FINAL REPORT

An Assessment of Alternative Measures for Determining 

Economically Distressed Counties and Areas in the 

Appalachian Region

 

Mark Partridge, Linda Lobao, P. Wilner Jeanty 

The Ohio State University 

Lionel J. “Bo” Beaulieu 

Southern Rural Development Center, Mississippi State University 

Stephan Goetz 

Northeast Regional Center for Rural Development, Penn State University 

Prepared for the Appalachian Regional Commission (ARC) 

March 28, 2008 

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TABLE OF CONTENTS 

Contents Page

TABLE OF CONTENTS ................................................................................................... i 

LIST OF TABLES............................................................................................................iii 

LIST OF FIGURES..........................................................................................................iv 

EXECUTIVE SUMMARY................................................................................................. v

PART I - BACKGROUND AND ISSUES IN CONSTRUCTING DISTRESS 

INDICATORS

1. Introduction ................................................................................................................. 1 

2. History and Background of ARC ................................................................................ 3 

2.1. The ARC Distressed Counties Program and Distress Indicators......................... 4 

2.2. Past Research on Alternative Distress Indicators for Appalachia and Other 

Regions ....................................................................................................................... 7 

3. Distressed Counties Programs of Other Federal Agencies...................................... 10 

4. Conceptual, Theoretical, and Empirical Issues ........................................................ 14 

4.1. Conceptual and Theoretical Issues in Producing Robust Indicators: A 

Framework to Guide Selection .................................................................................. 14 

4.2. Socioeconomic Indicators and Selection of Specific Variables for Distress 

Indexes...................................................................................................................... 16 

4.3. ARC Variables Measuring Distress.................................................................... 18

PART II - EVALUATING DISTRESS INDICATORS AND MEASURES

5. Appraisal of Backward and Forward Looking Indicators of Distress......................... 20 

5.1. Backward-looking Indicators of Distress ............................................................ 20 

5.1.1. Cost of Living Index ................................................................................................... 20 

5.1.2. Poverty Rate............................................................................................................... 21 

5.1.3. Unemployment Rate................................................................................................... 23 

5.1.4. Employment Rate/Labor Force Participation: Measures of Labor Market Strength 24 

5.1.5. Place of Work Employment Growth .......................................................................... 25 

5.1.6. Income per Person, Ratio of Income Maintenance and Other Payments to Income. 26 

5.1.7. Income Maintenance Payments ................................................................................. 27 

5.1.8. Retirement Income ..................................................................................................... 28 

5.1.9. Income from Dividends, Interest, and Rent ............................................................... 29 

5.1.10. Out-migration/Net Migration/Change in Population .............................................. 30 

5.1.11. Demographic Characteristics and International Immigrants................................. 32 

5.2. Forward Looking Measures of Distress ............................................................. 35 

5.2.1. Net Migration/Population Change ............................................................................ 36 
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5.2.2. Building Permits and Housing Measures................................................................... 36 

5.2.3. Housing Vacancy Rates ............................................................................................. 39 

5.2.4. Foreclosure Data....................................................................................................... 40 

5.2.5. Self-Employment and Entrepreneurship.................................................................... 41 

5.2.6. Educational Attainment ............................................................................................. 42 

5.2.7. Natural Amenities and Natural Capital..................................................................... 43 

5.2.8. Local Industry Composition....................................................................................... 44 

5.2.9. The Knowledge/Creative-Based Workforce............................................................... 45 

5.2.10. Health Status of the Population ............................................................................... 47 

5.2.11. Local Government Capacity .................................................................................... 50 

5.2.12. Social Capital and Its Link to Economic Well-Being .............................................. 51 

5.3. Regional and Sub-County Distress Measures ................................................... 54 

5.3.1. Multi-County Level Geographies............................................................................... 54 

5.3.2. Sub-County Indicators............................................................................................... 55 

5.4. New Federal Data Sources: Offering Expanded Assessment of Current 

Conditions.................................................................................................................. 55

PART III - EXPLORATORY ANALYSIS 

6. Assessing the Sensitivity of Current Indicators ........................................................ 58

PART IV- CONCLUSIONS AND RECOMMENDATIONS

7. General Principles and Recommendations for Developing Distress Indicators........ 79 

7.1. An Updated Economic Distress Index for the Appalachian Regional Commission

................................................................................................................................... 79 

7.1.1. General Principles for Further Analysis.................................................................... 80 

7.1.2. Related Questions for Further Analysis..................................................................... 81 

7.2. Recommendations for Selecting Variables and Developing a Distress Index.... 82 

7.2.1. Review of Current Indicators..................................................................................... 82 

7.2.2. Next Steps................................................................................................................... 82 

References.................................................................................................................... 88 

Appendices ................................................................................................................... 99 

Appendix A.1. Maps of Distress Indicators with the Refined Categories ................. 100 

Appendix A.2. Supplementary Information: Maps Showing Information of Potential 

Interest..................................................................................................................... 103 

Appendix A.3. Materials on Leading, Coincident, and Lagging Indicators ............... 104 
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LIST OF TABLES 

Table Page

Table 3.1: Distress Indicators Used across Distressed Counties Programs...........................13 

Table 5.1: Sources of Personal Income in the U.S., 2005..................................................27 

Table 5.2: Economy.com County-Level Employment and Forecast Availability...................38 

Table 5.3: Ten Quantitative Measures of Social Capital................................................53 

Table 6.1: Correlation Matrix of the Economic Indicators..............................................59 

Table 6.2: Change of County Economic Status in the United States...........................................62 

Table 6.3: Change of County Economic Status in Appalachia.........................................62 

Table 6.4: Economic Status Prediction in Appalachian States with the Added-education 

 Approach..........................................................................................64 

Table 6.5: Economic Status Prediction in Appalachian States with the Added-population

 Approach..........................................................................................65 

Table 6.6: Change in Economic Status of the Appalachian Sub-regions.............................66 

Table 7.1: Possible Outcomes from a Distress Index Evaluation......................................79 

Table 7.2: Data Dictionary..................................................................................85 
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LIST OF FIGURES 

Figure Page

Figure 2.1: Map of the Appalachian Region and Sub-regions...........................................4 

Figure 2.2: ARC County Economic Status Designation by National Index Value Rank............6 

Figure 5.1: Income Maintenance vs. Share of income...................................................28 

Figure 5.2: Trends in Retirement Income as a Share of Total Income................................29 

Figure 5.3: Trends in Income from Dividends, Interests, and Rent (Share in total).................30 

Figure 5.4: Housing Stress Counties, 2000...............................................................36 

Figure 5.5: Trends in Building Permit Issued in the U.S. (1000 units)...............................39 

Figure 5.6: Trends of Vacancy Rates in the U.S., 1965-2007..........................................40 

Figure 5.7: Trends in Rural Wage and Salary vs. Self-Employment Jobs and Ratio of Self – 

 Employed to Wage and Salary Workers, 1969-2005 .....................................41 

Figure 5.8: Unadjusted Mortality Rates, 1993–1997.....................................................47 

Figure 5.9: Age Adjusted Mortality Rates, 1993–1997..................................................48 

Figure 5.10: Suicide Rates among Girls Ages 10-14 in the United States, 2002-2004..............48 

Figure 5.11: Suicide Rates, 1989-1998.....................................................................49 

Figure 6.1: Distress Indicator Map for the ARC Region................................................67 

Figure 6.2: Distress Indicator Map for the U.S. as a Whole............................................68 

Figure 6.3: Poverty Rate Map in the Appalachian Region..............................................69 

Figure 6.4: Map of Poverty Rates in the United States.................................................70 

Figure 6.5: Map of Unemployment Rates in the United States and Appalachia.....................71 

Figure 6.6: Per Capita Market Income in the United States and Appalachia, 2003.................72 

Figure 6.7: Map of Educational Attainment in Appalachia (High School Completion Rates)....73 

Figure 6.8: Map of Total Population Change in Appalachia, 2000-2005.............................74 

Figure 6.9: Map of Distress Indicator with the Educational Attainment Index......................75 

Figure 6.10: Map of Distress Indicator with the Population Change Index..........................76 

Figure 6.11: Map Comparing the ARC’s Index with Our Index with Educational Attainment...77 

Figure 6.12: Map Comparing the ARC’s Index with Our Index with the Population Change.....78 

Figure A.1: Map of ARC’s Distress Indicator with the Refined Economic Status................100 

Figure A.2: Map of Prediction of Refined Economic Status Using Our Index with Educational 

 Attainment.....................................................................................101 

Figure A.3: Map of Prediction of Refined Economic Status Using Our Index with Population 

 Change.........................................................................................102 

Figure A.4: Nonmetro Counties with Above-average (18 percent or higher) Population 65 and 

 above, 2000....................................................................................103

Figure A.5: Nonmetro Employment Change, 2000-2006.............................................103 

Figure A.6: Average Commute Times in Nonmetro Counties, 2000................................103 

Figure A.7: E-forecasting Leading Economic Index...................................................104 
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EXECUTIVE SUMMARY 

The Appalachian Regional Commission (ARC) has played a vital role since 1965 in advancing 

the well being of people, communities, and institutions in the region. Due to sustained 

investments undertaken by ARC and other public and private sector entities, substantial inroads 

have been made against the economic instability, poverty, weak human capital, poor 

transportation corridors, and limited physical infrastructure that characterized the region at the 

time of ARC’s inception. 

This report responds to a proactive effort by the ARC to explore new avenues for assessing wellbeing. Despite well-recognized advances, chronic socioeconomic distress persists in various 

pockets in the region while other areas face increasing instability stemming from population 

shifts and global economic changes. The ARC has sought to develop meaningful indicators to 

document distress, with the goal of improving the ability of the federal office of the ARC and its 

state partners to target resources effectively to counties facing a diversity of barriers to achieving

economic progress. 

The purpose of this report is to offer additional insights on the set of distress indicators and their

respective measures that can prove comprehensive, practical, and valuable in guiding the future 

work of the ARC. Our report takes a fresh look at the current indicators employed by the ARC 

to classify counties as economically distressed. We outline the strengths and limitations 

associated with such indicators and evaluate a series of new indices and data sources that may 

promote greater accuracy in terms of monitoring the long-term socioeconomic complexion of 

counties in the region. These new indicators include “forward-looking” measures as well as 

indicators that tap a wider range of socioeconomic dimensions of distress, beyond the standard 

economic indices conventionally employed by the ARC. 

Our conclusion is that the ARC should update its current distress indicators to better reflect 

twenty-first century socioeconomic conditions. Though improved in recent years, the currentlyused distress index -- based on the poverty rate, unemployment rate, and per capita market 

income -- suffers from various shortcomings. Our analysis reveals that the poverty rate alone 

largely drives the variability in the current distress index. Therefore, the current index is not a 

valid and transparent measure that fully reflects all the dimensions of distress. Another problem 

is the use of the unemployment rate and per capita income. In particular, the unemployment rate 

does not capture contemporary labor market weaknesses to the degree that other indicators 

would. Finally, in sensitivity analysis, we find that the counties can shift in terms of their distress 

designation with only modest changes in how the distress index is calculated. 

After a careful analysis of over 50 indicators, we recommend that the ARC reevaluate its 

distress indicators in the following ways. First, it should consider the following candidate 

indicators in this analysis: (1) population change; (2) educational attainment; (3) income and 

earnings; (4) housing market conditions; (5) entrepreneurship and self employment; (6) 

improved measures of labor market strength; and (7) the poverty rate. These indicators capture 

dimensions of both current and forward-looking distress. Second, the analysis of candidate 

indicators should be statistical in nature, relying on regression approaches to determine the 

factors that have more power in explaining shifts in distress over time. This analysis should 
vi

consider variable measurement issues and proper weights for each indicator. The outcome of the 

proposed approach would be a small list of three to five variables that would constitute a new 

indicator of distress. Finally, the ARC should consider monitoring a secondary grouping of 

indicators to provide a broader context for benchmarking. These recommendations are more 

fully described in Section 7 of the report. 
1

PART I - BACKGROUND AND ISSUES IN 

CONSTRUCTING DISTRESS INDICATORS 

1. Introduction 

The purpose of this report is to assist in developing distress indicators and their respective 

measures that will be comprehensive, practical, and valuable in guiding the future work of the 

ARC. Our report takes a fresh look at the current indicators employed by the ARC to classify 

counties as economically distressed. We outline the strengths and limitations associated with 

such indicators and evaluate a series of new indices and data sources that may provide improved 

accuracy of the long-term socioeconomic viability of counties in the region. These new 

indicators include “forward-looking” measures as well as indicators that tap a wider range of 

socioeconomic dimensions of distress, beyond the standard economic indices conventionally 

employed by the ARC. 

Our analysis is the product of a multi-disciplinary research team that carefully appraised the 

findings of past empirical studies, examined agency reports, conducted preliminary empirical 

analyses, and met with federal and state ARC representatives and staff to secure their input. The 

project team consists of economists and sociologists specializing in community/regional wellbeing and spatial analysis. Throughout the six month duration of the project, the team met in 

Washington D.C. and in Columbus, OH, conducted numerous conference call meetings, and 

worked individually to assemble the information presented here. The task of writing of the 

report was collectively shared by all members of the team. 

This report is organized into three parts and seven sections. 

Part I provides the research and policy-oriented background for the selection of distress 

indicators. Our primary intent is threefold. First, in Section 2, we present an overview of the 

history and background of the ARC’s efforts to document distress. Agency reports and empirical 

research studying distress in the region are used to inform this overview. Second, we examine 

the comparability between ACR indicators and measures of distress with those of other federal 

agencies in Section 3. We find that many, if not most, agencies employ the same indicators used 

by the ARC -- poverty, unemployment, and income -- but some add other indicators more 

tailored to the respective needs of these agencies. Finally, Section 4 discusses conceptual, 

theoretical and methodological issues involved in developing indicators of distress. We 

document the types of decisions that need to be made and the problems that arise in selecting 

appropriate indicators and measures. 

Part II turns to an appraisal of indicators that can be employed to document distress. The overall 

strength and limitations of these indicators for covering “distress” as a concept are discussed in 

Section 5.1. Data issues involved in the measurement of these indicators -- such as timeliness, 

geographic coverage, and cost of data sources -- are documented. Our initial focus is on what we 

label “backward-looking” indicators, measures that benchmark contemporary conditions such as 

poverty rates, income, and unemployment. Some of these variables have been widely used as 

distress measures; others are alternative measures involving new income sources and population 

attributes such as aging and immigration, measures that have received limited attention with 
2

regard to their possible inclusion in socioeconomic distress indexes. In both cases, however, 

these indicators appear to perform better at tracking past or current conditions than in 

determining future well-being. 

In Section 5.2, we delineate a number of indicators that potentially offer a window into the future 

fortunes of the region. These include indicators of population change, housing starts, 

entrepreneurial and knowledge economy trends, local government capacity, and the social 

capital/civic health of local communities. Section 5.3 examines new data sources that can be 

tapped to construct some of the indicators above for different geographies and time periods. 

These include data available at the sub-county level and recently developed data sources, such as 

the American Community Survey (ACS) which will soon be available for the nation’s smallest 

counties (although with some time lags and as of yet unknown limitations). Taken as a whole, 

this section provides a list of indicators and their respective measures that we believe offer a 

more comprehensive and multi-faceted picture of distress relative to those presently in use. 

Part III, contained in Section 6, provides an exploratory sensitivity analysis of distress indicators 

using empirical data. The purpose is to determine the consistency of the use of different variables 

in classifying counties as distressed. We begin by examining the performance of ARC’s current 

distress indicators, namely, poverty, income, and unemployment. While all three have been 

perceived as key contributors to distress, our analysis reveals that the poverty rate alone largely 

drives the variability in the current distress index. 

We then turn to a “what-if” analysis, changing the assumptions of the current distress index by 

adding two potential new variables, change in population and educational attainment. We find 

that while there is some stability in the classification of counties as distressed when either of the 

two variables is added, shifts in the designated distress classification of some counties also occur. 

Our conclusion is that categorizing distress is somewhat sensitive to the variables used in 

constructing the index. 

Part IV, contained in Section 7, presents our conclusions and recommendations. We summarize 

the findings of our research review and exploratory empirical analysis. We document the range 

of scientifically sound indicators that are both contemporary and forward-looking measures of 

distress. These indicators should prove most useful to the ARC and other federal/state agencies) 

in its quest to develop a defensible system for determining levels of economic distress. Finally, 

we specify needed steps to refine the selection process of indicators and their accompanying 

measures, a process that can improve the benchmarking of both past progress and attainment of 

future goals. 
3

2. History and Background of ARC 

Created by an act of Congress in 1965, the Appalachian Regional Commission (ARC) is a 

regional economic development agency representing a unique partnership of federal, state, and 

local government. Local level participation in the Commission is assured through 72 local 

development districts with boards composed of elected officials, business people, and other local 

leaders. Similar to other agencies created to promote local planning and to address 

socioeconomic problems of lagging regions and communities in the U.S., ARC’s primary role is 

to promote economic competitiveness and social development of the Appalachian Region. 

In the Appalachian Regional Development Act, the legislation from which ARC derives its 

authority, Appalachia was originally defined to include 300 counties in 10 states. As a result of 

several amendments to the Act, the last of which was in 2002, the region now incorporates an 

area of 200,000 square miles and about 22.9 million people. It follows the spine of the 

Appalachian Mountain from Southern New York to Northern Mississippi and includes 410 

counties comprising all of West Virginia and parts of 13 states referred to as Appalachian states: 

Alabama, Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, Ohio, 

Pennsylvania, South Carolina, Tennessee, and Virginia. Governors of these 13 states, together 

with a federal co-chairman appointed by the President, make up the Commission. Figure 2.1 

presents the Appalachian Region and its sub-regions. 

To fulfill its mandate of reducing the socioeconomic gap between the Appalachian Region and 

the rest of the nation, the commission has put forth a wide range of activities and programs in the 

Region over the course of its history. In contrast to economic development agencies that are 

principally categorical grantmakers, ARC has implemented a multi-faceted approach which 

combines its special grant programs with advocacy, regional planning, and research activities. As 

a result, the commission has provided support for various projects since its inception, ranging 

from the Appalachian Development Highway System (ADHS); community water and sewer 

facilities for homes, businesses, and industries; health, education, and human resource 

development initiatives; to economic development programs and local capacity building and 

leadership development. 

In the spirit of its congressional mandate, ARC originally mobilized its efforts to employ a 

growth center policy aiming at promoting economic development in Appalachia’s urban areas 

with the assumption that development, once taken place in these localities, would spread to rural 

areas. Such policies, prominent among many world leaders and other development agencies such 

as EDA, were in line with the prevailing regional development theory in the 1960s and early 

1970s. The growth center approach was harshly criticized for not supporting the areas in greatest 

need. The ARC shifted its focus in the mid-1980s towards the more remote rural areas, allocating 

its resources primarily to these more economically disadvantaged counties. 

As a result of the Commission’s multi-pronged efforts, great strides have been made in reducing 

the economic imbalances between the Region and the rest of the U.S. In fact, Widner (1990) 

forcefully argues that the ARC’s endeavor to develop Appalachia (mostly through the Distressed 

Counties Program) has been the most comprehensive regional development effort ever 

undertaken in the country. However, a large number of communities in the region are still not 
4

up to par with the rest of the country in terms of economic vitality and living conditions. 

Appalachia continues to battle economic anguish, concentrated areas of high poverty, high 

unemployment rates, educational disparities, high rates of diseases, and population out-migration 

(ARC, 2004). 

 Source: ARC 

 Figure 2.1: Map of the Appalachian Region and Sub-regions 

2.1. The ARC Distressed Counties Program and Distress Indicators 

The ARC Distressed Counties Program is well documented in the academic literature (Glasmeier 

and Fuellhart, 1999; Wood and Bischak, 2000). The Distressed Counties Program (DCP) was 

proposed by ARC in a report to Congress in the early 1980s as a response to the threat of the 

Commission’s imminent demise. Congress had requested that ARC outline a plan for completing 

its programs in a timely manner. Not only was ARC not dissolved, but the Distressed Counties 

Program was formally adopted as ARC’s policy and took effect at the beginning of the fiscal 

year 1983. Implementing the program required that a reasonable share of the Commission’s 
5

funds be devoted to counties in the most dire economic need. From the inception of the DCP, 20 

percent of Area Development funds were allocated to the distressed counties. In fiscal year 

1997, Area Development allocation to distressed counties increased to 30 percent. But, this 

annual set-aside does not preclude these counties from benefiting from the rest of ARC’s funded 

programs. The Distressed Counties Program has been the ARC’s principal vehicle and 

predominant framework for providing adequate help to the most economically disadvantaged 

counties in the region. 

Putting to work the Distressed Counties Program involves identifying distressed counties using 

economic indicators. ARC elected to employ variables that not only would vary little over very 

short time periods, but also would identify counties having the structurally weakest economies. 

In the 1980s, ARC began its distressed county designation using four distress indicators selected 

from a larger list of 12: a three-year average unemployment rate, poverty rate, per capita market 

income (which is income excluding transfer payments), and a three-year average infant 

mortality. These indicators were used to rank all ARC counties. To qualify as distressed, 

counties had to be in the lowest quartiles in at least three of the four categories. However, the 

ARC continues to struggle with how to define distress and how the definition should evolve with 

the changing economy. For example, between December 1999 and July 2006, there were 33 

ARC meetings to discuss the distress indicators (Witte and Bischak, 2006). 

To date, ARC has made several revisions to the original distress measures in order to improve 

their consistency and relevancy over time. The first adjustment to the distress indicators occurred 

in fiscal year 1988. Specifically, ARC discontinued the use of infant mortality since the region’s 

mortality rate had improved to the point where it was consistent with the average rate for the 

nation. The ARC also indexed the remaining indicators to national averages. Rather than using a 

single county status designation, ARC defined two more categories, middle and competitive 

counties. Competitive counties were those with poverty rates at or below national averages, 

three-year unemployment rates at or below national averages, and per capita market income 

(PCMI) no less than 80 percent of the national average. In the same fiscal year, the Commission 

began tracking the counties located within its region using a five-category system: severely 

distressed, distressed, middle, strong, and very strong. 

A second revision, which occurred in fiscal year 1995, resulted in a change in the distress 

designation. No longer did a county need to meet all three criteria to qualify as distressed. 

Counties with poverty rates of at least 200 percent of the national average needed to meet one of 

the two remaining criteria to be considered distressed. The two remaining criteria were 150 

percent unemployment or two-thirds per capita market income. 

A fourth designation, attainment counties, was introduced in FY1997 to categorize the counties 

performing at or above national averages in all three criteria. The four categories were then as 

follows: distressed, transitional or middle, competitive, and attainment. In that year, Area 

Development funds increased to 30 percent and attainment counties were disqualified from 

receiving such funds. 

In fiscal year 1998, a fourth adjustment in the ARC distress measure was created. Congress 

charged ARC with the task of addressing the needs of severely and persistently distressed areas 
6

of the Region and focusing attention on the areas of greatest need to provide a fairer opportunity 

for the people of the Region to achieve a quality of life on par with that generally enjoyed by 

citizens across the United States. 

In fiscal year 2006, ARC introduced an “at risk” designation to include non-distressed counties 

which were nearly distressed. However, this designation (which was only for planning purposes) 

did not entitle counties to any additional funding. The at-risk category was viewed as useful in 

identifying the transitional counties with characteristics touching on the distress threshold. 

Criteria used to identify the at-risk category included a per capita market income that is twothirds of the national average or less, a three-year average unemployment rate that is at least 125 

percent of the national average, and a poverty rate that is at least 125 percent of the national 

average. ARC’s county designations now were expanded to five: distressed, at risk, transitional 

or middle, competitive and attainment. 

Finally in fiscal year 2007, ARC elected to transform its three traditional distress indicators into 

a national index. Under this new model, county designation and classification involves a threestep procedure. First, a county’s averages on three economic indicators -- three-year average 

unemployment rate, per capita market income, and poverty rate -- are compared with national 

averages. Second, the resulting values are summed and averaged to create a composite index 

value for each county. Each county in the nation is then ranked based on its composite index 

value. The higher the index values, the higher the levels of distress. Thirdly, each Appalachian 

county is classified into one of the five economic status designations based on its position in the 

national ranking. Figure 2.2 shows the ARC’s current economic status designation criteria. 

ARC assigns the status “distressed” to counties ranking in the worst 10 percent of the nation’s 

counties. Distressed counties are the most economically depressed. Counties ranking between the 

worst 10 percent and 25 percent of the nation’s counties receive the “at-risk” status, meaning that 

counties are at risk of becoming economically depressed. Such counties ranking between the 

worst 25 percent and the best 25 percent of the nation’s counties are assigned the “transitional” 

status. Counties ranking between the best 10 percent and 25 percent of the nation’s counties fall 

in the “competitive” category. Those that rank in the best 10 percent of the nation’s counties are 

classified as “attainment” counties, areas that are the most economically strong. 

Source: County Economic Status Fiscal Year 2007, ARC 

Figure 2.2: ARC County Economic Status Designation by National Index Value Rank 
7

2.2. Past Research on Alternative Distress Indicators for Appalachia and Other 

Regions

ARC distress measures have been criticized as being imperfect. For example, conventional 

unemployment rates do not fully capture local market conditions, especially in rural regions. 

That is, unemployment rates fail to measure underemployment, labor force participation, or job 

creation differences. Furthermore, unemployment does not encompass involuntary part-time 

workers and discouraged job seekers who exit the job markets. To obtain a better understanding 

of the local labor markets and the need for job creation, ARC commissioned two studies 

assessing underemployment (e.g., involuntary part-time employment) and labor force 

participation which are broader measures of labor market surplus than unemployment. 

A study by Bradley et al. (2001) focused on generating county-level measures of labor force 

participation and underemployment rates. Results from this study indicate that, more so than 

unemployment, both labor force participation and underemployment portray a higher degree of 

labor market surplus in Appalachia as compared to the U.S. Additionally, between 1993 and 

1998, underemployment and labor force participation apparently grew more slowly in 

Appalachia than in the U.S. as a whole. This study also revealed that job growth was slower and 

wage growth slightly less than for the entire U.S. over this time period. 

Following Bradley et al. (2001), Price and Wial (2005) analyzed underemployment by state and 

demographic group for each Appalachian state and the entire country for each year from 1996 to 

2004. They concluded that tremendous progress had been realized by 2004 in bridging the 

underemployment gap between nonmetro and metropolitan Appalachia relative to nonmetro and 

metropolitan areas outside of Appalachia. Most Appalachian states experienced statistically 

significant declines in underemployment. 

Another downside of the ARC distress measures is that data on poverty rates are decennial and 

as such become outdated over the course of a decade. Research conducted by Hammer (2000) 

and commissioned by ARC analyzed recent trends in poverty in the Appalachian region and 

examined the Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) effects on the 

ARC distress county designation. The SAIPE program was an attempt to remedy the deficiencies 

in economic distress measures such as the ten-year interval poverty rate and per capita income. 

The reason is that national levels and spatial distributions of income and poverty for small areas 

are not stable over time. The study focused on changes in total poverty in Appalachia between 

1979 and the mid-1990s, with particular emphasis paid to the post-1990 period. Hammer 

discussed the geographic distribution of poverty, especially child poverty. He contended that 

while SAIPE would provide overall better estimates of distress than the poverty estimates 

derived from a decade-old census, a simple substitution of the SAIPE point estimates for census 

poverty estimates might unjustifiably deny some counties distressed-status recognition. The 

author concluded that the availability of new sources of income and poverty data such as the 

American Community Survey should significantly improve the accuracy of the SAIPE, making 

them an even more viable option for the determination of distressed-status by the Appalachian 

Regional Commission. 
8

Another study by Wood and Bischak (2000) focused on progress and challenges in reducing 

economic distress in Appalachia from 1960 to 1990. They used data from several sources 

including the U.S. Census Bureau, the Bureau of Economic Analysis, and the Office of 

Economic Opportunity to identify time trends in the number of distressed counties in 

Appalachia, prior to the inception of ARC, as well as throughout much of the ARC’s existence. 

The study also extended its scope to include all the remaining U.S. counties. To identify 

distressed counties, ARC’s current distress criteria at the time of the study were applied to all 

U.S. counties for the decennial years 1960, 1970, 1980, and 1990. Illustrating the progress in the 

ARC region, their findings indicated that the number distressed counties had decreased by more 

than a half during the time period under scrutiny and only about one-quarter of the 1960 

distressed counties in Appalachia remained distressed. Measures used to identify distressed 

counties included the following: 

• A poverty rate that is 150 percent or more of the U.S. average 

• PCMI that is no more than 2/3 of the U.S. average 

• An unemployment rate that is 150 percent or more of the U.S. average 

• A county also qualifies as being distressed if it has a poverty rate that is at least 

200 percent of the national average and matches only one of the two remaining 

criteria (150 percent unemployment or two-thirds PCMI). 

In addition to identifying counties moving out of or remaining in distress, Wood and Bischak 

pointed to several factors affecting the distress status of counties using two logistic regression 

models, a socioeconomic model and an economic structure model. The dependent variable in 

both models took on the value of “1” if a county moved out of distress and “0” if the county 

remained distressed between 1960 and 1990. Based on the socioeconomic model, factors 

contributing to a county moving out of distress between 1960 and 1990 included rates of 

employment in manufacturing, high educational attainment rates, high percentage of the 

population living in urban areas, a low percentage of minorities, and a county’s location in the 

southern Appalachian sub-region. The results from their economic structure model point to 

factors such a county’s ability to attract retirees, high levels of manufacturing, and close location 

to a metropolitan area as being key factors in determining whether a county moved out of the 

distress category. 

Expanding and updating the work by Wood and Bischak (2000), Wood (2005) recently analyzed 

trends in national and regional economic distress from 1960 to 2000. Wood capitalized on the 

release of the 2000 census data to re-examine distress conditions in Appalachia. He found that 

distress was not random over time, but rather quite persistent. Counties that were distressed in 

2000 were distressed for the most part in 1960. Wood’s results were similar to those he obtained 

in the previous study with Bischak. Factors affecting the distressed county status include high 

minority populations, low educational attainment, low employment in manufacturing, high 

employment in mining, low employment in professional services, and location in a metropolitan 

area. 

Given the weaknesses of the ARC’s distress measure, Glasmeier and Fuellhart (1999) developed 

a surrogate additive index they labeled the economic health index (EHI). The EHI was composed 

of four individual indices: an unemployment rate index comparing county-level civilian 
9

unemployment rate to the national civilian unemployment rate; a per-capita market income index 

comparing a county’s income level, less transfer payments, to the national level; a labor force 

participation index; and a per-capita transfer payments to per-capita market income ratio index. 

The summation of the four individual indices yields the health economic index. The lower the 

health economic index scores, the better counties performed economically. 

To predict economic distress, a linear regression model was used with the 1994 county-level 

index values as the dependent variable. Socioeconomic variables, such as percent of population 

with four-year college degrees, percentage of income from manufacturing, and percentage of 

income from residential adjustment were significantly and positively associated with county 

economic health. On the other hand, variables such as single mothers with children under 18, 

females in the labor force, and those over 65 years of age, were negatively associated with the 

EHI. Location variables, such as adjacency to a metro area, were significantly and positively 

related to a better EHI, while location in the central Appalachia region was significantly and 

negatively associated with county economic health. Glasmeier and Wood (2005) later used the 

EHI to determine the economic characteristics of the counties that had received funds from the 

U.S. Economic Development Administration from 1965 to 1997. 

Feser and Sweeney (2003), in a study not commissioned by ARC, used data from the 1969 to 

1999 period to examine the spatial extent and temporal persistence of U.S. economic distress 

based on three different indicators: unemployment, low income, and out-migration-induced 

population decline. They utilized commuter zone as their unit of analysis. The study was an 

attempt to assist the U.S. Economic Development Administration (EDA) in a review of criteria 

used to assess target development assistance and to evaluate the incidence and geography of outmigration and population decline as compared with two of the most common distress measures: 

low income and unemployment. The authors excluded poverty rate due to the unavailability of 

yearly poverty data. A mixture of absolute and relative distress measures was used. While 

unemployment was expressed as a rate, the ratio of income maintenance transfer payments (for 

family assistance, food stamps, and other income maintenance programs) to total personal 

income was used instead of per capita income. 

To distinguish high-growth/high-out-migration places from places experiencing high outmigration/depopulation, Feser and Sweeney employed the following measure of outmigration/population loss (OPL): 

 













 −

















=

+

it

ti it

it

it

it P

P P

P

O

OPL , 1

*

where t and i index regions and year respectively; O is the number of out-migrants as reported by 

the Statistics of Income Division of the IRS; P* is estimated population from the IRS; and P is 

population from the Bureau of Economic Analysis. Unemployment and income data were from 

the BLS and the BEA’s Regional Economic Information System respectively. Distress thresholds 

were 8.4 percent for unemployment rate (75th percentile), 75th percentile for ratio of income 

maintenance payments to personal income, and 25th percentile for OPL respectively. 

 
10

Other studies have collected primary data to identify socio-economic problems characterizing 

distressed individuals, families, and communities. Fox and Chancey (1998), for example, 

analyzed the sources of economic distress, examining the relationship between six measures of 

economic stress and seven measures of individual and family well-being. Distress measures 

included income, perceived economic well-being, individual and spouse job instability, and 

individual and spouse job insecurity. Measures of individual and family well-being included 

psychological distress, self-affirmation, health, family accord, family conflict, and split-up. 

Results indicate that job and financial uncertainties negatively affected individual and family 

well-being for both men and women. For both employed men and all men, family conflict 

increased when a spouse experienced job instability and job insecurity. 

The Florida Legislative Committee on Interregional Relations (LCIR) surveyed the county and 

municipal governments in the state regarding problems facing distressed communities and the 

extent to which federal and state revitalization programs used by local governments were 

successful in addressing the needs of these communities. Results from the survey revealed that 

the set of socioeconomic problems that characterized most distressed communities included 

(LCIR, 2003): 

• Vacant and abandoned buildings 

• Loss of jobs and corresponding high unemployment rates 

• High dropout rates 

• Inferior public infrastructure: streets in need of repair, crumbling sidewalks, lack 

of adequate street lighting, antiquated sewer/water systems, among others 

• Low income households, 

• Concerns for public safety and high crime. 

Summary Evaluation: Past research has repeatedly identified key conditions related to 

whether a county is categorized as in distress or as having exited the distress category. 

These key conditions include poverty, labor market conditions, educational attainment, 

and net population loss. 

3. Distressed Counties Programs of Other Federal Agencies 

In addition to ARC, a wide range of federal and state agencies strive to counteract economic 

challenges in places of distress. Among these agencies are the Economic Development 

Administration (EDA), which under numerous program titles provides funding to projects in 

economically distressed places; the U.S. Department of Agriculture’s (USDA) rural development 

programs that addresses housing electricity, water and sewage, empowerment zones, and 

enterprise communities; the Department of Housing and Urban Development (HUD), focusing 

specially on areas with persistent economic distress; and the Federal Emergency Management 

Agency (FEMA) which assists areas in need of disaster assistance. The Bureau of Indian Affairs; 

and the Tennessee Valley Authority (TVA) also plays key development roles as well. 

Newly created entities established by Congress to address economic distress issues include the 

Delta Regional Authority (DRA), the Denali Commission, and the Northern Great Plains 

Regional Authority. Members of Congress have also proposed bills to establish regional 

development commissions in the Southwest Border Region and the Southeast Crescent region. 
11

Each of these established agencies has targeted specific areas with a variety of special programs 

if they meet the distress threshold that has been established by the relevant agency. However, the 

geographical distressed areas served by ARC overlap, at times, with those designated by these 

agencies. Because many of these agencies are modeled after ARC, it is not surprising that they 

rely on similar indicators for determining a county’s distress status. 

In terms of indicator usage across the established distressed programs, in a review of 16 federal 

and 18 state programs, Fullenbaum and McNeil (1995) noted that three distress indicators are the 

most commonly used: poverty, unemployment, and income. Most agencies employ at least one 

or all of the indicators used by ARC. Only rarely would an agency consider a single indicator at 

the county level. Six of the sixteen federal programs combine population change or outmigration with such measures as poverty rate and unemployment. 

Some federal programs, rather than relying on a set of criteria, have recently adopted an openended approach to determining distress eligibility. The EDA rests upon two primary measures of 

distress to determine a community’s eligibility to receive funding: per capita income and 

unemployment rate. To qualify as distressed, a community must have an average per capita 

income which is 80 percent or less of the national per capita income average and an 

unemployment rate, for the most recent 24-month period, at least one percentage point higher 

than the national average. Also qualifying as distressed are communities facing actual or 

threatened severe unemployment or economic adjustment problems resulting from short-term 

and long-term economic shocks, including the following (EDA, 2002): 

• Closure or restructuring of industrial firms essential to area economies; 

• Military base closures or realignments, defense contractor reductions-in-force, and 

Department of Energy defense-related funding reductions; federally declared natural or 

other major disasters or emergencies; extraordinary depletion of natural resources, such 

as fisheries, coal, and timber; 

• Substantial out-migration or population loss; 

• Underemployment; 

• Destructive impacts of foreign trade. 

Although DRA is patterned after ARC in terms of structure, it uses EDA’s criteria to identify 

distressed counties (Reeder and Calhoun, 2002). The Department of Housing and Urban 

Development (HUD) has recently proposed a change in the Community Development Block 

Grant (CDBG)’s formula which favors towns with large college student populations by including 

the incomes of these full-time dependent students in the calculation of poverty rate (HUD, 2006). 

To allocate funds, the proposed formula would be based on five variables including: 

• The number of households living in poverty excluding full-time students; 

• The number of overcrowded housing units; 

• The number of female headed households with minor children; 

• The number of homes 50 years or older occupied by a low-income family; 

• The per capita income of the community relative to the per capita income of its 

metropolitan area. 
12

Most programs consider absolute measures to determine distress eligibility, meaning that 

counties must reach a threshold to be considered distressed. Agencies such as HUD, USDA, and 

TVA adopt a relative standard on given indicators. Counties qualify for funds based on their 

ranks on selected distress measures. 

Geographic units utilized by federal programs can range from entire counties to small 

communities. While a county may not qualify as distressed, it may have distressed 

‘communities’ ranging from cities, towns, Indian tribes, census tracts, to subdivisions. 

Recognizing such a possibility, ARC designates as distressed areas census tracts within “at-risk” 

and “transitional” counties with a median family income no greater than two-thirds of the 

national average and a poverty rate at least 1.5 times the national average (ARC, 2006). 

Similarly, communities in non-distressed counties can qualify for EDA’s assistance if they meet 

the EDA’s eligibility definition. Sub-county distress measures based on census tract data are also 

used by the SBA and USDA’s empowerment zone and enterprise communities programs. Table 

3.1 provides examples of distress indicators used across programs. 
13

Programs Distress Indicators 

Poverty Income Employment Population Housing 

ARC Poverty rate Per capita market income 3-year Unemployment rate USDA1 High level of 

poverty rate greater than 30% 

USDA2 Median family income Unemployment rate Population change DRA 80% or less of national per capita 

income average 

Unemployment rate at least one point higher than the national average 

Substantial outmigration and population loss 

Ed Title1 Poverty rate EDA 80% or less of national per capita 

income average 

 Unemployment rate at least one point higher than the national average 

Substantial outmigration and population loss 

 

NGPA - % of pop below 

150% poverty level - % of pop below poverty level 

Unemployment rate as of 2003 

- %of pop ≤ 24 yrs 

- % of ≥ 65 yrs 

- Pop change 

 

HUD Number of 

households living in poverty, excluding full-time college students 

Per capita income of the community relative to the per capita income of its metropolitan area 

 Population size -Overcrowded 

housing - Substandard housing - Cost of housing production - Housing buiprior to 1940 - Housing with incomplete plumbing 

SBA Poverty rate 20% 

or more 

- Median household income in nonmetropolitan census tracts < 80% of the statewide median household income - 50% or more of households in metropolitan census tracts with income below 60 % the area median income 

Source: Bischak, 2002; Reeder and Calhoun, 2002; and HUD, 2006 Table 3.1: Distress Indicators Used across Distressed Counties Programs
14

4. Conceptual, Theoretical, and Empirical Issues

Academic and policy-oriented literature on the use and construction of socioeconomic indicators 

is vast. Indicators are summary tools used to delineate current status, problems, and trends. 

These tools enable policymakers and other decision-makers to assess important attributes of local 

and regional conditions, to evaluate specific programs, and determine the impacts of programs 

and policies (Miller, 1993). Academic researchers also use socioeconomic indicators to address 

a wide array of scholarly and policy-oriented questions. Socioeconomic indicators are 

innumerable in scope, but Sections 2 and 3 (Part I) explain that the most common measures of 

distress include poverty, unemployment, educational attainment, and income. 

It should be kept in mind that a variety of measures constructed from data sources that can be 

used to operationalize any indicator. For example, poverty can be operationalized by using the 

official U.S. poverty rate, which is considered an indicator of “absolute deprivation” or by other 

measures, such as the proportion of a population whose income falls below 50% (or some other 

proportion) of the national median family income, which is a measure of “relative deprivation” 

(Schiller, 2008). 

4.1. Conceptual and Theoretical Issues in Producing Robust Indicators: A 

Framework to Guide Selection 

Drawing from the academic and policy-oriented literature on socioeconomic indicators, we offer 

brief points relevant to analyzing distress in the Appalachian region. Most broadly, the selection 

of appropriate indicators needs to be based on a sound conceptual and theoretical framework. 

Such a framework helps avoid ad hoc data collection and analysis and allows for the selection of 

indicators that best target distress. We briefly describe such a framework. 

Defining the Meaning of “Distress”: To select appropriate indicators, the general concept 

being assessed needs to be defined. Glasmeier et al. (2003) note that there is no universally 

accepted measure of distress among federal agencies. A clear definition is particularly needed 

because government agencies’ use of “distress” tends to become blurred and imprecise as they 

respond to changing mandates. This in turn, affects the ability to meet big-picture goals. 

Distress, for example, can be conceptualized in terms of poverty alone, sufficiency or 

insufficiency along various other resource-related criteria (e.g., education, population increase, 

employment, income, health), and social exclusion or inclusion (e.g., isolation from nonpoor 

groups, social life, and services) (Garner and Short, 2002; Nelson et al., 1998; Schiller, 2008). 

The ARC originally characterized “distress” as “underdevelopment,” essentially focusing on 

economic development in the entire region (ARC, 1999). In 1983, with the introduction of the 

Distressed Counties Program, poverty and other insufficiency indicators assumed a more 

prominent role in defining distress. Our observation in reviewing materials from ARC is that 

current interest centers on distress as an indicator of persistent structural problems that reduce 

residents’ well-being, with a focus on economic insufficiencies. 

Using Government or Local Citizens’ Definitions of Distress: A related point is the question 

whose definition of distress should prevail? Social indicators can be conceptualized through 
15

“subjective indicators,” that is, indicators that local citizens or community residents themselves 

evaluate, such as their perceptions of personal and local economic conditions. These types of 

indicators are associated with a “bottom-up” approach to community development because 

residents themselves decide the indicators that matter to them (Pike et al., 2008). Alternatively, 

distress can be defined through “objective indicators” such as official statistics collected in 

censuses. These types of indicators are usually associated with top-down approaches to 

community development, whereby external evaluators such as government agencies or other 

policymakers, decide the standards that are to prevail. Each of these ways of conceptualizing 

distress provides a different, equally important view of local conditions—but the views do not 

necessarily coincide (Garner and Short, 2002). That is, residents may not perceive distressed 

conditions even in communities where the poverty rate is high. As is customary in governmental 

agencies, ARC’s measure of distress is based on objective indicators and our report centers on 

these indicators. 

Using Theory to Define Distress: In order to ideally define distress, analysts need to draw 

from theory or “a systematic explanation for the observed facts” (Babbie, 1989). Theory 

provides guidelines for selecting the indicators that best represent distress, assessing the causes 

of distress, and designing policies that are intended to alleviate it. Social scientists have 

developed a number of theories addressing the determinants of poverty and related forms of 

distress. A succinct review is provided by Schiller (2008). He notes that theories depend upon 

the degree to which individual attributes (such as individual decisions and responsibility), 

structural attributes (such as the local economy), and government (such as a weak or strong 

social safety net) are emphasized as determinants of poverty and other distress. There are also 

different theories addressing the contrasting question of regional well-being or development—its 

determinants, definition, and policies that can promote it. Pike et al. (2008) provide a recent 

review of these theories. The conceptualization and measurement of distress (and its obverse, 

regional development) should fit with the underlying theory that specifies its attributes and 

causal determinants. 

Specifying the Time Horizon: In conceptualizing distress, it is important to define the time 

horizon. Common distress indicators are often oriented at providing a backward view of 

distress; they are limited at identifying the risk of future distress. Thus, policies may be put in 

places that fail to address emerging problems if they rely on current distress indicators. For 

example, recent downturns in local housing markets could not be anticipated using conventional 

income-based measures. Glasmeier et al. (2003) note the ability to track both long-term and 

short-term distress conditions needs to be part of the mix. 

Validating Indicators of Distress: Once distress is defined, indicators should reflect its 

intended meaning—they should have validity. By validity, social scientists mean indicators 

should have several important properties that allow them to capture the underlying concept(s) 

(Babbie, 1989:124). One of these qualities is face-validity -- essentially transparency; indicators 

should reflect common agreement and shared understanding (such as among researchers, 

practitioners, and policy-makers) that the indicator is indeed tapping distress. As we show 

below, analysts debate whether some indicators such as a high elderly population should be 

treated as markers of distress. A second quality is predictive validity, the degree to which the 

indicator is predictive of the situation in the future. For example, past poverty rates tend to be 
16

very good predictors of those in the future, but past unemployment rates may not perform as 

well. A third quality is content validity-- the degree to which an indicator covers “the range of 

meanings included within the concept” (Babbie, 1989). 

Poverty, for example, tends to have a great deal of content validity in that it covers innumerable 

forms of distress caused by lack of income in a modern society. The U.S. poverty threshold 

itself was formulated to implicitly capture major forms of distress, extrapolating from food to 

housing and other consumption expenses. Poverty is thus a good umbrella indicator that depicts 

other forms of distress. As a fourth quality, indicators should have construct validity, which 

refers to how an indicator relates to other indicators. If the indicator is supposed to tap distress,

it should correlate with other like-indicators. Again, poverty is an excellent example of an 

indicator that correlates with common “distress” indicators -- such as unemployment, educational 

attainment, and adult and child health. At the same time, it should be kept in mind that 

indicators can be selected to tap different dimensions of distress, so the degree of correlation can 

vary among indicators. 

4.2. Socioeconomic Indicators and Selection of Specific Variables for Distress 

Indexes

Once the precise conceptual indicators of distress are defined, measures or variables can be 

constructed. Variables have strengths and limitations along several lines that affect their 

usefulness for inclusion in distress indexes like the one currently employed by ARC. 

First, measures raise the same issues of validity discussed above. Ideally, measures should be 

transparent and usefully predictive of present as well as future distress. Umbrella variables such 

as the “general poverty rate” allow indexes that can be constructed with greater parsimony. They 

minimize the number of variables that need to be included and the time, effort, and data steps 

required to create them. Such variables also simplify interpretation. On other hand, in an effort 

to create parsimonious indexes, important variables that provide new or independent information 

about distress may be left out. Thus, index construction needs to balance the trade-offs between 

inclusion and exclusion of pertinent variables. Typically, if variables are measuring the same 

indicator, they correlate with one another. However, if distress is defined as a multi-faceted 

concept—which we believe it should be—then variables measuring different indicators of this 

concept may not correlate highly.1

Second, since ARC employs “objective” or secondary-data indicators, selection of measures is 

dependent on available federal and other data sources, which vary in quality, geographic 

 

1

A mix of indicators is important to identify distress among the ARC counties. Roback (1982) demonstrated why 

employing only a few, select indicators can be inadequate. She shows that locations profitable for firms have higher 

wages and higher land or property values, but locations attractive to households have lower wages due to greater 

labor supply and higher land prices. Her analysis shows that vibrant communities can have high or low wages – and 

high or low per capita incomes. Similarly, depending on whether a region’s strengths are dominated by firm or 

household preferences, a vibrant region can have very low or high unemployment rates (Partridge and Rickman, 

1997). It hinges on whether residents are willing to remain in a region if they are unemployed. This analysis 

illustrates why certain indicators such as average wages, income, and unemployment rates can be imperfect 

measures of distress when used in isolation. The rationale also underlies Partridge and Rickman’s (2003) call for 

multiple measures to indicate whether a region is experiencing broad-based prosperity or distress. 
17

coverage, cost, and timeliness (Feeney et al., 1995). One quality that social scientists expect 

measures to have is reliability—the idea that repeated measures applied to the same observations 

will yield the same, consistent results (Babbie, 1989). Some measures are more reliable than 

others due to their data source. For example, where data are reported in a different manner by 

state, such is the case with the Census of Governments, reliability is more problematic (e.g. 

measurement error is introduced due to different methods of data collection procedures in each 

state). Another issue is geographic coverage. While numerous potentially useful measures exist, 

many are not be available across counties, particularly small ones. Cost considerations are also 

important. The frequency of data collection affects the degree to which data are timely enough 

to assess distress conditions. To fully capture distress, measures should enable the assessment of 

both long and short-term distress. 

Finally, once variables are selected, decisions need to be made about the manner by which 

counties are then classified as “distressed.” One method of classification is to use an absolute 

cut-off point, or threshold under which counties fall, to designate the county as distressed. For 

example, a county could be classified as distressed when its unemployment rate exceeds a certain 

threshold unemployment rate. A contrasting method of classification is to use a relative cut-off 

point in which some proportion of counties (e.g., the top 10%) is designated as distressed. 

Measures using absolute cut-off points are problematic because they tend to assume an 

unchanging threshold when this threshold may vary in relevance over time. Moreover, for most 

variables, no widely agreed upon standard threshold points have been set by researchers. For 

these reasons, absolute thresholds may produce unsatisfactory or unreliable results whereby 

distress is not consistently captured. Related discussions of this issue are found in Feser and 

Sweeney (2003) and Schiller (2008). By contrast, “relative” measures of distress have the 

advantage of changing over time as national conditions and norms change. Indeed, it may be 

more defensible to argue that a county is “distressed” if it falls in the bottom 10% of U.S. 

counties than try to defend that, for example, a 25% poverty rate indicates distress but a 20% 

poverty rate does not. 

Other problematic issues arise when the classification system is an index that combines different 

measures of distress. Variation in types of distress can become masked (Glasmeier et al., 2003). 

A county that fares markedly worse on one measure may be ranked as not distressed if other 

measures are favorable because summary indexes can wash-out the effects of any one individual 

measure. Classifying counties based on index scores into an “either/or” situation also adds 

arbitrariness. For example, the 78th county that just made the “distressed” category in FY 2008 is 

likely little worse off than the 79th county that was ranked at the top of the “at risk” category. 

Yet, they could be treated very differently in funding priorities. Our point is that distress is not 

likely to be captured with a threshold, but is more accurately reflected by a continuum (ARC, 

1999). 

One possible solution to the “either/or” question is to adopt an additive index with carefully 

selected weights for each indicator. A community is designated as being distressed if a particular 

threshold number is reached after taking into account a large number of distress measures. Yet, 

this still suggests that the ARC (or anyone else) can design the perfect weights and can identify 
18

the perfect indicators of distress, which may be too complex to reasonably expect.2 In FY 2007, 

the ARC adopted a more flexible meaning of distress (ARC, 2007). Rather than meeting some 

set threshold for each of the three distress indicators, the ARC now uses an additive measure 

across its three measures of distress. Specifically, if the additive sum of the three distress 

indicator measures fall in the lowest 10% of all U.S. counties, the county is deemed as falling 

into distress (ARC, 2007). While additive indexes still can mask distress among any one of their 

component variables, this is a more flexible and useful approach vis-à-vis past classification 

systems. Ranking distress relative to the universe of U.S. counties also gives the ARC a more 

transparent and defensible benchmark when describing “distress” to Congress and other 

stakeholders. 

4.3. ARC Variables Measuring Distress

As noted in section 3 above, government agencies tend to use a common set of indicators for 

distress—e.g., unemployment rate, net population change/out migration, poverty rate, per-capita 

income, employment growth, etc. The ARC’s historic reliance on the unemployment rate, percapita market income (PCMI), and the poverty rate, places the Commission in the mainstream. 

Yet, there are shortcomings with any set of measures, including those used by the ARC. 

Moreover, structural changes in the economy since the early 1980s (when the ARC first initiated 

the distress indicators) suggest an ongoing need to appraise alternative measures to assess 

whether they are still meeting current priorities. 

Our reading of the historic ARC usage of the term ‘distress’ is that it reflects persistent structural

problems that reduce the well-being of most residents of a given county. In practice, its common 

distress indicators have been backward looking, examining “past distress.” They are not 

necessarily indicative of future structural problems that may cause distress. This raises the 

question as to whether the ARC should be more strategic and consider future trends in 

determining distress? Is it that current ARC programs are more aimed at solving past problems 

and not proactive in mitigating emerging pockets of distress? 

The ARC’s usage of distress does not generally measure cyclical or short-term deprivation—e.g., 

a closing of a rural community’s pulp mill is painful but does not necessarily portend to 

structural problems. This would stand in contrast to the Economic Development Administration 

(EDA) which places considerably more weight on short-term cyclical measures (Feser and 

Sweeney, 2003; Glasmeier and Wood, 2005). A justification for the ARC’s targeting towards 

long-term distress is that its mission is to alleviate structural problems, leaving short-term 

problems to other agencies. 

In the following section, we summarize a series of potential ‘distress’ indicators, assessing 

strengths and weaknesses, their geography of coverage, cost, frequency, and timeliness. We 

begin with primarily “backward” measures of distress that are indicative of past structural 

 

2

 Another issue with weighting the relative contribution of measures is that weights would probably need to vary 

over time. So for example, unemployment typically would be given a stronger weight in eras where distress was 

more dependent upon this indicator (i.e., such as was the case in the 1970s) but lesser weight today where 

unemployment has become more uniform across regions. It becomes difficult to benchmark indicators over time 

when different weighting systems are used for each time period. 
19

problems. We then consider more “forward” looking measures that reflect emerging or 

continued evidence of distress. We give special attention to the current distress measures 

employed by the ARC, namely, PCMI, unemployment rate, and poverty rate. 
20

PART II - EVALUATING DISTRESS INDICATORS AND MEASURES 

5. Appraisal of Backward and Forward Looking Indicators of Distress 

5.1. Backward-looking Indicators of Distress 

5.1.1. Cost of Living Index 

Distress indicators are often based on a nominal dollar value. Some examples include PCMI and 

the poverty rate threshold. Differences in local cost of living imply that a given (say) $20,000 

poverty rate threshold is more binding in a high-cost location than a low-cost location. Thus, it 

seems logical that given its importance and interrelationships with other measures, we begin our 

discussion with the challenges in developing a local cost of living index. 

There are many constraints in adjusting for local cost of living, including data availability and 

conceptual issues. With regard to data availability, the U.S. government does not produce a local 

cost of living index (COLI) that allows for comparisons in living costs. There has been 

discussion that the U.S. Bureau of Economic Analysis may, in the future, produce a COLI at the 

state level, but even within a given state, the differences in cost of living can be tremendous. 

Other measures of local cost of living include the U.S. Chamber of Commerce’s ACCRA index, 

but this measure is only available for a subset of metropolitan areas, which would be inadequate 

for the needs of the ARC. 

There are some “stop-gap” solutions for constructing local cost of living indices. First, the 

primary reason that cost of living varies across local areas is due to differences in housing costs 

(U.S. General Accounting Office, 1995). Across local areas, the costs of other goods and 

services vary much less than does housing. For example, it has been proposed that local poverty 

rate thresholds be adjusted for differences in cost of living using the U.S. Department of Housing 

and Urban Development Fair Market Rent (FMR) (see Joliffe, 2006b for details). The FMR is 

essentially the cost to rent a “standard housing unit” at the 40th percentile of the housing 

distribution.3 The local COLI would be based on how much the local living costs are increased 

due to higher housing costs.4

 

The FMR COLI index has been criticized for its assumption that the cost of other goods and 

services do not vary across the nation. For example, prices of non-housing goods may 

 

3According to U.S. Housing and Urban Development (2007), “FMRs are gross rent estimates. They include the 

shelter rent plus the cost of all tenant-paid utilities, except telephones, cable or satellite television service, and 

internet service.” According to HUD, FMRs are annually calculated for 530 metropolitan and urban areas and 2,045 

nonmetropolitan counties and there is no data lag in their release. For more details of FMR, see 

http://www.huduser.org/datasets/fmr.html. 

4The specific adjustment proposed for the poverty rate is based on the notion that a low-income household spends 

about 44% of its income on shelter and 56% on other goods and services. The key assumption is that non-housing 

goods and services costs do not significantly vary across locations (i.e., any non-housing cost differences tend to 

offset one another). Based on these assumptions, the cost of living index for a county c relative to the national 

average U.S. would equal: COLIc = 1+ .44(FMRc/FMRUS), where FMR is the fair market rent of the standard 40th

percentile housing unit and the national average cost of living would equal 1. See Jolliffe (2006a, 2006b) for more 

details of how to adjust the poverty rate for local differences in cost of living. 
21

significantly vary around the national average even if they are equal on average across the 

nation. Similarly, Nord and Leibtag (2005) and Partridge and Rickman (2006) argue that the 

FMR COLI “over adjusts” the cost of living because less-populated remote areas may have 

higher prices for some products, or some products may not even be available in such locations 

(e.g., there are no “big-box” stores in remote communities). Indeed, Kurre (2003) finds that there 

are rural-urban price differences across many categories of products—calling into question the 

basic assumption of the FMR COLI. Finally, the assumed 44%/56% housing-non housing 

expenditure share split may not accurately reflect consumption patterns for many locations. 

There are also conceptual issues that call into the question the whole measurement of local cost 

of living. Economists have long recognized the pattern of compensating differentials (e.g., 

Roback, 1982). For example, locations with strong labor markets or “nice” amenities such as 

mountains, ocean views, or lakes will have relatively high housing costs and places that lack 

strong labor markets and other amenities will have low housing costs. Rather than indicating that 

a household needs less income to be as well off, low average housing costs often simply reflect 

the particular disadvantages of a location such as remoteness or poor environmental conditions 

(perhaps a toxic waste dump). The low housing costs compensate for these local disadvantages. 

Adjusting (say) PCMI upward to reflect a lower cost of living would overlook these other 

‘debilitating’ factors that lead to lower housing costs. 

Summary Evaluation: A county-level cost-of-living measure can be constructed on a 

timely basis, but there will remain conceptual problems in its interpretation as well as 

questions about its reliability. Possible gains in the use of this measure may be offset 

by concerns about the loss in precision. 

5.1.2. Poverty Rate

The poverty rate is a key measure of community distress and is routinely used by ARC and other 

agencies. Though the ARC only nominally places a one-third weight on poverty rates, the current 

ARC distress indicator in fact closely mimics a poverty rate measure (Partridge, 2007). This 

follows because much of the variation in ARC counties is across their measured poverty rates 

(per capita income and the unemployment rate are not as variable). This outcome may be 

acceptable—but such a weighting scheme favoring the poverty rate is more accidental than 

explicit. Instead, the ARC may wish to have other factors play a stronger role in determining 

distress. 

The official U.S. Census Bureau poverty rate is criticized for a variety of shortcomings. First, it 

is often denounced for not adjusting for local cost of living differences and it excludes informal 

or black market activity (Nord and Leibtag, 2005; Partridge and Rickman, 2006). Yet, as 

described in the local cost of living discussion in Section 5.1.1, it is not clear whether adjusting 

for “cost of living” differences would actually improve the poverty rate as a measure of 

community distress. Using poverty rates as a distress measure also introduces an arbitrary nature 

to the process. For example, households with incomes just above the poverty threshold would not 

be considered in “distress” even though they are clearly facing economic stress. Yet, a high local 
22

poverty rate appears to be closely associated with the local area having a relatively high share of 

“low-income” households, including households just above the official poverty rate.5 

The poverty rate also appears to be a very good measure of structural problems in a particular 

location. For example, local poverty rates are very persistent (Partridge and Rickman, 2006). 

Counties that had high (low) poverty rates 50 years ago tend to have high (low) poverty rates 

today. Thus, a high poverty rate is a good indicator of systemic structural problems that 

characterize underperforming locations both at present and over the long-term. 

One of the biggest historical drawbacks of using the poverty rate is that it has been most reliably 

measured in the ten-year Census of Population at the county level. Thus, it is increasingly 

inaccurate as time elapses (Glasmeier et al., 2003), though relative poverty rates tend to change 

little over time (Partridge and Rickman, 2006). Intercensus annual estimates of poverty rates can 

currently be obtained from the U.S. Census Bureau’s Small Area Income and Poverty Estimates 

(SAIPE) (Hammer, 2000).6

A potential advantage of SAIPE data is that it is available at county and school district levels. At 

the same time, a limitation associated with the SAIPE estimates is that they are available with 

about a three-year lag. Moreover, these estimates are survey-based and are not as accurate for 

less-populated counties (Wood, 2005). Measures of economic deprivation could be augmented 

by other indicators that are available on an annual basis such as the percentage of households that 

receive food stamps or the percentage of children who qualify for free or reduced-price meals (at 

the school district-level). Yet, this would require further statistical analysis to construct a proper

method to bridge these alternative measures to a conventional poverty rate. 

Beginning in 2010, the U.S. Census Bureau’s American Community Survey (ACS) will produce 

poverty rates at the county, census tract, and block group levels on an annual basis (U.S. Census 

Bureau, 2006). With the ACS replacing the decennial census as a source of local economic and 

demographic data, this will overcome current concerns that poverty rates are increasingly 

outdated as time elapses from the past census. Another advantage of the ACS is that it will be 

released with about a one-year lag (which is far superior to longer release lags for the decennial 

census). Yet, for smaller rural counties and sub-counties, the ACS will use a five-year moving 

average, meaning that there still will be a delayed response for the ACS. Moreover, the ACS is 

sample based, which is subject to some error. For persistent measures such as poverty rates, this 

error is likely to be less severe than for other measures that are more variable over time (e.g., 

employment growth). 

 

5

For example, Partridge et al. (1996) and Levernier et al. (1998) report that local measures of income inequality are 

highly correlated. 

6The U.S. Census Bureau’s SAIPE program was initiated to remedy the problem of the long ten-year interval in the 

census poverty rate. Annual county-level updated estimates for poverty and income are obtained using multiple 

regression analysis (with approximately a two-year delay). Predictors or independent variables used to create these 

estimates include the number of personal exemptions claimed on federal income tax returns by families with 

incomes at or below the poverty level, the number of people receiving food stamps, Census of Population, and 

Census Bureau population estimates. The Census Bureau 

(http://www.census.gov/hhes/www/saipe/techdoc/quantify.html) reports that the errors for the SAIPE program are

larger than those in the ten-year Census. See Hammer (2000) for more details of the SAIPE program. 
23

Summary Evaluation: The poverty rate is an essential distress indicator, though it has 

limitations in detecting future distress and it may not capture other dimensions of 

distress. ACS will likely prove to be an invaluable addition as a data source for the 

poverty rate, though it will reflect a five year moving average that will not be exactly 

current. 

5.1.3. Unemployment Rate 

 

The official unemployment rate reported by the Bureau of Labor Statistics has long been used by 

federal and state agencies as a core measure of economic distress (see the authors’ comparison 

Table 3.1) or the underutilization of labor resources. This rate is defined as the number of 

unemployed workers divided by the civilian labor force in a community or region. Note that a 

non-employed individual must be actively seeking work to be officially considered unemployed. 

As discussed in section 5.1.10 on population change and migration below, unemployment rates 

can fluctuate widely depending in part on the degree of attachment of workers and households to 

the local community. For example, unemployment rates in the Great Plains states are generally 

low not because the local economies are perpetually booming, but because people leave as soon 

as they lose their jobs given that they have little hope of finding new employment in the future 

(or they may not be attracted to remain in place with a harsh climate). The decision to leave is 

related, in part, to expectations about future wages; if expected wages and the odds of getting a 

job are high, then workers are willing to wait for some time, and be counted among the 

unemployed (e.g., Blanchflower and Oswald, 1995). Otherwise they will leave. 

Further, as many authors have pointed out, unemployment rates do not include discouraged 

workers (those who have given up looking for work and are no longer counted as part of the 

labor force), underemployed workers (those who would like to work more hours or who are not 

in the types of jobs that take full advantage of all the skills that they have), and labor market 

churning of part-time and seasonal workers (Price and Wial, 2005; Bradley et al., 2001). 

Unemployment rates also reflect year-to-year cyclical behavior that may not reflect structural 

rigidities. In 2005, additional concerns arose over changes in the LAUS (Local Area 

Unemployment Statistics) methodology (Bishak communication to Brown, Chief of LAUS, 

BLS, Jan. 27, 2006) and how those changes affected the selection of distressed ARC counties. 

Nonetheless, the unemployment rate remains a widely used distress indicator and it is available 

annually with a two-year lag. 

Furthermore, unpublished research on one southeastern Kentucky County found that the 

recorded unemployment rate rose dramatically after a major new manufacturing plant located 

there.7

 The reason was not only that more local workers decided to re-enter the workforce in that 

county, but also that a number of out-migrants, who had left previously because they had no 

local opportunities, decided to move back after the new plant opened. Thus, local 

unemployment rates and economic activity do not always move in opposite directions. 

 

7

 Eldon D. Smith, University of Kentucky, pers. comm., ca. 1990. 
24

In the ARC states of Ohio, Pennsylvania and New York, it is possible that unemployment rates 

have remained low despite on-going economic upheaval because residents have adjusted by 

leaving to find work elsewhere, especially in more recent years – e.g., see Grill’s (2003) 

discussion regarding Appalachian New York. In fact, our maps showing population change for 

2000-2005 suggest that this may be exactly what has happened in these communities. We know 

from supplemental information and human capital theory (Snyder et al., 2007) that it is often the 

youngest residents, the future workforce, who are the most likely to leave because they have the 

most to gain from relocating (Grassmueck et al., 2007). 

Summary Evaluation: The unemployment rate is easy to obtain and use, but it is also 

subject to considerable measurement error, and error of interpretation. We recommend 

that ARC explore the implications of dropping this variable as a measure of distress in 

favor of a population migration or change measure, coupled with a measure of job 

growth pending the results of the RFP on “Alternative Employment Measures of 

Economic Distress in the Appalachian Region,” referenced in section VIII of the present 

RFP. 

5.1.4. Employment Rate/Labor Force Participation: Measures of Labor Market 

Strength 

The employment rate and labor force participation are place of residence (POR) measures of 

labor market strength. As we note above, the unemployment rate is increasingly viewed as an 

unreliable and incomplete measure of economic distress. It misses discouraged workers, longterm unemployed, marginally attached individuals who are not seeking work, and it undercounts 

underemployed workers (Bradley et al., 2001; Price and Wial, 2005; Wood, 2005). 

For these reasons, Partridge and Rickman (2003) argue that the employment rate (employment 

rate/population that is sixteen and over), in conjunction with annual employment growth, is a 

better indicator of overall labor market strength (see also below). While this clearly improves 

upon the simple unemployment rate, accurately adjusting for the size of the local working age 

population from public statistics can be problematic. For example, some communities have 

more high school dropouts and more senior citizens who are forced to work because they did not 

save enough for retirement. In such cases, using only the strictly-defined civilian workforce 

could be problematic, but it is possible to make adjustments for these abnormalities by using 

Census data. 

Glasmeier et al. (2003) proposed an Economic Health Index (EHI) that included a labor force to 

total population measure (LFPOP). The LFPOP corrects for two potential shortcomings in the 

traditional unemployment rate. First, it adjusts for counties that have a high dependency ratio 

with a large youth share of the population. Second, it adjusts for cases where there is a large 

share of the population that is not actively involved in the workforce—most notably discouraged 

workers who do not seek work due to the lack of employment opportunities. 
25

In isolation, the LFPOP does not reflect the case where there is a large share of the labor force 

that is unemployed.8 Thus, to combine the influence of a high unemployment rate and a low 

labor force participation rate due to discouraged workers, the total residential employment over 

total population (EMP-POP) would address both concerns with one measure (Partridge and 

Rickman, 2006). If the ARC continues to measure distress with a labor market indicator, using 

EMP-POP would directly capture unemployment and discouraged worker effects and is more 

informative than an unemployment rate measure. 

Summary Evaluation: The place of residence (POR) employment is available on an 

annual basis with almost no delay as part of the Local Area Unemployment Statistics 

series at the Bureau of Labor Statistics. Thus, it would be available in as timely a 

manner as the unemployment rate and conceptually can be used to construct superior 

measures of labor market distress. 

5.1.5. Place of Work Employment Growth 

Place of work (POW) county employment captures employment by employers located in the 

county. POW employment would provide more of a measure of the economic health of the 

county’s employers. Conversely, place of residence (POR) employment better reflects the 

employment conditions of the county’s workforce. POR employment differs from POW 

employment primarily due to in- and out-commuting patterns. As is often the case in rural 

counties, there is significant out-commuting to urban locations, which would imply POR 

employment could greatly exceed POW employment (and visa versa in core urban counties). 

POW employment growth offers different information on the economic conditions of local 

employers and it also provides information about local job availability for local residents—to 

avoid commuting. Moreover, local job growth is very highly correlated with local population 

growth in the medium to long-term. For this reason, Partridge and Rickman (2003) argue that 

POW job growth could be a relatively complete measure of local economic conditions. Another 

advantage of the POW employment data (described below) is that it includes both wage and 

salary employment and self employment. Because self employment is sometimes associated with 

more entrepreneurial activities, this reflects another advantage of considering POW employment 

data. Monitoring self employment then serves another ARC goal of trying to encourage 

entrepreneurship (ARC, 2007). 

The largest possible shortcoming of POW job growth data is that it may not fully reflect 

conditions for employed residents (who may be out-commuting). Namely, ARC has primarily 

defined distress as it relates to the residents of a particular county—not the county’s employers. 

Summary Evaluation: Annual POW employment data are released by the U.S. Bureau 

of Economic Analysis with about an eighteen month delay. Thus, they are as timely as 

the per capita market income measure used in ARC’s current distress indicator. Yet, 

POW employment data do not perfectly reflect place-of-residence (POR) prosperity, 

which should be weighed in any decision to adopt it. 

 

8The Glasmeier et al. (2003) index also included the relative county unemployment rate. 
26

5.1.6. Income per Person, Ratio of Income Maintenance and Other Payments to 

Income

At first glance, income per capita would seem to be the single-best indicator of human welfare 

and well-being. Calculated as the total income earned in a place divided by the total population, 

it is a measure of the productivity of workers and the ability of owners of assets or entitlements 

to command resource flows. However, like the unemployment rate, this variable also has 

shortcomings as a measure of distress, or lack thereof, when it is applied to different geographic 

units. First of all, as discussed earlier, per capita income does not account for differences in the 

cost of living over space. For example, the prices of homes tend to be very high in cities or on 

the coasts of the U.S., as well as in high-amenity areas of the nation’s interior. This reflects not 

just differences in the quality or size of the home, but also different costs of land (rents). 

Residents may also be willing to give up income in exchange for the ability to live in a highamenity area. In this case, they may be just as well off as a higher-income household living in a 

low-amenity area (as mentioned above).9

Second, there may be variations in the size of the workforce earning the total income, and this 

can lead to misleading results. For example, Utah notoriously ranks near the bottom of all states 

in terms of per capita income (47th), but that does not mean that the average household or adult 

resident is poor. In fact, the state does much better in income per household rankings. The 

explanation for this finding is that average family size is quite large in Utah, with a relatively 

high number of children who are not of working age, so a given amount of total income is 

divided by more people. 

Per capita income also does not reflect average wages, or earnings power, because it does not 

adjust for labor force participation. As an alternative, growth in per capita income does indicate 

whether a place is moving towards “attainment,” all else being equal. Per capita income also 

does not measure income distribution, such as high levels of income inequality. Other than 

including the poverty rate, this drawback could be overcome by including measures of income 

inequality or median household income (these measures likely will be available annually from 

the American Community Survey (ACS) starting in 2010). One advantage of using income per 

capita as a measure of distress is that it is available annually, with about a two-year lag, from the 

U.S. Bureau of Economic Analysis, though median household income may become the preferred 

measure beginning in 2010 with the expansion of the ACS. Furthermore, it is easily understood 

as an indicator of distress, subject to the caveats discussed above. 

Personal income is derived from three sources: labor, transfers and property (see Table 5.1). The 

ARC has long recognized that transfers should not be included as a part of income when 

assessing distress, and as such, it uses a “market-income” measure instead. This excludes 

“retirement and disability insurance benefit payments, medical payments, income maintenance 

benefits payments (e.g., food stamps), unemployment insurance benefit payments, veterans 

 

9

 Earnings in cities (places with high population density) are higher not only because worker productivity is higher, 

but also because employers have to pay workers more so that they will be compensated for disamenities related to 

urban congestion. This is a complication for present purposes, since the ARC region includes both metro and nonmetro counties, and more generally, is “uneven” in how it includes or excludes cities within its states (Isserman and 

Rephann, 2005: 346). 
27

benefit payments, and other such payments” (ARC, 2007). Similarly, the Conference Board uses 

personal income minus transfer payments when it assesses the health of the national economy 

(see discussion of economic indicators below). Yet, omitting transfer payments does mean that 

government-based retirement income is left out, which could overstate the amount of distress in 

locations with significant retirees. 

Sources of Personal Income in the U.S. 2005

All numbers are per capita 

Personal income $34,471

Net earningsa 23,956

Personal current transfer receiptsb

5,149

 Income maintenance 532

 Unemployment insurance benefits 109

 Retirement and other 4,507

Dividends, interest, and rentc

5,366

Source: U.S. BEA Regional Economic Information System, 

2007

a. Labor, b. transfer, c. property income. 

 Table 5.1: Sources of Personal Income in the U.S., 2005 

Because of the recognized shortcomings of per capita personal income as a measure of distress in 

any given county, we examine three alternative measures here. In particular, we consider 

income maintenance, retirement and dividends, interest and rent (DRI) payments separately as a 

share of total personal income. Our examination reveals the relative importance of each of these 

in the different ARC counties. These relative indicators (rates) could potentially offer a number 

of interesting insights into the vitality of local county economies that previously have not been 

considered directly. While we are able to present only national average for these variables here, 

we know from experience that these measures vary widely across the individual counties of the 

ARC region. 

5.1.7. Income Maintenance Payments

The first indicator is income maintenance payments as a percent of total personal income in the 

county. This formulation is also used by Feser and Sweeney (2003), who argue that this 

approach: (a) not only gets around the problem of variations over space in relative costs of living 

differences, but (b) that it also generates a rate, which can more easily be used for benchmarking 

purposes.10 They argue that the fact that the level of income per capita has been increasing for 

the most part over time makes it difficult to establish a “distress threshold that facilitates 

comparisons of the incidence of distress over both time and space” (p.43). This ratio is 

calculated as total income maintenance payments divided by total personal income. 

 

10 Note that this is not a perfect correction, because income maintenance expenditures are not adjusted regional 

income maintenance expenditures are not adjusted for regional cost of living differences (just as federal income 

taxes paid by individuals do not depend on where they reside). Jolliffe (2004) reports a reversal of poverty rankings 

for metro and non-metro counties when HUD’s FMR adjustments are used to correct for cost of living differences 

(see: http://www.npc.umich.edu/publications/workingpaper04/paper13/04-13.pdf). He finds that the FMR index is 

20 percent lower in non-metro than in metro regions. Since the ARC region includes both metro and non-metro 

counties, the use of such adjustments would also impact county rankings. 
28

Figure 5.1 shows that the ratio of income 

maintenance payments to total personal 

income nationally only moves between 1 

and 1.6 percent, so it is quite small. 

However, transfer payments may be 

relatively more important in lower-income 

areas, such as in the Appalachian Region. 

While the ratio fell noticeably between 1995 

and 2000 (the period which included the 

major welfare reform act of 1996, 

PRWORA and a major economic 

expansion), it has increased consistently 

again since 2001. As expected, this measure 

tends to coincide, in general, with the 

national business cycle. 

The rationale for including this ratio as a 

measure of economic distress would be as 

follows. The larger the share of total income in a given community that is comprised of income 

maintenance payments, the less healthy – or distressed – is that community. Likewise, in a 

community that is becoming less distressed over time, this ratio will fall even if payments per 

capita are rising (so long as total income is rising at an even faster pace). This ratio could be 

used as an alternative to the poverty rate. As an important caveat, however, note that this ratio 

only appears superficially to be independent of the poverty rate. To the extent that eligibility for 

income maintenance transfers is poverty-based, the difference between the two series is more 

apparent than real. This measure also has the advantage of being more up-to-date, in theory, 

than the poverty rate previously based on the decennial Census to the extent that individuals have 

to reapply annually for transfer payments (though the expansion of the ACS will eliminate this 

advantage). Yet, a shortcoming is that transfer payments imperfectly reflect household 

 poverty or local economic degradation. 

5.1.8. Retirement Income 

A second, novel indicator to consider is retirement income in a county as a share of total personal 

income. Not surprising, given the baby boomer bubble working its way through the labor force, 

this share has been rising steadily over the last 35 years (see Figure 5.2); it has almost doubled, 

from 6.8 percent of all income in 1969 to 13.1 percent in 2005. Whether or not this ratio by 

itself is an indicator of current or future distress can be debated. In declining communities, a 

strategy of driving local economic growth based on retirees will succeed only if the pipeline of 

retained (i.e., new) or return retiree migrants remains full. Otherwise, it is only a matter of time 

before this source of income growth dries up, and such a strategy will not be sustainable. 

Similarly, older retirees may be a drain on local health and human services. 

Thus, in order to be reliable, this measure has to be viewed in conjunction with another – that of 

population change. If that population change is negative, then a relatively larger share of 

retirement income in total income would suggest that the community is in decline. Conversely, 

if the population is also growing, attracting well-to-do retirees, then the community is less likely 

Income Maintenance as a Share of Total Income

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

1969197119731975197719791981198319851987198919911993199519971999200120032005

Figure 5.1: Income Maintenance vs. Share of 

income (data source: BEA, REIS)
29

to be distressed. Before making a final recommendation about this indicator, and the others 

discussed here, some empirical sensitivity analyses would need to be carried out to assess the 

reliability of these numbers. However, like the other indicators considered here, this one is 

available annually with only a two-year lag from the Bureau of Economic Analysis. 

Retirement Income as a Share of Total Income

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1969197119731975197719791981198319851987198919911993199519971999200120032005

 Figure 5.2: Trends in Retirement Income as a Share of Total Income 

 (Data source is BEA, REIS) 

5.1.9. Income from Dividends, Interest, and Rent

Economists and others usually focus on income as a flow measure to assess the level of distress 

or well-being of a community. While labor income represents a return on individual’s work 

effort, another important component of total personal income is derived from a stock rather than 

a flow variable -- the payment of dividends, interest and rent based on assets owned or wealth 

held. Pryor (2007) presents empirical evidence that these kinds of payments are accounting for 

the growing income inequality in American society. He argues that to the extent that “wealth 

begets more wealth,” this trend will only increase in the future. 

Figure 5.3 shows this source of income fluctuating between 14 and 20 percent of total personal 

income, with a downward trend occurring since about 2000. That is, total personal income has 

been rising more rapidly than income from dividends, interest and rents. We believe that 

examining this measure as a timely, supplemental indicator of economic well-being in a 

community would be useful in future analysis. Often this kind of income can provide a buffer 

against short-term fluctuations in local labor markets—e.g., if a manufacturing plant closes. As 

the figure shows, this income stream tends to be relatively independent of the national business 

cycle, unlike the income maintenance payments. 

Other authors (e.g., Low, 2005) have also included the value of housing or an imputed rental 

value, as done by Pryor (2007), as well as the value of agricultural land, to measure local wealth 

in the form of fixed assets. A few of the counties in Central and Southern Appalachia score very 

high on the agricultural land value, but we hesitate to include this in the index. The reason is that 

the value of the land can only be extracted if it is sold. More generally, this discussion points to 

the fact that there are other important assets to consider in the region to assess the level of 

distress in a given county. 
30

Income from Dividends, Interest and Rent, Share in 

Total

0

0.05

0.1

0.15

0.2

0.25

1969197119731975197719791981198319851987198919911993199519971999200120032005

Figure 5.3: Trends in Income from Dividends, Interests, and Rent (Share in Total) 

 Data sources: BEA and REIS 

Summary Evaluation: While intuitively appealing and easy to collect, per capita market 

income is completely dominated in the current ARC formula by the poverty rate. This 

variable says very little about the distribution of income within a county. One approach 

is to look at change in income over time. Theoretically, we would expect to observe 

some convergence – at least among the rural counties. The ARC should evaluate how 

income maintenance and dividend, rent and interest payments as a share of total 

income are related to distress. The relative importance of retirement income also 

should be explored, in conjunction with population change. 

5.1.10. Out-migration/Net Migration/Change in Population

The notion that people “vote with their feet” in determining where to live is a widely-used 

measure of distress (Feser and Sweeney, 2003). Whether measured in terms of out-migration, 

net-migration, or change in population, it is apparent that people will on-balance leave areas that 

have some sort of combination of a weak economy or poor quality-of-life and move to areas that 

have strong economies and/or more favorable quality-of- life features. Thus, it reflects 

underlying economic conditions and quality of life as shown by the actual behavior of the 

residents – i.e., it is not an estimate produced by academics or public agencies. Because 

migration is both monetarily and psychologically costly, it also reflects the long-run expectations 

of the migrants as to where they will be most content to reside (Grassmueck et al., 2007). 

There is conceptual debate regarding what population change actually measures. For example, 

population loss could indicate distress or alternatively, it could indicate a desired adjustment to 

distress (Feser and Sweeney, 2003; Partridge and Rickman, 2006). Regarding the latter point, if a 

location is suffering from hard economic times, then it is often helpful if some residents relocate 

to regions that are experiencing greater economic growth. By reducing labor market competition, 

such relocation mitigates the economic pain in the origin region while helping the workers who 

find employment elsewhere. Many economists would argue that such migration is helpful in 
31

promoting economic adjustment and should not be impeded by providing government aid to the 

declining region. They say such aid would slow the needed adjustment—prolonging the agony 

(Glaeser, 2005). 

Other economists disagree and argue that population change is a direct indicator of distress. In 

particular, they note that a declining population may produce further distress. For example, it 

could lead to a vicious cycle of negative expectations that lead to a lack of private investment. 

Out-migration can be especially problematic for distressed communities if it is associated with a 

brain drain of the most talented or entrepreneurial individuals. Likewise, if fewer people are left 

to support a fixed public infrastructure, this would lead to higher taxes, which will further 

depress an area. Moreover, with concerns about sprawl and congestion in large growing 

American metropolitan areas, influxes of new residents from declining areas can lead to further 

increases in monetary costs for new infrastructure, as well as implicit costs. Finally, another 

advantage of using net-population change is that it also constitutes a forward-looking measure of 

a location’s vibrancy because residents who are considering relocating are weighing future 

economic and quality-of-life prospects in their origin versus their potential destination. 

In addition, the process of net migration or population change can also obscure some traditional 

measures of economic distress such as the unemployment rate (Grill, 2003; Partridge and 

Rickman, 1997, 2003, 2006). For example, Appalachian New York has experienced significant 

economic dislocation in recent decades. Rather than experiencing a sharp increase in the 

unemployment rate, this area has seen a steady out-migration of its residents. This pattern stands 

in stark contrast to the pattern in Central Appalachia where downsizing in natural resource based 

industries has led to sharp increases in local unemployment rates (because the unemployed tend 

to remain in their Central Appalachian homes). Therefore, using net migration would act as a 

complementary indicator to other labor market indicators. 

It should be noted that net migration (or relative population change) has some limitations. Not all 

households can easily move. Likewise, in other settings, rapid population growth may make 

matters worse for the existing residents of the destination community. Partridge and Rickman 

(2005) found that some low-income regions experience considerable population growth. In such 

cases, Nord (1998) noted that low-income areas often attract new high-poverty residents who 

desire low-skilled occupations and “affordable” housing. Brown, Lobao, and Digiacinto (1999) 

also found clusters of low-income counties in the Ohio River Valley region of Appalachia that 

were attracting migration streams of new high poverty residents. This type of in-migration may 

create local “poverty traps” (see also Glaeser et al., 2000). After weighing these strengths and 

weaknesses, when net-population change represents increased distress (not just an adjustment to 

past distress), it is most sensible to use it as a distress indicator. 

In the ARC region, the general pattern since about 1970 is that North Appalachia has faced 

significant net-out migration (especially New York and Pennsylvania) and South Appalachia has 

experienced significant net in-migration (Lichter et al., 2005; Pollard, 2005). The central region 

tends to fall in-between, though in far Southwest West Virginia and Southeast Kentucky, there is 

a clear distressed pocket that has experienced significant net-out migration (Pollard, 2005). This 

pattern would suggest that one advantage of using a net-population change measure is that it 

would better capture (potential) distress in North Appalachia (and parts of Central Appalachia), 
32

while other measures such as the poverty rate would better reflect distress in other parts of the 

ARC region. 

If a net-population change measure was adopted as a distress indicator, it should be calculated 

over a time period of sufficient length -- at least five years -- so that it adequately reflects distress 

and not short-term random variations or shocks. Likewise, when considered over a long enough 

timeframe, net population change is very closely linked to employment growth (Partridge and 

Rickman, 2003). 

Regarding the proper population change measure, out-migration by itself would be inadequate 

due to the significant churning that naturally arises. For example, the typical Appalachian county 

generally has significant out-migration regardless of the net change in population (Lichter et al., 

2005; Pollard, 2005). Indeed, rapidly growing regions like the northern suburbs of Atlanta have 

significant in-migration, but they also experience considerable out-migration (e.g., return 

migration). 

Summary Evaluation: Population change or migration would capture elements currently 

lacking in the existing ARC indicators. The preferred population measure would be 

either the change in total population or net migration. Over longer periods, net migration 

and change in total population are very highly correlated because the natural increase 

in population is relatively uniform across the country. For example, Atlanta is growing 

so rapidly not because its population has a high birth rate, but because it has high net 

in-migration. Another advantage of considering either net population change or net 

migration is that they are available on an annual basis with only about a nine month 

delay from the U.S. Census Bureau. 

An additional measure that may be helpful for planners in the ARC region is annual data on the 

origin of in-migrants to a given county or the destination of a county’s out-migrants. Are they 

moving to nearby areas or are they leaving Appalachia in general? Such in- and out-migration 

data can be obtained from the IRS through its income tax database. It is available with about a 

two year lag and it is very inexpensive ($500 per year for the entire U.S.).11 However, though 

income tax filers mostly represent the universe of migrants, it does miss undocumented workers 

and domestic non-tax filers, so it is not entirely complete. 

5.1.11. Demographic Characteristics and International Immigrants

Demographic characteristics such as race, ethnicity, age, gender, and immigration status may 

serve as distress indicators insofar as they denote the presence of at-risk or disadvantaged 

populations. Native Americans, African Americans, and Hispanic populations historically have 

had higher poverty rates than non-Hispanic whites. Poverty is also higher among recent 

immigrants, children under 18, single-parent households (especially those headed by women), 

and the elderly (particularly elderly women) (Glasmeier, 2006; Schiller, 2008). These general 

national relationships tend to apply across geographic territory—for example, counties with a 

higher proportion of such disadvantaged groups usually fare worse on poverty and other 

 

11More details of the IRS county-to-county migration data can be found at 

http://www.irs.gov/taxstats/indtaxstats/article/0,,id=96816,00.html. 
33

economic distress indicators (Glasmeier, 2006; Glasmeier et al., 2003; Partridge and Rickman, 

2006). A number of studies of Appalachian counties also find an association between the 

disadvantaged demographic groups above and greater distress using the ARC measures (Haaga, 

2004; Wood, 2000; Wood, 2005). 

County indicators of the presence of at-risk or disadvantaged populations can be constructed by 

simply using the proportion of the population in the socio-demographic groups above from the 

decennial censuses. Most studies assessing the determinants of poverty and other distress 

factors, including the ARC distress indicators, employ such proportional measures. But, they use 

them as determinant rather than outcome (i.e., distress) variables. A drawback of using 

sociodemographic variables is their timeliness. The U.S. Census Bureau’s annual American 

Community Survey (ACS) will provide five year averages on such variables for all counties 

beginning in 2010 (U.S. Census Bureau, 2006). In addition, the census contains finer-grained 

data whereby the proportion of the population in poverty (and other income-level variables) by 

gender, race/ethnicity, age, and family status are available by county. Finally, other 

(nonproportional) measures have been developed that pertain to the well-being of different 

groups and we discuss these below. 

Although numerous sociodemographic indicators can be measured, some are more pertinent than 

others for Appalachia. The recent ARC Strategic Plan (2004) notes that demographic shifts such 

as population aging and rapid increases of new populations with less proficient English language 

skills could affect future performance goals, so it is reasonable to scrutinize both age and 

immigration. 

With regard to age, the number of children (under age 18) and older adults (ages 65 and older) 

are sometimes combined in a measure termed the “dependent population.” This refers to the 

proportion of residents in age groups less likely to fully support themselves through participation 

in the labor market. Counties with higher dependent populations have greater economic distress 

nationally (Glasmeier et al., 2003; Wood, 2005), and to some degree, in the Appalachian region 

as well (Wood, 2005). However, this combined age measure is becoming less relevant to 

distress. There has been a long-term decline in the proportion of children, while the Appalachian 

elderly are growing as a proportion of the population due largely to net-outmigration of young 

adults (Haaga, 2004). The population over age 65 represents 14.3% of Appalachian residents 

compared to 12.4% of all U.S. residents (Haaga, 2004). 

Most aging is done in place -- in-migration of retirees to Appalachia is relatively low and 

concentrated in a few counties. As a distress indicator, the aging population is limited. Poverty 

rates among the elderly are lower than those of children. Haaga (2004) also points out that older 

Americans are now more of a resource for communities. Many are able to work and those up to 

age 75 are not heavy consumers of public services. Thus, as described in Section 5.1.8, an aging 

population itself will continue to be a less relevant indicator of distress in the future. 

International immigration at the county level is customarily measured using the data from the 

decennial censuses, which uses the term “foreign born population,” defined as people who are 

not U.S. citizens at birth (MPI, 2003). This population includes naturalized citizens, legal 

immigrants, legal non-immigrants (e.g., refugees and persons on student or work visas), and 
34

persons illegally residing in the United States.12 Recent immigrants (those who arrived in the 

U.S. five years prior to the Census) also are documented. 

Currently, immigration does not appear to be an indicator of distress as it pertains to most 

Appalachian counties. First, studies of ARC counties note that growth in the immigrant 

population has been much less in the region than the rest of the county, at least based on data 

from the 1990s (Lichter et al., 2005; Pollard, 2004). In 2000, the foreign born represented just 

2.7% of the population, compared to 11.1% for the total U.S. population (Lichter et al., 2005). 

Pockets of higher immigrant populations, however, are found in northern Georgia, university 

settings, and parts of the Carolinas (Pollard, 2004). Second, there are data limitations. Measures 

of immigration at the county level are dependent on the decennial censuses, although they will 

soon be available through the ACS. Other data sources currently do not provide coverage of 

small geographic areas (MPI, 2003). These sources include the Current Population Survey 

conducted by the Census Bureau for the Bureau of Labor Statistics as well as the New Immigrant 

Survey.13 Third, the degree to which new immigrants conceptually tap the concept of “distress” 

is debated. Some analysts argue that new immigrants, even undocumented immigrants, are 

beneficial to the economy while others argue the opposite. (See Martin and Midgley (2006) for a 

discussion of research findings on the topic). 

Another set of demographic variables, race and ethnicity, have been identified as important in 

assessing distress by ACR reports and published research. Wood (2005) notes “counties and 

regions that have been persistently distressed invariably share one characteristic in common: a 

relatively high level of minorities.” Ethnic concentration (density) and segregation have been 

studied by social scientists for many decades and are known correlates of economic distress 

(Glasmeier, 2006). In Appalachia, the non-Hispanic African American population remains the 

largest minority group, although the Hispanic population has grown rapidly (Pollard 2004). This 

contrasts with the U.S. as a whole where the Hispanic population now exceeds the African 

American population. Pollard (2004) notes that the minority population varies markedly in 

Appalachia, with southern Appalachia having a 19 percent minority population, compared to 7 

percent of northern and 4 percent of central Appalachia in year 2000. 

In addition to simple proportional measures, demographers and other social scientists have 

developed a variety of other measures that can be adapted to comparisons across the region. For 

 

12By way of comparison, the term native refers to people residing in the United States who are U.S. citizens, that is, 

people: born in one of the 50 states or the District of Columbia; born in the U.S. Insular Areas such as Puerto Rico 

or Guam; and born abroad of a U.S. citizen parent (MPI 2003). 

13The CPS is a monthly survey of approximately 60,000 households designed to assess monthly economic 

conditions. It represents the civilian non-institutional population, rather than the full resident population as 

represented by the decennial censuses. MPI (2003) notes: unlike “the Census 2000 and ACS data, with their sizable 

sample populations, robust analyses of CPS data are generally restricted to the national level and to select 

geographic areas with sizeable populations.” Other government surveys include the Survey of Income and Program 

Participation (SIPP), the National Health Interview Survey (NHIS), and the American Housing Survey (AHS) but 

their use for studying the foreign born is restricted by their relatively small sample sizes. The New Immigrant 

Survey (supported by NIH, INS, NSF, the Department of Education, and PEW Charitable Trusts) was piloted in 

1996 with the first wave conducted in 2003 and second wave conducted in 2007. Its geographic coverage is limited 

to the top 85 Metropolitan Statistical Areas (MSAs) and the top 38 counties, with a random sample of 10 MSAs 

from among the rest of the MSAs and a random sample of 15 county pairs from among the rest of the counties (see 

NIS, 2006). 
35

example, the Hoover index of concentration is a measure of the proportion of ethnic population 

in a county relative to its land area. It ranges from 0, where there is an extreme dispersal, to 100 

indicating extreme concentration (Lichter and Johnson, 2006). 

The most commonly used measure of segregation is the Index of Dissimilarity Dt which 

measures the degree to which two ethnic groups (e.g. whites/others) are evenly spread across 

geographic units, such as census tracts.14 Segregation type indicators, although widely used, 

raise several issues. Measures vary as to their particular strengths and limitations for 

highlighting geographic patterns (Brown and Chung, 2006). These indicators were developed 

for cities using neighborhood or tract data. For segregation measures like the Dt to be useful in 

comparisons among counties in Appalachia, data below the county level, such as block level 

(Lichter et al., 2007) and block-group level data (Lichter et al., 2008) can be used, but this entails

computational time. Multi-racial categories used first in the 2000 Census also add complexity for 

handling aggregate data (Lichter and Johnson, 2006). 

Other indicators from reports and published research that could be considered are variables 

measuring women’s employment and labor force participation (see 5.1.4). Wood and Bischak 

(2000) note, for metropolitan areas in the region in 1997, that female employment was overrepresented in less skilled jobs, while male employment had a relatively higher percentage share 

of higher skilled employment when contrasted with other U.S. metropolitan areas. Women’s 

status is often measured by the proportion of women in the labor force, which Brown et al. 

(2005) found was lower in the Ohio River Valley counties composing Appalachia. Measures 

based on proportional-based comparisons between men and women in employment sectors, labor 

force, and earnings can be constructed from decennial census data or the ACS after 2010. The 

Institute for Women’s Policy Research (Caiazza et al., 2004) provides examples of labor force 

and other variables that be constructed for states and many of these measures are applicable to 

counties. 

Summary Evaluation: The aging and immigrant populations do not appear to be highly 

relevant as distress indicators across most of Appalachia at present. Variables 

measuring race/ethnicity and gender could be given greater scrutiny as to their 

usefulness as markers of distress. 

5.2. Forward Looking Measures of Distress 

ARC distress indicators are backward looking in the sense they are measuring past structural 

problems. They do not necessarily reflect whether underlying conditions will change in the 

future and whether some currently non-distressed counties are at risk of falling into future 

distress. Thus, we will describe some other indicators of distress that we believe will better 

reflect future structural conditions in the county. To be sure, there will be some overlap. Many of 

 

14If the minority and white percentage are equal across all counties, then Dt is equal to 0, meaning racial segregation 

is low while a score of 100 means complete segregation. The Dt has as straightforward meaning. A score of 60 is 

high and indicates that 60% percent of either the white or the minority group must move to different geographic unit 

for the two groups to be equally distributed (Glasmeier, 2006). 
36

these measures will reflect both past and current conditions, as well as signal future expectations 

for structural problems. 

5.2.1. Net Migration/Population Change 

The previous discussion in Section 5.1.10 regarding net migration/population change noted that 

expectations regarding migration are inherently forward looking (Topel, 1986). Future 

expectations about the local economy and the quality of life help drive migration decisions by 

both potential in- and out-migrants. As noted above, data availability represents a key advantage 

of using net-migration or relative population change. However, it was noted that a key problem 

with using net migration is that it may reflect the needed adjustment process to structural 

problems, in which it alleviates distress rather than indicates distress. 

5.2.2. Building Permits and Housing Measures 

Along with food and clothing, housing is a basic human need. Homes are not only the single 

largest expenditure item (33% of the total) and the most important asset for many Americans, but 

a home address is essential for accessing gainful employment and to assure worker productivity. 

The quality of housing and its affordability are important dimensions of county-level distress 

used by HUD. Improving housing quality has long been a key operational objective of the ARC, 

and when the Economic Research Service (ERS) recently compiled a housing distress measure, 

the ARC region did not figure prominently on the resulting map (see Figure 5.4 below). The 

ERS map relies on Census 2000 data and may be out of date given the upheaval that has 

occurred in housing markets since 2006.15 

 Figure 5.4: Housing Stress Counties, 2000

Housing affordability is another way of looking at housing as a potential problem area. This 

measure relies on Census data and usually is based on the ratio of the median home price to the 

 

15 http://www.ers.usda.gov/briefing/rurality/typology/maps/Housing.htm 
37

median household income in a community (a value greater than 3.0 indicates distress), or the 

share of homeowners and renters who spend more than 30 percent of their income on housing. 

Housing affordability is more of a problem especially on the coasts and in high-amenity scenic 

areas, as noted in Section 5.1. High home vacancy or foreclosure rates in a community also 

reflect an inability of homeowners to afford the homes in question. 

Another more subtle indication of housing problems or shortages is the spatial mismatch 

phenomenon, where individuals cannot afford to live in the communities where they work. For 

example, Hilton Head, South Carolina is one such community, where service workers need to 

commute in from outlying areas. This is also a concern in a number of fringe counties in the 

ARC region (Mather, 2004). For policymaking purposes, however, it is sometimes difficult to 

ascertain how individuals voluntarily make the trade-off between nicer homes and longer 

commutes (e.g., residents of Pike County, PA have very long commutes into New York City, 

where many formerly lived, but in return, they can afford bigger homes with larger lots). 

Here we focus on a different dimension of housing than the quality of and access to the existing 

stock. In part, we do this because the measures that have been used in the past are based on 

Census data and there is some uncertainty about the reliability and timeliness of their release for 

small counties once ACS is fully operational. Second, we know that a strong correlation exists 

between housing values with poverty rates. Thus, including these static indicators does not 

really provide any new and independent information about local economic distress beyond what 

is already derived in current distress indicators. 

Thus, instead of looking at the traditional housing measures, we consider local county business 

permits as an independent forward-looking indicator of housing conditions. These permits can 

reveal predictions of future conditions in a county as assessed by the local real estate industry 

and other private investment decisions. Economy.com sells current county data at $200 per 

county or $2,000 for a regular subscription on building activity and housing affordability, 

updated quarterly. This data source is potentially relevant for this and other sections of our 

report, but their emphasis is on metropolitan areas and it is not clear how good their data are for 

non-metro areas. Further investigation would be needed to assess the reliability of 

Economy.com’s nonmetropolitan housing indicators, but their use seems to be a reasonable 

possibility. 
38

County Detailed Employment & Output Forecast 

• Payroll employment, industrial output and wage forecasts for all U.S. counties. 

• Includes both real and nominal output for 21 two-digit NAICS categories, 89 three-digit NAICS 

categories, and 284 four-digit NAICS categories. 

• Complete coverage of government, military personnel and farm employment. 

• History begins in 1970 and extends to 30 years of forecast for counties. History and forecasts are 

updated quarterly with adjustments monthly. Frequency is annual. 

County Forecast 

• Covers all U.S. counties. 

• Approximately 100 variables, including one-digit employment, total and wage & salary income, 

population and households, labor force and unemployment rate, bankruptcies and retail sales, 

residential permit issuance, single-family and multifamily housing stock, existing sales, sales 

price, affordability index and mortgage originations. 

• Annual forecast out 30 years. Updated monthly. 

Source: http://www.economy.com/home/products/databases.asp?pid=30-00004-

00&src=serviceOverview#30-00004-00 

Table 5.2: Economy.com County-Level Employment and Forecast Availability 

Housing construction is one of the ten variables that make up the index of leading economic 

indicators used to forecast national recessions and expansions. A key reason for using this 

variable is that as housing construction expands, so does the demand for consumer durables such 

as new refrigerators, cabinets, copper wiring, etc. In other words, new housing construction not 

only provides employment for construction workers, but it also has a multiplier effect in the 

economy at large. New housing construction in any one county is unlikely to have such a large 

multiplier effect, to the extent that manufacturing plants supplying the inputs may be located 

elsewhere. However, new home construction does indicate that the local private sector expects 

future growth in the community, and it creates important demand for the services of local 

bankers and real estate agents, among others, even as more and more of these transactions move 

to the web. 

In a community that is in decline, or in distress, new building activity is likely to be subdued or 

non-existent from one year to the next, whether measured on a per capita or existing housing 

stock basis.16 In vibrant communities, on the other hand, new building permits are likely to be 

issued on a regular basis, which would also be reflected in rising land values (e.g., Kilkenny and 

Johnson, 2007). These building permits data are available, at no cost, from the U.S. Census 

Bureau with only a one-year lag. This is an enormous advantage over other housing statistics, 

which are usually two years out of date before they are released. In addition, it is also possible to

gauge the quality of the new housing stock as measured by the average value of the new homes 

that are being built, which are also reported in these statistics. Yet, one weakness with the U.S. 

Census Bureau’s building permit data is that not all local governments regularly report their data 

as part of the county total (e.g., a rural township). One way to overcome this problem is to 

 

16In using this measure, one would need to control for factors such as age of the existing housing stock, cost of or 

availability of land, etc. For example, Allegheny County, PA is less likely to offer vacant green space for new 

housing construction at a relatively low price. One would also need to further investigate some abnormalities that 

occur in certain counties. For example, Columbia County, NY (just north of NY City) is seeing growth in the 

construction of new, high-end homes even as the county is depopulating and the local unemployment rate relative to 

the state’s average is rising. To capture this kind of (suspected) “gentrification” on the edges of the ARC region, the 

distress indicator selected needs to have multiple variables. 
39

monitor the change in permits, which would be more accurate if the reporting jurisdictions 

within each county remained constant over time. 

Limitations of Building Permits Data. The portion of construction measurable from building 

permit records is inherently limited since such records obviously do not reflect construction 

activity outside of the area subject to local permit requirements. For the nation as a whole, 

however, less than 2 percent of all privately owned housing units built are in areas that do not 

require building permits. 

The reported statistics are also influenced by the following factors. 1) Some building permit 

jurisdictions close their books a few days before the end of the month/year, so that the time 

reference for permits is not, in all cases, strictly the calendar month/year. 2) A study spanning 

four years showed that about 3 percent of the single-family houses built in permit-issuing places 

are built without a permit. 

To the extent that most of these limiting factors apply rather consistently over an extended 

period, they may not seriously impair the usefulness of building permit statistics as prompt 

indicators of trends in residential construction activity.17

 

 Figure 5.5: Trends in Building Permit Issued in the U.S. (1000 units)18

5.2.3. Housing Vacancy Rates 

Housing vacancy can be a sign of building neglect and abandonment, which in turn, result from 

economic distress. It is important to determine if this is correlated with foreclosure data. The 

national vacancy rate has recently edged up sharply to above 2.5 percent for the first time, even 

as housing affordability has become a major issue in some regions. In other regions, it is likely 

that the vacancy rate is being partially driven by foreclosures or depopulation, which can lead to 

a downward spiral of economic decline if it portends a future decline in residential construction. 

 

17 Questions should be directed to Manufacturing and Construction Division, U.S. Census Bureau, Washington, D.C. 

20233-6900. Phone: (301) 763-5160. http://www.census.gov/const/C40/Sample/placeprt.pdf, p.4 

18 Source: http://www.census.gov/hhes/www/housing/hvs/historic/histtab2.html

Building permits Issued, the US, in 1,000 housing units

0

500

1000

1500

2000

2500

1959

1962

196519681971

1974197719801983

19861989

1992

19951998

2001

2004

2006
40

In many communities, recent foreclosures are likely to be the result of excessive speculation 

associated with the housing bubble that was driven in part by subprime lending. It is not clear to 

what extent this has been an issue in the Appalachia. If this measure is used, it will be have to be 

based on the ACS. 

5.2.4. Foreclosure Data

Another sign of economic distress in a community is likely to be that of widespread housing 

foreclosures. This can set off a spiral of socioeconomic decline that includes rising crime rates.19

 

The RealtyTrac website (http://www.realtytrac.com/) makes foreclosure data available on a realtime basis, and this is one critical advantage of this data source. 

 Figure 5.6: Trends in U.S. Vacancy Rates, 1965-2007 

 (Basic data are from U.S. Census Bureau) 

According to the website, the numbers are updated daily. No other publicly collected and 

reported data sets even come close to matching this source in terms of timeliness. It is perhaps 

the single-best measure of what is happening in counties at this moment in time, rather than what 

happened two years ago. Yet, we caution that the recent attention on the number of home 

foreclosures will likely wane as past (discontinued) lending practices underlie much of the 

current ‘crises’. 

 

19 See the recent New York Times article “Foreclosures Force Suburbs to Fight Blight,” March 23, 2007; also 

Immergluck and Smith, 2006. The latter study suggests that increases in foreclosure rates are associated with higher 

neighborhood crime rates. 

Vacancy Rates for Home Owners (non-Rental), 

the United States

0

0.5

1

1.5

2

2.5

3

2007.........2005.........2003.........2001.........1999.........1997.........1995.........

1993\r.......

1991.........

1989\r.......

1987.........1985.........1983.........1971977.........1979.........1981.........

5.........

1973.........1971.........1969.........1967.........1965.........
41

Summary Evaluation: The housing sector is notoriously cyclical, often driven by 

speculation, but it may be too important as a measure of local economic well-being for 

ARC to ignore. The conventional measure of housing quality and affordability, however, 

may no longer be effective in distinguishing ARC from non-ARC counties. Building 

permits, and the rate at which they are or are not issued over time, are an important 

economic indicator about the future. But it is not clear if building activity itself is 

sufficiently large to have county-wide impacts. Housing also needs to be examined in 

the context of population change – are new homes just being built for wealthy inmigrants (in bedroom communities) who do not spend their money locally in any 

meaningful way, or does such construction reflect genuine expansion of local 

employment opportunities? One option, subject to caveats discussed above, is to use 

only a lack of new housing construction as an indicator of local stagnation, or distress. 

Local vacancy rates may be a better long-term indicator than foreclosure rates, but the 

former are available only with considerable lags, highlighting the trade-off between 

accuracy or relevance and timeliness of indicators.

5.2.5. Self-Employment and Entrepreneurship

One of the key facts of the “New Economy” is that individuals are increasingly working for 

themselves as opposed to others. Especially in rural regions, reported rates of self-employment 

have risen dramatically over the last 35 years (Goetz, 2008). Figure 5.7 shows an increase in the 

share of self-employed in rural areas from 18 to 27 percent, with similar changes occurring in 

urban areas. If these trends continue, then about one in three rural workers will be self-employed 

by 2015. Because self-employment is a forward-looking measure of future conditions, we view 

a county’s ability to sustain self-employment (as an alternative to unemployment) as one 

indicator of future well-being. 

Figure 5.7: Trends in Rural Wage and Salary vs. Self-Employment 

Jobs and Ratio of Self –Employed to Wage and Salary Workers, 1969-

2005 (Goetz, 2008; basic data are from BEA, REIS) 

Bradley et al. (2001) dismiss self-employment or proprietorship formation as offering much 

promise for the region because they believe it to be “motivated by the absence of alternative 

means of economic subsistence” (p. 50). Even so, they acknowledge that in attainment counties, 

higher rates of such activity could reflect the opportunity to earn higher incomes or achieve 

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

18000000

20000000

1969 1971 1973 1975 1977 1979 1981 1983 1985 198

7

1989 1991 1993 1995 1997 1999 2001 2003 2005

Number

0.17

0.18

0.19

0.2

0.21

0.22

0.23

0.24

0.25

0.26

0.27

Ratio

Wage & Salary Jobs

Ratio

Self-Employed Jobs
42

greater independence (the direction of causation here is not obvious). It is clear that selfemployment reflects the lack of other economic opportunities in some communities, and we 

cannot dismiss this possibility. 

Further, the returns to self-employment relative to wage-and-salary employment have been 

declining noticeably over time. It is not clear to what extent this relative decline is a result of the 

fact that productivity growth among the self-employed is not keeping up with that of wage-andsalary earners, whether it simply reflects under-reporting of earned income, or whether it 

represents a trend towards more casual forms of self employment.20 Despite these challenges, 

we believe that as a new reality of working, the concepts of self-employment or entrepreneurship 

should be considered as a potential measure of economic distress by ARC. Emerging research 

(e.g., Shrestha et al., 2007) also suggests unequivocally that self-employment has important 

second-round effects in terms of stimulating wage-and-salary employment at the county-level. 

Furthermore, Rupasingha et al. (2007) found that self-employment was associated with 

statistically significant reductions in poverty rates at the county-level during the decade of the 

1990s. 

One clue about the extent to which self-employment growth in an Appalachian county is radical

(response to opportunity) as opposed to reactive (response to necessity) may be found in the 

returns to self-employment. This aspect was not considered by Bradley et al. (2001), but these 

returns could be included in any assessment of entrepreneurship in a community. The ability to 

distinguish between reactive and radical self-employment is very important, and the ARC might 

consider further research in this area. By examining the so-called non-employer statistics, it may 

also be possible to assess changes by sector – e.g., manufacturing as opposed to basic or 

advanced producer services, although disclosure problems are likely to arise in less-populated 

counties. 

As an alternative and independent verification of entrepreneurial energy in a county, County 

Business Patterns data could be examined for changes in establishments, especially those that are 

smaller, in different sectors that could be separated into more or less advanced. These data are 

available with two year lags from the Census Bureau, U.S. Department of Commerce. 

Summary Evaluation: Given the increasing importance of entrepreneurship and selfemployment in terms of their numbers alone, we believe that more research is urgently 

needed to distinguish between self-employment as a necessity versus an opportunity 

within the ARC region. Such an indicator needs to be evaluated in the context of the 

FY2007 employment RFP issued by ARC.

5.2.6. Educational Attainment

The human capital model suggests that greater educational attainment would raise average wages 

(Borjas, 1996), and in turn, higher PCMI. Along with higher wages, greater average educational 

attainment is associated with lower unemployment and higher labor force participation. Thus, it 

 

20 For example, the so-called Tax Gap of non-reported income was estimated to be around $365 bn in 2001, the most 

recent year for which estimates are available. This gap likely accounts for an important part of the relative decline 

in returns to self-employment since it is easier to under-report this type of income than wage-and-salary earnings. 
43

is not surprising that if greater average education is associated with higher wages and labor force 

participation, it also is associated with lower poverty rates (Partridge and Rickman, 2006). For 

this reason, education attainment is clearly an underlying determinant of current levels of 

distress. 

Moreover, educational attainment is a very good predictor of future economic growth over long 

periods of time (Partridge, 1997; Glaeser and Shapiro, 2003; Simon, Curtis, and Nardinelli, 

2002). With decreasing emphasis on traditional natural-resource based and manufacturing 

sectors and a simultaneous increase in the importance of the New Economy, human capital and 

education’s role will likely grow over time. In sum, if a locality has low levels of educational 

attainment, it will likely have higher current levels of poverty and unemployment and lower 

PCMI, as well as higher future levels of these measures. For these reasons, average educational 

attainment would in many cases directly account for the underlying causes of current and future 

local distress. Conversely, poverty, unemployment, and low market income are often symptoms

of low educational attainment. 

In terms of data availability, a key drawback of using educational attainment has been that it has 

been only available at the county level from the decennial census. Yet, as described in the 

poverty rate discussion in Section 5.1.2, it will be annually available at the county and census 

tract levels beginning with the 2010 ACS. Using educational attainment does have some 

drawbacks. Though, on average, it is a reasonable proxy for human capital, it does not 

necessarily reflect the quality of education. For example, a high school degree in Chattanooga 

may be of different quality than in Huntington, and so on. Likewise, any measure of educational 

attainment has an arbitrary nature. For instance, if a distress indicator used the percent of the 

adult population with a high school degree, one could always ask why not use the percent with at 

least an Associates Degree or a Bachelors’ Degree. Of course, such problems are not unique to 

educational attainment. 

Summary Evaluation: Educational attainment should be considered an important 

candidate for inclusion in a distress index due to its availability after 2010 and its 

backwards and forwards looking nature. 

5.2.7. Natural Amenities and Natural Capital 

One of the strongest predictors of local growth dating back to the 1930s is natural amenities, 

especially climate (Rappaport 2004, 2007). Natural amenities are important because they are 

complementary to a local tourist industry and help attract workers—especially more-mobile high 

skilled workers (Partridge et al., forthcoming). Natural amenities also are conducive to attracting 

retirees and supporting businesses. As a measure of natural amenities, the U.S. Department of 

Agriculture’s Economic Research Service has produced an index of natural amenities based on 

climate, access to water, and landscape topography. 

Nonetheless, Deller et al. (2001), Deller and Lledo (2007), and Ferguson et al. (2007) note that 

natural amenities may be insufficient to stimulate local growth without adequate man-made 

facilities. For example, human investment is needed to build a ski resort. This creates an added 

measure of complexity when incorporating man-made facilities with natural amenities. 
44

Every several years, the USDA updates its National Resource Inventory. This inventory contains 

a large database of natural and manmade “amenities” at the county level. In attempting to 

combine this immense database into a usable form, Deller et al. (2001) and Deller and Lledo 

(2007) use the principal-component statistical approach to combine multiple measures into one 

unit of analysis. For example, they create a water recreation component by combining the 

number of boat launches, the number of lakes, the number of fishing sites, and so on into one 

variable. The problem with using a principal-component measure as an indicator of distress is 

that it has a “black-box” feel and the resulting lack of transparency may create controversy. 

Summary Evaluation: It is currently not feasible to use natural amenities or natural 

capital as an indicator of distress. Nonetheless, it may be helpful if the ARC produced 

indicators of natural capital, but these would probably be most useful as supplementary 

measures for federal, state, and local planning. 

5.2.8. Local Industry Composition

Communities with high shares of at-risk industries are more predisposed to experience future 

distress. Historically, Appalachian communities with economies most intensively engaged in 

natural resource activities such as manufacturing, timber, and mining have suffered due to laborsaving technological change. Likewise, since the mid 1990s, communities with significant shares 

of labor-intensive manufacturing have been at increasing risk due to global pressures (e.g., 

Bernard et al., 2005; Herzenberg, 2005). These patterns have been very persistent, dating back to 

the 1950s for natural resources and to the early 1970s for manufacturing-intensive communities. 

A measure of predicted future economic distress could be easily constructed based on expected 

industry trends. The shift-share prediction is simply the predicted county growth rate if all of its 

industries grew at the expected national growth rate. This prediction accounts for whether the 

county has a composition of fast or slow-growing industries (Blanchard and Katz, 1992).21 For 

example, it would predict slow growth for a county that has a high share of natural resource 

employment or manufacturing, with greater predicted growth for counties with emerging sectors. 

The shift-share measure performs quite well as a predictor of local economic growth, especially 

if the industry disaggregation is quite fine. Therefore, in using this measure, a given county can 

be viewed as a strong candidate for future distress if it fell below a certain threshold in terms of 

future predicted growth. 

The predictions for national industry growth can be derived from U.S. Department of Labor data. 

Private vendors such as EMSI, IMPLAN or REMI can also be employed at a moderate cost to 

produce national estimates.22 The advantage of using private vendors is that they can be used on 

short notice and be responsive to the Commission’s schedule, though this does entail an expense. 

 

21The shift-share measure is simply the sum of the product of initial county-level industry composition multiplied by 

the expected national industry growth rate. Specifically, for county i in period 0, the expected county growth rate 

over the next t years would be: INDMIXi = ∑j(Sharecj0)×NATGWTj(0-t), where j refers to industry, NATGWT refers 

to the predicted national growth for sector j between periods 0 and t and the summation is over all industries.

22For more details of EMSI’s products, see http://www.economicmodeling.com/index.php. Their pricing suggests 

that their entire package for the entire nation can be purchased for $20,000, with an annual fee for new data. 
45

Of the private vendors, EMSI is one firm that has been recommended to the authors as having 

innovative products. The novelties of EMSI include that its products are very easy to use on its 

web-based interface and they are flexible. For example, EMSI’s GIS based tool means that its 

products can be used to construct reports within a user-defined distance of a point—and thus, it 

can produce reports for an entire region surrounding a county (e.g., within 100 miles). Regarding 

county-level forecasts and economic development data, both EMSI and IMPLAN can produce 

results at the 6-digit NAICS level where they employ algorithms to fill in suppressed data that 

are not disclosed by the government. 

The ARC or private vendors could be called upon to produce individual county forecasts that are 

more complete than the simple shift-share forecast. However, such forecasts would be much 

more expensive to produce on an annual basis. 

Another related measure that would be of interest for the ARC, states, local economic 

development areas, and counties is whether the region or county has emerging industry clusters 

that could be “growth engines.” For example, a county with a particular high share of 

employment in a fast-growing set of related industries is blessed with an emerging cluster that 

could spur rapid economic growth. Various private vendors could help produce data on clusters. 

For example, using the EMSI tool, Purdue University has been working with the U.S. Economic 

Development Administration to provide county-level measures of clusters for economic 

development planning. Going forward, ARC and its partners could produce their own cluster 

analysis using EMSI, IMPLAN, REMI, or other vendors. 

Summary Evaluation: The ARC should consider more sophisticated measures of 

industry composition and clusters for its state and local partners. However, it is not 

clear how to include these measures in a distress index that only utilizes three or four 

primary indicators. While measures of industry composition and clusters may not have 

the priority of these other indicators, they will be useful adjunct or secondary indicators 

for planning purposes. 

5.2.9. The Knowledge/Creative-Based Workforce

For a number of years, adults living in rural areas could count on jobs in the agricultural, 

manufacturing, or extractive industries to secure gainful employment, regardless of their 

educational credentials. But as a result of technological advances, improved efficiencies in 

production, expansion of global competition, and greater government restrictions on mining 

activities, counties across Appalachia find themselves increasingly challenged in terms of 

maintaining and strengthening their local economic conditions. 

The ability of communities to compete in a global marketplace increasingly rests on their success 

in capturing and expanding their knowledge-based workforce (Munnich and Schrock, 2003). 

Knowledge is defined as an intangible resource that enables individuals to use information, 

education, and past experiences to create ideas and innovations (Henderson and Abraham, 2005; 

Metcalfe and Ramlogan, 2005). According to Henderson and Abraham (2005), knowledge is the 

key driver of the U.S. economy. As such, understanding how the ARC region compares in terms 

of its success in expanding its knowledge-based economy is worth considering. 
46

What remains a challenge is finding a metric that captures the knowledge sector of a local area. 

Recent studies have provided some straightforward approaches. For example, Henderson and 

Abraham (2004) view knowledge workers as those engaged in “management, business, financial, 

professional and related occupations,” positions that require a high level of knowledge in order 

to complete complicated job-related tasks. In a similar vein, Florida’s (2002) discussion of the 

“creative class” is closely aligned with the measurement proposed by Henderson and Abraham. 

The only exception is the inclusion of “high-end sales and sales management” workers in 

Florida’s measure of the “creative class.” 

Occupations that constitute the knowledge/creative sector of a county’s workforce are outlined 

below. They represent occupation categories now in use by the Census Bureau to describe the 

occupational complexion of counties in the U.S. counties. 

• Management occupations (except farmers and farm managers) 

• Business and financial operations occupations 

• Computer and mathematical occupations 

• Architecture and engineering occupations 

• Life, physical, and social science occupations 

• Legal occupations 

• Education, training and library sciences 

• Art, design, entertainment, sports, and media occupations 

• Health care practitioners and technical occupations

• High-end sales (composed of the following two sales categories: (a) sales representatives, 

services, wholesale and manufacturing; (b) Other sales and related occupations, including 

supervisors). 

Determining the proportion of the county’s workforce collectively employed in these 

occupational classes can offer some inkling on how ARC counties are faring in terms of its 

engagement in the type of knowledge/creative activities that are seen as a key source of longterm economic growth. Of special concern is determining the extent to which economic distress 

is tied to the inability of counties to grow the knowledge/creative sectors of their economies.23

 

Summary Evaluation: Monitoring changes in the workers in the ARC that are employed 

in occupations associated with the knowledge/creative-based economy would be useful 

in determining how well the region will perform in an important sector. 

 

23Research studies indicate the ability of local areas to capture and expand knowledge/creative-based jobs is 

dependent on a number of important factors. They include: (1) the availability of talented, well-educated and trained 

workers; (2) access to productivity-enhancing information and communication technologies; (3) public policies and 

local institutional systems that support innovation; (4) nearness to institutions of higher education that help facilitate 

the education and skill enhancement needs of knowledge workers, and serve as generators of new ideas and 

innovations; (5) physical proximity of the community to larger metropolitan areas; and (6) the availability of natural 

resource amenities (Barkley and Henry, 2004; Cortwright, 2002; Goetz and Rupasingha, 2003; Henderson and 

Abraham, 2004; Powell and Snellman, 2004; Romer, 1998). Having these important components in place could be a 

major challenge for nonmetro areas, according to Powell and Snellman (2004), given their historic dependence on 

physical inputs or natural resources, versus intellectual capacity, as the foundation of their economies. 
47

5.2.10. Health Status of the Population

Basic personal health is both a cause of and determinant of economic well-being (or distress). In 

the past, the ARC used infant mortality as in indicator of economic distress but dropped the 

measure once the region had caught up with the rest of the nation. Likewise, in less-populated 

counties, annual changes in infant deaths led to significant fluctuations in the infant mortality 

rate. Another difficulty in mortality data interpretation is sorting out the place of death (say a 

hospital in an urban county) vs. the parents’ county of residence. 

County-level data for different causes of death are available from the CDC annually, and could 

be used as supplemental measures of economic distress within ARC counties. However, 

according to the CDC website (accessed 12/30/07), geographic information will no longer be 

supplied effective with the 2005 data.24 While the CDC might provide such information to ARC 

upon request, including such a measure may not provide sufficient additional independent 

information to justify the costs to the extent that health status and income are relatively highly 

correlated. Even so, the mortality data can be used to demonstrate an important statistical feature 

of any indicator-type data, including some of the variables used by ARC to identify distressed 

counties. This is demonstrated using Figures 5.8 and 5.9. 

 

 

 

24 http://www.cdc.gov/nchs/products/elec_prods/subject/mortmcd.htm#description1 

Figure 5.8: Unadjusted Mortality Rates, 1993–1997

Red = High Mortality White = Normal Mortality 

Blue = Low Mortality
48

Source: James et al. (2004) 

 

Figure 5.9 maps the county-level mortality rates with and without adjustments for age. It is 

important to note the higher rates of mortality in the ARC counties once the data are adjusted for 

age of the population. The age-adjustment is an important correction in this case. More 

generally, the main point to note here is that ARC counties may show up as problem counties 

(distressed) only after we control for certain variables. This has to be considered in the selection 

of final variables to be included in the distress index. 

 Source: CDC 

 Figure 5.10: Suicide Rates among Girls 

 Ages 10-14 in the U.S., 2002-2004 

 

Suicide. Figure 5.10 shows suicide rates among young girls in the U.S. from 2002 to 2004. 

Suicide rates, especially among youth, can be an indicator of severe local economic distress, 

rather than reflecting only personal or family predisposition.25 For example, suicide rates among 

farmers tend to rise during periods of farm financial crises, while the same tends to be true of 

young adults who have lost hope for a positive future. Figure 5.11 suggests that relatively high 

 

25 The graphic is from http://www.cdc.gov/datastatistics/archive/youthsuicide.html 

Figure 5.9: Age Adjusted Mortality Rates, 1993–1997 

Red = High Mortality White = Normal Mortality 

Blue = Low Mortality
49

rates of suicide are a problem in parts of the Appalachian region; note that the problems seem 

even more pronounced in the West (often believed to be associated with higher gun ownership), 

but this is optically misleading because of the larger county sizes in the West. Also, suicide data 

do not capture unsuccessful attempts by individuals to end their own lives.

There is growing recognition in the literature that suicide is important – and increasingly 

common – in rural areas (e.g., Singh et al,. 2002), and that these areas are also ill-equipped to 

deal with the problem (Fiske et al., 2005). At the same time, researchers are finding that 

community- or neighborhood-level features, especially poverty and economic decline, are 

associated with higher suicide rates (Rehkopf et al., 2006). These regional characteristics, or the 

local ecology, may be just as important as individual-level characteristics (e.g., a family history 

of depression and suicide) in explaining suicide or other risky behaviors (Whitley et al., 1999; 

Hill et al., 2005, the latter based on United Kingdom data). 

The CDC does not report suicide rates for individual counties when the numbers in question are 

so small as to preclude anonymity of those afflicted. However, experts who have worked at 

CDC with the data suggest it is possible to obtain actual numbers by using “rolling” 5-year 

windows in the web-enabled data extraction software. Even so, given the costs involved in 

collecting the data relative to the additional new information provided, we feel it is not feasible 

or desirable at this point to include suicide data in the index constructed to measure distress in 

ARC counties. This does not mean, however, that pronounced changes occurring in any one 

county over time should be ignored as a supplemental measure of distress. 

Summary Evaluation: It is important to acknowledge the relationship between economic 

distress and human health measures, including mental health as reflected in suicide 

rates. Nevertheless, given the data limitations and recent changes at CDC in the 

geographic detail with which data are reported, it is not feasible at present to include 

these variables as indicators of economic distress. Suicide rates and related health 

data (deaths from cirrhosis of the liver, for example) could be tracked on an adhoc basis 

by ARC counties. 

Suicide Rates 1989-1998

Legend # of Counties

At or above the 90th 

NATIONAL percentile (308) 

At or above the 75th but 

less than the 90th 

NATIONAL percentile 

(460) 

Less than the 75th 

NATIONAL percentile (2304)

Data source: http://webappa.cdc.gov/cdc_mxt3/

Figure 5.11: Suicide Rates, 1989-1998
50

5.2.11. Local Government Capacity

Local government capacity is a general concept reflecting fiscal health and resources such as 

size, staffing, and expertise available to governments that increases ability to act on behalf of 

their citizens (Peterson, 1981; Reese and Rosenfeld, 2002). Limited government capacity can be 

considered an indicator of distress insofar as it constrains efforts to improve community 

conditions. Smaller, resource-poor governments usually provide fewer services for residents and 

local businesses (Kraybill and Lobao, 2005; Peterson, 1981; Reese and Rosenfeld, 2002). The 

ARC notes the importance of improving the capacity of local governments in its strategic plan 

for 2005-2010 (ARC, 2004). 

Although “capacity” may be intuitively understood by policymakers, actual measures are 

problematic. First, social scientists employ numerous indicators of capacity encompassing size, 

staffing, and fiscal health. There is little consistent use of these indicators and respective 

measures across studies, so that benchmarking standards are not established. 

Second, capacity indicators vary in relevance to assessing distress. Size of government variables 

(such as government employment per capita and general revenue) and staff attributes (such as the 

presence of different types of professionals) do not provide information about how governmental 

resources are allocated to match to population needs, nor do they denote the fiscal conditions of 

governments (Reese and Rosenfeld, 2002). Capacity, as measured by the fiscal health of 

governments, is more reflective of distress. These measures vary widely but most are 

constructed with the idea of assessing how fiscal conditions of local government are balanced 

with community attributes or needs. Conventional measures include ratio of own-source 

revenues to own-source expenditures, the ratio of own-source revenue to aggregate county 

income, property taxes per capita, and per capita tax revenues (Reese and Rosenfeld, 2002). 

Other measures of fiscal health include debt burden and bond ratings, but coverage of small 

governments is a problem with these measures (Hendrick, 2004). 

Fiscal health measures also have limitations. Reese and Rosenfeld (2002) note most measures of 

fiscal health focus on ability to raise revenue. These measures have much do with external 

conditions outside government, particularly residential income and wealth and public willingness 

to pay for services. Fiscal measures also are not straightforward distress indicators because they 

often result from complex processes involving long-term community adaptations (Reese and 

Rosenfeld, 2002). For example, Johnson et al. (1995) found that counties with higher poverty 

had low fiscal burdens as measured by the ratio of own-source revenue to aggregate county 

income, in contrast to their expectations. They explain this finding by noting that high poverty 

counties adapt to an environment of weak revenue generation and place less tax burden on 

residents but provide fewer services. In such cases, it is difficult to argue that local government 

capacity is related to higher quality of life for citizens. 

In addition to general issues above about local government capacity as an indictor of distress, 

there are issues specific to constructing measures for the ARC region. One is the unit of 

government to which data are referring. Measures could be constructed using county 

government as the reference point. However, this would neglect distress of municipalities and 

other local governments. To account for other local governments, measures could be constructed 
51

whereby all local governments (including county government) are aggregated to provide a 

county-area government capacity measures (see Johnson et al., 1995 for an example). Second, 

functions and ability to tax and spend vary by state and even within states property tax rates and 

other conditions vary. To account for such variations, measures should be general enough to be 

applicable across states and proportionate, where the numerator and denominator use relevant 

base figures. 

Third, data sources must be considered. The major data source on local governments is the 

Census of Governments and conventional variables on fiscal health described above are typically 

created from this source. However, the Census is conducted only every five years. Also, data 

quality is not uniform across all counties (Stephens and Wikstrom, 2002). For small counties, 

data are often less detailed and aggregated up to larger categories. Other measures can, in 

principal, be constructed using long- and short-term debt obligations from the Census and bond 

rating variables available for purchase from investment companies such as Moody’s. But small 

counties are less likely to have any data on debts and bonds available for them. 

Summary Evaluation: Local government capacity is not a transparent indicator of 

distress but could serve as an adjunct or secondary indicator to track needs of particular 

counties. We suggest use of conventional fiscal measures such as own-source 

revenues to expenditures noted above that can be derived from the Census of 

Governments. Although these measures have limitations, they are generally applicable 

across states and data are available for small counties. Fiscal measures should be 

evaluated for their association with external distress conditions (i.e. poverty, income, 

unemployment) to ensure that the former are capturing the local context appropriately. 

For a general assessment of government capacity, we suggest aggregating all local 

governments to create this measure for county areas. 

5.2.12. Social Capital and Its Link to Economic Well-Being

There is an expanding body of research that suggests that social capital has an important impact 

on the economic health of an area. Rooted in the research of Putnam (1993, 2000) and others 

(such as Bourdieu, 1993 and Coleman, 1988), studies show that core elements of social capital—

particularly the presence of a rich stock of social networks and sets of norms that govern the 

relationships among these networks—enhance the capacity of communities to act on issues of 

local importance (Schuller, 2001). Social capital is the “glue” that holds societies together and 

whose presence can spur the type of economic growth that brings benefits to the entire 

community (Grootaert, 1998; Putnam, 1993). In essence, it serves as a set of social resources 

that communities can tap when tackling local economic problems (Glaeser, 2001). 

In areas suffering from economic distress, measuring the state of social capital present in these 

localities may be a path well worth exploring. Simply put, in communities where good things 

are happening across the spectrum – in education, in job creation, in health care, in community 

services – a broad-based corps of civic-minded people and organizations is often in place to 

undergird these important activities (Woolcock, 2001). As Putnam (1993) notes, “Working 

together is easier in a community blessed with a substantial stock of social capital.” Thus, in the 
52

context of forward-thinking strategies, taking stock of the social capital attributes of a county or 

place may offer some important insights regarding the future capacity of ARC counties 

(particularly those suffering for economic distress) to undertake collective action on their major 

socioeconomic challenges. 

Social capital represents a multi-dimensional concept. It consists of “bonding” and “bridging” 

activities that occur within the local community setting, as well as “linkages” that tie community 

members to organizations and resources existing outside the community (what we commonly 

refer to as vertical ties). Bonding represents the strong interactions and intimate ties that people 

have with family, friends, neighbors, and close work associates. Bridging reflects the horizontal 

ties that individuals have with people and groups within the community with whom they have 

only limited interactions (Flora et al., 2008; Putnam, 2000). These constitute what Granovetter 

(1973) labels as “weak ties” that can be accessed in times of need. The third element, vertical 

linkages, offers an avenue for local people, organizations and communities to gain access to 

valuable resources and ideas from outside the community that can be used to support and guide 

local initiatives. According to Woolcock (2001), the presence of various combinations of 

bonding, bridging, and linking social capital shapes the range of social and economic outcomes 

that are possible in communities. As such, these interactions help build trust and create the 

social assets that can be tapped for future community endeavors (Putnam, 2002). 

If social capital is to be viewed as a viable tool that the ARC might consider for discriminating 

among counties that are best or least positioned to “act” on local economic and social challenges, 

then it is critical that sound measures of social capital be identified. Measuring bonding, 

bridging, and vertical relations, or assessing levels of trust or existing norms in communities, 

cannot be easily achieved without engaging in costly and time consuming qualitative data 

collection activities (Haezewindt, 2003). We would propose the use of a series of quantitative 

measures that have been found to be important corollaries of civic-minded communities or that 

contribute to the development of trusting relationships. Most important, they represent variables 

that are readily available at the county level. 

Table 5.3 outlines 10 key variables identified in the research literature as viable proxies for social

capital. For sake of clarity, we classify these variables into four major themes – all of which are 

associated in some way with the strengthening (or weakening) of social capital in a local area. 

Variables listed under community attachment represent factors that help people feel “rooted” in 

their communities. Voting represents active participation in the political process. The 

social/civic activeness of a locality is captured by the density of local organizations existing in 

the area. Age and education are included in the “social and civic participation” category as well 

since both give shape to the civic activeness of local residents. Finally, we propose four 

variables to assess the level of social cohesion/integration present in the community. 
53

Table 5.3: Ten Quantitative Measures of Social Capital 

Variables Contribution to Social Capital Data Source 

Community Attachment

Home Ownership  Increases membership in local 

organizations, voting 

participation, social trust 

Decennial Census 

ACS after 2010 

Length of Residence  Improves the strength and 

breadth of social networks, 

increases chances of being 

civically involved and engaged 

in local organizations 

Decennial Census 

ACS after 2010 

Political Participation

Voting Participation  Increases awareness of political 

affairs 

 Builds citizenship 

County and City Data Book 

(2004) 

Social and Civic Participation

Associational/Nonprofit 

Organizations 

 Builds horizontal ties across the 

community (i.e., expands 

connections and access to 

resources) 

 Enhances communication and 

sharing of information 

 Facilitates cooperation and 

collective action on local issues 

County Business Patterns 

(2005) and National Center 

for Charitable Statistics 

(2007) 

Years of schooling  Higher educational levels 

increase trust and community 

involvement 

Decennial Census 

ACS after 2010 

Age  Networks and relationships 

increase with age (until one 

surpasses age 60) 

 Social trust increases with age 

 Persons 30 and above are more 

likely to be involved in their 

communities 

Decennial Census and U.S. 

Census Bureau’s 

Population Estimates 

(2006) 

ACS after 2010 

Social Integration

Marital Status  Married persons are more 

trusting, more likely to provide 

social support to neighbors 

 Single persons are less likely to 

be civically active 

Decennial Census 

ACS after 2010 

Immigration/Ethnic 

Diversity 

 Immigration reduces community 

cohesion on the short term 

Decennial Census and U.S. 

Census Bureau’s 
54

 Ethnic diversity weakens social 

trust and results in lower 

political, social and civic 

participation 

Population Estimates 

(2006) 

ACS after 2010 

Income Inequality  Inhibits the development of 

social trust 

 Lowers involvement in local 

organizations 

Decennial Census and 

American Community 

Survey after 2010 

Residential Mobility  Disrupts the relationships/ties 

that individuals have with local 

people and organizations 

 Reduces membership in local 

organizations 

Decennial Census 

ACS after 2010 

Table 5.3: Ten Quantitative Measures of Social Capital—cont. 

Summary Evaluation: Social capital-type measures should be given scrutiny for future 

use at least as secondary or adjunct indicators of distress. The items could be 

examined as a series of independent factors, or a smaller set of indices (if appropriate), 

to assess how well they correlate with economic conditions in the ARC counties. While 

not perfect measures of social capital, the items described in Table 5.3 could be worth 

exploring with regard to their links to future economic distress. 

5.3. Regional and Sub-County Distress Measures 

5.3.1. Multi-County Level Geographies

A common feature of economic (and social) distress is that it tends to cluster into groups of 

contiguous counties and neighborhoods (Glasmeier et al., 2003; Partridge and Rickman, 2005; 

Rupasingha et al., 2002; Miller and Weber, 2004). For example, poverty rates are highest at the 

cores of county-level clusters in Appalachia, the historic Cotton belt, and the Mississippi Delta, 

and then taper off gradually towards the edge of the clusters (Partridge and Rickman, 2007). 

The ARC could develop more sophisticated statistical approaches to assess distress and for 

economic development planning. One approach is standard spatial econometric methods 

(Anselin, 1988) and more descriptive approaches such as Moran’s I and geographically weighted 

(or distance-weighted) approaches (Fotheringham et al., 2002).26 One advantage of these 

approaches is that they could formally account for the interdependence between neighboring 

counties. For example, economic distress manifested through a weak labor market in nearby 

counties may have spillover impacts on the county of interest. Another advantage is that they 

lend themselves quite well to GIS mapping and can be visually presented in a friendly way. 

Nonetheless, a clear shortcoming is that the current ARC staff is not sufficiently large to conduct 

this analysis on a widespread basis. 

 

26Geographically-weighted approaches would calculate the average of a particular indicator (such as the poverty 

rate) within a set distance of the county (e.g., within 100 miles). These statistics can then be mapped to illustrate the 

clustering in a visually appealing, transparent way. 
55

Summary Evaluation: There are many potential uses of indicators that would tap 

clustering of distress among counties or sub-regions within the greater ARC region, but 

the construction of these indicators would have to be weighed against the need for 

additional resources. 

5.3.2. Sub-County Indicators

It is conceptually easy to construct sub-county-level measures of distress—e.g., at the census 

tract level. Indeed, the ARC staff has produced distress measures at this level in the last few 

years. The practical problem is that it has been historically challenging to develop sub-county 

distress measures because most of these data have only been reported in the decennial Census. 

For this reason, as the decade moves forward, the ARC’s census tract measures tend to be more 

out-dated than their county-level counterparts. Some current exceptions to these data constraints 

include inter-census estimates of population and place-of-work employment indicators linked to 

establishment zip code (which are subject to confidentiality restrictions and are sometimes hard 

to reconcile to census tracts). Yet, the ACS’s expansion in 2010 will allow more annual subcounty analysis at the tract level or finer—though with the caveat that the ACS’s accuracy will 

not be perfect. 

Nevertheless, aside from issues of accuracy, before sub-county indicators of distress are widely 

used for funding allocations, there are the following conceptual and policy questions: (1) In the 

midst of an otherwise vibrant county, when does a cluster of “distressed” census tracks reach the 

critical mass such that they warrant further attention? (2) Do these proportions differ across “At 

Risk” counties and counties that are “Transitional?” (3) Some measures such as population 

change have very little meaning when discussing a neighborhood (census tract)—i.e., what does 

it mean if a census tract had out-migration when it is undergoing industrial or commercial 

development. (4) Does it make sense for ARC to be concerned with sub-county outcomes given 

its history as more of a regional economic development authority? Do sub-county issues fall 

more into the purview of other state and federal agencies such as EDA or Housing and Urban 

Development? 

Summary Evaluation: We recommend that the ARC engage in more research and 

stakeholder discussion before utilizing sub-county distress indicators for planning and 

funding allocations. In the meantime, the current ARC approach of presenting subcounty measures of distress is wise for planning purposes. 

5.4. New Federal Data Sources: Offering Expanded Assessment of Current 

Conditions 

Throughout this report, we have recommended that the ARC consider new variables and data 

sources in measuring economic distress and in their planning. In particular, there are three 

federal surveys that we believe have the most potential for providing expanded assessments of 

current and future conditions. 

The U.S. Census Bureau’s annual American Community Survey (ACS) will increasingly be a 

source of data at the county and sub-county levels. In 2008, the ACS will report three-year 
56

averages (2005 to 2007) for all counties with greater than 20,000 population. Beginning in 2010, 

it will report five-year averages for all counties and sub-counties (U.S. Census Bureau, 2006). 

The ACS is a rich source of data comparable to the decennial Census (which it will replace in 

terms of detailed local information). We are increasingly convinced that the ACS will provide 

relatively accurate measures that will be annually updated (with a very short lag into the 

following year). However, for smaller counties and for sub-counties, it will be based on five-year 

moving averages, meaning that it will not be perfectly up-to-date.27 Yet, given that the ACS 

generally reports demographic data that more slowly change over time, a five-year moving 

average should be relatively accurate at the scale of a county (though it may not pick up dramatic 

changes at the census-tract level). 

Summary Evaluation. As described earlier in the report, the ACS may prove to be an 

invaluable source for constructing future measures of distress. 

Second, the Longitudinal Employer-Household Dynamic (LEHD) data set maintained by the U.S. 

Census Bureau has tremendous potential as a major source for both current and forward-looking 

indicators of distress.28 Several forward-looking indicators can be obtained such as the overall 

number of new hires; number of new-hires into “stable” longer-lasting jobs; recent layoffs; and 

labor market turnover of hiring, quits, and layoffs. Even the average wages of all current 

employees and newly-hired employees are available. LEHD is currently publicly available at the 

county level with about a one-year lag. It also provides detailed assessments by gender on over 

21 different industries and 8 different age groups. 

One disadvantage is that only 11 of the ARC region’s 13 states participate in the LEHD program 

(as of December 17, 2007, New York and Ohio data are not reported). Fortunately, though not as 

rich as the LEHD data, the U.S. Department of Labor’s ES-202 data can potentially fill in some 

of the holes in non-participating ARC states, especially regarding wages and detailed 

employment conditions by industry. Yet, probably the key disadvantage of the LEHD data is that 

there are significant disclosure issues in less populated counties for particular industries, 

meaning that much of the detailed data is unavailable. There still would be some scope of filling 

in some of this data from private vendors such as EMSI or REMI. 

Summary Evaluation: The ARC could utilize LEHD data as a timely indicator of counties 

that are experiencing significant upswings in hiring or in layoffs to provide an early 

signal/warning of fundamental change. Yet, given that the ARC’s distress indicators 

have generally been structural or persistent measures of economic degradation, it is not 

clear how the ARC could incorporate LEHD data into a current distress index, although 

an ongoing pattern of layoffs could be a future indicator of structural distress. One 

drawback with using LEHD data is that there is not a long history of having such data at 

the county level, so it is not clear what shifts the data are tapping. There would need to 

be research as to how to use the data and more time devoted to learning about its longterm implications. 

 

27The ACS will use single-year estimates for locations with more than 65,000 people, three-year moving estimates 

for locations with more than 20,000 people, and five-year estimates for less-populated geographies (U.S. Census 

Bureau, 2006). 

28For more details, see the LEHD website at: http://lehd.did.census.gov/led/index.html. 
57

Third, when data on place-to-place migration flows were described above, we noted that the IRS 

county-to-county migration data could also be employed in constructing annual measures of 

migration. The IRS data could be utilized to assess the origin and destination of a particular 

county’s migrants. For example, are out-migrants staying in the nearby region or are they leaving 

the region for other locations? Such data could be invaluable for policymakers trying to assess 

the underlying local dynamics and in their design of mitigating policies. In particular, it may help 

in designing regional or multi-county approaches for alleviating pockets of distress. 

Summary Evaluation: The IRS migration data may prove to be a useful supporting tool 

for ARC’s planning with local partners. 
58

PART III - EXPLORATORY ANALYSIS 

6. Assessing the Sensitivity of Current Indicators

Here we conduct an exploratory analysis to examine the sensitivity of different measures of 

distress. The purpose is to examine the degree of consistency in classifying counties based on 

changing assumptions about relevant variables that can be included in distress indexes. 

Figure 6.1 shows the current distress indicator map for the ARC region, whereas Figure 6.2 

reports the pattern for the U.S. as a whole. At the ARC level, distress is concentrated in Central 

Appalachia in Southwest West Virginia and Southeast Kentucky, as well as in Northeast 

Mississippi. Under the current formula, there are no distressed counties in New York and 

Georgia and there is only one distressed county in Pennsylvania. In the rest of the country, 

distress is most apparent in the Mississippi Delta, along with counties in the Rio Grande, in the 

Historic Cotton Belt, and in Native American reservations in the West. 

Figure 6.3 reports poverty rates for the ARC region, while Figure 6.4 presents the same figures 

for the U.S. The clear pattern in both cases is that measures of distress closely correspond to the 

poverty rate. There are two clear causes of this pattern. One, the other indicators (unemployment 

and PCMI) are both correlated with poverty rates, as shown in Table 6.1. The other is that the 

poverty rate is more prone to vary across the nation, meaning it drives the variability in the 

distress indicator. Thus, the current ARC distress indicator is really a “high-poverty” indicator. 

Likewise, because poverty is so persistent, the current listing of ARC distressed counties would 

closely correspond to counties that had very high poverty rates in the late 1970s (Partridge, 

2007). Conversely, the two candidate indicators for our analysis display a somewhat different 

pattern. Population change has lower correlations with the other variables, suggesting it provides 

independent information, but high school attainment is highly correlated with the existing 

indicators. 

Figure 6.5 shows the relative three-year average (2002-2004) unemployment rate in the United 

States and the ARC region. As noted above, the unemployment rate does not vary greatly across 

the region, being slightly higher in Central Appalachia and slightly lower in North Alabama and 

North Georgia. Figure 6.6 reports the relative PCMI in 2003. The pattern is that relative PCMI is 

lowest in South and especially Central Appalachian counties, and is highest in the Northern 

counties. 

As noted in Section 5, two indicators that appear to be especially worthy of being potential 

distress indicators are educational attainment and percent change in population change. For the 

ARC region, Figure 6.7 reports educational attainment as the percent of the adult population over 

25 years of age that have achieved at least a high school degree. Likewise, Figure 6.8 illustrates 

the percent population change over the 2000-2005 period. 

The figures repeat the consistent pattern of distress in Central Appalachia—in this case, being 

depicted by low educational attainment and significant population loss. Yet, there are differences 

between the two measures. Educational attainment is lower in Central and South Appalachia, but 

only modestly below the national average in North Appalachia. Population growth is much 
59

weaker in North Appalachia, while actually quite robust in parts of South Appalachia—

especially in North Georgia (Lichter et al., 2005). If educational attainment was used as a 

measure of distress, the range of distressed counties would likely tilt south, with the opposite 

being the case if population change was used as a distress indicator. Nonetheless, regardless of 

the choice of distress indicators, there will be most assuredly a cluster of distressed counties in 

Central Appalachia as well as in Northeast Mississippi, while North Georgia will appear to be 

relatively prosperous. Other changes would likely occur elsewhere. 

Therefore, we briefly examine the sensitivity of the distress measures to include population 

change and educational attainment. This analysis is only for illustrative purposes and does not 

necessarily represent the best measures of distress, which would require a complete statistical 

analysis and assessment of the proper weights. Specifically, we will consider two alternatives, 

one where we replace the unemployment rate with relative education and another where we 

replace the unemployment rate with the percent change in population over the 2000-2005 

period.29 Further analysis would need to assess other possibilities. 

Poverty 

rate 

PCMI Unemployment Population 

change 

High school 

completion 

rate 

Poverty rate 1.00 

PCMI -0.61* 1.00 

Unemployment 0.46* -0.41* 1.00 

Population 

change 

-0.26* 0.22* -0.08* 1.00 

High school 

completion 

rate 

-0.71* 0.60* -0.46* 0.13* 1.00 

* Significant at the one percent significance level, N=3108 U.S. counties. 

 Table 6.1: Correlation Matrix of the Economic Indicators 

Our methodology is the same as that currently used by the ARC to determine distressed counties. 

Namely, we calculate each variable relative to the U.S. average, sum the scores, and rank them 

relative to the universe of over 3,100 U.S. counties. If an ARC county falls in the bottom 10% of 

the national ranking, it is deemed “ distressed,” if it falls in the bottom 10 to 25%, it is labeled 

“at risk,” and so on (ARC, 2007). Of course, it is very unlikely that an optimal distress index 

would give these variables equal weight—if only because they would have different standard 

deviations and thus, have more or less impact at the extremes (i.e., a variable with higher 

standard deviation would be more prone to push a county down into the distressed category or 

above into the attainment category). Likewise, it is by no means clear that the ARC distress 

indicator list would exactly include three variables (e.g., Glasmeier et al., (2003) propose using 

more distress indicators). Moreover, it still needs to be determined which specific indicators 

should compose a distress indicator list. 

 

29We remove the unemployment rate because of our impression from key informants that it is no longer a good 

measure of distress. Further research would be needed to confirm this point. 
60

Our alternative variable for educational attainment is the 2000 Census national adult share of the 

population with at least a high school degree divided by the corresponding county share [(U.S. 

national value/county value)*100]. This is for illustrative purposes, as further assessment should 

also consider the college graduate share. For population change, we use an analogous approach, 

though we cannot exactly use a parallel measure. The problem arises because we would have to 

divide by negative population change when counties lost population—which would not be 

desirable for an additive index. Instead, we create a distress measure of population change that is 

normalized to have the same standard deviation as the relative poverty rate (County Poverty 

rate/U.S. national poverty rate).30 

We present the results for these two alternative indicators. First, Tables 6.2 and 6.3 show 

changes in economic status in the United States and Appalachian counties respectively, as result 

of removing unemployment rate from the ARC’s national index for the fiscal year 2007 and 

replacing it with educational attainment and population change. For example, for the United 

States as a whole, 51 “at-risk” and 20 “transitional” counties now fall in the “distressed” 

category when including the population change index and 52 “at-risk” counties are now 

“distressed” when considering the educational attainment index. Fourteen and three counties 

shift from “distressed” to “transitional” with the population change and education attainment 

indices, respectively. Counties only shift from “distressed” to “at-risk” and “transitional” status. 

In Appalachia, counties also switch from “at-risk” to distressed status. Six at-risk counties switch 

to distressed with the population change index and 15 with educational index. No counties 

switch from the transitional, competitive, and attainment categories to the distressed category in 

Appalachia. Counties mostly switch from distressed to at-risk when using our approach. 

Compared to the current ARC distress indicator, 19 counties either switch into or out of distress 

using population change and 22 counties change distress status when using educational 

attainment. Thus, about one-fourth to one third of the counties classified as distressed would 

differ from the current ARC distress classification. 

 

30First, we calculate a z-score, or how many standard deviations a county is either below the mean U.S. county 

population growth rate over the 2000-2005 period, or how many standard deviations a county is above the mean 

U.S. county growth rate. To correspond to the notion that the bigger the number, the greater the distress, we then 

calculate a “normalized” population standard deviation. By normalized, we mean for counties with below the 

national average county population growth, we assign the number of standard deviations a positive number, while 

for counties with above-average population growth, we give the corresponding standard deviation figure a negative 

number (i.e., the negative z-score). We then calculate the standard deviation of the relative poverty rate (County 

Poverty Rate/U.S. Poverty Rate), which equals 0.527. That is, the relative county poverty rate has a standard 

deviation of about 53 percentage points around the mean. Thus, the relative population number used in calculating 

our refined distress index is derived as: 

Pop Index Measure = 100 - 52.7×(normalized population standard deviations from the national average). We use 

52.7 since the relative poverty rate is multiplied by 100. 

Normalizing the Pop Index Measure to have the same standard deviation as the relative poverty rate gives poverty 

and net population about equal weight in the distress index. To give an example in constructing this number, Gilmer 

County, West Virginia’s 2000-2005 population growth was 1.18 standard deviations below the mean U.S. county 

average. The resulting Pop Index Measure for Gilmer County then equals 100 - (52.7)*(-1.18)=162.2. Of course, 

further analysis would need to assess whether the standard deviation of population index should be benchmarked to 

the poverty rate. See Feser and Sweeney (2003) for a similar discussion of the difficulties of normalizing 

population-change metrics. 
61

Figures 6.9 and 6.10 portray the new distress status under our approach. For the United States, 

these two Figures need to be compared with Figure 6.2 to determine the change in economic 

status on a state-by-state basis. In Michigan for example, three counties appear distressed under 

the ARC’s approach, but at-risk under our alternative using the educational attainment variable. 

Under the alternative with the population change variable, one of them remains distressed and 

two become at-risk. 

Tables 6.4 and 6.5 present predictions of county economic status in the Appalachian states by 

both the ARC’s index and our two alternatives. In Ohio, for example, the ARC approach 

produces three distressed counties while our alternatives produce zero and one distressed county, 

respectively. In such states as Alabama and Mississippi, the number of distressed counties 

remains the same regardless of approaches. Nonetheless, the specific counties that fall into 

distress can differ between the two approaches. Likewise, no indictors predict a distressed county 

in Georgia, Maryland, New York, North Carolina, and South Carolina. While our alternative 

index with the educational attainment variable predicts more distressed counties in Kentucky, 

Tennessee, and Virginia; the opposite is observed when using our index with the population 

change variable. In total, across all five distress designation categories, 91 ARC counties would 

change their current status if the population growth alternative was used and 61 counties would 

change their designation if the education alternative was used. 

Table 6.6 reports the results for the Appalachian sub-regions. Neither approach predicts a single 

attainment county in Central Appalachia. Counties are either distressed, at-risk of becoming 

distressed, or transitional, though one county falls into the competitive category under our addedpopulation approach. While more than 65% of the Central Appalachia counties are classified as 

distressed under our alternative with the educational attainment variable, about 50% of them are 

so labeled under the two other indicators. In Northern Appalachia, both approaches put about 

two-thirds of the counties in the transitional category. Whereas the ARC’s approach predicts 

attainment counties only in the Southern Appalachia, our approach predicts such counties in 

Northern and Southern Appalachia. It is worth noting that more attainment counties are predicted 

in Southern Appalachia by our added-population approach, since population growth is stronger 

in parts of this sub-region as shown in Figure 6.8.

The results above indicate that, in predicting distressed counties, in some cases our approach is 

consistent with that of ARC, while in other cases, the two approaches disagree. Although we are 

not endorsing our sensitivity measures (they are only for illustrative purposes), they do clearly 

show that small subtle changes can produce different counties categorized as in distress, 

illustrating some sensitivity to the analysis. 

For the United States a whole, Figures 6.11 and 6.12 consist of a pair of maps comparing our 

approach with that of ARC in terms of predicting distressed and non-distressed counties. Red 

indicates cases where both methods produce the same prediction for a distressed county 

(scenario 1); blue represents cases where the ARC approach produces a distressed county 

prediction, but our alternative does not (scenario 2); green represents cases where our method 

produces a distressed county prediction, but the ARC approach does not (scenario 3); while 

white represents cases where both methods suggest the county is not distressed (scenario 4). 
62

Alternative 

Distressed Alternative At-risk Alternative Transitional Alternative Competitive 

ARC ‘s Distress status Population Education Population Education Population Education Population EducatioDistressed - - 57 49 14 3 0 0 At-risk 51 52 - - 149 93 6 0 Transitional 20 0 144 96 - - 167 118 Competitive 0 0 5 0 188 124 - - Attainment 0 0 0 0 36 0 99 65 The Row indicates current ARC classification and the column is the classification for the alternative indicator. 

Education indicates an alternative indicator index where unemployment is replaced with the education index

Population indicates an alternative indicator index where unemployment is replaced with the population index

Table 6.2: Change of County Economic Status in the United States 

Alternative 

Distressed Alternative At-risk Alternative Transitional Alternative Competitive 

ARC’s Distress status Population Education Population Education Population Education Population EducatioDistressed - - 13 7 1 0 0 0 At-risk 6 15 - - 25 9 0 0 Transitional 0 0 10 15 - - 18 4 Competitive 0 0 0 0 4 8 - - Attainment 0 0 0 0 0 0 1 2 The Row indicates current ARC classification and the column is the classification for the alternative indicator. 

Education indicates an alternative indicator index where unemployment is replaced with the education index 

Population indicates an alternative indicator index where unemployment is replaced with the population index

Table 6.3: Change of County Economic Status in Appalachia 
63

The results for Appalachia suggest that for seven out of 13 states, the ARC’s approach agrees 

with our alternative distress indicator approach in indicating a distressed county (see Tables 6.4 

and 6.5). Figure 6.11 shows that our added-education indicator tends to predict more distress in 

Kentucky, Tennessee, and Virginia and less distress in Ohio and Pennsylvania. Figure 6.12 

shows that the added-population approach tends to add more cases in Virginia, while there are 

fewer distressed counties in Kentucky, Ohio, Tennessee, and West Virginia. 

To provide further refinement, we create an additional category for descriptive purposes only—

“weakly” and “strongly” transitional counties, which is respectively distinguished by a county 

being below or above the national average. Thus, the “weakly transitional” counties include 

those ranking between the worst 25 and 50% of the U.S. counties and the “strongly transitional” 

counties are those ranking between the best 25 and 50% of the U.S. counties. Figures A-1, A-2,

and A-3 in appendix present the refined economic status for both ARC and our approaches. 

Summary Evaluation: Even when using ad hoc alternative distress indicators that are 

only modestly different from those used in the current ARC distress index, both the 

number and types of counties that fall into distress can be somewhat different. It is likely 

that using other indicators would produce very different results. Moreover, the current 

ARC distress index implicitly places more weight on the poverty rate due to poverty’s 

high variability. Changing the variable weights to z-scores would likely further shift the 

distress index. The point is that for a variety of reasons, measures of distress are 

somewhat sensitive to the underlying assumptions. 
64

States Distressed At-risk Transitional Competitive Attainmen

ARC Education ARC Education ARC Education ARC Education ARC Educa

Alabama 3 3 9 11 23 21 1 1 1 1 Georgia 0 0 0 3 26 28 6 3 5 3 Kentucky 34 41 11 4 6 6 0 0 0 0 Maryland 0 0 0 0 2 2 1 1 0 0 Mississippi 9 9 11 11 4 4 0 0 0 0 New York 0 0 0 0 14 14 0 0 0 0 North Carolina 0 0 7 5 18 20 4 4 0 0 Ohio 3 0 10 9 15 19 1 0 0 1 Pennsylvania 1 0 1 1 45 42 5 9 0 0 South Carolina 0 0 1 0 4 5 1 1 0 0 Tennessee 7 11 12 12 27 26 4 1 0 0 Virginia 1 3 4 4 15 13 1 1 1 1 West Virginia 16 15 16 20 21 18 2 2 0 0 Total 74 82 82 80 220 218 26 23 7 6 ARC indicates the ARC’s current index composed of three indicators: poverty, income, and unemployment 

Education indicates an alternative indicator index where unemployment is replaced with the education index 

*There is one missing value for the education attainment variable in Virginia. **The composition of specific counties differs across the different distress indicator approaches. 

Table 6.4: Economic Status Prediction in Appalachian States with the Added-education Approach 
65

States Distressed At-risk Transitional Competitive Att

ARC Population ARC Population ARC Population ARC Population ARC 

Alabama 3 3 9 9 23 20 1 4 1 Georgia 0 0 0 0 26 14 6 11 5 Kentucky 34 32 11 10 6 8 0 1 0 Maryland 0 0 0 0 2 2 1 1 0 Mississippi 9 9 11 10 4 5 0 0 0 New York 0 0 0 0 14 14 0 0 0 North Carolina 0 0 7 2 18 21 4 5 0 Ohio 3 1 10 8 15 19 1 0 0 Pennsylvania 1 0 1 2 45 45 5 3 0 South Carolina 0 0 1 0 4 5 1 1 0 Tennessee 7 4 12 9 27 33 4 4 0 Virginia 1 3 4 5 16 14 1 1 1 West Virginia 16 14 16 19 21 18 2 2 0 Total 74 66 82 74 220 218 26 33 7 

ARC indicates the ARC’s distress indicator index composed of poverty, income, and unemployment 

Population indicates an alternative indicator index where unemployment is replaced with the population index. 

*The composition of specific counties differs across the different distress indicator approaches. Table 6.5: Economic Status Prediction in Appalachian States with the Added-population Approach
66

Distress 

status 

Indicatorsa Central Northern Southern Total 

ARC 45 14 15 74 

Distressed Population 43 9 14 66 

Education 57 9 16 82 

ARC 22 26 34 82 

At-risk Population 23 26 25 74 

Education 16 27 37 80 

ARC 20 95 106 221 

Transitional Population 20 98 100 218 

Education 14 95 109 218 

ARC 0 9 17 26 

Competitive Population 1 6 26 33 

Education 0 12 11 23 

ARC 0 0 7 7 

Attainment Population 0 5 14 19 

Education 0 1 5 6 

Total 87 144 179 410 

a

ARC indicates the ARC’s current index composed of poverty, income, and unemployment 

Education indicates our alternative index where unemployment is replaced with the education index 

Population indicates our alternative index where unemployment is replaced with the population index 

*The composition of specific counties differs across the different distress indicator approaches.

Table 6.6: Change in Economic Status of the Appalachian Sub-regions 
67

Figure 6.1: Distress Indicator Map for the ARC Region 
68

Figure 6.2: Distress Indicator Map for the U.S. as a Whole 
69

Figure 6.3: Poverty Rate Map in the Appalachian Region 
70

Figure 6.4: Map of Poverty Rates in the United States 
71

Figure 6.5: Map of Unemployment Rates in the United States and Appalachia 
72

Figure 6.6: Per Capita Market Income in the United States and Appalachia, 2003 
73

High School C

45677

Educational Attainment in Appalachia, 2000

(High School Completion Rates)

Map create

December 27

Data source: U.S. Census B2000 Census SF3

This map shows the 

of adult population 25 

over that have achiev

a high school de

U.S. average 

Appalachia avera

Georgia

Ohio

North

Carolina

Virginia

South

Carolina

Alabama

Mississipi

Pennsylvania

New York

Kentucky

Tennessee

West

Virginia

0 

115 

MD

Figure 6.7: Map of Educational Attainment in Appalachia (High School Completion Rates) 
74

Figure 6.8: Map of Total Population Change in Appalachia, 2000-2005 
75

Figure 6.9: Map of Distress Indicator with the Educational Attainment Index 
76

Figure 6.10: Map of Distress Indicator with the Population Change Index 
77

Scenarios

Scenario 1Scenario 2Scenario 3Scenario 4Appalachian Region

0 320 160 Miles

0 390 195 Miles 0 140 70 Miles

Distressed and Non-distressed Status Prediction:

A Comparison between the ARC's Index and Our Index with Educational Attainm

.

Scenario 1 = cases where both methods produce a distressed county.Scenario 2 = cases where ARCmethod produces a distressed county but our method does not.Scenario 3 = cases where ourmethod produces a distressed county, but ARC method doesnot.Scenario 4= cases where both methods suggest the county isnot distressed.

Figure 6.11: Map Comparing the ARC’s Index with Our Index with Educational Attainment 
78

Scenarios

Scenario 1Scenario 2Scenario 3Scenario 4Appalachian Region

0 320 160 Miles

0 400 200 Miles 0 150 75 Miles

Distressed and Non-distressed Status Prediction:

A Comparison between the ARC's Index and Our Index with Population Chan

.

Scenario 1 = cases where both methods produce a distressed county.Scenario 2 = cases where ARCmethod produces a distressed county but our method does not.Scenario 3 = cases where ourmethod produces a distressed county, but ARC method doesnot.Scenario 4= cases where both methods suggest the county isnot distressed.

Figure 6.12: Map Comparing the ARC’s Index with Our Index with the Population Change
79

PART IV - CONCLUSIONS AND RECOMMENDATIONS 

7. General Principles and Recommendations for Developing Distress 

Indicators

7.1. An Updated Economic Distress Index for the Appalachian Regional Commission 

As set forth in the RFP, the new distress index should satisfy the following general criteria: 

• It should be credible, transparent and likely to be acceptable to ARC, Congress and OMB. 

• The component indicators selected for the index should be as up-to-date or delivered with as 

little time lag as possible. 

• The index should be accurate in terms of capturing economic distress in the ARC counties, 

without identifying false positives (a county that is not really distressed but is identified as such 

by the index) or false negatives (a county that is in reality distressed but does not show up as 

such on the index). 

Evaluation of the Index County is in fact: 

Index shows county is: Distressed Not Distressed

 Distressed Correct False 

positive 

 Not Distressed False 

negative 

Correct 

 Table 7.1: Possible Outcomes from a Distress Index Evaluation 

Given the fact that economic distress is a multi-dimensional phenomenon, the proper construction of 

an index requires multiple indicator measures or variables as “inputs.” Thus, each variable needs to 

have an explicit weight associated with it (whether that weight is 1.0, 1/n where n is the number of 

indicators, or some other number). Not choosing a weight for an indicator (by default) is exactly the

same as choosing one, and thus, results in explicit value judgments as to the importance of each factor. 

In other words, a weight of 1 is just as arbitrary as a weight of 2, 33 or 0.33. For example, as we noted 

above, the current passive ARC distress index, is, in the end, a de facto measure of the poverty rate. 

And, as we showed in our modest sensitivity analysis using educational attainment and population 

change, changing variables and/or weights leads to different rankings and to the identification of 

different counties being labeled as distressed. 

Our point is that more empirical work is needed to develop a credible distress index, using actual 

county-level data going back through time, combined with some ground-truthing with knowledgeable 

local observers. Thus, a new index can be calibrated against the one current one. Future ARC efforts to 

adopt a new index should consider adding an additional option that involves “listening” to local 

experts across the region in order to gauge the range for which indicator variables should be weighted

in a distress index—i.e., to ensure that the indicators reflect the reality on ground. For example, should 

a proper index place considerably more weight on variation in poverty rates (as is currently the case by 

default rather than by design) relative to other indicator variables? Likewise, as we have noted 
80

repeatedly, the proper indicators would also need to be determined to gauge distress in the 21st

Century. 

7.1.1. General Principles for Further Analysis 

A well-constructed distress index should track how the ARC counties are performing relative to nonARC counties, and how they are performing relative to one another. Though the current ARC index 

may be lacking in some dimensions, it does reasonably well in terms of being benchmarked to the U.S. 

average. However, the following principles should be applied with regard to the possible revision of 

the index: 

1. The ARC should consider reducing the dominating influence that the poverty rate currently has on 

the overall distress index. This would be accomplished by generating z-scores that standardnormalize each indicator variable by subtracting its mean and dividing by its standard deviation. Of 

course, each variable would need to be appropriately weighted and empirical verification would be 

necessary. Such a standard normalization would even the playing field for the variables comprising 

the index in the sense that each variable is given a more equal relative weight, regardless of how 

much it varies statistically. This is also relevant for regression analyses discussed in point 7 below. 

In revising the distress index, future research should assess how much of a difference standard 

normalizing each indicator variable would make. 

2. An explicit weighting of each variable—perhaps equal to one—will create greater transparency 

regarding how each variable affects a given county’s distress ranking. For example, using z-scores, 

a variable could be transparently given a weight of 2 by multiplying the variable’s z-score by 2. 

Conversely, under the current ARC distress measure, it is not transparent how much more weight 

the poverty rate receives as compared to the other two indicators. Yet, as Partridge (2007) notes, 

the current ARC distress index corresponds quite closely to a poverty mapping from the late 1970s. 

3. The current ARC indicators—poverty, unemployment and PCMI—may not describe the current or 

future situation in a county because they reflect the cumulative effects of previous economic 

forces. Instead, they describe the situation as it existed two (or more) years earlier. We strongly 

recommend that the ARC consider adopting better indicators that help predict future distress for 

directing government expenditures (Appendix A.2 presents materials on lagging, coincident and 

leading indicators.) 

4. In the past, ARC has had to measure distress using indicators that were at least two or even more 

years out of date. This should be less of a problem in the future with the American Community 

Survey. 

5. As a supplementary measure, ARC could consider using a leading economic indicator – in the form 

of building permits issued or other relevant variables (see Appendix A.2). Building permit numbers 

are released with only a relatively short delay of 12 months or less, and they help describe local 

economic conditions (subject to caveats discussed elsewhere). The Conference Board uses building 

permits as a leading measure of U.S. national economic activity.31

 

 

31The Conference Board’s methodology for constructing their indexes of economic activity can be found at: 
81

6. The ARC should weigh whether economic conditions in nearby counties are components of a given 

county’s level of distress. If neighboring counties are distressed, then economic spillovers across 

county-lines may increase the distress burden of the county in question. This phenomenon could be 

assessed using geographical weighting procedures described in Section 5.3.1. 

7. To validate potential distress indicators, we strongly recommend using econometric modeling to 

determine how well the contemporaneous measures of distress presage future economic conditions. 

For example, using models developed from regression analysis, one can simulate over time how 

well a particular variable predicts structural economic (distress) conditions in a future period. A 

general approach for accomplishing this as a validity check could be to regress outcomes over the 

period 2000-2007 on initial conditions in 2000. 

8. Another ‘reform’ that the ARC should consider is a finer delineation of distress to better address

the absolute threshold issue of either being “in” or “out” of distress or the related issue of greater

severity of distress. The advantage of a finer delineation is that it would target more funding to the

most severely distressed cases, while at the same time, including counties that fall just below the 

“distressed threshold.” Clearly, the group of “most at risk” counties faces structural issues much 

like their distressed cousins and including them for funding would partially mitigate the concerns 

of counties that fall just below the “distressed” threshold. 

7.1.2. Related Questions for Further Analysis

1. High poverty rates have persisted for decades in certain ARC counties. Other counties in the region 

have had low rates since ARC was first formed. Are there other key correlates (additional 

information) that account for these differences? A conceptual framework would provide guidelines 

for selecting the indicators that best represent distress, assessing the causes of distress, and 

designing policies that are intended to alleviate it. 

2. Counties in the northern reaches of the ARC have low poverty and unemployment rates, as well as 

high educational attainments compared to other ARC counties. Thus, these counties tend not to 

show up as distressed. To what extent then, are there sub-regional variations in distress? If a range 

of different indicators were evaluated, would the northern ARC counties still tend to have lower 

distress rankings? 

3. Even though individuals who are left behind in the northernmost counties may not live in poverty 

or experience high rates of officially-recorded unemployment. However, they do suffer from the 

(unmeasured) externalities or spillovers associated with the departure of long-time residents 

(Kilkenny and Johnson, 2007). To what extent should these spillovers be considered in the funding 

formula? 

4. Increasing urbanization in the southern areas of the ARC has resulted in general reductions in 

distress. Many remote areas of southern and central Appalachia have not experienced this relative 

prosperity. Issues of race and class still plague many communities. What additional social issues 

and problems continue to influence patterns of distress in Appalachia? 

 

http://www.e-forecasting.com/US_Leading_Economic_Indicator.htm. 
82

7.2. Recommendations for Selecting Variables and Developing a Distress Index 

7.2.1. Review of Current Indicators

Section 4, 5 and 6 discussed various shortcomings of the current ARC distress index consisting of 

poverty rates, unemployment rates, per-capita market income (PCMI). The key concern is that the 

current index is a de facto poverty rate measure. Moreover, because the poverty rate is so persistent, 

the current list of distressed counties is almost synonymous with lagged poverty rates from decades 

ago. Our point is not that poverty is a weak measure of distress, but rather that it may be receiving too 

much weight in the process of determining the distress status of counties. 

Another shortcoming with the current indicators is that the poverty rate varies significantly across the 

ARC region, as well as the country. By contrast, PCMI and the unemployment rates vary less across 

the region and country. Because the poverty rate is more variable, it drives whether a county appears

to be distressed or falls into the attainment category. This could be corrected by weighting each 

variable differently in the construction of the overall distress index. A final problem is that the 

unemployment rate and PCMI are increasingly flawed measures of distress due to socioeconomic 

shifts. For example, differences in unemployment rates no longer clear reflections of economic 

conditions due to migration patterns—i.e., at the local level, job growth and changes in unemployment 

are very low. 

For these reasons, we encourage the ARC to reexamine its current distress index to better reflect 

socioeconomic shifts since the 1980s. Likewise, indicators could better capture future distress in order 

to guide funding to areas that are designed to mitigate future problems rather than providing band-aids 

for past characteristics of distress. Finally, future efforts need to rigorously examine the weights that 

should be placed on each variable so that the distress indicator fully reflects the intended structural 

weaknesses. 

7.2.2. Next Steps

In a quest to assess persistent distress, a rigorous appraisal of the underlying structural factors that 

predict future distress would be in order. Such a conceptual framework would inform the ensuing 

statistical investigation. 

From this conceptual framework, we envision a new distress index that consists of three to five major 

components (each of which may have a number of sub-components), that are to be standardnormalized to facilitate a transparent weighting process. Weights would be determined based on 

consultation with key informants and on the basis of the outcomes (that is, how well they predict or 

correlate with actual conditions of local distress). 

The ARC would be presented with several different distress indexes. Key information that would be 

considered would include the counties that changed status—i.e., enter the distress category or exit the 

distress category. Our basic sensitivity analysis in Section 6 provides a good example of how such an 

analysis could present such findings. We envision the ARC being presented with maps and tables 

showing the results of an extensive sensitivity analysis. 
83

We believe that the following variables deserve further close examination: 

After evaluating over 50 indicator variables, we believe the following form the core indicators of 

future analysis for a distress indicator. They are transparent, valid, reliable, easy for stakeholders to 

understand, and available at a relatively low cost. We also offer a set of secondary indicators that 

would be of value for stakeholders for tracking progress. A data dictionary for the key core and 

secondary variables we recommend for consideration is provided in Table 7.2. 

1. Population Change: Population change -- including its key component such as out-migration -- 

forms an important mechanism for dealing with economic decline. As such, it should be 

considered for use in any future distress index. Population loss is a real measure of economic 

deterioration, and counties should not be penalized in the distressed counties formula for tackling 

their economic problems through out-migration. Over longer periods, population change also 

closely proxies for employment change, capturing a key economic component as well. Further 

assessment of the composition of population change would be warranted. For instance, is distress 

more related to migration of young workers, or is distress linked more to changes in the numbers of 

retirees? 

Adjustment assistance to counties experiencing significant population loss can be motivated with 

two arguments: (1) Those staying behind have to deal with real negative consequences of a smaller 

population base; and (2) Assistance could stem or even reverse the outmigration (See section 5.1 

for further discussion). 

2. Educational Attainment: Education is the prime measure of human capital, is an underlying 

determinant of an individual’s current and future earnings capacity, and is highly correlated with 

other factors such as poverty. It is also associated with an individual’s ability to adjust to economic 

change and succeed in the knowledge economy. For these reasons it is connected to current distress 

and is a forward-looking measure of future distress. Indeed, adding high school educational 

attainment was more important than adding population change in terms of affecting county 

exit/entry into/from distress, as shown by the sensitivity analysis in Section 6. A full set of 

measures should be considered, including the proportion with an associate degree, four-year 

college degrees, and net changes in educational attainment as a way of determining whether there 

is an ongoing problem with brain drain. 

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

distress, and it should be considered as a potential candidate in a new index (see section 5.2 for 

further discussion). Yet, as noted above, lower per-capita income can reflect amenities, lower cost 

of living, and other factors that are not related to distress. For these reasons, other measures of 

income and earnings should be weighed as well, including recent trends in changes in income. 

4. Housing or Housing Change: Some measure of building permits, coupled with changes in 

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

Property values denote local on-the-ground assessments of the future direction of the community 

by private entrepreneurs (the market). Changes in new home construction are also good forecasts 

of changes in future population. 
84

5. Entrepreneurship and Self-Employment: A forward-looking measure of the local community’s 

ability to compete is having a strong entrepreneurial capacity. Though “entrepreneurial capacity” 

cannot be directly measured from any federal data source, a good proxy is self employment as 

owners of small businesses. Because small business formation is motivated by a host of favorable 

and unfavorable reasons, the ARC should consider investing resources to sort out “reactive” from 

“radical” entrepreneurship in the region. As section 5.2.5 described, one clue regarding the extent 

to which self-employment growth in an Appalachian county is radical (response to opportunity) as 

opposed to reactive (response to necessity) may be found in the returns to self-employment. This 

distinction could be an important measure of a county’s ability to adjust to new employment 

realities. 

6. Labor Market Strength: Several measures of labor market conditions should be considered in a 

refined distress index. As noted above, the unemployment rate fails to adequately capture labor 

market conditions. As described in Section 5.1.4, the employment rate (employment 

rate/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 also proves more 

informative than the conventional unemployment rate measure. 

7. Poverty Rate: The poverty rate should remain one of the core variables that indicate distress. 

Yet, future assessment should consider the proper weight to place on the poverty rate. Moreover, 

this investigation should consider the overlap of poverty with the other indicators included in the 

complete distress index. 

8. Other Alternative Measures: Our final recommendation is for the ARC to report a more 

complete listing of indicators for the region beyond those narrowly interpreted as distress. These 

would include a large number of the alternative indicators discussed in various sections of this 

report, such as the more innovative measures of social capacity. Though many of these measures 

are not suited to be among the four or five core selected indicators of distress, they would offer the

ARC counties and interested parties a more comprehensive/holistic gauge of their progress towards 

meeting regional goals aside from “distress.” By providing a more complete list of measures, 

individual counties can customize their benchmarks to monitor progress. Such measures could be 

reported at a relatively low cost on the ARC web-site for access by interested users. 

Some candidates for inclusion in a secondary list of indicators include: 

• Social capital proxies such as voter participation rates, home ownership, and residential 

stability to help assess various forms of social development and capacity-building; 

• Local government capacity to provide services; because of limitations in Census of 

Government data, a full delineation of fiscal capacity will require data collection from taxation 

and revenue departments at the state level, which may be more costly; 

• Foreclosures and vacancy rates for local housing; 

• Amenities and quality of life indicators; 

• Demographic change including changes in immigrants;

• An index of leading economic indicators for each county; 

• Knowledge-economy occupational changes; 

• Share of local employment in industries that are vulnerable to international competition. 
85

Indicators Overall assessment, strengths, and

weaknesses 

Geography Cost Source Freq

Current 

Poverty rate Good measure of structural problems 

of a location, but households just above poverty not included 

County, Census tract 

No cost 

U.S. Census Bureau, ACS1

Decebut wbecoannuACS 

Unemployment rate 

Easy to obtain, use, and interpret; but may not reflect labor market conditions 

County, Census tract 

No cost 

U.S. Census Bureau, Bureau of Labor Statistics, and ACS 

Annu

Employment rate Better than unemployment rate, but 

cannot adjust for the size of the local working age population 

County and census tract 

No cost 

Local Area Unemployment Statistics, Bureau of Labor Statistics, and ACS 

Annu

Employment growth 

Can give clues about local economic conditions, but does not reflect place of residence prosperity 

County No 

cost 

U.S. Bureau of Economic Analysis (BEA) 

Annu

Per capita income 

A good measure of well-being, but does not account for differences in the cost of living over space, does not reflect average wages, and does not measure income inequality 

County and Census tract 

No cost 

Regional Economic Information System, U.S. Dept of Commerce, and BEA 

Annu

Population or net migration rate 

Reflects many elements missing in the existing ARC indicators. 

County, Census tract 

No cost 

U.S. Census Bureau, IRS 

Annu

1: See Section 5 for more details. American Community Survey (ACS) will be available in 2010. Table 7.2: Data Dictionary
86

Indicators Overall assessment, strengths, and

weaknesses 

Geography Cost Source Freq

Housing conditions 

Can predict future conditions in a county, but quality of data for nonmetro areas could be of concern 

County No cost ACS, U.S. 

Census Bureau 

UpdaQua

Population change/In- and out-migration 

Useful to track down origin and destination of migrants, but not entirely complete 

County $500 per 

year for the entire U.S. 

IRS income tax database, ACS 

Annu

Forward-looking 

Building permits Do not reflect construction activity 

outside areas subject to local permit requirements, not all local governments regularly report their data 

County No cost U.S. Census 

Bureau 

MontAnnu

Foreclosure (F) and vacancy rates (VC) 

Important measure of local economic well-being, but need to be examined in the context of population change 

County No cost F: 

RealtyTrac.com, VC: ACS 

F: DaVC: annuwith 

Selfemployment/ entrepreneurship 

A good indicator for future well-being, but research is needed to separate radical from reactive self-employment 

County No cost U.S. Census 

Bureau, Dept. of Commerce, BEA, ACS 

Annu

Educational attainment 

Very good predictor of future economic growth, but does not reflect quality of education and has an arbitrary nature 

County and census tract 

No cost ACS Annu

Natural amenities/ natural capital 

Good indicator of natural capital base, but not a good indicator of distress. 

County No cost Economic 

Research Service (USDA) 

Everseveyear

Table 7.2: Data Dictionary, Cont.
87

Indicators Overall assessment, strengths, and 

weaknesses 

Geography Cost Source Freq

Social Capabilities 

Local government capacity 

Reflects fiscal health and resources, data available for small counties, but data quality not uniform across all counties 

County No cost Census of 

Governments 

Everyear

Social capital See Table 5.3 for a list social capital variables and their sources. These variables can be obta

nominal fee. 

Table 7.2: Data Dictionary, Cont.
88

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Appendices 
100

Appendix A.1 Maps of Distress Indicators with the Refined Categories Figure A.1: Map of ARC’s Distress Indicator with the Refined Economic Status 
101

Figure A.2: Map of Prediction of Refined Economic Status Using Our Index with Educational Attainment 
102

Figure A.3: Map of Prediction of Refined Economic Status Using Our Index with Population Change 
103

Appendix A.2 Supplementary Information: Maps Showing Information of Potential 

Interest 

Figure A.5: Nonmetro Employment 

Change, 2000-2006 

Figure A.6: Average Commute 

Times in Nonmetro Counties, 

2000 

Figure A.4: Nonmetro Counties with 

Above-average (18 percent or higher) 

Population 65 and above, 2000 
104

Appendix A.3 Materials on Leading, Coincident, and Lagging Indicators

The following describes leading, coincident, and lagging economic indicators 

used to construct the national index of leading, lagging, and coincident economic 

indicators.

Ten Components of the U.S. National Leading Economic Indicator: Vendor performance, 

average weekly manufacturing hours, manufacturers' new orders for nondefense capital goods, 

stock prices, average weekly initial claims for unemployment insurance (inverted), index of 

consumer expectations, real money supply, building permits, interest rate spread, and 

manufacturers' new orders for consumer goods and materials 

Four Components of the Coincident Economic Indicator: Personal income less transfer 

payments, industrial production, employees on nonagricultural payrolls, and manufacturing and 

trade sales. 

Seven Components of the Lagging Economic Indicator: Change in CPI for services, commercial 

and industrial loans outstanding, change in labor cost per unit of output, ratio of consumer 

installment credit to personal income, average prime rate charged by banks, average duration of 

unemployment (inverted) and the ratio of manufacturing and trade inventories to sales. 

Source: Conference Board 

http://www.e-forecasting.com/U.S._Leading_Economic_Indicator.htm

Figure A.7: E-forecasting Leading Economic Index