -2.66 2654 311 -3.11 2583 289 -2.82 2722 318 -3.15 2570 279 -2.71 2638 294 -2.86  Lawrence County, Ohio -2.63 2645 306 -3.42 2657 307 -2.33 2538 269 -3.52 2646 295 -2.74 2646 300 -2.43  Mahoning County, Ohio -1.21 2099 199 -1.16 1897 165 -0.92 1794 155 -1.26 1966 160 -1.31 2144 186 -1.02  Meigs County, Ohio -3.74 2892 366 -4.73 2897 366 -3.98 3002 379 -4.91 2892 358 -3.92 2885 356 -4.16  Monroe County, Ohio -3.42 2832 349 -4.13 2803 340 -4.01 3007 381 -4.28 2802 336 -3.57 2832 343 -4.15  Morgan County, Ohio -3.66 2880 360 -4.81 2903 369 -3.99 3004 380 -5.03 2906 363 -3.89 2881 353 -4.22  Muskingum County, Ohio -1.56 2260 230 -1.93 2227 225 -1.72 2252 215 -2.01 2257 217 -1.64 2293 226 -1.80  Noble County, Ohio -1.70 2333 252 -2.86 2509 271 -2.65 2667 299 -2.73 2465 260 -1.57 2263 218 -2.52  Perry County, Ohio -1.67 2317 246 -2.52 2424 260 -2.58 2641 293 -2.66 2444 257 -1.80 2361 240 -2.71  Pike County, Ohio -3.59 2865 357 -4.47 2854 354 -3.22 2839 342 -4.71 2864 350 -3.83 2873 352 -3.46  Ross County, Ohio -1.21 2103 200 -1.59 2087 199 -1.80 2286 220 -1.68 2114 187 -1.30 2137 182 -1.89  Scioto County, Ohio -3.45 2838 351 -4.28 2827 345 -3.40 2896 354 -4.19 2788 332 -3.37 2787 331 -3.31  Trumbull County, Ohio -1.17 2081 195 -1.25 1937 170 -1.34 2030 187 -1.35 2004 165 -1.27 2119 177 -1.43  Tuscarawas County, Ohio 0.01 1442 105 -0.21 1502 109 -0.72 1669 131 -0.48 1647 110 -0.26 1627 107 -0.98  Vinton County, Ohio -2.59 2634 303 -3.76 2728 321 -3.15 2816 337 -4.04 2753 322 -2.87 2666 305 -3.44  Washington County, Ohio -0.24 1569 116 -0.34 1556 116 -0.60 1589 118 -0.30 1580 100 -0.20 1590 102 -0.56 

 Pennsylvania 

Allegheny County, Pennsylvania 1.36 841 48 3.06 513 24 2.49 503 25 2.55 642 29 0.85 1088 57 1.99 
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Armstrong County, Pennsylvania -1.68 2324 249 -1.67 2123 206 -1.99 2381 237 -1.99 2249 215 -2.00 2438 256 -2.32 

 Beaver County, Pennsylvania -0.05 1471 109 0.04 1393 92 -0.79 1712 138 0.09 1420 82 -0.01 1506 93 -0.75  Bedford County, Pennsylvania -1.23 2112 202 -1.72 2145 211 -2.12 2454 255 -2.09 2288 225 -1.60 2277 220 -2.49  Blair County, Pennsylvania -0.68 1820 150 -0.84 1751 136 -1.05 1870 167 -1.15 1923 151 -0.99 2008 162 -1.36  Bradford County, Pennsylvania -0.55 1738 142 -0.85 1760 139 -1.03 1864 166 -1.21 1949 158 -0.91 1975 157 -1.39  Butler County, Pennsylvania 2.43 513 34 3.29 472 21 2.28 548 34 2.92 556 22 2.07 640 36 1.91  Cambria County, Pennsylvania -1.66 2313 244 -2.07 2283 237 -2.26 2513 266 -2.43 2389 247 -2.03 2449 257 -2.63  Cameron County, Pennsylvania -1.60 2282 237 -1.89 2217 224 -2.38 2556 272 -2.27 2338 236 -1.98 2431 255 -2.76  Carbon County, Pennsylvania -0.37 1642 125 -0.46 1608 121 -1.18 1934 173 -0.59 1686 117 -0.49 1757 119 -1.31  Centre County, Pennsylvania 2.58 473 30 2.96 533 25 2.90 423 19 1.76 848 47 1.38 871 44 1.70  Clarion County, Pennsylvania -1.17 2075 192 -1.36 1985 178 -1.13 1908 170 -1.98 2242 213 -1.79 2354 235 -1.76 

Clearfield County, Pennsylvania -1.41 2207 220 -1.86 2202 220 -1.83 2298 224 -2.28 2340 237 -1.84 2373 242 -2.25 

 Clinton County, Pennsylvania -0.54 1734 140 -0.96 1803 148 -1.25 1978 179 -1.26 1967 161 -0.84 1943 151 -1.55  Columbia County, Pennsylvania 0.01 1446 107 -0.09 1454 101 -0.42 1473 107 -0.61 1696 120 -0.51 1772 123 -0.93  Crawford County, Pennsylvania -0.79 1877 158 -1.26 1942 173 -1.42 2075 192 -1.71 2126 190 -1.24 2108 175 -1.87  Elk County, Pennsylvania -0.37 1648 126 -0.28 1528 113 -1.17 1926 172 -0.43 1627 108 -0.52 1775 125 -1.31  Erie County, Pennsylvania 0.25 1307 85 0.22 1308 82 0.10 1223 81 -0.24 1561 99 -0.21 1596 104 -0.36  Fayette County, Pennsylvania -2.66 2653 310 -3.23 2613 296 -2.53 2617 290 -3.67 2684 306 -3.10 2715 315 -2.96  Forest County, Pennsylvania -1.95 2439 268 -3.32 2633 302 -2.38 2554 271 -3.65 2679 303 -2.27 2521 270 -2.70  Fulton County, Pennsylvania -0.29 1594 117 0.01 1407 95 -0.45 1489 111 -0.49 1655 113 -0.79 1912 147 -0.94 
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 Greene County, Pennsylvania -2.34 2562 287 -3.00 2556 282 -2.52 2614 289 -3.54 2650 296 -2.88 2668 306 -3.06 

Huntingdon County, Pennsylvania -0.90 1942 167 -1.45 2022 185 -1.93 2345 233 -2.07 2281 223 -1.53 2246 212 -2.56 

 Indiana County, Pennsylvania -1.19 2089 198 -1.26 1941 172 -0.65 1621 120 -1.82 2171 194 -1.76 2343 232 -1.21  Jefferson County, Pennsylvania -0.91 1947 168 -1.27 1948 174 -1.58 2178 205 -1.64 2099 184 -1.28 2121 179 -1.95  Juniata County, Pennsylvania 0.50 1177 76 0.49 1192 71 -0.58 1574 116 -0.05 1484 91 -0.03 1513 94 -1.11 

Lackawanna County, Pennsylvania 0.09 1391 99 0.39 1226 76 -0.01 1267 87 0.06 1441 85 -0.25 1619 105 -0.35 

 Lawrence County, Pennsylvania -1.06 2021 178 -1.36 1986 179 -1.50 2123 201 -1.62 2092 181 -1.31 2143 185 -1.75  Luzerne County, Pennsylvania -0.31 1612 119 -0.11 1463 102 -0.66 1635 122 -0.31 1586 101 -0.51 1773 124 -0.86 

Lycoming County, Pennsylvania -0.51 1721 138 -0.58 1663 124 -0.92 1799 157 -0.59 1689 118 -0.52 1782 128 -0.93 

 McKean County, Pennsylvania -1.16 2070 190 -1.33 1976 176 -1.56 2167 204 -1.63 2095 183 -1.45 2203 204 -1.85  Mercer County, Pennsylvania -0.39 1657 130 -0.63 1680 126 -1.26 1982 180 -0.99 1851 140 -0.76 1899 146 -1.62  Mifflin County, Pennsylvania -1.17 2078 194 -1.73 2152 212 -1.93 2343 232 -2.33 2362 242 -1.77 2347 234 -2.52  Monroe County, Pennsylvania 3.19 348 15 3.33 466 20 2.37 520 27 3.11 528 20 2.97 394 13 2.14  Montour County, Pennsylvania 1.09 931 54 1.85 796 47 1.09 857 54 1.14 1031 52 0.38 1303 74 0.38 

Northumberland County, Pennsylvania -1.15 2068 188 -1.40 2004 182 -1.76 2267 218 -1.85 2184 199 -1.60 2278 221 -2.21 

 Perry County, Pennsylvania 0.78 1048 62 0.96 1028 61 -0.09 1307 90 0.62 1203 65 0.44 1270 72 -0.42  Pike County, Pennsylvania 3.21 343 13 3.35 463 19 2.32 537 31 3.32 478 16 3.18 353 12 2.29  Potter County, Pennsylvania -1.63 2294 239 -1.52 2061 191 -1.54 2157 202 -1.69 2118 188 -1.79 2355 236 -1.71 

Schuylkill County, Pennsylvania -1.18 2083 197 -1.51 2055 189 -2.03 2398 243 -1.83 2176 196 -1.50 2239 211 -2.34 
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 Snyder County, Pennsylvania 0.00 1447 108 -0.17 1484 106 -0.88 1770 150 -0.83 1787 131 -0.66 1854 139 -1.54  Somerset County, Pennsylvania -1.01 1993 176 -1.51 2054 188 -2.11 2443 254 -1.97 2235 211 -1.48 2227 209 -2.57  Sullivan County, Pennsylvania -1.18 2082 196 -1.77 2170 214 -2.17 2480 258 -2.01 2258 218 -1.42 2187 201 -2.42 

Susquehanna County, Pennsylvania -0.10 1494 111 -0.26 1519 112 -0.87 1760 148 -0.37 1609 106 -0.21 1594 103 -0.98 

 Tioga County, Pennsylvania -0.87 1922 165 -1.52 2059 190 -1.63 2202 207 -1.70 2121 189 -1.04 2031 168 -1.81  Union County, Pennsylvania -0.93 1960 171 -1.13 1886 161 -1.44 2087 195 -1.83 2177 197 -1.63 2287 224 -2.14  Venango County, Pennsylvania -1.56 2263 232 -1.98 2247 229 -2.10 2436 252 -2.33 2363 243 -1.91 2405 248 -2.44  Warren County, Pennsylvania -0.81 1889 160 -0.96 1802 147 -1.32 2016 186 -1.13 1913 149 -0.98 2002 161 -1.48 

Washington County, Pennsylvania 0.69 1084 66 1.57 864 49 0.68 976 61 1.21 1004 49 0.34 1324 77 0.33 

 Wayne County, Pennsylvania 0.64 1107 68 0.38 1233 77 -0.25 1383 97 0.28 1344 76 0.54 1220 68 -0.35 

Westmoreland County, Pennsylvania 0.72 1075 65 1.29 936 53 0.39 1094 69 1.05 1058 55 0.48 1251 71 0.15 Wyoming County, Pennsylvania 0.08 1396 100 0.00 1409 96 -0.67 1638 123 -0.16 1528 98 -0.08 1541 97 -0.84 

 South Carolina 

Anderson County, South Carolina 0.28 1292 83 0.22 1309 83 0.00 1261 86 0.23 1368 77 0.29 1346 80 0.02 Cherokee County, South Carolina -1.06 2025 180 -1.69 2133 207 -1.42 2078 193 -2.02 2259 219 -1.39 2173 194 -1.75 Greenville County, South Carolina 2.78 422 22 3.51 424 18 3.04 395 16 3.36 468 15 2.64 477 20 2.89 

 Oconee County, South Carolina -0.20 1549 114 -0.13 1467 103 -0.32 1427 104 -0.33 1596 104 -0.41 1713 115 -0.53  Pickens County, South Carolina 0.35 1251 80 0.06 1383 91 0.27 1139 74 -0.10 1501 93 0.19 1401 84 0.11 
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Spartanburg County, South Carolina 0.56 1150 70 0.56 1166 69 0.33 1113 73 0.52 1241 69 0.51 1239 70 0.29 

 Tennessee 

 Anderson County, Tennessee 0.03 1429 104 0.19 1324 85 0.34 1110 72 -0.01 1468 87 -0.17 1577 101 0.15  Bledsoe County, Tennessee -3.18 2783 336 -3.92 2756 332 -2.92 2750 324 -4.17 2779 330 -3.43 2800 335 -3.17  Blount County, Tennessee 1.76 708 43 1.82 806 48 1.32 805 51 1.80 838 46 1.74 751 40 1.30  Bradley County, Tennessee 0.42 1212 78 0.42 1212 73 0.51 1043 67 0.52 1242 70 0.52 1234 69 0.61  Campbell County, Tennessee -3.62 2872 358 -4.53 2863 356 -3.12 2805 333 -4.91 2893 359 -4.00 2902 363 -3.50  Cannon County, Tennessee -0.76 1856 156 -0.98 1810 151 -0.90 1780 153 -1.46 2044 170 -1.25 2110 176 -1.39  Carter County, Tennessee -1.33 2163 212 -2.11 2297 240 -1.31 2014 185 -2.23 2327 234 -1.44 2201 203 -1.43  Claiborne County, Tennessee -2.96 2730 325 -3.79 2736 325 -2.64 2661 298 -4.25 2794 334 -3.43 2798 334 -3.10  Clay County, Tennessee -3.66 2879 359 -4.68 2890 364 -3.37 2887 349 -5.30 2931 373 -4.28 2935 374 -3.98  Cocke County, Tennessee -3.46 2840 352 -4.59 2873 359 -2.75 2700 310 -4.89 2890 356 -3.76 2864 350 -3.05  Coffee County, Tennessee 0.78 1045 61 0.82 1068 66 0.67 981 62 0.59 1220 67 0.55 1213 66 0.44  Cumberland County, Tennessee -0.49 1706 136 -1.05 1840 156 -0.74 1688 135 -1.24 1959 159 -0.68 1862 141 -0.94  DeKalb County, Tennessee 0.15 1361 94 -0.29 1536 114 0.06 1237 83 -0.83 1790 132 -0.39 1701 113 -0.48  Fentress County, Tennessee -3.13 2771 332 -4.08 2791 338 -2.49 2604 286 -4.89 2889 355 -3.94 2888 358 -3.30  Franklin County, Tennessee 0.33 1263 81 -0.15 1478 104 -0.68 1642 126 -0.33 1591 103 0.15 1419 85 -0.86  Grainger County, Tennessee -1.70 2335 253 -2.50 2416 258 -1.81 2295 222 -3.03 2541 275 -2.23 2504 266 -2.34  Greene County, Tennessee -1.36 2177 215 -1.54 2068 194 -0.81 1718 139 -1.93 2217 207 -1.75 2341 230 -1.20  Grundy County, Tennessee -4.29 2963 385 -5.30 2955 378 -3.39 2893 351 -5.94 2990 384 -4.94 2994 387 -4.04  Hamblen County, Tennessee -0.59 1762 144 -0.85 1757 137 -0.56 1565 114 -0.99 1846 138 -0.73 1884 144 -0.70 
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 Hamilton County, Tennessee 1.38 829 47 2.21 709 41 1.95 629 42 2.31 708 36 1.48 828 42 2.05  Hancock County, Tennessee -5.81 3054 404 -7.59 3070 409 -4.85 3075 404 -8.28 3077 410 -6.50 3069 409 -5.53  Hawkins County, Tennessee -1.07 2033 183 -1.66 2119 204 -1.45 2095 197 -1.89 2198 203 -1.31 2140 184 -1.69  Jackson County, Tennessee -2.68 2660 313 -3.48 2672 311 -2.41 2570 275 -4.01 2745 318 -3.21 2746 321 -2.94  Jefferson County, Tennessee 0.10 1384 98 -0.50 1626 122 -0.13 1327 91 -0.65 1718 122 -0.05 1524 95 -0.29  Johnson County, Tennessee -3.57 2860 356 -4.85 2907 370 -3.76 2962 370 -5.28 2929 372 -4.01 2904 364 -4.19  Knox County, Tennessee 2.82 411 21 3.63 407 16 3.26 361 13 3.28 488 17 2.47 521 27 2.91  Lawrence County, Tennessee -2.36 2572 290 -3.26 2623 299 -3.08 2791 330 -3.63 2675 301 -2.73 2643 298 -3.45  Lewis County, Tennessee -1.89 2413 264 -2.94 2532 277 -2.49 2603 285 -3.01 2533 273 -1.96 2424 253 -2.56  Loudon County, Tennessee 1.79 699 42 2.20 712 42 1.55 733 47 2.16 753 39 1.75 744 39 1.51  Macon County, Tennessee -1.11 2048 185 -1.64 2099 200 -1.08 1884 168 -1.99 2244 214 -1.46 2212 206 -1.43  Marion County, Tennessee -1.17 2077 193 -1.47 2034 186 -1.65 2208 208 -1.59 2086 178 -1.29 2129 181 -1.76  McMinn County, Tennessee -1.26 2129 207 -1.69 2134 208 -1.21 1950 174 -1.87 2192 200 -1.44 2196 202 -1.39  Meigs County, Tennessee -2.31 2551 283 -3.03 2563 284 -2.10 2439 253 -3.33 2608 284 -2.60 2616 288 -2.40  Monroe County, Tennessee -0.60 1773 145 -1.25 1938 171 -0.92 1796 156 -1.53 2068 174 -0.88 1964 154 -1.19  Morgan County, Tennessee -3.51 2852 355 -4.53 2861 355 -3.52 2925 361 -4.91 2891 357 -3.90 2883 354 -3.90  Overton County, Tennessee -1.89 2418 265 -2.85 2508 270 -2.06 2420 248 -3.56 2654 297 -2.60 2615 287 -2.77  Pickett County, Tennessee -3.18 2784 337 -4.42 2849 351 -3.92 2997 378 -4.91 2894 360 -3.68 2856 348 -4.42  Polk County, Tennessee -1.65 2306 242 -2.26 2341 248 -2.21 2496 260 -2.55 2410 250 -1.94 2421 251 -2.50  Putnam County, Tennessee 0.22 1329 89 -0.05 1434 98 0.99 881 57 -0.62 1701 121 -0.34 1672 111 0.43  Rhea County, Tennessee -1.92 2430 266 -2.68 2463 262 -2.21 2499 262 -2.69 2457 259 -1.94 2418 250 -2.23 
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 Roane County, Tennessee -0.55 1737 141 -0.66 1689 129 -0.51 1526 113 -0.72 1742 124 -0.62 1827 135 -0.58  Scott County, Tennessee -3.11 2769 331 -4.37 2844 349 -3.41 2899 355 -4.83 2880 353 -3.57 2833 344 -3.87  Sequatchie County, Tennessee -0.32 1618 121 -0.83 1749 134 -0.24 1380 96 -1.16 1930 154 -0.65 1851 138 -0.57  Sevier County, Tennessee 2.26 569 38 2.48 651 36 2.09 600 38 2.44 672 32 2.23 597 33 2.06  Smith County, Tennessee 0.01 1443 106 -0.17 1485 107 -0.43 1482 110 -0.60 1692 119 -0.42 1718 116 -0.86  Sullivan County, Tennessee -0.43 1678 134 -0.30 1540 115 -0.32 1426 103 -0.42 1622 107 -0.54 1789 131 -0.43  Unicoi County, Tennessee -1.68 2326 250 -2.26 2340 247 -2.07 2422 249 -2.38 2377 245 -1.80 2360 239 -2.19  Union County, Tennessee -1.94 2436 267 -2.84 2503 268 -1.78 2273 219 -3.21 2582 281 -2.31 2534 273 -2.15  Van Buren County, Tennessee -2.84 2695 318 -3.37 2647 304 -2.71 2684 308 -4.14 2771 327 -3.60 2839 345 -3.48  Warren County, Tennessee -1.63 2298 241 -2.26 2343 249 -2.02 2397 242 -2.58 2417 251 -1.95 2422 252 -2.35  Washington County, Tennessee 1.10 927 53 1.13 977 55 1.49 752 49 0.89 1096 59 0.85 1087 56 1.24  White County, Tennessee -2.23 2530 278 -3.25 2620 297 -2.67 2675 301 -3.56 2656 298 -2.54 2598 284 -2.99 

Virginia 

Alleghany + Covington + Clifton Forge, Virginia -3.01 2744 328 -3.49 2673 312 -3.89 2995 377 -3.43 2634 291 -2.95 2689 309 -3.83 

 Bath, Virginia 0.73 1069 64 1.19 958 54 0.13 1210 79 1.17 1018 50 0.71 1143 58 0.11  Bland, Virginia -1.52 2245 226 -2.21 2329 245 -2.47 2594 283 -2.07 2283 224 -1.39 2175 196 -2.33  Botetourt, Virginia 2.77 426 23 3.74 396 15 2.31 541 32 3.77 396 11 2.79 440 18 2.34  Buchanan, Virginia -5.40 3041 399 -6.40 3038 397 -5.34 3088 412 -6.77 3038 396 -5.78 3045 398 -5.72  Carroll + Galax, Virginia -2.37 2575 291 -3.10 2582 288 -3.15 2811 335 -3.28 2595 282 -2.55 2601 285 -3.32  Craig, Virginia -0.14 1512 112 -0.23 1511 111 -1.01 1844 162 -0.45 1635 109 -0.36 1680 112 -1.22  Dickenson, Virginia -4.78 3003 391 -6.03 3014 392 -5.03 3079 406 -6.35 3014 388 -5.11 3007 388 -5.36 
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 Floyd, Virginia 0.08 1399 101 -0.42 1594 119 -0.78 1704 137 -0.77 1766 129 -0.28 1639 109 -1.13  Giles, Virginia -0.32 1617 120 -0.88 1774 142 -1.48 2115 199 -1.03 1864 143 -0.47 1745 118 -1.63  Grayson, Virginia -3.18 2785 338 -4.10 2795 339 -3.70 2956 367 -4.37 2818 341 -3.46 2807 337 -3.98  Henry + Martinsville, Virginia -3.27 2798 339 -3.81 2739 326 -3.51 2920 360 -3.82 2705 312 -3.28 2766 324 -3.52  Highland, Virginia -1.43 2214 222 -1.56 2074 195 -1.93 2346 234 -1.62 2093 182 -1.49 2232 210 -1.99  Lee, Virginia -3.79 2902 370 -4.79 2899 367 -3.54 2930 363 -5.06 2910 366 -4.06 2915 368 -3.81 

Montgomery + Radford, Virginia 0.74 1067 63 0.42 1214 74 1.46 764 50 -0.32 1590 102 0.00 1504 92 0.72 

 Patrick, Virginia -1.72 2340 254 -2.69 2468 263 -2.71 2682 306 -2.90 2506 266 -1.93 2413 249 -2.92  Pulaski, Virginia -0.85 1910 164 -0.86 1764 140 -1.02 1852 163 -0.55 1671 115 -0.55 1796 133 -0.72 

Rockbridge + Buenavista + Lexington, Virginia -0.96 1973 175 -0.95 1801 146 -1.25 1975 178 -1.56 2080 177 -1.56 2258 216 -1.85 

 Russell, Virginia -3.27 2801 340 -4.30 2834 347 -3.77 2967 373 -4.48 2827 344 -3.45 2805 336 -3.96  Scott, Virginia -3.02 2747 330 -3.87 2746 328 -3.39 2895 353 -4.16 2776 329 -3.30 2777 327 -3.68  Smyth, Virginia -2.13 2501 275 -2.72 2479 264 -2.41 2567 274 -2.81 2490 261 -2.21 2499 265 -2.49  Tazewell, Virginia -2.34 2561 286 -2.84 2505 269 -2.41 2571 276 -2.86 2497 262 -2.36 2550 275 -2.43  Washington + Bristol, Virginia -1.57 2270 234 -1.65 2107 201 -1.56 2165 203 -1.68 2113 186 -1.60 2280 222 -1.59  Wise + Norton, Virginia -3.15 2774 333 -3.88 2749 329 -2.85 2731 320 -4.02 2747 320 -3.28 2768 325 -2.99  Wythe, Virginia 0.27 1299 84 -0.18 1490 108 -0.65 1625 121 -0.10 1504 94 0.35 1318 76 -0.57 

 West Virginia 

 Barbour County, West Virginia -3.36 2821 344 -4.29 2831 346 -3.01 2776 327 -4.89 2888 354 -3.96 2891 360 -3.61  Berkeley County, West Virginia 2.67 456 28 2.80 568 28 1.99 623 41 2.51 656 30 2.38 542 31 1.71 
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 Boone County, West Virginia -3.73 2890 364 -4.59 2871 358 -4.09 3016 383 -5.01 2904 361 -4.15 2920 369 -4.51  Braxton County, West Virginia -3.48 2845 353 -4.63 2880 361 -3.48 2915 359 -5.17 2920 370 -4.02 2909 366 -4.02  Brooke County, West Virginia -1.67 2320 248 -2.01 2264 232 -2.48 2598 284 -2.14 2305 228 -1.80 2359 238 -2.61  Cabell County, West Virginia -1.06 2026 181 -1.15 1892 163 -0.48 1504 112 -1.40 2025 167 -1.31 2146 187 -0.73  Calhoun County, West Virginia -4.46 2981 387 -5.82 2998 388 -4.70 3066 400 -6.58 3024 392 -5.22 3015 391 -5.45  Clay County, West Virginia -4.91 3013 393 -6.35 3036 396 -4.96 3078 405 -7.13 3054 401 -5.68 3039 397 -5.73 

Doddridge County, West Virginia -2.85 2698 319 -3.65 2715 319 -3.22 2838 341 -4.11 2766 325 -3.31 2778 328 -3.68 

 Fayette County, West Virginia -4.22 2950 381 -5.17 2943 376 -3.76 2963 371 -5.55 2954 376 -4.60 2965 381 -4.14  Gilmer County, West Virginia -2.60 2639 304 -3.40 2650 306 -2.17 2475 257 -4.03 2752 321 -3.24 2756 323 -2.80  Grant County, West Virginia -1.44 2216 223 -2.00 2253 230 -2.03 2406 246 -2.62 2428 253 -2.06 2459 260 -2.65 

Greenbrier County, West Virginia -2.02 2456 269 -2.41 2396 254 -1.97 2362 236 -2.86 2499 263 -2.47 2580 282 -2.42 Hampshire County, West Virginia -0.74 1850 155 -1.47 2037 187 -1.28 1999 183 -2.06 2277 221 -1.33 2151 189 -1.87 

 Hancock County, West Virginia -1.83 2394 260 -2.22 2333 246 -2.61 2648 294 -2.23 2325 233 -1.83 2370 241 -2.61  Hardy County, West Virginia 0.14 1365 95 -0.35 1560 117 -0.70 1658 130 -0.75 1759 128 -0.26 1631 108 -1.11  Harrison County, West Virginia -1.74 2354 255 -1.57 2078 197 -0.98 1824 160 -1.88 2194 202 -2.05 2457 259 -1.29  Jackson County, West Virginia -1.66 2312 243 -2.31 2360 251 -2.22 2500 263 -2.29 2343 238 -1.63 2291 225 -2.19 

Jefferson County, West Virginia 3.21 344 14 4.12 338 10 3.03 398 17 3.72 406 12 2.81 435 17 2.62 Kanawha County, West Virginia -0.39 1654 129 0.41 1218 75 0.23 1164 77 0.04 1449 86 -0.75 1896 145 -0.14 

 Lewis County, West Virginia -2.56 2629 302 -3.08 2575 287 -2.51 2607 288 -3.56 2658 299 -3.04 2703 313 -2.99 
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 Lincoln County, West Virginia -4.90 3011 392 -6.00 3013 391 -4.38 3047 393 -6.45 3019 390 -5.34 3023 393 -4.83  Logan County, West Virginia -4.96 3016 395 -5.69 2990 386 -4.55 3058 397 -5.91 2986 383 -5.18 3010 389 -4.78  Marion County, West Virginia -1.25 2128 206 -1.38 1992 180 -1.24 1966 176 -1.54 2074 176 -1.42 2184 200 -1.40  Marshall County, West Virginia -2.65 2650 308 -2.87 2511 272 -2.63 2656 296 -2.88 2501 264 -2.66 2629 292 -2.64  Mason County, West Virginia -3.16 2777 334 -3.97 2769 334 -3.52 2926 362 -4.28 2801 335 -3.47 2808 338 -3.83 

McDowell County, West Virginia -8.82 3100 420 -10.45 3101 420 -7.30 3102 420 -11.17 3102 420 -9.55 3102 420 -8.03 

 Mercer County, West Virginia -3.28 2802 341 -3.88 2750 330 -2.81 2721 317 -4.08 2759 323 -3.47 2810 339 -3.01  Mineral County, West Virginia -1.24 2123 205 -1.76 2161 213 -1.70 2236 213 -1.92 2211 205 -1.40 2180 198 -1.86  Mingo County, West Virginia -6.08 3065 409 -6.97 3054 404 -5.40 3090 413 -7.24 3058 405 -6.35 3064 407 -5.67 

Monongalia County, West Virginia 2.27 565 36 2.75 581 31 2.96 412 18 1.81 835 45 1.32 896 45 2.01 

 Monroe County, West Virginia -2.35 2568 289 -3.27 2625 300 -3.32 2874 347 -3.65 2680 304 -2.74 2645 299 -3.70  Morgan County, West Virginia -0.08 1482 110 0.34 1253 80 -0.34 1436 105 -0.08 1495 92 -0.51 1766 121 -0.76  Nicholas County, West Virginia -3.16 2778 335 -3.92 2759 333 -3.32 2873 346 -4.42 2822 343 -3.66 2847 347 -3.81  Ohio County, West Virginia -0.88 1929 166 -0.35 1564 118 -0.07 1294 89 -0.75 1756 126 -1.27 2120 178 -0.47 

Pendleton County, West Virginia -1.39 2193 218 -1.80 2183 216 -2.01 2391 239 -2.25 2333 235 -1.85 2376 243 -2.46 Pleasants County, West Virginia -2.07 2477 272 -2.43 2401 255 -2.71 2681 305 -2.64 2439 256 -2.28 2522 271 -2.92 Pocahontas County, West Virginia -2.46 2599 297 -3.22 2610 294 -3.12 2806 334 -3.73 2694 308 -2.98 2695 310 -3.64 

 Preston County, West Virginia -1.29 2144 210 -1.81 2186 217 -1.59 2182 206 -2.32 2358 240 -1.80 2358 237 -2.10  Putnam County, West Virginia 1.56 772 44 2.06 742 45 1.27 818 52 1.93 804 44 1.42 858 43 1.14 
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 Raleigh County, West Virginia -2.27 2539 281 -2.50 2417 259 -2.25 2510 264 -2.63 2434 255 -2.40 2562 278 -2.37 

Randolph County, West Virginia -2.08 2481 273 -2.48 2409 256 -1.84 2306 225 -3.00 2532 272 -2.61 2618 289 -2.36 

 Ritchie County, West Virginia -2.46 2598 296 -3.03 2565 285 -2.71 2683 307 -3.02 2538 274 -2.45 2576 280 -2.71  Roane County, West Virginia -4.23 2952 383 -5.34 2963 380 -4.11 3019 384 -5.81 2981 381 -4.69 2972 384 -4.58 

Summers County, West Virginia -4.68 2995 389 -6.09 3019 394 -4.55 3057 396 -6.66 3033 394 -5.25 3018 392 -5.12 

 Taylor County, West Virginia -2.32 2559 284 -3.12 2586 291 -2.66 2671 300 -3.47 2640 294 -2.67 2632 293 -3.01  Tucker County, West Virginia -3.01 2745 329 -3.68 2716 320 -3.47 2913 358 -4.15 2774 328 -3.48 2815 341 -3.95  Tyler County, West Virginia -3.81 2907 371 -4.71 2893 365 -4.13 3023 386 -4.66 2857 349 -3.76 2868 351 -4.09  Upshur County, West Virginia -2.07 2476 271 -2.80 2491 266 -1.97 2360 235 -3.44 2639 293 -2.71 2641 296 -2.61  Wayne County, West Virginia -2.89 2709 322 -3.52 2682 313 -2.69 2678 303 -3.78 2703 311 -3.15 2732 319 -2.95  Webster County, West Virginia -5.19 3031 398 -6.47 3040 398 -4.85 3074 403 -7.16 3056 403 -5.88 3053 402 -5.54  Wetzel County, West Virginia -3.44 2836 350 -4.04 2779 335 -3.83 2983 375 -4.29 2804 337 -3.69 2858 349 -4.08  Wirt County, West Virginia -2.53 2616 300 -3.77 2731 322 -3.20 2829 340 -4.17 2783 331 -2.93 2681 308 -3.60  Wood County, West Virginia -1.22 2108 201 -1.35 1982 177 -1.14 1914 171 -1.19 1944 157 -1.07 2045 169 -0.99 

Wyoming County, West Virginia -5.63 3049 402 -6.58 3046 401 -5.24 3084 410 -7.08 3050 399 -6.12 3060 404 -5.73 

Notes: For each of the six Candidate Indexes, the table shows the Z-score for each ARC region county and the county’s relative ranking amcounties and the relative ranking among the 3,102 U.S. counties. The final three columns provide the underlying data and rankings for the 

Index. See Sections 3 and 4 for more details of the construction of the Candidate Index. Details of the ARC Distress Indicator can be found 
117 | Page

Table A.7: Distressed Counties in the ARC States, by Index

NOTE: The table shows number of counties for each ARC state that is categorized as distressed using the ARC Distress Index for F

well as the number resulting from each of the six Candidate Indexes. 

Index 4.1 Index 4.2 Index 4.3 Index 4.4 Ind

ARC 

‘10 

ARC 

‘07 

Population change 96-06, Employment/population ratio 07, Percent of adults with a four-year college degree 00, Poverty Rate 07 

Population change 96-06, Employment/population ratio 07, Percent of adults with a four-year college degree 00, Poverty Rate 07, Percapita market income 06 

Population change 96-06, Employment/population ratio 07, Percent of adults with a four-year college degree 00, Percapita market income 06 

Population change 96-06, Employment/population ratio 07, Percent of adults with at least one year of college education 00, Poverty Rate 07, Percapita market income 06 

Population Employmeratio 07, Peadults with year of coll00, Poverty capita mar

Alabama 2 3 5 4 4 4 Georgia 0 0 0 0 1 0 Kentucky 40 34 32 33 31 36 Maryland 0 0 0 0 0 0 Mississippi 12 9 11 11 9 9 New York 0 0 0 0 0 0 North Carolina 0 0 0 0 0 0 Ohio 6 3 5 5 6 4 Pennsylvania 1 1 0 0 0 0 South Carolina 0 0 0 0 0 0 Tennessee 9 7 7 9 8 11 Virginia 1 1 5 5 9 5 West Virginia 11 16 17 15 22 18 

Total 82 74 82 82 90 87