Recent Trends in Poverty in the Appalachian Region:

The Implications of the U.S. Census Bureau Small Area 

Income and Poverty Estimates 

on the ARC Distressed Counties Designation

A Report Presented to:

The Appalachian Regional Commission

Prepared by: 

The Applied Population Laboratory

University of Wisconsin

1450 Linden Drive

Madison, WI 53706

August 2000
i

TABLE OF CONTENTS

Page

TABLE OF CONTENTS i

LIST OF FIGURES iii

LIST OF TABLES iv

EXECUTIVE SUMMARY v

SECTION I 1

Introduction 1

Small Area Income and Poverty Estimates Program 2

SECTION II 6

Overview of Total Poverty (all ages) in Appalachia during the 1990s 6

Total Poverty in the Sub-Regions of Appalachia 7

Total Poverty by State in Appalachia 9

Geographical Distribution of Total Poverty, 1989, 1993, and 1995 11

Development Districts 15

Total Poverty by Metropolitan Status 19

Total Poverty by Nonmetropolitan Social and Economic Function 20

Considering the Starting Level of Total Poverty and Subsequent Change 21

SECTION III 28

Child Poverty 28

Changes in Child Poverty, 1989-1995 28

Considering the Starting Level of Child Poverty and Subsequent Change 36

Child Poverty by Age group (0-4 and 5-17) 39

Considering the Starting Level of Young Child Poverty and 49

Subsequent Change

SECTION IV 53

The ARC Distressed County Designation 53

Distressed Counties in 1980 and 1990 54

The Accuracy of Distressed Status at the end of the 1980s 59

SECTION V 64

The Effect of Using Post-Censal Poverty Estimates on 

Distressed Status during the 1990s 64

Distressed Status, by State, 1990 to 1994 65

Distressed Status, by State, 1990 to 1996 66

Causes of Distressed Status Transition, 1990 to 1994 67

Causes of Distressed Status Transition, 1990 to 1996 70

Causes of Distressed Status Transition, 1994 to 1996 72

Counties Distressed Due to poverty at 200 percent of 
ii

U.S. average and Above 72

SECTION VI 77

Conclusions and Recommendations 77

REFERENCES 81

APPENDICES 84

Appendix A - Small Area Income and Poverty Estimate Program 84

Methodology

Appendix B- Economic Research Service Economic and Policy 87

Typology Definitions

Appendix C - Appalachian Poverty Measures 89 

Appendix D - Distressed Status Designation Methodology 94

Appendix E - Appalachian Distressed Counties 1980 – 1996 95
iii

LIST OF FIGURES

Page

2.1 Total Poverty, ARC Counties, 1989 (Census) 12

2.2 Total Poverty, ARC Counties, 1993 (SAIPE) 13

2.3 Change in Poverty, ARC Counties, 1989-1993 14

2.4 Total Poverty, ARC Counties, 1995 (SAIPE) 16

2.5 Change in Poverty, ARC Counties, 1993-1995 (SAIPE) 17

2.6 Change in Poverty, ARC Counties, 1989-1995 18

2.7 Relative Poverty Position, ARC Counties, 1989-1993 25

2.8 Relative Poverty Position, ARC Counties, 1993-1995 (SAIPE) 27

3.1 Child Poverty (ages 0-17), ARC Counties, 1989 (Census) 29

3.2 Child Poverty (ages 0-17), ARC Counties, 1993 (SAIPE) 30

3.3 Change in Child Poverty, ARC Counties, 1989-1993 31

3.4 Child Poverty, ARC Counties, 1995 (SAIPE) 33

3.5 Change in Child Poverty, ARC Counties, 1993-1995 (SAIPE) 34

3.6 Change in Child Poverty, ARC Counties, 1989-1995 35

3.7 Relative Child Poverty Position, ARC Counties, 1993-1995 (SAIPE) 38

3.8 Young Child Poverty (ages 0-4), ARC Counties, 1989 (Census) 41

3.9 Young Child Poverty (ages 0-4), ARC Counties, 1993 (SAIPE) 42

3.10 Change in Young Child Poverty (ages 0-4), ARC Counties, 1989-1993 43

3.11 Young Child Poverty (ages 0-4), ARC Counties, 1995 (SAIPE) 44

3.12 Change in Young Child Poverty, ARC Counties, 1993-1995 (SAIPE) 45

3.13 Change in Young Child Poverty, ARC Counties, 1989-1995 46

3.14 Relative Young Child Poverty Position, ARC Counties, 

1993-1995 (SAIPE) 52

4.1 ARC Distressed Counties, 1990 57

4.2 ARC Distressed Counties, 1980 58

4.3 Comparison of 1980 Census and 1990 SAIPE to 1990 Census 62

4.4 Comparison of 1980 Census and 1990 SAIPE Upper Bound 

to 1990 Census 63

5.1 ARC Distressed Counties by type and change, 1990-1994 69

5.2 ARC Distressed Counties by type and change, 1990-1996 71

5.3 200 Percent Poverty Distressed Counties, 1990-1994 75

5.4 200 Percent Poverty Distressed Counties, 1990-1996 76
iv

LIST OF TABLES

Page

2.1 Total Poverty rates for Appalachia and Non-Appalachia 7

2.2 Total Appalachian Poverty by Sub-Region 8

2.3 Total Poverty rates by Metropolitan Status in Appalachia 19

2.4 Relative Poverty Position of Appalachian Counties, 1989-1993 22

2.5 Relative Poverty Position of U.S. Counties, 1989-1993 23

2.6 Relative Poverty Position of Appalachian Counties, 1993-1995 24

2.7 Relative Poverty Position of U.S. Counties, 1993-1995 24

2.8 Poverty levels for Appalachian and U.S. Counties, 1995 (SAIPE) 26

3.1 Poverty rate for children age 0-17 yrs, 

Appalachian and Non-Appalachian Counties 28

3.2 Relative Child Poverty Position of Appalachian Counties, 1993-1995 37

3.3 Relative Child Poverty Position of U.S. Counties, 1993-1995 37

3.4 Poverty rate for children ages 0-4, 

Appalachian and Non-Appalachian Counties 39

3.5 Poverty rate for children ages 5-17, 

Appalachian and Non-Appalachian Counties 39

3.6 Poverty rate for children ages 0-17, by region within Appalachia 40

3.7 Poverty rates for children ages 0-17, by state within Appalachia 47

3.8 Poverty rate for children ages 0-17, 

by 1993 metropolitan status within Appalachia 48

3.9 Poverty rates for children 0-17, by 1993 Urban Continuum

(Beale Code) within Appalachia 49

3.10 Relative Young Child Poverty Position of ARC Counties, 1993-1995 50

3.11 Relative Young Child Poverty Position of U.S. Counties, 1993-1995 50

4.1a ARC Distressed Counties by State, 1980 and 1990 55

4.1b ARC Distressed Status Changes by Cause of Change 59

4.2 Comparison of 1980 Census and SAIPE in 

Determining 1990 ARC Distressed Status 61

5.1 ARC Distressed Counties by State, 1990 and 1994 65

5.2 ARC Distressed Counties by State, 1990 and 1996 66

5.3 ARC Distressed Status Changes by Cause of Change 67

5.4 200 Percent Poverty Distressed Counties, 1990, 1994, and 1996 73
v

EXECUTIVE SUMMARY

The following report, funded by the Appalachian Regional Commission (ARC), explores recent

poverty trends for the 399 counties that comprise Appalachia, and examines the Census Bureau’s 

Small Area Income and Poverty Estimates’1 effects on the ARC distressed county designation. 

We begin with an examination of the changes in total poverty in Appalachia between 1979 and 

the mid-1990s, with particular emphasis paid to the post-1990 period. The gap in poverty

between Appalachia and the rest of the country declined as poverty outside Appalachia increased 

during the 1980s while remaining virtually unchanged in Appalachia. The U.S. average poverty

rate declined from 15.1 percent to 13.8 percent between 1993 and 1995, while poverty among

Appalachian counties declined from an average of 16.1 percent in 1993 to 14.6 percent in 1995. 

The ARC counties with relatively higher rates of poverty are generally concentrated in

Kentucky, as well as West Virginia, southern Ohio, and Mississippi. Although Appalachia has

long been struggling economically, Appalachia’s total poverty rate in 1995 was only slightly

higher than in the rest of the country.

Child poverty in Appalachia increased slightly between 1989 and 1995, following the national

pattern. In particular, young children in Appalachia have experienced the greatest increases in

poverty, compared with older children and the general population. The geographical patterns of

total poverty and child poverty are overwhelmingly similar, with higher rates of child poverty

concentrated in eastern Kentucky, and significant portions of northern Tennessee, West Virginia,

southern Ohio, and Mississippi. Between 1993 and 1995 relative increases in child poverty were 

most expansive in Alabama, the Carolinas, and New York, followed by Kentucky, West

Virginia, Virginia, Pennsylvania, Mississippi, and Georgia. Only Ohio and Tennessee

experienced fairly consistent relative declines in child poverty during the period. Similar to the 

overall poverty rates for the sub-regions, the Central sub-region continued to experience the

highest child poverty rates within Appalachia. More than one-third of the children who lived in

the Central sub-region lived in households with incomes under the poverty line, with the poverty

rates in the other regions just over 20 percent. Poverty rates for children ages 0-4 years were, 

 

1

 The Census Bureau’s Small Area Income and Poverty Estimates are abbreviated as SAIPE. These will also be 

referred to as “SAIP estimates” to focus on the numerical estimates themselves rather than the overall statistical 

estimates program
vi

and continue to be, considerably higher than for children ages 5-17 years both nationally and in

Appalachia. This gap was even wider for Appalachian counties than for the remainder of the

U.S., with 27.3 percent of children ages 0-4 in poverty, compared to 19.5 percent for children

ages 5-17 in 1995. 

The ARC has used the distressed county designation for almost twenty years to identify counties 

with the most structurally disadvantaged economies. Each year the ARC updates the distressed 

status of counties based on more current information on unemployment and per capita market

income. However, reliable county-level poverty rates have, until recently, only been available

from the decennial census at the beginning of each decade. The Census Bureau SAIPE program 

has produced county-level poverty estimates for 1989, 1993 and 1995, giving the ARC the

option of using more recent poverty data to classify counties. We evaluate the influence of postcensal estimates of poverty on the traditional distressed county classification, which uses only

the estimates of poverty from the most recent census, during both the 1980s and the early 1990s. 

Of the 399 Appalachian counties, the number designated as distressed increased between 1980

and 1990 by 50 percent.2 This increase reversed a two-decade decline in the number of

distressed counties. Changing relative poverty levels were a factor in 10 of the 12 transitions 

out of distressed status during the 1980s. Poverty did not contribute quite as greatly to the much 

larger number of counties that became distressed in the 1980s. 

Principally, we use two analyses to evaluate the viability of the SAIPE for the ARC designation

of distressed counties. We first evaluate the accuracy of the distressed status designation at the 

end of a decade, comparing the 1980 census with the 1989 SAIPE (using the 1990 census as the 

standard of accuracy). With certain caveats, the results from the 1980s demonstrate that as a

decade progresses, the SAIP point estimates more accurately predict the status of both distressed 

and non-distressed counties than the poverty estimates from the previous census. Then we

examine the causes of status transitions that would occur in the early 1990s incorporating the

SAIPE into the distressed county designation. The number of counties that have been affected

 

2

 The number of distressed counties in 1990 does not correspond to the number of counties officially designated 

distressed by ARC because distress levels were frozen during the 1988-1992 period awaiting the release of 1990 

census poverty data (Wood and Bischak 2000). The distressed designation uses three year averages of 

unemployment and per capita market income. Numbers in Table 4.1a are based on a formula for defining distressed 

counties that incorporates poverty estimates from the last census, not the Census Bureau’s post-censal SAIPE 

estimates.
vii

by economic change in the 1990s can be better evaluated and joint changes in unemployment, 

income, and/or poverty can be distinguished from changes in poverty alone. Between 1990 and 

1994 the number of distressed counties in Appalachia declined sharply (38 percent), due more to 

overall economic improvement in Appalachia relative to the U.S. as a whole than by substitution

of the SAIPE for the 1990 census poverty estimates. Moreover, relative shifts in unemployment 

played a more important role as an independent cause of these transitions out of distressed status 

than did shifts in poverty. 

The distressed status accuracy results from the end of the 1980s suggest that the SAIPE would 

provide a better determinant of distressed status than the poverty estimates derived from a decade 

old census. The magnitude and causes of distressed status transitions in the first half of the

1990s indicate that using the SAIP estimates would alter the counties that would be designated

distressed by the ARC but not to a radical degree. However, both of these analyses demonstrate 

that a simple substitution of the SAIP point estimates for census poverty estimates may

unjustifiably deny some counties distressed status recognition. As an antidote to this situation it 

might be more defensible to combine the SAIP point estimate and the SAIP upper bound

estimate in the future determination of distressed status. This would accomplish the objective of

utilizing more current estimates of poverty while reducing the negative consequences of utilizing

an estimate of poverty with greater statistical variation than decennial census derived estimates. 

Overall, the analysis of the 1990s indicates that the number of distressed counties has declined in

Appalachia during the decade. The Small Area Income and Poverty Estimates indicate a decline 

in poverty in Appalachia relative to the U.S. as a whole, which reflects a concomitant relative

decline in unemployment and a relative increase in per capita market income. Determination of

distressed status using the 2000 Census of Population and Housing poverty rates should confirm

this decline. During the next decade, the accuracy of the SAIPE program should improve

significantly as new sources of income and poverty data, especially the American Community

Survey (ACS), become available, making them an even more viable option for the determination

of distressed status by the Appalachian Regional Commission. 
1

SECTION I

Introduction

Since its formation in 1965, the Appalachian Regional Commission has pursued a

comprehensive program of regional development to improve socioeconomic conditions and

alleviate poverty. Initially, 85 percent of ARC funds were allocated to highway construction in

order to overcome the region’s remoteness and physical isolation from the rest of the country, 

not withstanding Appalachia’s close proximity to the population concentrations of the Eastern

United States (Isserman and Rephann, 1995). Although highway construction has remained an

important activity for ARC, from its inception, funds have also been appropriated for hospitals 

and treatment centers, land conservation and stabilization, mine land restoration, flood control

and water resource management, vocational education facilities, and sewage treatment works

(Isserman and Rephann, 1995). The ARC and state and local governments have spent more than 

$15 billion on economic and social development in the region (Wood and Bischak 2000). 

Although Appalachia continues to be a region of the U.S. with relatively high levels of poverty, 

it has made significant gains during the past 25 years. Numerous articles, books and

documentaries have highlighted the plight of the Appalachian people over the years (Harrington,

1962; Caudill, 1963; Weller, 1965; Lyson and Falk, 1993; Couto, 1994). In this mountainous, 

geographically remote, and disproportionately rural region, residents have traditionally

contended with a cyclical economy, lower than U.S. average earnings, and higher than average

poverty levels (PARC, 1964; ARC, 1972; ARC, 1979). Besides the rural and geographically

isolated nature of the region, the socioeconomic differences between Appalachia and other parts 

of the country have been shaped by a number of factors including the relative lack of highskill/high-wage manufacturing, limited industrial diversity, sensitivity of the region’s industries

to recession, dependence on extractive industries, export of capital, and lack of investment in the 

human capital of the region (Dix, 1978; Raitz and Ulack, 1984; Duncan, 1992; Haynes, 1997). 

The following report explores recent poverty trends for the 399 counties that comprise

Appalachia. The Appalachian Regional Commission (ARC) has provided funding for this
2

research. The analysis examine the Census Bureau’s Small Area Income and Poverty Estimates

(abbreviated as SAIPE, which will also be referred to as “SAIP estimates” to focus on the

numerical estimates themselves rather than the overall statistical estimates program) and their

effects on the ARC distressed county designation. We begin with a discussion of the SAIP 

estimates. This is followed by an examination of the changes in total poverty in Appalachia

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

including a discussion of the geographical distribution of poverty. While our analysis covers the 

total population (all ages), we focus in greater detail on child poverty. We conclude with an

evaluation of the impact of using the SAIPE estimates for the years 1989, 1993 and 1995 to

assign the economically distressed status designation used by the ARC.3

Small Area Income and Poverty Estimates Program

Detailed poverty and income levels for states and sub-state geographic areas, especially counties, 

are among the most important products of the decennial census of population and housing. 

However, the ten-year interval between the census enumerations leaves a relatively long time

span without more current data on the changes in poverty levels and rates in sub-state areas. 

Measuring poverty at ten-year intervals does not capture fluctuations within the period and is

seldom coincident with the timing of major economic shifts. Moreover, national poverty trends

do not uniformly affect all states and sub-state areas, nor do these national trends consistently

affect all age groups within the population. This ten-year gap between censuses undermines the 

ability of many federal, state, and local programs designed to alleviate poverty to effectively

identify and reach their target populations. 

The Census Bureau’s Small Area Income and Poverty Estimates Program was initiated to

remedy this deficiency by providing post-censal county estimates of income and poverty. We

provide a brief summary of this program in this report; more detailed information on the Small 

Area Income and Poverty Estimates Program can be found at the Census Bureau’s website 

(http://www.census.gov /hhes/www/saipe/saipe93 /origins.html), and in reports from the

 

3

 We have used the 1990 Census estimates for poverty when referring to poverty change since 1990. See appendix 

A for a further discussion of the Census Small Area Income and Poverty Estimates.
3

National Research Council (1998 and 2000). The primary reason for developing post-censal 

estimates of income and poverty for small areas is that the national levels and spatial

distributions of these characteristics are not stable over time. If decennial census data are used 

to benchmark poverty relief programs for an entire decade, the programs remain fixed on the

decennial targets even when income and poverty levels rise or fall nationally, or the relative

levels of poverty for population groups, states, or local areas change. The Census Bureau (under 

authorization from Congress) prepares poverty estimates for children ages 5-17. These statistics 

are for use by the U.S. Department of Education in allocating federal funds under Title I of the 

Elementary and Secondary Education Act for education programs to aid disadvantaged schoolage children. In this report we examine levels and changes in poverty among the entire

population, among children ages 0-4, and among children ages 0-17, while recognizing that the 

poverty estimates for children 5-17 and the models that generate them have been subjected to 

greater scrutiny and more thorough evaluation (National Research Council, 1998). 

 The principal aim of the Census Bureau’s SAIPE program has been to produce post-censal 

estimates of median income and poverty for states, counties, and school districts in the absence 

of actual measures collected in a large-scale survey or a census. To accomplish this goal, the 

Census Bureau uses multiple regression statistical modeling to generate updated county-level 

estimates of income and poverty. Multiple regression is a statistical technique that attempts to

explain or predict the level of a single dependent variable based on the levels of a set of

independent variables (Vogt 1993). In the absence of a single source of reliable estimates for

income and poverty, regression modeling leverages several data sources and time periods in

order to optimize precision (National Research Council 1997). 

The SAIPE multiple regression models have produced biennial estimates of income and poverty

beginning in 1993. The SAIPE model uses several county-level independent (predictor)

variables, 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, the

1990 census of population, and the Census Bureau population estimates. The statistical model

incorporates county-level data on income and poverty from the Demographic Supplement of the 

Current Population Survey (CPS) conducted in March each year, as the dependent variable. The 
4

SAIPE model combines three years of CPS data to improve the precision of the estimates. This 

technique is similar to ARC’s use of three-year averages for unemployment and per capita

market income in the designation of distressed counties. Because the CPS sample does not

include all counties, the relationship between the predictor variables and the dependent variable

is estimated for the subset of counties included in the CPS sample, and then applied to all

counties. 

In 1994, Congress authorized a study by the National Research Council (NRC) to assess the

production, appropriateness, and the reliability of the updated poverty estimates for children ages 

5-17. Upon evaluation of the original model and poverty estimates for 1993, the NRC Panel 

concluded that the 1993 estimates represented a substantial step toward the production of postcensal poverty estimates. The panel further recommended the use of these estimates (together

with poverty estimates from the 1990 Census) for allocations for school year 1997-98 under the 

terms of Title I of the Elementary and Secondary Education Act allocations (National Research

Council, 1997). Subsequent revisions of the 1993 estimates were evaluated by the NRC Panel

and recommended for use in Title I allocations for school year 1998-99 (National Research

Council, 1998). The Panel concluded that the estimates, although containing strengths and

weaknesses, were superior to continued use of child poverty rate data from the outdated 1990

Census for allocations under Title I. Poverty estimates for counties and school districts for 1995 

were also evaluated by the NRC Panel. The 1995 estimates for children ages 5-17, released in 

1999, were recommended for Title I allocations for school year 1999-2000 (National Research

Council, 1999).

The decision to use the Census Bureau’s post-censal poverty estimates for funding allocations is 

a tradeoff between precision obtained in the decennial census and more current (if less precise) 

post-censal estimates. The 1990 census estimates of poverty are more precise in a statistical

sense because they are based on a very large sample (approximately one-sixth of all households). 

However, they describe the income and poverty situation only as of 1989. The 1993 and 1995

estimates are considerably less precise, but because of their relative currency, they provide a

better description of poverty and economic conditions in the post-1990 period. The Census

Bureau plans to continue research and development efforts to improve the estimation models and 

potentially reduce the time lag between the reference year of the estimates and their release date.

National income and poverty patterns changed between 1989 and the 1993 and 1995 SAIP
5

estimates. Between 1989 and 1993, Census Bureau estimates suggests that, median household 

income declined by 7 percent, the number of people below the poverty level increased by 25

percent, and the number of poor children ages 5 to 17 increased by 24 percent. These belie the 

heterogeneity of economic shifts in counties across the country. In the National Research

Council Panel’s preliminary analysis of poor school-age children for U.S. counties, several

categories of counties experienced trends that, in the Panel’s judgement, warranted further

investigation. For example, large metropolitan central city counties experienced a high-implied 

percentage change in child poverty between the 1989 census estimates and the 1993 model-based 

estimates (42%). This change declined systematically with decreasing population size for

metropolitan counties and continued the decline to the most remote, rural non-metropolitan 

counties. Counties with higher percentages of Native Americans had lower implied increases in

child poverty; however, there was no particular pattern of change for counties containing

reservations. Farm counties had an implied decline in child poverty, while non-farm nonmetropolitan counties had an implied increase in child poverty. Some of this change may be

related to systematic biases in the estimation models (see National Research Council, 1998) but 

in all likelihood also represents actual changes in levels of poverty and its geographic

distribution during this period.
6

SECTION II

Overview of Total Poverty (all ages) in Appalachia during the 1990s

Although Appalachia has long been struggling economically, Appalachia’s total poverty rate in

1995 was only slightly higher than in the rest of the country. Table 2.1 compares the poverty

rates for the 399 Appalachian counties with the rest of the country and the entire U.S.4 In 1979 

(based on the 1980 Census), poverty rates were two percentage points higher in Appalachia than 

in the remainder of the U.S. For 1989, we have two measures of poverty, the SAIPE and the 

census (1990 Census). According to the SAIPE figures, the gap in poverty between Appalachia 

and the rest of the country declined as poverty outside Appalachia increased during the 1980s

while remaining virtually unchanged in Appalachia. Nationally, the 1989 SAIPE indicate that the

proportion of people in poverty was slightly lower than indicated by the 1989 census.5 In

Appalachia, the SAIPE poverty rate was about 6.4 percent lower than the census rate.

In 1993, the poverty gap between Appalachian counties and counties in the remainder of the U.S. 

was one percentage point, and by 1995 it had declined to just under one percentage point. The 

SAIP estimates suggest that this apparent compression occurred because the poverty in

Appalachian counties had not increased as much as it had outside of Appalachia. While the net 

change in poverty for Appalachia was an increase of one half of one percentage point between

1989 and 1995, poverty rates in counties outside of Appalachia increased by 1.5 percentage

points. Relative to the rest of the United States, Appalachian poverty continues to decrease, a 

trend apparent in decennial census data since the 1960s.

 

4

 Throughout this report, poverty figures are labeled with the year that they measure income. For example, the 1990 

census measures income from 1989 and are labeled as 1989 census poverty rates.

5

 The 1989 SAIP estimates of the numb er of people in poverty are 4.4% lower than the 1989 census figures. This 

includes adjustments made for the differences in the populations included in the poverty universe (U.S. Census 

Bureau, 1999).
7

Table 2.1: 

Total Poverty rates for Appalachian Counties and U.S counties outside of Appalachia

1979 Census 1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Appalachian 

counties 

14.1% 14.1% 15.3% 16.1% 14.6%

U.S. counties 

outside of 

Appalachia

12.2% 12.7% 12.9% 15.1% 13.7%

Total 12.4% 12.8% 13.1% 15.1% 13.8%

Total Poverty in the Sub-Regions of Appalachia

As the total poverty rates in Table 2.2 indicate, the economic fortunes of the three sub-regions of 

Appalachia have shifted over the last few decades. Until recently, the northern sub-region 

enjoyed higher incomes and lower poverty than the other sub-regions of Appalachia (PARC,

1964; ARC, 1972; ARC 1979, ARC 1981). Since the late 1960s however, the decline in the 

manufacturing base and the gradual erosion of the higher paying jobs associated with this

industry has caused a relative decrease in income and higher poverty levels in northern

Appalachia. The poverty rate for the northern sub-region of Appalachia was higher in 1989 than 

in either 1979 or 1969 (Couto, 1994). Between 1989 and 1993, the poverty rate increased

slightly by one to 2.5 percent, depending upon the estimate, SAIPE or census. But by 1995, the 

poverty rate in northern Appalachian counties had declined slightly to 13.6 percent, remaining

above 1969 and 1979 levels. 

In direct contrast to the northern sub-region, the southern sub-region has seen improvement in

incomes and poverty levels over the last three decades. Between 1979 and the 1995, the gap in 

poverty levels between northern and southern Appalachia disappeared. Part of this convergence 

may have been due to the geographical changes in manufacturing that occurred during the last 25 

years. Studies have noted that northern Appalachia has been losing manufacturing plants and
8

employment at the same time that southern Appalachia has been experiencing manufacturing

growth (Jensen, 1998; Raitz and Ulack, 1984). Additionally, the metropolitan areas of Atlanta,

Birmingham and Winston-Salem, with their strong economies, have helped lower the overall rate 

of poverty southern Appalachia. The SAIP estimates suggest that Southern Appalachia

experienced a 2.5 percentage point increase in poverty between 1989 and 1993 and then the same 

percentage point decrease between 1993 and 1995. In other words, according to the SAIP

estimates, there has been no net change in poverty in this part of Appalachia during the first half 

of the 1990s.

Table 2.2: 

Total Appalachian Poverty by Sub-Region

1979 

Census

1989 

SAIPE

1989 

Census

1993 

SAIPE

1995 

SAIPE

North 11.3% 12.5% 14.0% 15.0% 13.6%

Central 22.7% 24.2% 25.9% 26.0% 24.1%

South 15.3% 13.6% 14.3% 15.1% 13.6%

ARC counties 14.1% 14.1% 15.3% 16.1% 14.6%

The central Appalachian sub-region has undergone its own distinct pattern of recent change in

poverty. The poverty rate of the Central sub-region has been consistently higher than for the two 

other sub-regions. There are two differences between the central sub-region and the other two 

regions of Appalachia that partially account for the difference in poverty. First, the lack of

diversification of industry has forced this area to rely on one primary industry, coal mining, for

most of the century. Many authors have discussed the problems of extractive industries in

general and the crisis of mining and exporting the coal of central Appalachia in particular

(Duncan, 1985; Goodstein, 1989; Haynes, 1997). The profits from mining activities have largely

flowed out of the region as a result of ownership in the industry being predominated by distant 

individuals and corporations, thereby exacerbating the economic uncertainty inherent in coal

extraction for the workers of Eastern Kentucky, Southern West Virginia, Western Virginia and

Northern Tennessee (Duncan, 1992). The original President’s Appalachian Regional
9

Commission in 1964 noted that, “Much of the wealth produced by coal and timber was seldom 

seen locally. It went downstream with the great hardwood logs; it rode out on rails with the coal 

cars; it was mailed between distant cities as royalty checks from non-resident operators to

holding companies who had bought rights to the land for 50 cent or a dollar an acre. Even the 

wages of the miners returned to faraway stockholders via company houses and company stores”

(Isserman and Rephann 1995). The second factor distinguishing central Appalachia is that is it is 

much more rural than the other parts of the region. There are only two metropolitan areas in

central Appalachia (Huntington, West Virginia–Ashland, Kentucky and Lexington, Kentucky). 

Central Appalachia, like other nonmetropolitan areas nationally, suffers from higher than

average poverty rates. However poverty rates for central Appalachia are high even when

compared with other predominantly nonmetropolitan areas.

During the 1970s, the level of poverty in central Appalachia declined greatly. Increases in the

demand for coal, such as occurred with the 1970s energy crisis, generally meant increased

employment and lower poverty levels. During the 1980s as the energy crisis subsided, poverty

rates rose. The Central sub-region had a poverty rate in 1979 of 22.7 percent. This rate

increased to around 24 percent in 1989 according to the SAIP estimates or to around 26 percent 

according to the 1990 census. The sub-region’s poverty rate was at 26 percent in 1993 and by 

1995 it was close to the 1989 rate of 24 percent. Throughout this period it remained much higher 

than the adjoining areas of Appalachia. Even though there is evidence that employment in central

Appalachia is diversifying, potentially easing poverty and unemployment to levels similar to the

rest of the region or the nation, it has been a slow transformation. Positive changes have been

concentrated mainly in manufacturing (reducing reliance on extractive industries) but they are

less evident in the service sectors. 

Total Poverty by State in Appalachia

Rates of total poverty in Appalachia are not homogeneous across states, but instead show wide 

disparities (Appendix C, Table 1). Eastern Kentucky, the part of the state that is in Appalachia, 

and the entire state of West Virginia exhibited high rates of poverty throughout the period

examined in this report. This can be partially attributed to the high unemployment rates of these 
10

states and to the extractive and cyclical nature of the industries there. The portion of Mississippi

located in Appalachia has also had a higher than average rate of poverty. Although only a small 

part of Mississippi is in Appalachia, the state as a whole has a higher than average rate of

poverty. The Appalachian portions of Georgia, New York, North Carolina, Pennsylvania and

South Carolina have experienced rates of poverty below the Appalachian average include. For 

some of these states, lower rates of poverty among the ARC counties may be a result of greater 

diversification in the economic base of those counties. For Georgia in particular, many of the 

Appalachian counties are suburban areas in the Atlanta metropolitan area. The difference

between the 1989 SAIPE and 1989 census poverty rates is greater for individual states than it is 

for Appalachia as a whole or for the three sub-regions of Appalachia. Census Bureau tabulations 

show that the greatest differences between 1989 SAIPE and 1989 Census poverty estimates are 

for states in the Northeast and Midwest regions. The 1989 SAIPE estimates tend to be lower 

than the 1989 Census estimates for Appalachian states in the Northeast and Midwest regions

(U.S. Census Bureau, 1999). 

Consistent with the sub-regional change in poverty rates over the period, a north-south 

divergence arises. SAIPE estimates for Appalachian counties in New York suggest a 3.5

percentage point increase (a 30 percent increase) in poverty between 1989 and 1995. Poverty

rates in Pennsylvania, Maryland, and West Virginia also increased between 1989 and 1995. 

Poverty rates in Ohio decreased between 1989 and 1995 but the poverty rate was higher for all 

three SAIPE years than it was in 1979. The three southeastern, Atlantic coastal Appalachian

states, North Carolina, South Carolina and Georgia, all showed small increases in poverty from

1989 to 1995. More interestingly, the Appalachian counties in these states have poverty rates 

that have been declining for decades and are now among the lowest in Appalachia. Tennessee, 

Virginia, Mississippi and Alabama enjoyed declining poverty rates during the 1990s.

Since Appalachia encompasses 13 states and 399 counties, it is a heterogeneous region and each 

state does not contain an equal share of the Appalachian population. Pennsylvania, for example, 

contains more than one-quarter of the Appalachian population and therefore has a large influence 

on the overall poverty rate of the region. Since the poverty rates of Pennsylvania’s Appalachian

counties are lower than the rest of Appalachia and lower than the U.S. as a whole, Pennsylvania 
11

lowers the overall rate of poverty for Appalachia. And, since the poverty rate in Pennsylvania 

has increased since 1979, the overall decrease in Appalachia has been attenuated.

Geographical Distribution of Total Poverty, 1989, 1993, and 1995

The 1990 census’s total poverty rates for Appalachian counties are shown in Figure 2.1 as a

proportion of the total U.S. poverty rates. The four color categories correspond to poverty rates 

relative to the U.S. average rate. We compare the Appalachian counties to U.S. average rates to 

control for changes that merely reflect national trends and because in the calculation of

distressed status the comparisons are also made to U.S. averages. The counties with relatively

higher rates of poverty in 1989 were noticeably concentrated in Kentucky, as well as West

Virginia, southern Ohio, and Mississippi. 

A cursory examination of SAIPE poverty rates in 1993 (Figure 2.2) indicates that relative

poverty rates have a similar geographical distribution across Appalachia as they did in 1989,

particularly the concentration in eastern Kentucky and West Virginia, although the northern

Kentucky/southern Ohio region had somewhat lower relative poverty rates in 1993. Figure 2.3 

allows a closer examination of the change between 1989 (1990 Census) and the 1993 SAIP

estimate. For example, although both Figure 2.1 and 2.2 indicate that eastern Kentucky had

relatively high concentrations of poverty in both time periods, the black and white areas in

Figure 2.3 indicate which counties experienced either decreases in their total poverty rates or

below average increases compared to the U.S as a whole. Nearly all the eastern Kentucky

counties experienced a relative decline in poverty of at least three percent better than the national 

average over the period and the remainder experienced a more moderate relative decline. The 

significant increases in poverty (more than three percent above the national average) in

Appalachia between 1989 and 1993 according to the SAIP estimates were few and were isolated 

counties in West Virginia, Pennsylvania, northern Virginia, Tennessee, and Georgia. Poverty in

eight of the ARC counties in eastern Tennessee increased at a greater rate than the national

average, as did a few counties in northern Georgia and in the western Carolinas. Counties that 

experienced relative improvement from 1989 to 1993 were especially clustered in Mississippi,

Alabama, eastern Kentucky, southern Ohio, and West Virginia.
12

aplapl

Applied Population Laboratory

University of Wisconsin - Madison

Poverty Rate Relative

To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 2.1:

Total Poverty,

ARC Counties, 1989 (Census)

0 100 200 300 Miles
13

aplapl

Applied Population Laboratory

University of Wisconsin - Madison

Poverty Rate Relative

To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 2.2:

Total Poverty,

ARC Counties, 1993 (SAIPE)

0 100 200 300 Miles
14
15

Again, the distribution of total poverty across Appalachia in 1995 looked remarkably similar to

1989 and 1993 with higher poverty counties clustered in eastern Kentucky and West Virginia

(Figure 2.4). In contrast to the map of change between 1989 and 1993 (Figure 2.3), which

indicated a relative decrease in poverty among most ARC counties, a large majority of ARC

counties did not perform as well as the national average between 1993 and 1995 (Figure 2.5). 

The U.S. average poverty rate declined from 15.1 percent to 13.8 percent between 1993 and

1995, while poverty among Appalachian counties declined from an average of 16.1 percent in

1993 to 14.6 percent in 1995. The prevalence of light gray and dark gray colored counties in

Figure 2.5 highlights the fact that distinct and concentrated areas of Appalachia did not perform

as well as the national average. Indeed, eastern Kentucky, West Virginia, western sections of

North Carolina and Virginia, and much of Alabama fall into this category. However,

Mississippi, Pennsylvania, and especially Tennessee, and Ohio did experience relative declines

in poverty during the decade. During the 1989 to 1995 period overall, Ohio and Mississippi

experienced the most consistent relative declines in poverty across Appalachian counties

followed by Pennsylvania, West Virginia, Virginia, Kentucky, Tennessee, and Georgia (Figure 

2.6). Only the southern tier of New York counties consistently experienced a relative increase in 

poverty. 

Development Districts

We also compiled total poverty rates for Appalachian counties by development district

(Appendix C, Table 2). There are patterns through the early and mid 1990s that are worth

highlighting. Many of the development districts continue to struggle with much higher than

average poverty levels. Most of these districts are in Eastern Kentucky (Buffalo Trace, Gateway 

Area, Big Sandy Area, Lake Cumberland, Cumberland Valley and Kentucky River) and one of

these districts is in Alabama (South Central Alabama). These districts started out with 1989

poverty rates of at least 25 percent and continued to have poverty rates of at least 25 percent in 

1995. One district, the East Central district of Mississippi, started out with a high rate of poverty 

but according to the SAIP estimates, experienced a substantial decline in poverty between 1989 

and 1995. This district’s poverty rate declined from 33.0 percent to 24.1 percent over the sixyear period. One district, West Virginia’s district 4, experienced a large increase in poverty from 
16

aplapl

Applied Population Laboratory

University of Wisconsin - Madison

Poverty Rate Relative

To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 2.4:

Total Poverty,

ARC Counties, 1995 (SAIPE)

0 100 200 300 Miles
17
18
19

1989 to1995. It should be noted that every district in West Virginia experienced an increase in 

poverty during the period.

Total Poverty by Metropolitan Status

Similar to the U.S. as a whole, there is a difference in total poverty levels between metropolitan

and non-metropolitan counties in Appalachia.6 Non-metropolitan counties historically have had

higher poverty rates than metropolitan counties (Fuguitt, Brown and Beale, 1989; Lichter and

McGlaughlin, 1995). This has also been the case in Appalachia. Throughout the period nonmetropolitan counties have had an aggregate poverty rate about five percentage points higher

than metropolitan counties (Table 2.3). This held true even in 1993 when the estimates tended to 

show that overall U.S. poverty increased in metropolitan areas while it stayed the same in nonmetropolitan counties. The one exception to the difference is the 1989 Census poverty figures 

with a slightly greater, six percentage point difference, between metropolitan and nonmetropolitan Appalachian counties. For 1989, the SAIPE poverty estimates did not capture the 

same increase in poverty between 1979 and 1989 measured by the decennial census. This could 

be an indication of the 1989 SAIPE model’s relative inability to accurately predict poverty for 

counties with smaller populations.

Table 2.3: 

Total (All Ages) Poverty Rates by Metropolitan Status in Appalachia

Number of 

counties

1979 

Census

1989 

SAIPE

1989 

Census

1993 

SAIPE

1995 

SAIPE

Metro 109 11.8% 12.0% 12.8% 14.0% 12.5%

Nonmetro 297 17.2% 17.1% 18.8% 18.9% 17.4%

 

6

 We use the 1993 delineation of metropolitan status (U.S. Census Bureau, 1992).

ARC 

counties 

406 14.1% 14.1% 15.3% 16.1% 14.6%
20

For more detailed information on the effect of population size and proximity to metropolitan

counties, Table 3 in Appendix C provides aggregate Appalachian total poverty rates by the 1993 

rural-urban continuum codes developed by the Economic Research Service of the U.S.D.A.

(Butler and Beale, 1994). Overall, there is a gradient of poverty rates based on the metropolitan 

hierarchy code. The poverty rates among metropolitan counties are inversely related to their size 

classification. Thus the largest and core metropolitan counties have the lowest poverty rates. 

For non-metropolitan counties, the same pattern holds true with the caveat that adjacency status 

also matters. Counties that are less urban (fewer people) and not adjacent to metropolitan

counties are more likely to have higher poverty rates. Over time, there isn’t much change in this 

pattern. The only movement is that the largest counties have seen their poverty rates increase

faster than the other counties. Additionally, the suburban counties in the largest metropolitan

areas and the counties with no urban places have seen their poverty rates decrease over the

period.

Total Poverty by Nonmetropolitan Social and Economic Function

Appendix C, Table 4 shows total poverty rates broken down by non-metropolitan social and

economic function as developed by the Economic Research Service of the USDA (Cook and

Miser 1994; See Appendix B for definitions). The table reflects the higher poverty rates that

persist in Appalachian non-metropolitan counties as a whole. In each of the functional

categories, the poverty rate for classified counties has decreased during the 1990s. Throughout 

the period, manufacturing and retirement destination counties have had the lowest poverty rates 

in Appalachia. By the mid-1990s, poverty in Appalachian retirement-destination counties had

fallen below the national average. Not surprisingly, counties with the persistent poverty

designation have had the highest rates of poverty throughout the period. These are counties that 

have maintained high poverty levels since the 1960 census. Persistent poverty counties in

addition to government and agricultural counties do demonstrate the biggest decreases in the

percent of persons living at or below poverty during the nineties. Lastly, the mining counties

highlight the changes mentioned earlier with a large increase in poverty rates between 1980 and 

1990 that remained high throughout the period. 
21

Considering the Starting Level of Total Poverty and Subsequent Change

Examining changes in poverty without a starting reference point can obscure the fact that while

there are counties that significantly increased their total poverty rate, many of these counties still

had relatively low rates even after the increase. The worsening trend, therefore, does not

necessarily place these counties in a worse position relative to counties with higher rates of total

poverty. For example, between 1989 (1990 Census) and 1993, counties could experience among 

the highest rates of increase in poverty, yet their poverty level among counties could remain low. 

This example illustrates our conviction that a comparison of changes in total poverty rates is

more meaningful when the relative starting levels of county poverty are taken into account. To 

study change, therefore, we jointly consider shifts in total poverty and starting levels prior to

those shifts. We cross-classify counties according to their relative levels of total poverty in 1989 

(above or below average) with their subsequent change in poverty between 1989 and 1993

(above or below average). Likewise, counties are jointly grouped according to their relative

levels of total poverty in 1993 and their relative change in poverty rates between 1993 and 1995. 

The following tables and corresponding maps show how Appalachian counties fit into the four

categories based on the comparison of individual counties with the national level of poverty at

the beginning of the period and the comparison with the national change in poverty during the 

period. Those counties labeled “Best” (light gray) had below average levels of total poverty and

decreased their poverty over the time period, or had below average increases. Those counties 

labeled “Worrisome” (dark gray) also began with below average levels of poverty, but

experienced above average increases in poverty over the time period. Counties labeled

“Hopeful” (white) started the period with above average levels of poverty, but decreased their

poverty rates, or experienced below average increases, over the time period. Counties labeled 

“Worst” (black) had above average levels of total poverty and above average increases in 

poverty. 

Table 2.4 shows a cross-tabulation of the 1989 poverty rates in Appalachia as determined by the 

1990 Census and by the change in poverty rates between 1989 and the 1993 SAIP estimates. 

Here, the national benchmark for initial level of total poverty is 13.1 percent and the national
22

change in the total poverty rate over the four years was an increase of 3.8 percent. The percent 

of Appalachian counties with higher than average poverty rates was over 76 percent. A higher 

percentage of counties (85.2 percent) had poverty rates that were either decreasing or not

increasing as rapidly as the national average. The largest proportion of counties (70.4 percent) 

fit into the Hopeful category with a higher than average starting level of poverty in 1989 but a 

lower than average change in poverty between 1989 and 1993. Over 14 percent were

considered to be in the Best Position (low starting rates and smaller than average increases),

while only 6.3 percent of Appalachian counties were categorized as Worst (high starting rates

and higher than average increases).

Table 2.4: 

Relative Poverty Position of Appalachian Counties, 1989-1993

 Level

Change in Total (all ages) Poverty 

Rate Less Than U.S.

(< +3.8%)

Change in Total (all ages) Poverty 

Rate Greater Than U.S.

 (> +3.8%)

Total

Counties Below U.S. 

Poverty Rate in 1989 

 (< 13.1%)

Best

59

14.8%

Worrisome

34

8.5%

93

23.3%

Counties Above U.S. 

Poverty Rate in 1989 

 (> 13.1%)

Hopeful

281

70.4%

Worst

25

6.3%

306

76.7%

Total

340

85.2%

59

14.8%

399

100%

The comparison between Tables 2.4 and 2.5 allows us to contrast the distribution of these county 

types in Appalachia to the U.S. as a whole. The distribution of U.S. counties among these four 

categories differs somewhat, with almost a quarter of U.S. counties categorized as Best between 

1989 and 1993, and only 5.1 percent categorized as Worst. A somewhat smaller percentage of 

U.S. counties were categorized as Hopeful and a higher percentage were categorized as

Worrisome, relative to Appalachian counties.

Figure 2.7 displays the spatial distribution of these four county types for the time period 1989-

1993. All of the Appalachian counties in Kentucky that had relatively high poverty in 1989 

either decreased their poverty rates, or increased less than the national average and are therefore 

labeled Hopeful (white). There were no strong clustering patterns of Best counties, although 

western North Carolina and Pennsylvania had a disproportionate share. Pennsylvania, New
23

Table 2.5: 

Relative Poverty Position of all U.S. Counties, 1989-1993

Level

Change in Poverty Rate 

Less Than U.S. 

(< +3.8%)

Change in Poverty Rate 

Greater Than U.S. 

 (> +3.8%)

Total

Counties Below U.S.

Poverty Rate in 1989 

(< 13.1%)

Best

722

23.1%

Worrisome

424

13.5%

1,146

36.6%

Counties Above U.S.

Poverty Rate in 1989 

(> 13.1%)

Hopeful

1,824

58.3%

Worst

160

5.1%

1,984

63.4%

Total

2,546

81.3%

584

18.7%

3,130

100%

York, and Georgia had a significant number of counties with lower than average poverty rates in

1989, but many of these counties increased their poverty rates at a rate greater than the national

average of 5.8 percent for the period, and therefore were labeled Worrisome (dark gray). The 

Appalachian counties labeled Worst were largely clustered in West Virginia, and to a lesser

degree along the Tennessee/North Carolina border. Two counties in Georgia and two in New

York were also labeled worst due to having poverty rates just above the national average in 1989 

and then experiencing a greater than average increase in poverty during the period.

Tables 2.6 and 2.7 show the Relative Poverty Positions for Appalachian and U.S. counties

between 1993 and 1995. In contrast to the increase in poverty between 1989 and 1993, the U.S. 

experienced a decline in poverty (-4.3 percent) between 1993 and 1995. About 41 percent of 

Appalachian counties experienced an even more significant decline in poverty rates than U.S.

counties on average, while 59 percent did not perform as well. Only thirteen percent of

Appalachian counties were considered to be in the Best category, compared to 25.7 percent of all 

U.S. counties. Appalachia also had proportionately more counties categorized as Worst than did 

the U.S. (39.8 percent versus 35.3 percent). It is important to remember that counties whose 

poverty rates declined, but not as much as the national average, would be categorized as

experiencing a relative worsening trend in total poverty. This could partially account for the

significant jump in counties categorized as Worst in Appalachia. 
24

Table 2.6: 

Relative Poverty Position of Appalachian Counties, 1993-1995

Level

Change in Poverty Rate 

Less Than U.S. 

 (< -4.3%)

Change in Poverty Rate 

Greater Than U.S. 

(> -4.3%)

Total

Counties Below U.S.

Poverty Rate in 1993 

(< 15.1%)

Best

52

13.0%

Worrisome

77

19.3%

129

32.3%

Counties Above U.S.

Poverty Rate in 1993 

(> 15.1%)

Hopeful

111

27.8%

Worst

159

39.8%

270

67.7%

Total

163

40.9%

236

59.1%

399

100%

Table 2.7: 

Relative Poverty Position of U.S. Counties, 1993-1995

Change in Poverty Rate 

Less Than U.S. 

 (< -4.3%)

Change in Poverty Rate 

Greater Than U.S. 

(> -4.3%)

Total

Counties Below U.S.

Poverty Rate in 1993 

(< 15.1%)

Best

805

25.7%

Worrisome

780

24.9%

1,585

50.6%

Counties Above U.S.

Poverty Rate in 1993 

(> 15.1%)

Hopeful

442

14.1%

Worst

1,105

35.3%

1,547

49.4%

Total 1,247

39.8%

1,885

60.2%

3,132

100%
25
26

The spatial distribution of these four county types for the time period 1993-1995 appears in

Figure 2.8. Although most of the Appalachian counties in Kentucky had been labeled Hopeful

between 1989 and 1993, between 1993 and 1995 their designation predominantly changed to

Worst. The Worst relative position and change counties were concentrated in Kentucky, West 

Virginia, western Virginia, along the North Carolina/Tennessee border, and along the eastern and

western boundaries of Alabama. Again, we emphasize that certain counties labeled as “worst”

may have decreased their rates of poverty, but less than the national average. Therefore, while 

those counties may have improved their position compared to the previous time period, their

relative position with regard to U.S. averages remained or became “worst.” 

Finally, Table 2.8 provides the breakdown of counties for Appalachia and the U.S. as a whole by 

status above or below the national poverty level. Appalachian counties were still more likely to 

have poverty rates above the national average than all U.S. counties. Slightly more than twothirds of Appalachian counties had poverty rates above the U.S. national poverty rate. During the 

time period covered by this analysis, a declining number of Appalachian counties exhibited these 

high poverty rates. Between the 1979 census and the 1995 SAIP estimates, a net of 38 counties 

moved from having higher than average poverty rates to lower than average poverty rates. 

Interestingly, most of this decline occurred between the 1979 and 1989 census, a period when the 

overall Appalachian poverty rate increased faster than the national poverty rate.

Table 2.8: 

Poverty levels for Appalachian and U.S. counties using SAIPE estimates for 1995.

Appalachia United States

Below U.S. Poverty Rate in 1995 

(< 13.1%)

128

31.5%

1,485

47.3%

Above U.S. Poverty Rate in 1995 

(> 13.1%)

278

68.5%

1,656

52.7%
27
28

SECTION III

Child Poverty (ages 0-17)

We now shift our attention from total (all ages) poverty to poverty among the Appalachian child 

(ages 0-17) population. Child poverty is an important indicator of overall child well being. 

Although many factors put children at risk, nothing predicts bad outcomes for a child more

powerfully than growing up poor. Children who spend their early years in poverty often suffer

negative health, social and cognitive outcomes and are much more likely to be poor as adults. 

Child poverty is a particularly persistent condition for minority children, whereas white children 

are more likely to live in poverty for a relatively shorter time. Of great concern is the increasing

number of poor children in the U.S. during the last couple decades. In 1974, 10 million American 

children lived below the poverty line; by 1994 the number had risen to over 15 million. This 

represents an increase from 15 percent to 22 percent of all children, a poverty rate that is among

the highest in the developed world. Child poverty in Appalachia increased slightly between 1989 

and 1995, following the national pattern. In particular, young children in Appalachia have

experienced the greatest increases in poverty, compared with to older children and the general

population.

Changes in Child Poverty, 1989-1995

Child poverty followed a pattern similar to that of overall poverty in Appalachia and the United 

States, with increases between 1989 and 1993, followed by declines between 1993 and 1995. 

However, child poverty in non-Appalachian counties in the U.S. increased significantly more

between 1989 and 1993. Still, the absolute level of child poverty was slightly higher in

Appalachian counties than in non-Appalachian counties (Table 3.1).

Table 3.1: 

Poverty rate for children age 0-17 years, Appalachian Counties and U.S. Counties outside of 

Appalachia

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Appalachian counties 20.5% 20.1% 23.3% 21.6%

U.S. Counties outside of Appalachia 19.6% 18.1% 22.6% 20.7%

Total 19.6% 18.3% 22.7% 20.8%
29

aplapl

AppliedPopulation Labor atory

University of Wisconsin - Madison

Child Poverty Rate

Relative To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 3.1:

Child Poverty (ages 0-17),

ARC Counties, 1989 (Census)

0 100 200 300 Miles
30

aplapl

AppliedPopulation Labor atory

University of Wisconsin - Madison

Child Poverty Rate

Relative To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-8/00

Figure 3.2:

Child Poverty (ages 0-17),

ARC Counties, 1993 (SAIPE)

0 100 200 300 Miles
31
32

Figures 3.1, 3.2, and 3.3 display the geographical distribution of child poverty (0-17 year olds) 

among Appalachian counties for the years 1989 (Census) and 1993. As we might expect,

counties that experienced higher total poverty rates, also experienced higher child poverty rates. 

While the maps of total poverty and child poverty are not identical, it is apparent that the patterns 

are overwhelmingly similar. The counties with higher rates of child poverty in 1989 were

noticeably concentrated in eastern Kentucky, and significant portions of northern Tennessee, 

West Virginia, southern Ohio, and Mississippi. The geographic pattern of child poverty also

shifted between 1989 and 1993, in a similar pattern to the shifts for total poverty.

The geographical distribution of SAIPE child poverty rates across Appalachia in 1993 (Figure

3.2) is quite similar to the 1989 distribution, particularly the concentration in eastern Kentucky

and West Virginia. 

Figure 3.3 allows us to examine changes in child poverty rates between 1989 (1990 Census) and 

the 1993 SAIP estimate more closely. For example, although both Figure 3.1 and 3.2 indicate 

that eastern Kentucky had relatively high concentrations of child poverty in both time periods,

change in all the ARC Kentucky counties was a fairly evenly distributed relative improvement. 

The dominance of black and white counties in Figure 3.3 indicates that between 1989 and 1993 

Appalachia experienced a reduction in child poverty that was greater than the national average. 

The most significant relative increases in child poverty in Appalachia between 1989 and 1993

were in West Virginia, northern Georgia, Tennessee, and the southern tier of New York. 

Again not surprisingly Appalachian child poverty in 1995 (Figure 3.4) was distributed similarly to 

total poverty, with higher child poverty counties clustered in eastern Kentucky and West Virginia. 

However, between 1993 and 1995 a considerable majority of ARC counties either did not

decrease their child poverty rates as much as the U.S. averages, or increased their child poverty 

rates during the two-year period (Figure 3.5). During this period relative increases in child

poverty were most expansive in Alabama, the Carolinas, and New York, followed by Kentucky, 

West Virginia, Virginia, Pennsylvania, Mississippi, and Georgia. Only Ohio and Tennessee

experienced fairly consistent relative declines in child poverty during the period. Finally, Figure

3.6 examines change in child poverty between the 1990 Census and the 1995 SAIP estimate
33

aplapl

AppliedPopulation Labor atory

University of Wisconsin - Madison

Child Poverty Rate

Relative To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 3.4:

Child Poverty (ages 0-17),

ARC Counties, 1995 (SAIPE)

0 100 200 300 Miles
34
35
36

(1989-1995). The relative increases in child poverty experienced between 1989 and 1993 were 

tempered by the declines between 1993 and 1995. The most significant relative declines in child 

poverty between 1989 and 1995 were clustered in southern Ohio and Mississippi. Increases in

child poverty over the six-year period were most notably clustered in the New York, West

Virginia, northern Georgia and Alabama, and the western Carolinas. Many of these counties,

however, still had relatively low child poverty rates in 1995.

Considering the Starting Level of Child Poverty and Subsequent Change

As discussed with our comparison of total poverty rate changes, comparison of changes in child 

poverty rates is more meaningful when the relative starting levels of county child poverty are

taken into account. Therefore we examine the Relative Child Poverty Position of ARC counties 

for the most recent period, 1993-1995. Table 3.2 tabulates the 1993 poverty rates in Appalachian 

counties and the change in poverty between 1993 and 1995. The national benchmark for level of 

child poverty was 22.7 percent and the national change in the child poverty rate over the two

years was a decrease of 4.2 percent. The percent of counties in Appalachia with higher than

average child poverty rates was about 57 percent. A higher percentage of counties (69.7

percent) had child poverty rates that were either increasing, or decreasing less than the national

average. The largest proportion of Appalachian counties (40.1 percent) fit into the Worst

category with a higher than average starting level of child poverty in 1993, and a worse than

average change in child poverty between 1993 and 1995. Only 13 percent were considered to be 

in the Best Position (low starting rates and better than average declines). As would be expected, 

compared to the U.S. as a whole (Table 3.3), Appalachia has a significantly greater proportion of 

its counties in the Worst position, and significantly fewer in the Best position.

Figure 3.7 examines the geographic distribution of the relative child poverty position among

Appalachian counties between 1993 and 1995. As Table 3.2 above describes, over 40 percent of 

Appalachian counties were categorized as “worst” between 1993 and 1995. Those counties with 

high rates of child poverty in 1993 and worse than average change in the following two year

period were clustered in eastern Kentucky, West Virginia, and in parts of Mississippi and

Alabama. There were smaller clusters of these worst category counties in New York, Virginia, 

North Carolina, and Georgia. Of the 13 percent of Appalachian counties that were categorized
37

Table 3.2: 

Relative Child Poverty Position of Appalachian Counties, 1993-1995

 Level

Change in Child Poverty Rate 

Less Than U.S.

(< -4.2%)

Change in Child Poverty Rate 

Greater Than U.S.

 (> -4.2%)

Total

Counties Below U.S. 

Child Poverty Rate in 

1993 (< 22.7%)

Best

52

13.0%

Worrisome

118

29.6%

170

42.6%

Counties Above U.S. 

Child Poverty Rate in 

1993 (> 22.7%)

Hopeful

69

17.3%

Worst

160

40.1%

229

57.4%

Total

121

30.3%

278

69.7%

399

100%

Table 3.3: 

Relative Child Poverty Position of U.S. Counties, 1993-1995

 Level

Change in Child Poverty Rate 

Less Than U.S.

(< -4.2%)

Change in Child Poverty Rate 

Greater Than U.S.

 (> -4.2%)

Total

Counties Below U.S. 

Child Poverty Rate in 

1993 (< 22.7%)

Best

752

24.0%

Worrisome

1,108

35.4%

1,860

59.4%

Counties Above U.S. 

Child Poverty Rate in 

1993 (> 22.7%)

Hopeful

254

8.1%

Worst

1,018

32.5%

1,272

40.6%

Total

1,006

32.1%

2,126

67.9%

3,132

100%

as “best”, there did not appear to be any significant geographic concentrations, except in the

counties adjoining the Atlanta, Georgia, Cincinnati and Columbus, Ohio metropolitan areas, as

well as the westernmost Appalachian counties in Tennessee along Interstate 65. Counties

classified as “worrisome” seemed to follow a sickle-shaped pattern from New York and

Pennsylvania south, along the western Virginia border, the western Carolina borders, into

northern Georgia and Alabama. “Hopeful” counties, with above average child poverty but better 

than average change in child poverty, were predominantly located in Tennessee but also appeared 

in Ohio and West Virginia. 
38
39

Child Poverty by Age Group (0-4 and 5-17)

While poverty certainly has negative consequences for the general population, considerable 

research has shown that poverty can be particularly detrimental to the development of very young

children. Poverty rates for children ages 0-4 years were, and continue to be, considerably higher 

than for children ages 5-17 years both nationally and in Appalachia. This gap was even wider for 

Appalachian counties than for the remainder of the U.S., with 27.3 percent of children ages 0-4 in 

poverty, compared to 19.5 percent for children ages 5-17 in 1995. 

Table 3.4: 

Poverty rate for children ages 0-4, Appalachian Counties and U.S. Counties outside of

Appalachia

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Appalachian counties 24.9% 22.8% 28.7% 27.3%

U.S. counties outside of

Appalachia

23.9% 19.9% 27.8% 25.5%

Total 23.9% 20.1% 27.8% 25.7%

Table 3.5:

Poverty rate for children ages 5-17, Appalachian Counties and U.S. Counties outside of 

Appalachia

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Appalachian counties 18.7% 19.2% 21.1% 19.5%

U.S. counties outside of 

Appalachia

17.7% 17.4% 20.4% 18.7%

Total 17.7% 17.5% 20.4% 18.7%

In light of the markedly higher poverty rates in Appalachia for young children (aged 0-4), we 

focus on this age group in the following maps. In 1989, the spatial patterns for young child
40

poverty and total child poverty were similar among Appalachian counties (Figure 3.8). In 1993 

(Figure 3.9) the geographic distribution of total child poverty and young child poverty were

remarkably similar. Despite similarities in the spatial patterns, of child poverty, the actual rates

of young child poverty were significantly higher in 1993 (see Tables 3.4 and 3.5, above). 

Comparing change in young child poverty between 1989 and 1993 (Figure 3.10), with change in

total child poverty over the same period (Figure 3.3), change in young child poverty was very

similar relative to the U.S. average change. Only in Virginia did a recognizably greater number 

of counties experience on average significant increases in young child poverty compared to

overall child poverty.

Figure 3.11 provides the geographic distribution of the 1995 SAIP estimates for young child

poverty. Again, the higher than average (compared to U.S.) poverty counties were concentrated 

in eastern Kentucky and West Virginia. Between 1993 and 1995 different patterns emerged with 

regard to change in young child poverty (Figure 3.12). Compared to change in overall child

poverty (Figure 3.5), a significant cluster of counties in eastern Kentucky, western Virginia, and

in southern West Virginia performed much better than national average. Relatively poor

performance between 1993 and 1995 was observed for Alabama, the western Carolinas,

Pennsylvania, and New York. 

Table 3.6 provides a breakdown of child poverty rates for the three sub-regions of Appalachia. 

Similar to the overall poverty rates for the sub-regions, the Central sub-region continued to

experience the highest child poverty rates within Appalachia. According to the 1995 estimates,

more than one-third of the children who lived in the Central sub-region lived in households with

incomes under the poverty line, with the other three regions ranging from 20.1 percent to 21.6

percent.

Table 3.6: 

Poverty rate for children age 0-17 years, by region within Appalachia

Number of 

counties

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Northern 144 18.6% 19.2% 22.2% 20.3%

Southern 177 18.3% 18.1% 21.3% 20.1%

Central 85 37.6% 32.9% 37.2% 34.7%

Appalachia 406 20.5% 20.1% 23.3% 21.6%
41

aplapl

AppliedPopulation Labor atory

University of Wisconsin - Madison

Young Child Poverty Rate

Relative To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 3.8:

Young Child Poverty (ages 0-4),

ARC Counties, 1989 (Census)

0 100 200 300 Miles
42

aplapl

AppliedPopulation Labor atory

University of Wisconsin - Madison

Young Child Poverty Rate

Relative To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 3.9:

Young Child Poverty (ages 0-4),

ARC Counties, 1993 (SAIPE)

0 100 200 300 Miles
43
44

aplapl

AppliedPopulation Labor atory

University of Wisconsin - Madison

Young Child Poverty Rate

Relative To U.S. Average

Below U.S. average

100% - 150% of U.S. average

150% - 200% of U.S. average

Above 200% of U.S. average

APL-aeh-9/00

Figure 3.11:

Young Child Poverty (ages 0-4),

ARC Counties, 1995 (SAIPE)

0 100 200 300 Miles
45
46
47

Among the Appalachian states, counties within Kentucky had, by far, the highest child poverty

rates in 1989 (see Table 3.7). Child poverty in these Kentucky counties increased through 1993, 

as it did in the majority of Appalachian counties. Georgia had the lowest child poverty rates

among its Appalachian counties in 1989, and maintained this relative position, although they did

experience overall increases over the next several years. 

Table 3.7: 

Poverty rates for children ages 0-17 years, by state within Appalachia

Number of 

counties

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Alabama 37 20.6% 20.6% 23.3% 23.0%

Georgia 37 12.7% 12.1% 16.4% 15.1%

Kentucky 49 41.4% 36.1% 39.8% 37.8%

Maryland 3 18.4% 17.2% 19.3% 18.7%

Mississippi 22 28.0% 28.6% 28.6% 27.1%

New York 14 14.2% 16.4% 21.0% 20.7%

North Carolina 29 16.2% 15.5% 18.2% 18.5%

Ohio 29 24.5% 23.6% 24.8% 21.8%

Pennsylvania 52 16.8% 17.4% 19.8% 18.0%

South Carolina 6 14.1% 14.9% 17.9% 18.0%

Tennessee 50 21.9% 21.0% 25.8% 22.5%

Virginia 23 26.4% 21.5% 25.5% 22.5%

West Virginia 55 26.1% 26.2% 32.6% 30.0%

Appalachia 406 20.5% 20.1% 23.3% 21.6%

Metropolitan status is another county-level characteristic that may influence child poverty rates. 

Table 3.8 indicates that non-metropolitan counties in Appalachia have had, and continue to have, 

significantly higher rates of child poverty than metropolitan counties. While both county types

follow the same general trend between 1989, 1993 and 1995, the particular economic conditions 

that exist in non-metropolitan Appalachia, including high unemployment, industry and job loss,

and lack of adequate infrastructure may contribute to the sustained nature of their higher child

poverty rates.

Table 3.9 provides more specific information regarding county types and child poverty rates.

While in general non-metropolitan counties in Appalachia have higher child poverty rates than

do metropolitan counties, the Urban Continuum code (used earlier in Section II) provides an

even closer correlation with poverty rates. For example, in the 1989 Census, metro-core
48

Table 3.8: 

Poverty rates for children age 0-17 years, by 1993 metropolitan status within Appalachia.

Number of 

counties

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Metropolitan 109 16.9% 17.2% 20.5% 18.9%

Nonmetropolitan 297 25.0% 24.0% 27.0% 25.3%

Appalachia 406 20.5% 20.1% 23.3% 21.6%

counties in Appalachia had a 14.4 percent child poverty rate. The child poverty rate increased 

along the Urban Continuum scale to 26.5 percent for nonmetro, 20,000 urban population, nonadjacent to metro Appalachian counties. The child poverty rate was somewhat lower (23.9

percent) for the next category of counties, but increased again to 30.6 percent, fell to 27.6

percent, and then rose to a 35.5 percent child poverty rate for Non-metro, rural adjacent to metro

Appalachian counties. The 1993 and 1995 SAIP estimates followed this exact pattern of relative 

child poverty rates along the Urban Continuum.
49

Table 3.9: 

Poverty rates for children age 0-17 years, by 1993 Urban Continuum (Beale Code) within 

Appalachia.

1993 Beale Category Number of 

counties

1989 SAIPE 1989 Census 1993 SAIPE 1995 SAIPE

Metro-core 7 14.2% 14.4% 17.2% 15.0%

Metro-fringe 12 16.6% 15.6% 18.7% 16.4%

Metro-medium 59 17.8% 17.8% 21.7% 20.0%

Metro-small 31 17.8% 18.9% 21.9% 21.1%

Non-metro, 20,000 

urban population, 

adjacent to metro

20 19.8% 20.3% 23.2% 21.6%

Non-metro, 20,000 

urban population, nonadjacent to metro

11 20.7% 22.6% 26.5% 24.4%

Non-metro, 2,500-

19,999 urban 

population, adjacent to 

metro

83 21.3% 21.1% 23.9% 22.6%

Non-metro, 2,500-

19,999 urban 

population, nonadjacent to metro

78 29.2% 27.4% 30.6% 28.6%

Non-metro, rural, 

adjacent to metro

40 25.8% 24.9% 27.6% 25.9%

Non-metro, rural nonadjacent to metro

65 39.8% 32.5% 35.5% 34.2%

ARC counties 406 20.5% 20.1% 23.3% 21.6%

Considering the Starting Level of Young Child Poverty and Subsequent Change

We examine the Young Child Relative Poverty Position of ARC counties for the most recent 

period, 1993-1995 in order to provide a more meaningful analysis of change when considering

the starting levels of young child poverty. Table 3.10 shows a cross-tabulation of the 1993

young child poverty rates in Appalachian counties and the change in poverty rates between 1993 

and the 1995 SAIP estimates. The national benchmark for level of young child poverty was 27.9 

percent and the national change in the young child poverty rate over the two years was a

decrease of 3.7 percent. The percent of counties with higher than average young child poverty
50

rates was almost 63 percent. A similar percentage of counties (63.4 percent) had young child 

poverty rates that were either increasing, or decreasing less than the national young child poverty

level. The largest proportion of counties (38.6 percent) fit into the Worst category with a higher 

than average starting level of young child poverty in 1993, and a worse than average change in 

young child poverty between 1993 and 1995. Only 12.3 percent of Appalachian counties were 

considered to be in the Best Position (low starting rates and greater than average declines). 

Compared to the U.S. as a whole (Table 3.11), Appalachia had a similar proportion of its

counties in the Worst position, but significantly fewer in the Best position.

Table 3.10: 

Young Child Poverty Relative Position of Appalachian Counties, 1993-1995

 Level

Change in Young Child Poverty 

Rate Less Than U.S.

(< -3.7%)

Change in Young Child Poverty 

Rate Greater Than U.S.

 (> -3.7%)

Total

Counties Below U.S. 

Young Child Poverty 

Rate in 1993 (< 27.9%)

Best

49

12.3%

Worrisome

99

24.8%

148

37.1%

Counties Above U.S. 

Young Child Poverty 

Rate in 1993 (> 27.9%)

Hopeful

97

24.3%

Worst

154

38.6%

251

62.9%

Total

146

36.6%

253

63.4%

399

100%

Table 3.11: 

Relative Young Child Poverty Position of U.S. Counties, 1993-1995

 Level

Change in Child Poverty Rate 

Less Than U.S.

(< -3.7%)

Change in Child Poverty Rate 

Greater Than U.S.

 (> -3.7%)

Total

Counties Below U.S. 

Child Poverty Rate in 

1993 (< 27.9%)

Best

721

23.0%

Worrisome

1020

32.6%

1741

55.6%

Counties Above U.S. 

Child Poverty Rate in 

1993 (> 27.9%)

Hopeful

382

12.2%

Worst

1008

32.2%

1390

44.4%

Total

1103

35.2%

2028

64.8%

3131

100%

Figure 3.14 provides the spatial distribution of the relative young child poverty position for ARC 

counties between 1993 and 1995. The geographic patterns are very similar to the patterns of

starting position and change for overall child poverty with a few exceptions. The Appalachian 

counties that were categorized as “best” again were not markedly geographically clustered
51

except in the counties adjoining the Atlanta, Georgia, Cincinnati and Columbus, Ohio

metropolitan areas. Notably the best category cluster that appeared in total child poverty among

the westernmost Appalachian counties in Tennessee along Interstate 65 does not appear in young 

child poverty. Counties classified as “worrisome” again seemed to follow a sickle-shaped 

pattern from New York and Pennsylvania south, along the western Virginia border, the western

Carolina borders, into northern Georgia and Alabama. Finally, those counties with high rates of

child poverty in 1993 and worse than average change in the following two-year period were

again clustered in eastern Kentucky, West Virginia, and in parts of Mississippi and Alabama. 

Like for overall child poverty, there were smaller clusters of these worst category counties in

New York, Virginia, and North Carolina, but unlike overall child poverty there was not a cluster 

in Georgia. The pattern of worst counties in Kentucky was quite distinct with a solid line several 

counties wide following the entire western border of Appalachia and then extending into

Tennessee. The geographic distribution of hopeful counties for young children is quite different

than it was for all children. Tennessee is not as dominant in this category and there is a large and 

contiguous cluster of hopeful counties in eastern Kentucky, Western Virginia, and Virginia. 
52
53

SECTION IV

The ARC Distressed County Designation

The Appalachian Regional Commission (ARC) has used the distressed county designation for

almost twenty years to identify counties with the most structurally disadvantaged economies. 

Up to 30 percent of ARC’s Area Development Funds are targeted at distressed counties through 

allocation of ARC grants to distressed counties, requiring only a 20 percent match from the state 

and/or local government, which is lower than the state/local match required from non-distressed 

counties. From 1983, the inception of the distressed counties program, through 1999 the ARC 

has provided $266 million dollars in single-county grants to distressed counties. This sum

constituted 42 percent of such single-county grants awarded across Appalachia (Wood and

Bischak, 2000). 

The ARC has modified the variables and the formulae used to determine distressed status several 

times during the past two decades, adopting its present form in FY 1995. The current criteria for 

distressed status compare the poverty, unemployment, and per capita market income of

Appalachian counties with national averages. Three-year rolling averages are utilized for

unemployment and per capita market income to moderate the effect of annual variation caused

by short-term economic fluctuations. Currently, a county qualifies as distressed if its poverty

rate and its unemployment rate are greater than or equal to 150 percent of the corresponding

national average and its per capita market income is less than or equal to 2/3 of the national

average. A county with a poverty rate of 200 percent or more of the national average need only 

meet the criteria on one of the other two measures in order to be designated distressed. The ARC 

also designates transitional, competitive, and attainment counties, although these categories will

not be addressed in this report. 

Each year the ARC updates the distressed status of counties based on more current information

on unemployment and per capita market income. However, reliable county-level poverty rates 

have, until recently, only been available from the decennial census at the beginning of each

decade. In the years between decennial censuses, poverty rates for individual counties change,

and the distribution of poverty within the region shifts. Using the poverty rates from the most
54

recent census ignores the subsequent changes in poverty conditions as the decade proceeds. 

Post-censal updates of poverty paralleling the updated estimates for unemployment and per

capita market income could improve the distressed county designation. The Census Bureau’s

Small Area Income and Poverty Estimates (abbreviated as SAIPE, which will also be referred to 

as “SAIP estimates” to focus on the numerical estimates themselves rather than the overall

statistical estimates program) program offers a potential solution to this problem. The Census

Bureau’s SAIPE program initially published county-level poverty estimates for 1993 (and 1989

for comparison with 1990 census poverty estimates) with updates scheduled on a biennial basis

during the remainder of the decade. In this section of the report, we incorporate the SAIPE 

post-censal poverty estimates for 1989, 1993, and 1995 into the ARC distressed status

designation. We evaluate the influence of post-censal estimates of poverty on the traditional

distressed county classification, which uses only the estimates of poverty from the most recent

census, during both the 1980s and the early 1990s.

Distressed Counties in 1980 and 1990

To provide a context for the introduction of the SAIPE into the distressed county designation, we 

first examine distressed counties in 1980 and 1990, using the poverty estimates from the

respective censuses (Appendix D Distressed Status Designation Methodology). Of the 399

Appalachian counties, the number designated as distressed increased between 1980 and 1990,

from 71 counties in 1980 to 105 in 1990, nearly a 50 percent increase (Table 4.1a).7 This 

increase reversed a two-decade decline in the number of distressed counties. Between 1960 and 

1980 the number of distressed counties declined from 214 to only 78, according to designations 

made using a slightly modified distress formula, with single year income and unemployment

estimates rather than three-year averages (Wood and Bischak 2000). During the 1970s alone, the 

number of distressed counties declined by more than 50 percent from 161. 

 

77

 The number of distressed counties in 1990 does not correspond to the number of counties officially designated 

distressed by ARC because distress levels were frozen during the 1988-1992 period awaiting the release of 1990 

census poverty data (Wood and Bischak 2000). The distressed designation uses three year averages of 

unemployment and per capita market income. Numbers in Table 4.1a are based on a formula for defining distressed 

counties that incorporates poverty estimates from the last census, not the Census Bureau’s post-censal SAIPE 

estimates.
55

In 1980, Kentucky contained the largest number of distressed counties among Appalachian states 

at 32, with Tennessee a distant second at 16. This represented 65 percent of the ARC counties in 

Kentucky and 32 percent of the Tennessee ARC counties. The already high number of distressed 

counties in Kentucky increased by five, making 75 percent of Kentucky’s ARC counties

distressed. During the 1980s West Virginia experienced an increase of 20 distressed counties or 

nearly triple its 1980 number moving it into second place, with 27 distressed counties, behind

Kentucky. While only 13 percent of West Virginia ARC counties were distressed in 1980, 50 

percent were in 1990. Over 60 percent of the distressed counties in 1990 were located in just 

two states, Kentucky (37) and West Virginia (27). Ohio also had more than 2.5 times as many 

distressed counties in 1990 than in 1980 with 13, or 24 percent of the ARC counties in Ohio. 

Mississippi gained seven distressed counties during the decade, more than doubling the number 

of distressed counties, and moving the percent of ARC counties distressed in that state from 29 

percent to 62 percent. Only Tennessee lost a substantial number of distressed counties between 

1980 and 1990, seven or just under one half of its distressed counties, moving it from the state 

with the second most distressed counties in 1980 to fourth in 1990. 

Table 4.1a:

ARC Distressed Counties by State, 1980 and 1990

ARC 1980 Distressed 1990 Distressed Change

State Counties # % # % # %

Alabama 35 3 8.6 7 20.0 4 133

Georgia 35 1 2.9 0 0.0 -1 -100

Kentucky 49 32 65.3 37 75.5 5 16

Maryland 3 0 0.0 0 0.0 0 0

Mississippi 21 6 28.6 13 61.9 7 117

New York 14 0 0.0 0 0.0 0 0

North Carolina 29 3 10.3 2 6.9 -1 -33

Ohio 29 2 6.9 7 24.1 5 250

Pennsylvania 52 0 0.0 0 0.0 0 0

South Carolina 6 0 0.0 0 0.0 0 0

Tennessee 50 16 32.0 9 18.0 -7 -44

Virginia 21 1 4.8 3 14.3 2 200

West Virginia 55 7 12.7 27 49.1 20 286

TOTAL 399 71 17.8 105 26.3 34 48
56

Given the distribution of distressed counties across states in 1990, it is not surprising that

distressed counties were also geographically clustered (Figure 4.1). A large, contiguous group of

distressed counties encompassed nearly all the Appalachian counties in Kentucky then extended

several counties deep into West Virginia along the boundary of those two states. This cluster

also extended into a line of counties stretching northeast in Ohio and to a lesser degree into a

pocket of four distressed counties in Tennessee and two in North Carolina. A second large

cluster barely separated from the first by a single row of counties located just to its northeast,

was comprised of 20 counties in West Virginia. Principally Mississippi but also Alabama shared 

two smaller agglomerations of distressed counties. In 1990 there were only six isolated

distressed counties that did not at least touch a corner of another distressed county. 

The change in the geographic distribution of distressed counties between 1980 and 1990 not only 

reflected an increase in the number and extent of distressed counties but also a substantial shift 

northward and somewhat eastward in the bulk of distressed counties. This is especially evident 

in the two large 1990 clusters of distressed counties in central Appalachia with the Kentucky

group growing, moving out of Tennessee and into Ohio and West Virginia and the second West 

Virginia group emerging. The cluster of four contiguous distressed counties in West Virginia

that existed in 1980 grew to about 5 times that size in 1990 (Figure 4.2). 

The large cluster centered in Kentucky in 1990 was also considerably larger than in 1980, having 

grown significantly into West Virginia and Ohio. In 1980 a much larger portion of this cluster

was located in Tennessee, extending nearly to its southern border. In 1980, there was also a 

somewhat more continuous line of distressed counties stretched along the Tennessee/North

Carolina border than in 1990. In contrast, the cluster of distressed counties along the southern

tier of Appalachia in Mississippi and Alabama was much smaller in 1980, containing only five

counties, compared to 10 in 1990. The cluster along the northern border of Mississippi,

extending into Alabama did not exist at all in 1980, with only two scattered distressed counties, 

one distressed county in Mississippi and another in Alabama. 

Throughout both periods, 1980 and 1990, 282 counties remained non-distressed, while 59

counties remained distressed (Table 4.1b). Of the 12 counties that transitioned from distressed to 

non-distressed status during the 1980s, the majority of them (seven) did so solely as a result of

changes in poverty. An additional two resulted from joint changes in poverty and unemployment
57

aplapl

Applied Population Laboratory

University of Wisconsin - Madison

Distress Status

Nondistressed

Distressed

0 100 200 300 Miles

APL-aeh-9/00

Figure 4.1:

ARC Distressed Counties, 1990
58

aplapl

Applied Population Laboratory

University of Wisconsin - Madison

Distress Status

Nondistressed

Distressed

Figure 4.2:

ARC Distressed Counties, 1980

APL-aeh-9/00

0 100 200 300 Miles
59

and one other resulted from joint changes in poverty and income. The remaining two transitions 

out of distressed status resulted from changes in unemployment. Therefore, changing relative

poverty levels were a factor in 10 of the 12 transitions out of distressed status during the 1980s. 

Poverty did not contribute quite as greatly to the much larger number of counties (46) that

became distressed in the 1980s. The largest group of counties transitioning into distressed status 

experienced changes in both income and poverty (10), followed by eight counties with changes

in unemployment and income, seven counties with changes in unemployment alone, seven

counties with changes in poverty alone, and six counties with changes in all three indicators of

distress.

Table 4.1b:

ARC Distressed Status Changes by Cause of Change

Number

No Status Change 341

 Non-Distressed 282

 Distressed 59

Distressed to Nondistressed 12

 Unemployment 2

 Poverty and Unemployment 2

 Poverty and Income 1

 Poverty 7

Nondistressed to Distressed 46

 Unemployment 7

 Income 3

 Unemployment and Income 8

 Poverty and Unemployment 5

 Poverty and Income 10

 Poverty, Unemployment, and Income 6

 Poverty 7

The Accuracy of Distressed Status at the End of the 1980s

As noted, a significant problem with the determination of distressed status during the course of a 

decade is the diminishing relevance of the Census poverty rates as the decade progresses. 

Substitution of the SAIP estimates in the determination process may more accurately identify

distressed counties, especially near the end of each decade. This section examines the change in

1990 distressed status when the SAIP estimates are substituted for the census-based estimate of 

poverty during the 1980s. We compare the accuracy of “old” census poverty estimates (1980
60

census) with SAIP estimates (for 1989 poverty), when each is used to predict 1990 distressed 

status as measured by the 1990 census. We calculated four versions of distressed status for 1990 

using four different measures of poverty for calendar year 1989. The per capita market income 

and unemployment figures are the same in all four versions of distressed status. The first

distressed status designation is identical to the 1990 distressed status used in the comparison of

1980 and 1990 above and includes poverty rates for 1989 as measured by 1990 census. The 

second uses poverty rates from the 1980 census, which previously would have been the only

available measure of poverty at the end of a decade prior to the release of data from the new

census. Further, using the SAIPE we calculate two sets of distressed status designations for each 

year. As in the analysis of the 1990s below, we incorporate the actual SAIP point estimate

(which will be referred to as the estimate or the point estimate) in one distressed status

designation and we incorporate the 95 percent confidence interval upper bound SAIP estimate

(which will be referred to as the upper bound or UB in the tables) to create a fourth measure of 

distressed status. Table 4.2 compares the accuracy of the 1980 census and the two SAIPE 

measures in replicating 1990 distressed status as determined by the 1990 census. 

Of the 294 non-distressed counties in Appalachia in 1990 (i.e., as determined by the 1990

Census), both the SAIP point estimate and the 1980 census correctly categorized 281 of those 

counties (Table 4.2). The 1980 census incorrectly classified 12 of those counties as distressed, 

while both the 1980 census and the SAIP point estimate incorrectly categorized one of those

counties. As such, the SAIP point estimate correctly categorized 99.7 percent of the nondistressed counties while the 1980 census did so for 95.6 percent of those counties. The SAIPE 

upper bound incorrectly classified a greater number of counties as distressed than did the other

two measures; a total of 22 counties for an accuracy of 92.5 percent. The upper bound estimate 

would be expected to classify a greater number of nondistressed counties as distressed since it is 

the upper estimate of poverty at a 95 percent confidence level. All three measures correctly

categorized a very high percentage of the non-distressed counties, over 90 percent. 

For the 105 counties that were distressed in 1990, the 1980 census categorized 28 of those

counties as non-distressed. Although, the SAIP point estimate only incorrectly categorized 19 of 

these counties, this was an accuracy level of only 80 percent, while the 1980 census accuracy

was lower at 73.3 percent. The SAIP upper bound distressed categorization was much more

accurate than the other two in categorizing distressed counties with only three counties

incorrectly classified and an accuracy of 97.1 percent.
61

Table 4.2: 

Comparison of 1980 Census and SAIPE in Determining 1990 Distressed Status

Point 

Estimate

Upper Bound 

Estimate

Non-Distressed 294 294

 1980 Census and SAIPE Correct 281 267

 Only 1980 Census Incorrect 12 5

 Only SAIPE Incorrect 0 14

 Both Incorrect 1 8

SAIPE (% Correct) 99.7% 92.5%

1980 Census (% Correct) 95.6% 95.6%

Distressed 105 105

 1980 Census and SAIPE Correct 71 77

 Only 1980 Census Incorrect 13 25

 Only SAIPE Incorrect 6 0

 Both Incorrect 15 3

SAIPE (% Correct) 80.0% 97.1%

1980 Census (% Correct) 73.3% 73.3%

Neither the 1980 census nor the 1989 SAIP point estimate adequately anticipated the overall

expansion in distressed counties in terms of their northward shift (Figure 4.3). A sizeable

portion of the distressed counties in the West Virginia cluster were classified as non-distressed 

by both the 1980 census and the SAIP point estimate. The 1980 census misclassified an

additional five of those counties. Both indicators also largely missed the increase in the number 

of distressed counties in Ohio but this was true of the 1980 census to a greater extent. At the 

other end of that geographic cluster of distressed counties, the 1980 census did not accurately

predict the improving status of counties in Tennessee and counties along the Tennessee/North

Carolina border. Nor did these two indicators accurately anticipate the expansion of distressed 

counties in the border region of Mississippi and Alabama, although in this case the SAIPE

incorrectly categorized more of the counties. However, the SAIPE upper bound does correctly

categorize those distressed counties (Figure 4.4). The upper bound indicator also more

accurately predicted the expansion of distressed counties in Ohio and West Virginia. However, 

it did misclassify a number of non-distressed counties as distressed, although those generally

were not clustered but were scattered throughout Appalachia. 
62
63
64

SECTION V

The Effect of Using Post-Censal Poverty Estimates on Distressed Status during the 

1990s

 

The Census Bureau SAIPE program has produced county-level poverty estimates for 1989, 1993 

and 1995, giving ARC the option of using more recent poverty data to classify counties. The 

post-censal SAIPE produced by the Census Bureau may more accurately reflect current poverty

rates in Appalachian counties. However, the use of post-censal estimates of poverty with

relatively large standard errors must be weighed against use of the outdated but more precise

census-based estimates of poverty. 

In the following section we determine the distressed status of Appalachian counties in 1990,

1994, and 1996 using the poverty levels (for calendar year 1989) measured by the 1990 census. 

We then substitute the 1993 and 1995 SAIPE for the 1990 census poverty estimate in the

distressed county determination, thus determining distressed status according to the SAIPE for

fiscal years 1994 and 1996. In this manner, the number of counties that have been affected by 

economic change in the 1990s can be better evaluated and joint changes in unemployment,

income, and/or poverty can be distinguished from changes in poverty alone. 

Further, using the SAIPE we calculate two sets of distressed status for each year, the first

incorporating the actual SAIP point estimate (which will be referred to as the estimate or the

point estimate) and the second incorporating the 95 percent confidence interval upper bound

SAIP estimate (which will be referred to as the upper bound or UB in the tables). Using the 

upper bound SAIP estimate enables us to evaluate the statistical significance of distressed status 

changes that are due to changes the in poverty indicator. This constitutes a more conservative 

approach to incorporating the SAIPE into the distressed county designation, since only counties

whose poverty is below the distressed threshold to a statistically significant degree are

designated non-distressed. As such, we do not incorporate the SAIPE lower bound estimates 

into the distressed status designation, since the lowest poverty rate within a 95 percent

confidence limit does not hold the same consequences of unjustifiably removing counties from

the distressed designation. 
65

Distressed Status by State, 1990 to 1994

Using the 1993 SAIP estimates, between 1990 and 1994 the number of distressed counties in

Appalachia declined sharply by 38 percent from 105 in 1990 to only 65 in 1994 (Table 5.1).

Kentucky and Mississippi accounted for nearly one half (9 and 10 counties respectively) of the 

decline in the number of distressed counties. This represented a 24 percent decline in the

number of distressed counties in Kentucky but a 77 percent decline for Mississippi. Seven fewer 

West Virginia counties (26 percent) were distressed in 1994, as were six fewer Alabama counties 

(86 percent). 

Table 5.1: 

Distressed Counties by State, 1990 and 1994

ARC 1994 SAIPE 1994 SAIPE Upper Bound

State Counties 1990 Distressed Change % Change Distressed Change % Change

Alabama 35 7 1 -6 -86 1 -6 -86

Georgia 35 0 0 0 0 0 0 0

Kentucky 49 37 28 -9 -24 36 -1 -3

Maryland 3 0 0 0 0 0 0 0

Mississippi 21 13 3 -10 -77 8 -5 -38

New York 14 0 0 0 0 0 0 0

North Carolina 29 2 1 -1 -50 2 0 0

Ohio 29 7 4 -3 -43 5 -2 -29

Pennsylvania 52 0 0 0 0 0 0 0

South Carolina 6 0 0 0 0 0 0 0

Tennessee 50 9 6 -3 -33 8 -1 -11

Virginia 21 3 2 -1 -33 3 0 0

West Virginia 55 27 20 -7 -26 23 -4 -15

TOTAL 399 105 65 -40 -38 86 -19 -18

Substituting the upper bound of the SAIPE, the decline in the number of distressed counties is

reduced by one half with 86 distressed counties as opposed to 65. Kentucky is most affected by 

this substitution, with a decrease of only one county, as opposed to nine using the estimate. The 

number of distressed counties in Mississippi also declines by only five, as opposed to 10 using

the estimate. The number of distressed counties in West Virginia was also affected by the use of 

the upper bound SAIPE with a decrease of only 4. Alabama, the state that experienced the

largest percentage decline in the number of distressed counties, lost the same number of

distressed counties regardless of whether the point or upper bound estimate was used.
66

Distressed Status by State, 1990 to 1996

Using the point estimates to calculate distressed status in 1996 results in a 12 percent increase in 

the number of distressed counties after 1994. The number of distressed counties increased from

65 in 1994 to 73 in 1996, although the number remained 32 percent lower than in 1990 (Table 

5.2). Between 1994 and 1996, the number of distressed counties in Kentucky and Mississippi

increased by three and four respectively, yet remained substantially below the number of

distressed counties in each of those states in 1990. Among Appalachian states, only Ohio lost 

distressed counties during both time periods, with a decrease of three counties between 1990 and 

1994 and a decrease of two counties between 1994 and 1996, a cumulative 71 percent decline. 

Table 5.2: 

Distressed Counties by State, 1990 and 1996

ARC 1996 SAIPE 1996 SAIPE Upper Bound

State Counties 1990 Distressed Change % Change Distressed Change % Change

Alabama 35 7 1 -6 -86 1 -6 -86

Georgia 35 0 0 0 0 0 0 0

Kentucky 49 37 31 -6 -16 39 2 5

Maryland 3 0 0 0 0 0 0 0

Mississippi 21 13 7 -6 -46 8 -5 -38

New York 14 0 0 0 0 0 0 0

North Carolina 29 2 1 -1 -50 2 0 0

Ohio 29 7 2 -5 -71 6 -1 -14

Pennsylvania 52 0 0 0 0 0 0 0

South Carolina 6 0 0 0 0 0 0 0

Tennessee 50 9 8 -1 -11 11 2 22

Virginia 21 3 3 0 0 4 1 33

West Virginia 55 27 20 -7 -26 25 -2 -7

TOTAL 399 105 73 -32 -30 96 -9 -9

Using the upper bound SAIPE in 1996 alters the pattern of change with a decrease of only nine 

counties to 96. The number of distressed counties in Kentucky increases by two, to 39, as does 

the number of distressed counties in Tennessee, while Virginia increases by one. The number of

distressed counties using the upper bound in Alabama and Mississippi remains the same as in

1994 with a loss of six and five respectively compared to 1990. 
67

Causes of Distressed Status Transition, 1990 to 1994

The dramatic decline in the number of distressed counties during the early part of the decade

(1990 to 1994) was due more to overall economic improvement in Appalachia relative to the

U.S. as a whole than by substitution of the SAIPE for the 1990 census poverty estimates (Table 

5.3). Moreover, relative shifts in unemployment played a more important role as an independent 

cause of these transitions out of distressed status than did shifts in poverty. Of the 42 counties 

that transitioned from distressed to non-distressed status between 1990 and 1994, 12 did so as a 

result of change in unemployment alone while an additional eight did so as a result of changes in 

both unemployment and poverty. Another two counties moved from distressed to non-distressed 

Table 5.3: 

Distressed Status Changes by Cause of Change

90-94 90-96 94-96

Estimate UB Estimate UB Estimate UB

No Status Change 355 368 359 368 385 385

 Non-Distressed 292 288 290 283 323 301

 Distressed 63 80 69 85 62 84

Distressed to Non-distressed 42 25 36 21 3 3

 Unemployment 12 9 6 7 1 0

 Income 2 4 1 5 2 1

 Unemployment and Income 0 1 0 4 0 0

 Poverty and Unemployment 8 2 5 1 0 0

 Poverty and Income 5 2 3 0 0 0

 Poverty, Unemployment, and Inc. 5 4 6 2 0 0

 Poverty 10 3 15 2 0 2

Non-distressed to Distressed 2 6 4 10 11 11

 Unemployment 2 2 3 4 9 6

 Income 0 0 1 1 2 2

 Poverty and Income 0 0 0 0 0 1

 Poverty 0 4 0 5 0 2

as a result of change in per capita income, while five counties did so as a result of change in both 

poverty and income. Combined changes in unemployment, income, and poverty resulted in five 

counties leaving distressed status. Changes in poverty in combination with changes in

unemployment or income or both accounted for 2/3rds (28 counties) of the transition from

distressed to non-distressed status between 1990 and 1994. Less than 1/4th of the total transitions 

(10) can be solely attributed to the substitution of the SAIP estimate. Two counties moved into 

the distressed category as a result of change in unemployment.
68

Substituting the upper bound SAIPE further reduces the influence of changes in poverty on

distressed status, with only three counties moving to non-distressed status solely due to poverty

and 11 doing so as a result of changes in poverty combined with changes in one or both of the 

other indicators. Using the upper bound, four counties become distressed between 1990 and

1994. 

Counties that were designated as distressed in both 1990 and 1994 were largely clustered in

eastern Kentucky and in central West Virginia. With regard to the causes of transitions from

distressed status to non-distressed, no clear cut geographic patterns emerge. That said, eight of

the 12 counties in which change in unemployment alone caused a status change, were located on

the perimeter or just inside the eastern Kentucky distressed cluster (Figure 5.1). The remaining

four counties were located in Mississippi, three contiguous and one on the northern border. 

Between 1990 and 1994 the five counties that transitioned from distressed to non-distressed due 

to changes in poverty and unemployment combined were located in three states, but were all

situated along those state’s borders. Importantly, the 10 counties where changes in poverty alone 

accounted for status shifts are located outside the major clusters of distressed counties in

Kentucky and West Virginia.
69
70

Causes of Distressed Status Transition, 1990 to 1996

Changes in distressed status between 1990 and 1996 follow a pattern similar to the changes

occurring between 1990 and 1994, although the independent role of change in unemployment in 

transitions out of distressed status was markedly diminished, accounting for six of the 36

counties leaving distressed status (Table 5.3). Joint changes in all three of the indicators

accounted for six of the transitions out of distressed status, five were due to changes in poverty 

and unemployment, and six resulted from changes in poverty and income. As a singular factor, 

change in poverty was responsible for a much larger portion (15 counties or 40 percent) of

transitions out of distressed status between 1990 and 1996 than between 1990 and 1994. As a 

result of changes in unemployment, three counties that were not distressed in 1990 became

distressed in 1996. During this interval one county became distressed as a result of income

changes. 

Compared to using the 1996 SAIPE estimate, the substitution of the 1996 SAIPE upper bound 

has an even greater effect on designations than it did in 1994. The independent effect of poverty 

on distressed status is virtually eliminated, accounting for transition out of distressed status for

only two counties as opposed to 15. Status changes due to the joint changes of poverty and one 

or both of the other indicators also diminish considerably. Using the upper bound, only 21

counties overall would move out of distressed status compared to 36 using the SAIP point

estimates. 

Referring again to point estimate designations, counties that were designated as distressed in

both 1990 and 1996 remained clustered in eastern Kentucky and central West Virginia, although 

Mississippi had more than half of its distressed counties in 1990 remain distressed in 1996 (see 

Figure 5.2). This was a different pattern than was observed between 1990 and 1994. With

regard to the causes of transitions from distressed to non-distressed status, changes in poverty

alone accounted for this shift in several counties in southern Ohio as well as a few counties

surrounding the eastern Kentucky distressed cluster. Joint changes in poverty, unemployment 
71
72

and income were responsible for five of the six previously distressed counties in Alabama

transitioning to non-distressed status. Otherwise, there did not appear to be any other discernible 

geographical patterns of causes of county status change. 

Causes of Distressed Status Transition, 1994 to 1996

While the transition from the 1990 census based poverty estimates to the SAIP estimates is

important for understanding the implications of changes in poverty for the distressed county

designation, comparison of the two SAIPE years is also important (Table 5.3). Not surprisingly, 

a larger number of counties maintain the same status between 1994 and 1996 (385 compared to 

355 between 1990 and 1994 and 359 between 1990 and 1996). Between 1994 and 1996, only 

three counties transitioned from distressed to non-distressed; one of these was solely a result of

improving unemployment and the other two were a result of improving income and poverty. 

Compared to the transitions from the 1990 census poverty estimates, a large number of counties 

became distressed during the 1994 to 1996 period. Nearly all of those (9 of 11) were due to 

increasing unemployment, while one was due to change in per capita market income. None of

the status changes in the 1994 to 1996 period were due solely to changes in poverty. However, 

substituting the upper bound SAIPE, poverty was solely accountable for two counties moving

out of distressed status and for two counties moving into distressed status.

 

Counties Distressed Due to Poverty at 200 Percent of National Average and Above 

Counties with a poverty rate of 200 percent of the national average or higher that meet the

criteria for distressed status on one of the other two indicators, unemployment or per capita

market income, are designated distressed. These counties, which will be referred to as 200

percent poverty distressed counties, may be disproportionately affected by the substitution of the 

SAIP estimates for the decennial census poverty rates, due to the higher level of poverty

necessary to maintain their distressed status. Although it does not have direct implications for 

the use of the SAIPE in the distressed designation, in this section we examine 200 percent

poverty distressed counties to assess the effect using the SAIPE estimates would have on their

status. In 1990, there were 13 such counties, 12 of which were distressed due to meeting the per 

capita market income criteria, and one due to meeting the unemployment criteria (Table 5.4). In

the table, counties distressed on all 3 indicators are designated by PUI, while those distressed on 

two indicators are designated by P*U or P*I.
73

In 1994, six of these counties (Athens, Ohio; Lincoln, Casey, and Rowan, Kentucky; Oktibbeha, 

Mississippi, and Boone, West Virginia) transitioned out of distressed status due to changes in

poverty indicated by the SAIP estimates. One of these counties would not have been designated 

distressed in 1994 regardless of the use of SAIP estimates (Monroe, Kentucky) since its per

capita market income also exceeded the distressed threshold in 1994. Substituting the 1994

upper bound SAIP estimates preserves the distressed status of three of the counties (Casey, and 

Rowan, Kentucky; and Oktibbeha, Mississippi). 

Table 5.4: 

200 Percent Poverty Distressed Counties, 1990, 1994, and 1996

1994 1996

County State 1990 Estimate Upper Bound Estimate Upper Bound

Monroe Kentucky P* I P P P P*

Athens Ohio P* I P I P I I P I

Lincoln Kentucky P* I P I P I P I P*U

Casey Kentucky P* I P I P* I P I P*U

Rowan Kentucky P* I P I P* I P I P*U

Oktibbeha Mississippi P* I P I P* I P I P*U

Owsley Kentucky P* I P* I P* I P*U P*U

Whitley Kentucky P* I P* I P* I P*U P*U

Hancock Tennessee P* I P* I P* I P*U P*U

Clinton Kentucky P* I P* I P* I PUI PUI

Cumberland Kentucky P* I P* I P* I PUI PUI

Knox Kentucky P* I P* I P* I PUI PUI

Boone West Virginia P*U PU P*U PU P*U

Leslie Kentucky PUI P* I P* I P*U P*U

Wayne Kentucky PUI P* I P* I P*U P*U

Bell Kentucky PUI P* I P* I PUI PUI

Clay Kentucky PUI P* I P* I PUI PUI

McCreary Kentucky PUI P* I P* I PUI PUI

Noxubee Mississippi PUI P* I P* I PUI PUI

Mingo West Virginia PUI P*U P*U PUI PUI

Jackson Kentucky PUI PUI PUI P*U P*U

Lee Kentucky PUI PUI PUI P*U P*U

Wolfe Kentucky PUI PUI PUI P*U P*U

P = County poverty rate at or above 150% of national average

P* = County poverty rate at or above 200% of national average

U = County unemployment rate at or above 150% of national average

I = County per capita market income at or below 2/3 of national average

With regard to the geographic distribution of the 200 percent poverty distressed counties, during

the 1990 to 1994 period, both the stability and transition of this status occurred principally in

southern Kentucky, proximate to the Tennessee border (Figure 5.3). Near this border, four

counties were categorized as 200 percent poverty distressed in both 1990 and 1994 (dark gray), 

while another five changed to 200 percent poverty distressed counties (black) during the period. 

The counties that changed from 200 percent poverty distressed counties to non-distressed 

(vertical stripes) were scattered throughout the Appalachian region.
74

In 1990 there were seven counties that met the criteria for distressed status on all three indicators 

but were distressed by virtue of meeting the distressed criteria on two of the indicators by 1996. 

Six of these counties, (Leslie, Wayne, Bell, Clay, and McCreary, Kentucky; and Noxubee,

Mississippi), experienced changes in unemployment but remained distressed due to a poverty

rate of 200 percent of the national average or higher and a per capita market income of 2/3 or 

less of the national average. The sixth county (Noxubee, Mississippi) experienced changes in

income but remained distressed. Since the poverty rates for these counties in the SAIP point

estimates were at or above 200 percent of the national average, substituting the upper bound

estimates would not affect their status. All six of the counties that were 200 percent poverty

distressed counties in 1990 and remained so in 1994, which were all located in Kentucky, were 

also designated distressed in 1996. However, the indicators responsible for their distressed

categorizations changed. Three of the counties (Owsley, Whitley, and Hancock, Kentucky)

remained 200 percent poverty distressed categories but in 1990 and 1994 poverty and per capita 

market income exceeded the distressed threshold while in 1996 unemployment exceeded the

threshold and income did not. The other three counties (Clinton, Cumberland, and Knox,

Kentucky) became distressed on all three indicators. The seven 1990 200 percent poverty

distressed counties that were not designated distressed in 1994, remained non-distressed in 1996 

and the poverty rate for Athens County, Ohio fell below 150 percent of the national average. 

Substituting the upper bound estimates, five of those counties would again remain distressed. 

However, whereas four of them (Lincoln, Casey, Rowan, and Oktibbeha, Mississippi) were

distressed due to their per capita market income levels in 1990 and 1994, they are distressed due 

to their unemployment rates in 1996. The other county (Boone, West Virginia) would remain

distressed due to its poverty and unemployment rates. 

As would be expected from the 1990 to 1996 period, many of the 200 percent poverty distressed 

counties were concentrated in southern Kentucky (Figure 5.4). All three of the counties that

moved from two indicator distressed to three indicator distressed between 1990 and 1996 were 

located in this area. Whereas during the 1990 to 1994 period five counties along the 

Kentucky/Tennessee border moved from three category distressed to two category distressed in

1996 all but two of those counties reverted to three category distressed. Unlike in 1994, a small 

cluster of three more northerly Kentucky counties (Jackson, Lee and Wolfe) transitioned from

three indicator to two indicator distressed.
75
76
77

SECTION VI

Conclusions and Recommendations 

Principally, we have used two analyses to evaluate the viability of the SAIP estimates for the

ARC designation of distressed counties. We first evaluate the accuracy of the distressed status 

designation at the end of a decade, comparing the 1980 census with the 1989 SAIPE (using the 

1990 census as the standard of accuracy). Then we examine the causes of status transitions that 

would occur in the early 1990s incorporating the SAIPE into the distressed county designation. 

Incorporating the SAIPE into the ARC distressed county designation formula is complicated by

the threshold rather than continuous designation. The extent of error in the SAIP estimates, or 

any other estimates, cannot be known precisely. In a continuous funding allocation formula,

error in the estimates might increase or reduce a county’s funding, but in the case of a threshold 

formula the error might completely eliminate funding for a county (See National Research

Council, 2000). Counties that meet the thresholds on the distress indicators are eligible for

distress funding while those that do not are not eligible for the funding. Therefore, error in the 

estimates could result in a county unjustifiably being denied distressed status funding

completely. These consequences must be considered carefully in assessing the impact of the

SAIPE on the ARC distressed county designation. 

The 1980s were an anomalous decade for distressed status in Appalachia and nationally with a 

significant increase in the number of distressed counties following two decades of decline. It is 

noteworthy that the distress trends for the U.S. as a whole were the same as those for Appalachia, 

with distress rising in the 1980s. So it was not that Appalachia was different, but that the decade 

was a break in the secular decline of distress nationally and regionally. This somewhat

undermines the usefulness of comparing the accuracy of the SAIPE and the 1980 census in

determining distressed status at the end of the decade. The SAIPE and decade old census results 

might not behave in the same manner during a more typical decade in which the number of

distressed counties declines. With these caveats, the results from the 1980s demonstrate that as a 

decade progresses, the SAIP point estimates more accurately predict the status of both distressed 

and non-distressed counties than the poverty estimates from the previous census. 

Both the SAIPE and the 1980 census categorized 1990 non-distressed counties very well (99.7 
78

percent and 92.5 percent respectively), but they both largely failed to capture the increase in the 

number of distressed counties. The SAIPE incorrectly categorized one in five of the distressed 

counties and the 1980 census incorrectly categorized one in four. The SAIPE upper bound

estimate with its higher estimates of poverty than the point estimate did very well in categorizing 

distressed counties with 97.1 percent accuracy. This would be expected in this decade of

relatively increasing poverty in Appalachia. The upper bound performed worse than the other

two measures in categorizing non-distressed counties but still correctly categorized 92.5 percent 

of them. Based on the available evidence, we would conclude that the SAIPE upper bound

estimates most accurately categorize counties into the distressed status. However, this is a result 

of the dramatic and unprecedented shifts in poverty during the decade in Appalachia and may not 

hold true in future decades.

Although there will not be an independent confirmation of the accuracy of the SAIPE for

determining distressed status prior to the release of the 2000 census poverty rates, the impact of 

the SAIPE on distressed status can be determined for 1994 and 1996, using the SAIP poverty

estimates for 1993 and 1995. Using the SAIP estimates in assigning distressed status to counties 

during the 1990s did not independently account for the majority of status transitions. While it

did independently account for between approximately 24 and 40 percent of the transitions

(depending on the end year of the time period, 1994 or 1996), its greater utility was demonstrated 

in combination with the other indicators. To a considerable extent, changes in poverty follow the 

general patterns of economic change measured by unemployment and income. Of the three

distress indicators, unemployment had the largest independent effect on change in distressed 

status between 1990 and 1994 (accountable for 12 of 42 transitions out of distressed status and 

both the transitions into distressed status), although poverty affected distressed status

independently more frequently between 1990 and 1996 (accounting for 15 of 36 transitions out 

of distressed status). Income independently affected distressed status change for only a small

number of counties. Therefore, during the early 1990s the ARC distressed county designation

was affected more by independent changes in unemployment than by the substitution of the

SAIPE for census-based poverty estimates. It should be noted that for the small number of

counties moving from distressed to non-distressed status during the 1980s (12), changes in

poverty alone accounted for nearly three quarters of them. 
79

Substituting the SAIP upper bound poverty estimates into the distressed status designation

formula, further reduces the independent effect of poverty on distress. During the 1990 to 1994 

period, the number of transitions out of distressed status solely due to changes in poverty would 

be reduced from 10 to 3 with a reduction in the total number of transitions out of distressed

decreasing from 42 to 25. Likewise, during the 1990 to 1994 period, the number of transitions 

out of distressed status solely due to changes in poverty would be reduced from 15 to just 2 with 

a reduction in the total number of transitions out of distressed decreasing from 36 to 21. This 

substitution would also result in four net additional counties moving into the distressed

designation in the 1990 to 1994 period and six net additional counties in the 1990 to 1996 period. 

In sum, use of the upper bound estimate significantly reduces the independent effect of poverty

on distressed status during the early 1990s. 

Overall, the analysis of the 1990s indicates that the number of distressed counties has declined in

Appalachia during the decade. The Small Area Income and Poverty Estimates indicate a decline 

in poverty in Appalachia relative to the U.S. as a whole, which reflects a concomitant relative

decline in unemployment and a relative increase in per capita market income. Determination of

distressed status using the 2000 Census of Population and Housing poverty rates should confirm

this decline. 

The distressed status accuracy results from the end of the 1980s suggest that the SAIPE would 

provide a better determinant of distressed status than the poverty estimates derived from a decade 

old census. The magnitude and causes of distressed status transitions in the first half of the

1990s indicate that using the SAIP estimates would alter the counties that would be designated

distressed by the ARC but not to a radical degree. However, both of these analyses demonstrate 

that a simple substitution of the SAIP point estimates for census poverty estimates may

unjustifiably deny some counties distressed status recognition. As an antidote to this situation it

might be more defensible to combine the SAIP point estimate and the SAIP upper bound

estimate in the future determination of distressed status. This would accomplish the objective of

utilizing more current estimates of poverty while reducing the negative consequences of utilizing

an estimate of poverty with greater statistical variation than decennial census derived estimates. 

In effect, use of both the point and upper bound estimates would serve as a statistical holdharmless provision under which counties would not lose their distressed designation unless their
80

poverty rate fell to a level below the distressed threshold with 95 percent confidence. More

specifically, the SAIP point estimate initially could be substituted into the distressed designation. 

Then for counties designated as distressed at the beginning of the decade according to the census 

but moving out of distressed status later in the decade solely due to change in poverty according

to the SAIPE (that is without concomitant changes in unemployment and/or per capita market

income), the SAIP upper bound estimates could be substituted into the distressed designation. In 

this way, only counties for which the magnitude of the change in poverty responsible for

removing their distressed status designation was statistically significant in the SAIPE would be

negatively affected by the use of the SAIP estimates. This solution still would not benefit nondistressed counties with actual relative changes in poverty of a sufficient magnitude to move

them into distressed status, yet undetected by the SAIPE. However, that is a problem that could 

only be addressed in hindsight, as in the analysis of distressed status at the end of the 1980s in

this report. 

During the next decade, the accuracy of the SAIPE program should improve significantly as new

sources of income and poverty data, especially the American Community Survey (ACS), become 

available. When fully implemented in 2003, the ACS sample will include approximately

250,000 households each month compared to approximately 57,000 in the March CPS. The

sampling design will also select households in each county across the country, unlike the CPS. 

The ACS will thus sample approximately three million households annually and 15 million

households over a five-year period. Poverty estimates for small areas will be derived by taking

multiple-year averages from the ACS, up to five years for the least populous counties. However, 

the ACS slightly modifies the measure of poverty with questions asking about income during the 

12 months preceding the interview, rather than the preceding calendar year as in the census and 

the CPS. The ACS may provide an additional predictor variable in the SAIPE model or a

substitute for the current CPS derived dependent variable. Continued funding is a critical issue

for incorporating the ACS into the SAIPE program since insufficient funding would likely

necessitate reducing the sample size and introducing other discontinuities (NRC 2000). 

However, the anticipated increased accuracy of the SAIPE will make them an increasingly viable 

option for the Appalachian Regional Commission’s designation of distressed counties.
81

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D.C.: National Academy Press.

National Research Council, 1998. Small-Area Estimates of School-Age Children in Poverty: 

Interim Report 2: Evaluation of Revised 1993 County Estimates for Title I Allocations. 

Washington, D.C.: National Academy Press.

National Research Council, 1999. Small-Area Estimates of School-Age Children in Poverty: 

Interim Report 3: Evaluation of 1995 County and School District Estimates for Title I 

Allocations. Washington, D.C.: National Academy Press.

National Research Council, 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 

and Beyond. Washington, D.C.: National Academy Press.

President’s Appalachian Regional Commission. 1964. Appalachia: A Report by the President’s 

Appalachian Regional Commission. Washington, DC: US Government Printing Office.

Raitz, Karl B., and Richard Ulack. 1984. Appalachia: A Regional Geography. Boulder, CO: 

Westview Press.

U.S. Census Bureau. 1982. Census of Population and Housing, 1980: Summary Tape File 3

[machine-readable data files]. Washington: The Bureau [Producer and Distributor].
83

U.S. Census Bureau. 1992. Census of Population and Housing, 1990: Summary Tape File 3

[machine-readable data files]. Washington: The Bureau [Producer and Distributor].

U.S. Census Bureau. 1999a. Small Area Income and Poverty Estimates: Intercensal Estimates 

for States, Counties, and School Districts. Washington, D.C.: U.S. Census Bureau. 

Website: www.census.gov/hhes/saipe/techdoc. 

U.S. Census Bureau. 1999b. USA Counties 1998 [CD-ROM]. Washington: The Bureau 

[Producer and Distributor].

U.S. Bureau of Economic Analysis. 2000. Regional Economic Information System 1969-98. 

[CD-ROM]. Washington: The Bureau [Producer and Distributor].

Vogt, W. Paul. 1993. Dictionary of Statistics and Methodology : A Nontechnical Guide for the 

Social Sciences. Newbury Park, CA: Sage Publications.

Weller, Jack E. 1965. Yesterday’s People: Life in Contemporary Appalachia. Lexington, 

Kentucky: University of Kentucky Press.

Wood, Lawrence E., and Gregory A. Bischak. 2000. Progress and Challenges in Reducing 

Economic Distress in Appalachia: An analysis of National and Regional Trends Since 1960. 

Washington: Appalachian Regional Comission. 
84

APPENDIX A 

Small Area Income and Poverty Estimate Program Methodology 

The data analyzed in this report are based in part on estimates of people in poverty prepared by

the U.S. Census Bureau as part of its Small Area Income and Poverty Estimates (SAIPE)

program. Portions of these estimates have been the subject of considerable analytical scrutiny due 

to the fact that federal legislation calling for estimates of the numbers of related children ages 5-

17 living in poverty also called on the National Research Council (NRC) of the National

Academy of Sciences to establish a panel of experts to study the estimates and to make a

recommendation to the Secretary of the Department of Education regarding their statistical

adequacy for allocating to federal funds to school districts under Title I of the Elementary and

Secondary Education Act. Findings by the NRC Panel on Estimates of Poverty for Small

Geographic Areas have, to date, been reported in three published interim reports (National

Research Council, 1997, 1998, 1999).

With respect to the Census Bureau’s estimates for counties of children ages 5-17 in families with 

incomes below the poverty level, the NRC panel, on the basis of its own studies as well as Census 

Bureau evaluations of its estimation model and reasonable alternative models, issued a carefully

and narrowly worded recommendation: "[T]hat the Census Bureau’s revised 1993 county

estimates of poor school-age children be used in the Title I allocations for the 1998-1999 school 

year" (National Research Council, 1998:3).

Three things must be noted with respect to this recommendation:

First, the scope of the recommendation pertains exclusively to the use of the Census Bureau’s

estimates of poor school-age (5-17) children for making allocations to counties under the Title I 

program. The recommendation is based on the panel’s conclusion that use of the revised modelbased estimates for 1993 is preferable to using outdated 1990 Census-based estimates for such

allocations, and, in particular, the panel withholds any mention of the potential utility of these

numbers for other uses. Indeed, the Census Bureau’s own technical documentation for the SAIPE

program (U.S. Census Bureau, 1999a) specifically cautions the user who wishes, for example, to 
85

compare census-based estimates of poverty with inter-censal model-based estimates of poverty, or 

inter-censal estimates for two different years, exactly what we attempt to do in the present report.

Second, the recommendation pertains only to the Census Bureau’s estimates of poor school-age 

(5-17) children. At the present time, the Bureau’s SAIPE program is producing estimates of the

number of poor and poverty rates for age groups 0-17, 5-17 and all ages. While the full extent of 

the Census Bureau’s examination of the models for age groups 0-17 and all ages is unknown,

some evaluation data are available on the Census Bureau’s homepage (U.S. Census Bureau,

1999a). It is important to note, however, that the models for estimates of poverty of persons 0-17 

and all ages were not examined by the NRC panel. It is the estimates of total poverty that are the 

focus of the present report.

Third, even the estimates of poor school-age (5-17) children suffer from errors that often are quite 

large. Based on the NRC panel studies (see, in particular, National Research Council, 1998), we 

know that the revised 1993 estimates of poor school-age children are based on a model which

suffers a small bias in the estimates with respect to county size and proportion of Hispanic

residents. Moreover, the model shows evidence of "variance heterogeneity with respect to both

CPS sample size and poverty rate" (p. 41). Both are observations that prompted the panel to

suggest further research into alternative model specifications. Indeed, while the NRC panel

concluded that the Census Bureau’s estimation model performed as well as, or better than,

alternative models that were tested, the panel recommended considerable further model

experimentation and testing. Data users who have examined the county estimates have noted, as

do we, that most of the estimates have uncomfortably wide confidence intervals.

Finally, the NRC panel has drawn attention to the fact that census-based estimates of poverty and 

CPS-based model estimates of poverty have systematic differences that arise from differences in

data collection procedures between the decennial census and the CPS (see, in particular, National

Research Council, 1997:Appendix B). There is some evidence that when compared to the 1990 

Census, CPS-based poverty estimates of the number and proportion of school-age children are 

higher. This fact, when considered alongside the additional fact that census-based estimates of

poverty themselves are based on a sample of the population (and thereby suffer from sampling
86

error), adds further complexity to the task of gauging the trends in poverty among counties

between 1990 and any subsequent year -- a task we herein undertake.
87

APPENDIX B

Economic Research Service Economic and Policy Typology Definitions

Farming dependent counties: Farming contributed a weighted annual average of 20 percent of

more of total labor and proprietor income over the three years from 1987 to 1989.

Mining dependent counties: Mining contributed a weighted annual average of 15 percent or

more of total labor and proprietor income over the three years from 1987 to 1989.

Manufacturing dependent counties: Manufacturing contributed a weighted annual average of

30 percent or more of total labor and proprietor income over the 3 years from 1987 to 1989.

Government dependent counties: Government contributed a weighted annual average of 25

percent of more of total labor and proprietor income over the 3 years from 1987 to 1989.

Services dependent counties: Service activities (private and personal services, agricultural

services, wholesale and retail trade, finance and insurance, transportation and public utilities)

contributed a weighted annual average of 50 percent of more of total labor and proprietor income 

over the 3 years form 1987 to 1989.

Non-specialized counties: Counties not classified as a specialized economic type over the 3 years 

from 1987 to 1989.

Retirement destination counties: The population aged 60 years and over in 1990 increased by

15 percent of more during 1980-90 through in-migration of people.

Federal lands counties: Federally owned lands made up 30 percent of more of a county’s land 

area in the year 1987.

Commuting counties: Workers aged 16 years and over commuting to jobs outside their county of 

residence were 40 percent or more of all the county’s workers in 1990.
88

Persistent poverty counties: Persons with poverty-level income in the preceding year were 20 

percent of more of total population in each of 4 years: 1960, 1970, 1980 and 1990.

Transfer dependent counties: Income from transfer payments (Federal, state and local) 

contributed a weighted annual average of 25 percent or more of total personal income over the 3 

years from 1987 to 1989.
89

APPENDIX C

Appalachian Poverty Measures

Table C.1: 

Appalachian Poverty by State

1979 

Census

1989 

estimates

1989 

Census

1993 

estimates

1995 

estimates

Alabama 16.9% 15.6% 16.1% 16.8% 15.5%

Georgia 12.5% 9.8% 10.2% 11.5% 10.5%

Kentucky 26.0% 27.0% 29.0% 28.5% 26.9%

Maryland 11.9% 12.0% 12.5% 13.2% 12.9%

Mississippi 22.3% 20.4% 22.6% 21.1% 18.7%

New York 12.0% 10.6% 12.9% 14.3% 14.1%

North Carolina 13.8% 11.9% 12.4% 13.1% 12.4%

Ohio 12.6% 16.0% 17.4% 16.8% 14.4%

Pennsylvania 10.0% 11.2% 12.5% 13.3% 11.9%

South Carolina 12.6% 10.9% 11.6% 12.6% 11.6%

Tennessee 16.6% 15.6% 16.1% 17.8% 14.9%

Virginia 15.6% 17.5% 17.9% 17.6% 16.5%

West Virginia 15.0% 17.2% 19.7% 21.7% 19.9%

ARC counties 14.1% 14.1% 15.3% 16.1% 14.6%

Note: Poverty rates by state within Appalachia only include counties designated as Appalachian.
90

Table C.2: 

Poverty Rates by Developmental Districts for Appalachia.

1979 

Census

1989 

Census

1989 

estimates

1993 

estimates

1995 

estimates

ALABAMA

 Northwest Alabama (1a) 15.5% 14.9% 16.7% 15.6% 14.9%

 North Central Alabama (1b) 16.7% 13.5% 14.4% 14.5% 13.4%

 Top of Alabama (1c) 15.6% 12.8% 13.6% 14.2% 13.3%

 West Alabama (1d) 22.4% 20.1% 21.9% 20.6% 19.7%

 Birmingham Regional (1e) 15.5% 15.9% 15.3% 17.0% 14.7%

 East Alabama (1f) 18.0% 16.2% 17.2% 18.5% 18.0%

 Central Alabama (1h) 18.2% 14.6% 14.5% 15.9% 13.7%

 South Central Alabama (1i) 33.0% 34.1% 34.5% 35.3% 34.4%

GEORGIA

 Coosa Valley (2a) 13.2% 12.4% 12.5% 14.0% 14.2%

 Georgia Mountains (2b) 14.4% 11.9% 12.2% 13.4% 12.1%

 Chattahoochee-Flint (2c) 15.8% 13.4% 14.9% 13.4% 12.1%

 Atlanta Regional (2d) 7.0% 4.5% 4.7% 6.6% 5.8%

 Northwest Georgia (2e) 16.5% 14.0% 15.7% 16.7% 15.2%

 North Georgia (2f) 14.9% 12.3% 12.5% 13.8% 13.4%

KENTUCKY

 Buffalo Trace (3a) 27.8% 25.9% 28.1% 26.5% 25.1%

 FIVCO (3b) 18.2% 19.1% 21.8% 22.5% 21.5%

 Bluegrass Area (3c) 22.2% 20.0% 22.3% 21.8% 19.7%

 Gateway Area (3d) 26.2% 26.0% 28.5% 28.0% 26.4%

 Big Sandy Area (3e) 22.4% 27.1% 29.5% 29.7% 28.5%

 Lake Cumberland (3f) 30.1% 28.0% 29.1% 27.9% 26.2%

 Cumberland Valley (3h) 30.2% 32.3% 33.5% 32.7% 31.0%

 Kentucky River (3i) 30.5% 33.6% 36.3% 35.3% 33.6%

 Barren River (3j) 29.1% 23.6% 26.9% 24.3% 23.7%

MARYLAND

 Tri-County W. Maryland (4a) 11.9% 12.0% 12.5% 13.2% 12.9%

MISSISSIPPI

 Northeast Mississippi (5a) 21.7% 21.5% 23.8% 21.4% 18.8%

 Three Rivers (5b) 18.8% 15.4% 17.8% 16.9% 14.9%

 Golden Triangle (5c) 25.6% 24.6% 26.6% 25.2% 22.6%

 East Central (5d) 37.2% 33.0% 35.1% 29.8% 24.1%

 North Central (5e) 24.6% 24.4% 26.4% 26.1% 23.9%

NEW YORK

 Southern Tier West (6a) 12.5% 13.3% 14.0% 17.0% 16.0%

 Southern Tier Central (6b) 11.5% 10.7% 12.4% 14.3% 13.8%

 Southern Tier East (6c) 12.0% 9.2% 12.5% 13.1% 13.4%

NORTH CAROLINA

 Southwestern North Carolina (7a) 18.8% 15.7% 17.2% 16.8% 16.4%

 Land of Sky (7b) 13.6% 12.3% 11.9% 13.1% 12.3%

 Isothermal (7c) 13.0% 10.7% 11.6% 12.5% 12.1%

 Region D (7d) 18.4% 15.1% 16.8% 15.3% 14.8%
91

1979 

Census

1989 

Census

1989 

estimates

1993 

estimates

1995 

estimates

 Western Piedmont (7e) 10.0% 9.7% 10.3% 11.2% 10.9%

 Northwest Piedmont (7i) 12.1% 10.4% 10.6% 11.8% 10.7%

OHIO

 Ohio Valley (8a) 14.2% 17.1% 18.4% 17.2% 14.7%

 Buckeye Hills-Hocking (8b) 14.3% 17.5% 20.3% 18.6% 16.1%

 Ohio Mid-Eastern (8c) 10.9% 14.1% 14.6% 15.2% 13.0%

PENNSYLVANIA

 Northwest Pennsylvania (9a) 9.9% 12.3% 13.6% 14.8% 13.6%

 North Central Pennsylvania (9b) 10.1% 11.7% 13.4% 14.2% 12.6%

 Northern Tier (9c) 13.5% 11.9% 13.1% 14.0% 12.9%

 ECD of Northeastern Pennsylvania 

(9d)

9.9% 9.7% 10.4% 11.7% 10.2%

 Southwestern Pennsylvania (9e) 9.3% 11.4% 12.5% 13.4% 11.8%

 Southern Alleghenies (9f) 11.3% 12.7% 13.9% 14.7% 13.6%

 SEDA (9g) 11.9% 10.0% 12.5% 12.1% 11.1%

SOUTH CAROLINA

 South Carolina Appalachian (10a) 12.6% 10.9% 11.6% 12.6% 11.6%

TENNESSEE

 Upper Cumberland (11a) 20.2% 16.9% 18.6% 19.0% 16.2%

 East Tennessee (11b) 16.8% 15.7% 16.0% 17.4% 14.6%

 First Tennessee (11c) 16.1% 15.4% 16.5% 17.9% 15.3%

 South Central Tennessee (11d) 15.3% 13.8% 14.8% 16.1% 13.6%

 Southeast Tennessee (11e) 15.2% 15.1% 15.0% 18.0% 14.6%

VIRGINIA

 LENOWISCO (12a) 20.1% 25.7% 23.4% 24.2% 22.7%

 Cumberland Plateau (12b) 16.1% 21.6% 21.5% 21.5% 21.2%

 Mount Rogers (12c) 14.6% 16.0% 15.5% 16.1% 14.7%

 New River Valley (12d) 15.7% 14.2% 17.8% 15.7% 14.2%

 Fifth Planning (12e) 8.7% 8.4% 7.7% 8.6% 7.7%

 Central Shenandoah (12f) 14.3% 11.2% 13.7% 11.6% 11.5%

WEST VIRGINIA

 Region 1 (13a) 17.3% 22.0% 24.1% 26.7% 24.5%

 Region 2 (13b) 17.1% 20.6% 24.0% 25.0% 23.4%

 Region 3 (13c) 11.7% 15.1% 16.7% 19.7% 17.3%

 Region 4 (13d) 17.1% 20.9% 23.3% 25.4% 26.3%

 Region 5 (13e) 13.7% 15.1% 18.6% 20.2% 19.1%

 Region 6 (13f) 15.5% 16.6% 19.3% 20.7% 18.9%

 Region 7 (13g) 18.8% 21.5% 23.9% 26.0% 24.4%

 Region 8 (13h) 17.3% 14.1% 15.9% 17.0% 16.3%

 Region 9 (13i) 14.1% 9.6% 11.4% 14.6% 13.0%

 Region 10 (13j) 10.6% 14.5% 16.8% 18.2% 16.6%

 Region 11 (13k) 9.3% 12.3% 14.9% 16.0% 14.7%

Note: Some developmental districts include non-Appalachian counties that do not appear in this table.
92

Table C.3: 

Poverty Rates by Urban Continuum Code for Appalachia

Beale Code (93) Number of 

counties

1979 

Census

1989 

estimates

1989 

Census

1993 

estimates

1995 

estimates

Metro-core 7 8.4% 9.6% 10.3% 11.3% 9.8%

Metro-fringe 12 12.4% 11.3% 12.2% 12.9% 11.2%

Metro-medium 59 12.8% 12.8% 13.3% 14.9% 13.1%

Metro-small 31 12.6% 12.8% 14.3% 15.2% 14.2%

Non-metro, 20,000 

urban population, 

adjacent to metro

20 13.1% 14.1% 15.4% 15.9% 14.5%

Non-metro, 20,000 

urban population, nonadjacent to metro

11 15.9% 15.1% 18.2% 18.5% 16.6%

Non-metro, 2,500-

19,999 urban population, 

adjacent to metro

83 15.6% 15.0% 16.4% 16.9% 15.5%

Non-metro, 2,500-

19,999 urban population, 

non-adjacent to metro

78 18.9% 19.7% 21.6% 21.6% 19.9%

Non-metro, rural, 

adjacent to metro 40 19.6% 17.4% 19.7% 19.5% 18.1%

Non-metro, rural nonadjacent to metro

65 25.1% 24.9% 26.0% 25.1% 23.7%

ARC counties 409 14.1% 14.1% 15.3% 16.1% 14.6%
93

Table C.4: 

Poverty Rates by USDA Non-metropolitan Economic and Policy Functions 

ERS Code Number of 

counties

1979 

Census

1989 

SAIPE

1989 

Census

1993 

SAIPE

1995 

SAIPE

Commuting 71 19.7% 18.3% 19.9% 19.9% 18.1%

Farming 6 25.2% 20.7% 22.6% 20.6% 19.6%

Federal Lands 24 19.1% 17.3% 18.5% 18.3% 17.3%

Government 24 25.9% 26.2% 28.5% 26.6% 24.8%

Manufacturing 128 15.4% 14.5% 15.8% 16.2% 14.9%

Mining 41 19.3% 22.5% 25.3% 26.4% 23.8%

Not Specified 65 18.9% 18.4% 19.5% 19.9% 18.2%

Poverty 93 27.0% 27.1% 28.9% 28.0% 26.0%

Retirement 19 15.7% 11.9% 12.9% 13.4% 12.4%

Service 32 15.4% 16.0% 17.8% 18.3% 16.8%

Transfer 91 22.2% 25.4% 27.4% 27.2% 25.3%

ARC Nonmetro297 17.2% 17.1% 18.8% 18.9% 17.4%
94

APPENDIX D

Distressed Status Designation Methodology

This report determines distressed status of Appalachian counties using the current ARC 

indicators. The poverty data to determine distressed status was derived from the 1980 and 1990 

U.S. Censuses of Population and Housing (U.S. Census Bureau, 1982 and 1992) and the U.S. 

Census Bureau Small Area Income and Poverty Estimates (U.S. Census Bureau, 1999a). This 

report uses three-year average unemployment rates derived from USA Counties 1998 (U.S. 

Census Bureau, 1999b) and three-year average per capita market income derived from the 

Regional Economic Information System 1969-98 (U.S. Bureau of Economic Analysis, 2000). The 

use of the three-year averages for unemployment and per capita market income accounts for the 

differences in distressed counties between this report and Wood and Bischak (2000). 

National Averages and Distressed Standards (in parentheses)

1980 

Poverty Rate - 12.4% (18.6%; 24.8%)

Unemployment Rate - 6.87% (10.3%)

Per Capita Market Income - $9,124 ($6,083)

1990 

Census Poverty Rate - 13.1% (19.7%; 26.2%)

SAIPE Poverty Rate - 13.1% (19.7%; 26.2%)

Unemployment Rate – 5.9% (8.85%)

Per Capita Market Income - $18,114 ($12,076) 

1994 

SAIP Poverty Rate - 15.1% (22.7%; 30.2%) 

Unemployment Rate – 6.14% (9.21%)

Per Capita Market Income - $21,271 ($14,181) 

1996 

SAIPE Poverty Rate – 13.8% (20.7%; 27.6%)

Unemployment Rate – 5.25% (7.87%)

Per Capita Market Income - $23,089 ($15,393)
95

APPENDIX E

Appalachian Distressed Counties 1980 – 1996

Table E.1: 

Distressed Counties, 1980, 1990, 1994, and 1996 (Bold Text = Distressed)

1994 1996

County State 1990 1990 Estimate

Upper 

Bound Estimate

Upper 

Bound

Bibb Alabama PUI PUI I P I None P

Fayette Alabama None PUI None None None P

Franklin Alabama U PUI None None None None

Lawrence Alabama PUI PUI None None None None

Pickens Alabama PUI PUI PUI PUI PUI PUI

Talladega Alabama PU PUI I P I P I P I

Winston Alabama UI PUI None None None P

Union Georgia P* I None None None None None

Adair Kentucky P* I P I P I P I PUI PUI

Bath Kentucky PUI PUI PUI PUI PUI PUI

Bell Kentucky P* I PUI P* I P* I PUI PUI

Breathitt Kentucky P* I PUI PUI PUI PUI PUI

Carter Kentucky PUI PUI PUI PUI PUI PUI

Casey Kentucky P* I P* I P I P* I P I P*U

Clay Kentucky P* I PUI P* I P* I PUI PUI

Clinton Kentucky P* I P* I P* I P* I PUI PUI

Cumberland Kentucky P* I P* I P* I P* I PUI PUI

Elliott Kentucky P* I PUI PUI PUI PUI PUI

Estill Kentucky PUI PUI P I P* I P I P*U

Floyd Kentucky P PUI PUI PUI PUI PUI

Green Kentucky P I P I I P I UI PUI

Harlan Kentucky P* PUI PUI PUI PUI PUI

Jackson Kentucky PUI PUI PUI PUI P*U P*U

Johnson Kentucky P PUI P I P* I PUI PUI

Knott Kentucky PUI PUI PUI PUI PUI PUI

Knox Kentucky P* I P* I P* I P* I PUI PUI

Laurel Kentucky P P I P I P* I P I P I

Lawrence Kentucky PUI PUI PUI PUI PUI PUI

Lee Kentucky PUI PUI PUI PUI P*U P*U

Leslie Kentucky P* I PUI P* I P* I P*U P*U

Letcher Kentucky PUI PUI PUI PUI PUI PUI

Lewis Kentucky PUI PUI PUI PUI PUI PUI

Lincoln Kentucky PUI P* I P I P I P I P*U

McCreary Kentucky PUI PUI P* I P* I PUI PUI

Magoffin Kentucky PUI PUI PUI PUI PUI PUI

Martin Kentucky P* PUI PUI PUI PUI PUI

Menifee Kentucky PUI PUI PUI PUI PUI PUI

P = County poverty rate at or above 150% of national average

P*= County poverty rate at or above 200% of national average

U= County unemployment rate at or above 150% of national average

I = County per capita market income at or below 2/3 of national average
96

1994 1996

County State 1990 1990 Estimate

Upper 

Bound Estimate

Upper 

Bound

Monroe Kentucky P* I P* I P P P P*

Morgan Kentucky PUI PUI PUI PUI PUI PUI

Owsley Kentucky PUI P* I P* I P* I P*U P*U

Perry Kentucky P PUI PUI PUI PUI PUI

Pike Kentucky P PU PU P*U PU P*U

Powell Kentucky PUI PUI P I P* I P I P*U

Rockcastle Kentucky P* I PUI P I P* I P I P*U

Rowan Kentucky P I P* I P I P* I P I P*U

Russell Kentucky PUI PUI P I P I PUI PUI

Wayne Kentucky P* I PUI P* I P* I P*U P*U

Whitley Kentucky PUI P* I P* I P* I P*U P*U

Wolfe Kentucky PUI PUI PUI PUI P*U P*U

Benton Mississippi P I PUI P I P* I PUI PUI

Chickasaw Mississippi P I PUI None P U PU

Choctaw Mississippi P* I PUI P I P* I PUI PUI

Clay Mississippi P I PUI PUI PUI PUI PUI

Kemper Mississippi P* I PUI P I P* I PUI PUI

Marshall Mississippi P* I PUI PUI PUI PUI PUI

Noxubee Mississippi PUI PUI P* I P* I PUI PUI

Oktibbeha Mississippi P* I P* I P I P* I P I P*U

Prentiss Mississippi I PUI I I I P I

Tippah Mississippi P I PUI I P I I I

Tishomingo Mississippi None PUI I I UI UI

Webster Mississippi P I PUI I P I P I P I

Winston Mississippi P* I PUI P I P* I PUI PUI

Graham North Carolina PUI PUI UI PUI UI PUI

Madison North Carolina P* I P I None None None P

Swain North Carolina PUI PUI PUI PUI PUI PUI

Adams Ohio PUI PUI PUI PUI UI PUI

Athens Ohio P I P* I P I P I I P I

Jackson Ohio U PUI I P I I P I

Meigs Ohio None P I PUI PUI PUI PUI

Monroe Ohio None PUI U U UI PUI

Pike Ohio PUI PUI PUI PUI UI PUI

Scioto Ohio U PUI PUI PUI PUI PUI

Vinton Ohio I PUI UI PUI UI PUI

Bledsoe Tennessee PUI I P I P I P I P I

Campbell Tennessee PUI PUI P I P* I PUI PUI

Claiborne Tennessee PUI P I P I P I P I P I

Clay Tennessee P* I P I None P U PU

Cocke Tennessee PUI PUI PUI PUI PUI PUI

Cumberland Tennessee PUI I None None None None

Fentress Tennessee PUI PUI PUI PUI PUI PUI

Grundy Tennessee PUI PUI PUI PUI PUI PUI

Hancock Tennessee PUI P* I P* I P* I P*U P*U

P = County poverty rate at or above 150% of national average

P*= County poverty rate at or above 200% of national average

U= County unemployment rate at or above 150% of national average

I = County per capita market income at or below 2/3 of national average
97

1994 1996

County State 1990 1990 Estimate

Upper 

Bound Estimate

Upper 

Bound

Jackson Tennessee PUI P I I P I I I

Johnson Tennessee P* I PUI PUI PUI PUI PUI

Meigs Tennessee U PUI UI UI UI PUI

Monroe Tennessee PUI UI I P I UI PUI

Morgan Tennessee P* I PUI P I P I I P I

Overton Tennessee PUI UI I P I UI PUI

Pickett Tennessee P* I P I P I P I PUI PUI

Rhea Tennessee P UI UI PUI UI UI

Scott Tennessee PUI PUI PUI PUI PUI PUI

Buchanan Virginia P PU PU PU PUI PUI

Dickenson Virginia U PUI PUI PUI PUI PUI

Lee Virginia P* I PUI PUI PUI PUI PUI

Russell Virginia None PUI UI PUI UI PUI

Barbour West Virginia P PUI PUI PUI PUI PUI

Boone West Virginia None P*U PU P*U PU P*U

Braxton West Virginia PUI PUI PUI PUI PUI PUI

Calhoun West Virginia PUI PUI PUI PUI PUI PUI

Clay West Virginia PUI PUI PUI PUI PUI PUI

Doddridge West Virginia P I P I PUI PUI P I P*U

Fayette West Virginia U PUI PUI PUI PUI PUI

Gilmer West Virginia P I PUI PUI PUI PUI PUI

Jackson West Virginia U PUI U PU None P

Lewis West Virginia I PUI PUI PUI PUI PUI

Lincoln West Virginia PUI PUI PUI PUI PUI PUI

Logan West Virginia None PUI PU P*U PUI PUI

McDowell West Virginia PU PUI PUI PUI PUI PUI

Mason West Virginia U PUI U PU UI PUI

Mingo West Virginia P PUI P*U P*U PUI PUI

Monroe West Virginia P I PUI I P I I P I

Nicholas West Virginia U PUI PUI PUI PUI PUI

Pocahontas West Virginia U PUI U PU U PU

Preston West Virginia PUI UI UI PUI UI PUI

Randolph West Virginia None PUI PU PU PU PU

Ritchie West Virginia UI PUI PUI PUI PUI PUI

Roane West Virginia UI PUI PUI PUI PUI PUI

Summers West Virginia PUI PUI PUI PUI PUI PUI

Taylor West Virginia UI PUI PUI PUI PUI PUI

Tucker West Virginia I UI UI UI UI PUI

Upshur West Virginia I PUI PUI PUI PUI PUI

Wayne West Virginia P PUI P I P I P I P I

Webster West Virginia PUI PUI PUI PUI PUI PUI

Wirt West Virginia UI PUI PUI PUI PUI PUI

Wyoming West Virginia PU PUI PUI PUI PUI PUI

P = County poverty rate at or above 150% of national average

P*= County poverty rate at or above 200% of national average

U= County unemployment rate at or above 150% of national average

I = County per capita market income at or below 2/3 of national average