Manufacturing Wage Inequality in the Appalachian 

Region 

A Report for the Appalachian Regional Commission 

By 

James K. Galbraith and Pedro Conceição 

Lyndon B. Johnson School of Public Affairs 

The University of Texas at Austin 

Austin, Texas 78713-6450 

May 2001 

1
Executive Summary 

This study examines inequality in manufacturing pay between manufacturing plants 

within states and counties of the United States, and compares the trends in such inequality 

with those in Appalachia over the 1963 to 1992 period. The statistical measure of 

inequality used in this study, the Theil statistic, is applied to measure the dispersion of 

average payrolls across manufacturing plants within and between each state and county. 

This approach allows one to determine whether the patterns of inequality differ across 

states and regions, and to assess whether the experience of the Appalachian Region, taken 

as a whole, differs from that of the rest of the nation. 

While a measure of inequality across manufacturing facilities cannot substitute for broader 

measures of inequality in household income distribution, and other measures of wellbeing, it does have two advantages. First, it provides an accurate measurement at the 

county level that permits the grouping of counties—using this harmonized measure of 

inequality—at the state and regional level. Second, the trends in the dispersion of pay 

across manufacturing plants are likely to track broader measures of inequality that cannot 

be directly measured at this level of geography. 

For comparative purposes, the analysis of manufacturing pay inequality proceeds from the 

national, to the state and county level, and thence to the regional level. At the national 

level, the analysis indicates that there was a reduction in pay inequality within states from

1963 to 1967, followed by a steady increase at an annual average rate of 2 percent from 

1967 to 1992. Over this time, increasing inequality in manufacturing pay overall has been 

largely driven by a widening average pay gap within states, rather than an increasing gap 

between rich and poor states. This does not mean that there has been a convergence of 

average wages across states, only that between-state inequality has not worsened 

markedly, while inequalities within-states have grown. 

The measure of inequality within states can be broken into two components: the 

contribution to pay inequality of the dispersion between counties, and the contribution of 

dispersion within counties. This analysis shows that changes within counties account for 

the largest share of the widening pay gap, increasing from 69 percent in 1963 to 76 

percent in 1992 of total measured inequality. Thus, the importance of differences across 

states has diminished, while the increase in manufacturing pay inequality is largely due to 

the growing dispersion of wages within states and counties. 

In order to analyze the relative effect of changes in inequality in the Appalachian Region, 

the inequality measure is divided into five components. The first component measures 

inequality between Appalachian counties; while the second component measures 

dispersion of wages within Appalachian counties. The third and fourth components 

measure inequality between and within non-Appalachian counties. The final component 

measures dispersion between the group of Appalachian counties and the group of nonAppalachian counties. 

2
In general most of the Appalachian states exhibit a similar overall pattern of a decrease in 

inequality from 1963 to 1967, and then an increase in inequality from 1972 to 1992. The 

exceptions are the states of Kentucky, Tennessee and Virginia, which exhibited a halt to 

increasing inequality after 1987, and a decrease through 1992. 

Differences between Appalachian and non-Appalachian counties are small for every state, 

which indicates that there are not sharp differences in average wages. In Alabama and 

Mississippi the differences narrowed, but most states showed stable differences. The 

differences between Appalachian and non-Appalachian counties increased in Kentucky, 

Maryland, Pennsylvania and Virginia from 1987 to 1992. 

Inequality between and within non-Appalachian counties accounted for the largest share 

of wage dispersion within most Appalachian states, except in West Virginia, Alabama, 

Tennessee and Pennsylvania where the Appalachian portion of the state makes up all or a 

significant share of employment and wages. 

In all 13 Appalachian states inequality within Appalachian counties is higher than 

inequality between Appalachian counties. In other words average wage levels are similar 

across Appalachian counties. Furthermore, inequality within Appalachian counties is more 

volatile over time than inequality between counties. Six states exhibit an increase in 

inequality within Appalachian counties from 1967 to 1992 (Alabama, Georgia, 

Mississippi, Ohio, Pennsylvania, and South Carolina). Four states show increases in 

inequality within Appalachian counties from 1967 to 1987, and then declines from 1987 to 

1992 (Kentucky, Maryland, Tennessee, and Virginia). Three states exhibit fluctuations in 

inequality within Appalachian counties, but over the whole period registered higher levels 

of inequality in 1992 than in 1963 (New York, North Carolina, and West Virginia). 

A comparison of the most unequal and most equal counties in the Appalachian and nonAppalachian counties in the region seems to indicate that Appalachian counties are neither 

substantially more nor substantially less unequal than non-Appalachian counties. 

A comparison of relative contributions to overall pay inequality in manufacturing shows, 

not surprising, that the contribution of differences between Appalachian and nonAppalachian counties is extremely small. Perhaps more interesting is the finding that there 

are indications of a widening gap between Appalachian and non-Appalachian counties 

beginning in 1982. Overall, however, the regional trend toward widening inequality of pay 

within counties mirrors the national pattern. 

Finally, the study reflects on the role of macroeconomic factors on the evolution of 

inequality across geographic regions. The study presents evidence that the national 

economic situation, particularly trends in national unemployment, conditions local 

changes in wages and wage inequality. Thus, during prosperous times such as 1963-67, 

the preponderance of counties experienced rising wages and falling inequality. In tougher 

times, such as 1977-82, falling wages and rising inequality predominate. In essence, 

periods of exceptional prosperity are necessary to reduce inequality in American 

manufacturing pay. Conversely, steep recessions tend to worsen pay inequality. 

3
1. Introduction 

This report presents and describes a new measure of inequality in manufacturing pay, 

suitable for assessing geographic patterns in the change of inequality over time in the 

United States. The measure is a between-groups component of Theil’s T statistic, 

calculated between manufacturing plants within counties of the United States. Data are 

drawn from the non-public tabulations of the Longitudinal Research Database of the 

Bureau of the Census1

. We consider only data for the census years of 1963, 1967, 1972, 

1977, 1982, 1987 and 1992. The data set thus provides a time-series measure of 

inequality for each covered county, with the potential for extension as the Economic 

Census for 1997 becomes available. Due to limitations on disclosure, data are available 

for 1,486 counties out of 3,150. In addition, the data set includes an aggregated index of 

inequality for those counties within each state that could not be individually disclosed. 

 Until recently, measures of inequality in the United States have been almost 

exclusively available at the national or state level only. This is due to a wide reliance on 

sample surveys and the need for very large samples if inequality is to be measured 

separately over a large number of distinct spatial units. There has also been a tendency in 

the economic literature on the evolution of inequality to suppose that the forces affecting 

inequality—whether technological change, trade, or such macroeconomic factors as the 

rate of unemployment—apply with equal force to all regions. In effect, this amounts to 

the implicit assumption that a single uniform national labor market exists.

Such an assumption may or may not be correct. A national measure of inequality 

can mask different patterns of inequality itself, and of changes in inequality over time in 

different regions of the country. In a study of inequality across American states, Bernard 

and Jensen (1998) argue that patterns of inequality do differ. In particular, these authors 

find that education premia differ across states, and that these differences have persisted 

over time. Topel (1994) also disaggregates the dynamics of US inequality, again finding 

that differences exist across Census regions. 

Our results indicate that national conditions seem to determine the local dynamics 

of inequality, which suggests that, at least at the level of analysis at which our data allows 

us to draw inferences, the assumption of a national labor market is valid. Still, this report 

takes the measurement of inequality down to a much smaller fundamental unit of 

geographic analysis than States or Census regions: the county. The sacrifice involved, as 

compared with prior analysis, lies in the fact that one cannot obtain measures of individual 

or household income at the county level; the unit of observation in the Economic Census 

is the manufacturing plant or other business facility. Thus, the measure of dispersion 

within each county is a measure of inequality across the average wages of manufacturing 

plants2

. 

At this level, we find that national conditions seem to be the most important 

determinants of changes in inequality. However, we still found significant differences in 

 

1

 The Longitudinal Research Database is described in Appendix 3. 

2

 This average does not consider any control for industry, urban/rural differences or any other possibly

relevant characteristics of firms. The average wage lumps all establishments of each county together and

includes both production and non-production workers. 

4
the dynamics of average wages in ARC and non-ARC counties from the 1980s onwards. 

These suggest that in this period wage growth in Appalachia was slower than elsewhere, 

on average. 

Our approach necessarily excludes many forms of income that are relevant for a 

larger analysis of social welfare. It also excludes all those sources of pay that lie outside of 

manufacturing facilities. Indeed, it is fair to state that a measure of inequality across

manufacturing facilities provides at most a narrowly focused and limited frame of 

reference from which to address the question of economic inequality broadly speaking. It 

cannot substitute—and we do not suggest that it can—for research into household income

and well-being using other sources of data. 

Nevertheless, the approach has two basic advantages. The first lies in achieving 

accurate measurement of this aspect of inequality in very fine geographic detail. And the 

second lies in the mathematical properties of the Theil index itself, which permit us to 

group counties together and so to achieve harmonized measures of inequality at the state 

and regional levels. Not least, this permits us to assess whether the experience of the 

Appalachian region, taken as a whole, differs from that of the rest of the country. We also 

believe that trends in the dispersion of pay across manufacturing plants are likely to 

indicate the direction of trends in broader measures of inequality that we do not observe. If 

pay in the garment shops is declining relative to pay in the mines, for instance, we think it 

highly likely – though we cannot prove the point from the evidence before us -- that other 

forms of inequality are increasing as well. 

Figure 1 presents a map showing the coverage of our data set—those counties for 

which an inter-plant measure of inequality could be disclosed. The number of plants in 

each county ranges from 19,964 in New York County in 1967 to 10 establishments in 

1987 and 1992 in Hancock County (Georgia). In 1992, the county with the most 

establishments (18,439) was Los Angeles County. 

5
Figure 1. Coverage of Between-Plant Pay Dispersion measures, 1963-1992, U.S. Counties. Shaded

counties were used in the data set. Appalachian Region counties are shown within the thick border. 

 

In what follows, we develop the information in this rich new source of inequality 

measures in hierarchic fashion, beginning at the national and state levels, and working our 

way toward an analysis of inequality at the county level. 

6
2. Manufacturing Pay Dispersion at the National Level 

This section describes the dynamics of dispersion in our measure of inter-plant pay 

dispersion at the national level. We first present a picture of the evolution of this 

dimension of economic inequality in the country as a whole, including a decomposition 

into the contributions of the between state and within state components of national 

inequality. 

Figure 2 shows the Theil measure of pay inequality across manufacturing plants at 

the national level. There is a reduction from 1963 to 1967, following which inequality 

steadily increased from 1967 to 1992, at an annual average rate of 2%, in a pattern that 

resembles a “check mark.” 

Figure 2. Manufacturing pay dispersion aggregated to the national level: relative importance of

inequality within and between states. 

0

0.02

0.04

0.06

0.08

0.1

0.12

1963 1967 1972 1977 1982 1987 1992

Between States Within States

Figure 2 also shows the contribution to US inequality of the dispersion in wages 

due to differences across states (area shaded in black) and the contribution that is 

accounted for by differences within states (white area). The contribution of cross-state 

inequality has remained stable throughout the period. Since overall inequality almost 

doubled from 1963 to 1992, the contribution of the between states component has 

decreased almost by half. This leads us to an opening inference. Clearly, the dynamics of 

7
rising national pay inequality have been driven by the within-states contribution. They 

have not been driven by an increasing average gap between rich states and poor. 

The within states component of inequality can be further decomposed into two

sub-components: the dispersion of wages across counties and the dispersion within 

counties (Figure 3). Again, changes within the smaller geographic units predominate. It is 

a changing within-counties component that is driving the increase in manufacturing pay 

dispersion. 

Figure 3. Contributions of Inequality Across Counties and Within Counties. 

0

0.02

0.04

0.06

0.08

0.1

0.12

1963 1967 1972 1977 1982 1987 1992

Between States Between Counties Within Counties

Table 1 shows the relative contribution of each of the components depicted in 

Figure 3 to US inequality. As the charts indicate, the largest contribution to US inequality 

comes from the within counties component. The share of the within counties component 

in total inequality increased from 69% in 1963 to 76% in 1992. Note that this increase in 

the share of the within counties component was made “at the expense” of the between 

states contribution, which has seen its share decrease by half, from 16% in 1963 to 8% in 

1992. The share of the between counties component remained relatively stable throughout 

the period at around 15%. 

8
Table 1. Contributions of the Between States, Between Counties and Within Counties Components 

1963 1967 1972 1977 1982 1987 1992

Between States 16% 16% 15% 14% 10% 9% 8%

Between Counties 15% 15% 14% 16% 14% 14% 15%

Within Counties 69% 69% 70% 70% 76% 77% 76%

These results indicate that differences in average wages across states in the US 

have become less important, with a steady decrease of the between states contribution to 

US inequality since 1967. The largest drop in this contribution occurred between 1977 and 

1982. Note, however, that this decrease in the share of the between state inequality does 

not mean that there has been convergence in average wages across states. As we saw 

above, the level of inequality between states has remained constant. What the results 

suggest, instead, is that the increase in inequality in the US since 1967 is accounted for 

mainly by a growing dispersion of wages within and across counties, within states. 

Yet another way to compare these three components of inequality is to contrast 

their dynamics. Figure 4 plots a line for the Theil index of each of the three components. 

Given the differences in magnitude, two scales are used, with the left-hand scale 

corresponding to the between states and to the between counties Theil, which are about 

one-third smaller than the within counties Theil (which corresponds to the scale on the 

right-hand side). 

Figure 4. The Dynamics of Pay Inequalities: Between States, Between Counties and Within Counties 

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

1963 1967 1972 1977 1982 1987 1992

Between States and Between Counties

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Within Counties

Between States Between Counties Within Counties

The dynamics of the between states component have remained essentially stable. 

There are two intervals of slight decreases (1963 to 1967 and 1977 to 1987) with the 

9
between states Theil increasing modestly in the remaining time periods. The dynamics of 

the between-states Theil seems to be independent of the evolution of the two other 

components. 

On the other hand, the dynamics of the between counties component and of the 

within counties component are essentially the same. For both components, there is a 

decrease in the Theil from 1963 to 1967, with the Theil increasing steadily from 1963 to 

1977. It is clear that a positive change in one component is associated with a positive 

change in the other component, and vice-versa. 

This section has therefore shown that after a decrease in wage inequality in the US 

from 1963 to 1967, wage dispersion in US manufacturing steadily increased through the 

early 1990s. The importance of differences across states has diminished; virtually all of 

the increase in US inequality is due to growing dispersion of wages within states. 

10
3. Manufacturing Pay Inequalities in the Appalachian Region 

About four hundred counties in thirteen US states define the Appalachian region. This 

section describes the dynamics of manufacturing wage dispersion in each of the thirteen

states with counties that are members of the Appalachian Regional Commission (ARC). 

The share of population in ARC counties varies from state to state. Taking 1990 as a 

reference, Table 2 shows that the population in the ARC goes from 100% in West Virginia 

(where all counties are in the ARC) to 5% in Maryland. 

Table 2. Share of State Population in ARC Counties 

1990

West Virginia 100%

Alabama 63%

Pennsylvania 49%

Tennessee 44%

Kentuc ky 28%

South Carolina 25%

Georgia 24%

Mississippi 20%

North Carolina 20%

Ohio 13%

Virginia 10%

New York 6%

Maryland 5%

Source: ARC and Bureau of the Census. 

The shares of manufacturing employment in the ARC (Table 3) are similar, but not 

exactly the same, as the shares of population. For most states, shares of manufacturing 

employment in the ARC are higher than the corresponding population shares. The 

exceptions occur for Pennsylvania, Kentucky and Ohio. 

11
Table 3. Share of State Manufacturing Employment in Plants Located in ARC Counties 

1963 1967 1972 1977 1982 1987 1992

West Virginia 100% 100% 100% 100% 100% 100% 100%

Alabama 70% 71% 70% 68% 66% 67% 67%

Tennessee 52% 52% 52% 51% 51% 49% 49%

Pennsylvania 46% 46% 47% 47% 45% 42% 44%

South Carolina 40% 39% 38% 37% 37% 37% 38%

Mississippi 30% 29% 31% 31% 33% 35% 34%

Georgia 25% 26% 28% 29% 28% 30% 32%

North Carolina 25% 24% 23% 23% 24% 23% 22%

Kentuc ky 13% 13% 15% 17% 18% 18% 18%

Virginia 14% 16% 15% 15% 14% 14% 14%

New York 7% 8% 7% 8% 8% 8% 9%

Maryland 9% 10% 10% 9% 9% 8% 8%

Ohio 8% 7% 7% 8% 8% 8% 8%

The shares of manufacturing wages in the ARC (Table 4) are nearly identical to 

the employment shares3

. Both the employment and the wage shares provide a way to see 

the importance of the ARC region in each state. However, the wage shares are particularly 

relevant in our analysis because wage shares are used to weight the Theil index 

components for each county when computing cross-county measures of inequality. 

Table 4. Share of State Manufacturing Wages in Plants Located in ARC Counties 

1963 1967 1972 1977 1982 1987 1992

West Virginia 100% 100% 100% 100% 100% 100% 100%

Alabama 75% 74% 73% 70% 67% 68% 68%

Tennessee 53% 53% 51% 51% 51% 48% 47%

Pennsylvania 46% 45% 46% 47% 45% 40% 41%

South Carolina 38% 38% 38% 37% 36% 36% 38%

Mississippi 26% 25% 28% 28% 29% 32% 33%

Georgia 22% 23% 26% 27% 25% 27% 29%

North Carolina 26% 25% 23% 24% 24% 23% 21%

Kentuc ky 10% 11% 13% 15% 15% 14% 14%

Virginia 13% 15% 13% 13% 12% 11% 11%

New York 7% 7% 7% 8% 9% 8% 8%

Ohio 7% 6% 6% 7% 8% 6% 7%

Maryland 9% 10% 10% 9% 9% 7% 7%

For each state with county-members of the ARC, we divide the evolution of 

inequality into five components. The first component measures inequality across ARC 

counties; the second component measures dispersion in wages within ARC counties. 

These two components are weighted by the ARC wage shares as they enter into the overall 

 

3

 Data from the County Business Pattern for 1996 suggests that the share of employment in plants located in

ARC remained virtually the same as 1992. 

12
state inequality measure. Two other components account for inequality between and 

within non-ARC counties in the state. The fifth and final component measures the 

dispersion between the group of ARC and the group of non-ARC counties. 

The following thirteen charts, one for each state, show the contributions of each 

component to the state level of wage dispersion. Most states exhibit the same pattern in 

overall inequality: a decrease in inequality from 1963 to 1967 and a steady increase from

1972 to 1992. In Kentucky, Mississippi, Tennessee and Virginia the trend of increasing 

inequality stops in 1987, with a decrease in inequality from 1987 to 1992. 

The component of inequality associated with differences between the ARC and the 

non-ARC counties is small for every state, which indicates that there is not a sharp 

difference in average wages in manufacturing for plants that are in ARC counties as 

compared with those that are in non-ARC counties. In Alabama and in Mississippi the 

between ARC and non-ARC counties component decreased over the period under 

analysis, but for most states this component has either remained stable or has witnessed a 

slight increase. The increase in this component has occurred primarily in the last period, 

from 1987 to 1992, especially in Kentucky, Maryland, Pennsylvania and Virginia. 

The remaining four components of the Theil index represented in the charts are 

associated with inequality between counties and inequality within counties for plants 

located in ARC and in non-ARC counties. The two components of inequality associated 

with non-ARC counties (between-counties and within-counties) dominate the level of 

state inequality in most cases. The obvious exceptions are those states for which 

employment and wage shares of ARC counties are large. These exceptions include West 

Virginia, Alabama, Tennessee and Pennsylvania. 

13
Alabama

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-ARC 
Georgia

0

0,020,040,060,08

0,1

0,12

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-ARC 
Kentucky

0

0.010.020.030.040.050.060.070.080.09

0.1

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-ARC 
Maryland

0

0,010,020,030,040,050,060,070,08

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
Mississippi

0

0,010,020,030,040,050,060,070,080,09

0,1

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
New York

0

0,020,040,060,08

0,1

0,120,14

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
North Carolina

0

0,020,040,060,08

0,1

0,12

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
Ohio

0

0,010,020,030,040,050,060,070,08

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
Pennsylvania

0

0,010,020,030,040,050,060,070,080,09

0,1

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
South Carolina

0

0,010,020,030,040,050,060,070,080,09

0,1

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
Tennessee

0

0,010,020,030,040,050,060,070,080,09

0,1

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
Virginia

0

0,020,040,060,08

0,1

0,12

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-AR
West Virginia

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Between ARC Counties Within ARC Counties Between Non-ARC Counties Within Non-ARC Counties Between ARC and Non-ARC Counti

Figure 5 - Inequality in States with ARC Counties 
The major determinant of the importance of the ARC-related components to state 

inequality is the wage share of plants in ARC counties. Figure 6 shows the relationship of 

the contribution of ARC counties’ inequality to the statewide inequality, on the one hand, 

and the share of wages of plants in ARC counties, on the other. The chart shows the 

relationship for 1992, which indicates that the two variables are almost perfectly 

correlated. The relationship is the same for any other year. 

The only state that is almost out of place is New York, where the share of wages 

in ARC counties is much larger than the contribution of ARC related counties to the 

state’s inequality. This indicates that inequality in New York is largely driven by what 

happens outside the counties included in the ARC region – not a surprising finding, to be 

sure. 

Alabama

Georgia

Kentucky

Maryland

Mississippi

New York

North Carolina

Ohio

Pennsylvania

South Carolina

Tennessee

Virginia

West Virginia

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Share of the Theil Index due to Inequality in the ARC

Share of Wages of Plants in the ARC

Figure 6- Relationship between Wage Share of ARC counties and their Contribution to Inequality, 

1992. 

It is also of interest to look at the dynamics of the “pure inequality” components, 

that is, at the evolution the between- and within-counties components of inequality in the 

ARC region. Figure 7 shows a sequence of thirteen charts, each showing, in the same

scale for all states, two lines, one representing inequality within ARC counties (the 

thicker line) and the other inequality between ARC counties. 

27
Alabama

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Georgia

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Kentucky

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Maryland

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Mississippi

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
New York

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
North Carolina

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Ohio

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Pennsylvania

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
South Carolina

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Tennesse

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
Virginia

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties
West Virginia

0

0.020.040.060.08

0.1

0.12

1963 1967 1972 1977 1982 1987 

Within ARC Counties Between ARC Counties

Figure 7- Within ARC Counties and Between ARC Counties Components of State Inequality 
The dynamics of the two components vary from state to state, but there are some

common features. First, inequality within counties is always higher than inequality 

between counties: compared to differences across plants within counties, the average 

levels of wages across counties are relatively similar. Secondly, inequality within 

counties (across plants) is more volatile and is subject to larger changes than inequality 

between counties. Beyond these general features, clearly common to all states, the 

dynamics of the two components differ from state to state. For a number of states there is 

a co-evolution of the two components throughout the period under analysis, but for other 

states the two components diverge, either since the beginning of the period or from some

later point in time. We look at the dynamics of the two components in each state below. 

In Alabama the levels of the Theil index for the within and between counties 

components were the same in 1963, a singular feature unique to this state. While the 

between-counties component remained stable, which means that there was not any 

divergence across the average wage levels across counties, inequality within counties 

increased steadily. In 1992 inequality within counties is about two times as large as 

inequality between counties. It must also be noted that the level of inequality across 

counties in Alabama, while stable, is large when compared with the other states. The 

Theil index measuring inequality across counties in Alabama never drops below 0.03. 

In Georgia the dynamics of the two components is very similar, with the 

“distance” between the two lines remaining the same from 1963 to 1987. In 1992 

inequality within counties increases sharply, with inequality between counties increasing 

as well from its 1987 value of 0.015 to about 0.02. 

The dynamics of the two components also track each other in Kentucky, but in 

this state the level of inequality within counties is much larger than inequality between

counties. In fact, inequality within counties in Kentucky is the largest of all states, 

reaching a Theil of 0.1 in 1987. The evolution of the two components is close to each 

other in both Maryland and in Mississippi, but in different ways. While in Maryland the 

two components have a rather volatile behavior, in Mississippi the within counties 

component increases monotonically, with the between counties component increasing as 

well or remaining virtually stable. In New York, again, the dynamics of the within and of 

the between counties components is similar to each other, with the dispersion in average 

wages across counties remaining small throughout the period. 

In North Carolina and in Ohio the dynamics of the two components are the same

until 1982. From 1967 to 1982 the difference between the measure of inequality within 

counties and the measure between counties remains rather stable, with both components 

increasing. From 1982 to 1987 the within counties component continues to increase in 

both states, but the between counties component shows a slight decrease. In North 

Carolina the trends are reversed from 1987 to 1992, with inequality within counties 

decreasing slightly and inequality between counties showing a small increase. In Ohio, 

both components recover an upward movement. 

Pennsylvania, like Alabama, shows divergent behavior of the two components, 

with inequality within counties increasing steadily from 1967 onwards, while inequality 

between counties oscillates around the 0.02 level. From 1982 on, inequality between 

counties actually decreases, while the within counties component continues the upward 
trend. In 1992 the difference between the two lines is more than five times larger than the 

difference in 1963. 

The dispersion across counties in South Carolina is virtually nonexistent until 

1982. The increase in the within county inequality is also modest up to 1977, but it picks 

up then, more than doubling its Theil level from 1977 to 1992. The between counties 

component also starts to increase in 1982, doubling from 1982 to 1987 and then doubling 

again from 1987 to 1992. 

In Tennessee and in Virginia the within counties component increases up to 1987 

and decreases from1987 to 1992. The between counties component in Tennessee 

decreases steadily throughout the period, while in Virginia it remains stable. For West 

Virginia the two lines represent the level of overall inequality in the state, since all 

counties belong to the ARC. The increase in inequality within counties starts, as with 

most states, in 1967, but stops in 1982, resuming again in 1987. From 1987 to 1992 the 

between counties component, which had remained virtually stable, starts to increase as

well. 

We next consider the issue of inequality in ARC counties at a more detailed level. 

As we have already seen, the major portion of inequality is due to the dispersion of wages 

within counties, regardless of whether we are considering ARC or non-ARC counties. Is 

there systematically higher inequality within ARC counties than within non-ARC 

counties? To explore this question, we list in Table 5 the thirty counties with the highest 

level of the Theil index in 1967 and in 1992. For most states, as for the US as whole, 

1967 corresponds to the year of lowest inequality and 1992 to the peak year. 

There is not an overwhelming presence of ARC counties in the two lists. In fact, 

ARC counties are almost absent from the 1967 list, with only five making the top thirty 

of inequality. In 1992 the number of ARC counties increases to eight, but the results of 

the two lists suggest that there is no reason to conclude that ARC counties are particularly 

unequal. The distribution of the most unequal counties among states reveals that there is 

no concentration of very unequal counties in a single state, or even a handful of states. 

The table indicates that there have been major changes in the structure of the 

geographic distribution of inequality across counties, since few that were most unequal in 

1967 list make the list in 1992. An important exception is New York County, which is in 

the top ten in 1967 and is the most unequal US county in 1992, with a level of the Theil 

index far higher than any other county. 

42
Table 5. The Most Unequal Counties in States with ARC Counties in 1967 and in 19924

STATE COUNTY THEIL STATE COUNTY THEIL

1 Maryland Somerset 0.17 New York New York 0.26

2 Virginia Northumberland 0.12 South Carolina Saluda 0.19

3 Maryland Talbot 0.11 Alabama Shelby 0.19

4 Georgia NonARC Counties 0.11 Georgia Whitfield 0.18

5 North Carolina Lenoir 0.10 New York Westchester 0.18

6 Tennessee ARC Counties 0.09 Kentucky Muhlenberg 0.17

7 Kentucky ARC Counties 0.09 North Carolina Columbus 0.16

8 New York New York 0.09 Mississippi Franklin 0.16

9 Alabama Marengo 0.09 Virginia Fairfax 0.15

10 Georgia Haralson 0.09 Alabama Russell 0.15

11 Kentucky NonARC Counties 0.09 Alabama Marengo 0.15

12 Mississippi NonARC Counties 0.09 Georgia Washington 0.14

13 Virginia Westmoreland 0.09 Virginia Prince Edward 0.14

14 Tennessee McMinn 0.08 Alabama NonARC Counties 0.14

15 Maryland Dorchester 0.08 Virginia Amherst 0.14

16 North Carolina Columbus 0.08 Mississippi Marion 0.14

17 Virginia Arlington 0.08 Alabama Clarke 0.14

18 Alabama Escambia 0.08 Tennessee NonARC Counties 0.13

19 Alabama Russell 0.08 Kentucky ARC Counties 0.13

20 North Carolina Pamlico 0.08 Alabama Limestone 0.13

21 Alabama NonARC Counties 0.07 Georgia Thomas 0.13

22 Georgia Glynn 0.07 Mississippi Monroe 0.13

23 New York Schoharie 0.07 Georgia Greene 0.13

24 Virginia Hampton 0.07 Georgia Barrow 0.13

25 Georgia Lowndes 0.07 South Carolina Dorchester 0.13

26 Kentucky Boone 0.07 Virginia Pulaski 0.13

27 Virginia Lancaster 0.07 Alabama Dallas 0.13

28 South Carolina Clarendon 0.07 Georgia Carroll 0.13

29 Alabama Dallas 0.07 Georgia Ben Hill 0.12

30 Kentucky Christian 0.07 Kentucky Warren 0.12

1967 1992

Note: ARC counties are in bold. 

If ARC counties are not particularly unequal, could it be that they are especially 

equal? Table 6 shows the least unequal counties in the US again for 1967 and for 1992. 

Again, ARC counties, while more abundant here than in the most unequal list, do not 

appear to be particularly more equal than the non-ARC counties. However, there is now a 

concentration of least unequal counties in two states, since Georgia and Virginia have, 

together, about half of the most equal counties in the US. 

 

4

 The listings of “ARC counties” and of “Non-ARC counties” correspond to groupings of either ARC or

Non-ARC counties in each state due to disclosure concerns. 

43
Table 6. The Most Equal Counties in States with ARC Counties in 1967 and in 1992

STATE COUNTY THEIL STATE COUNTY THEIL

1 Virginia Floyd 0.003 New York Schoharie 0.002

2 North Carolina Cabarrus 0.005 Georgia Hancock 0.005

3 Mississippi Scott 0.005 Virginia Essex 0.012

4 North Carolina Macon 0.005 Tennessee Marshall 0.013

5 Virginia Louisa 0.006 Georgia Randolph 0.014

6 Tennessee Carroll 0.006 North Carolina Rutherford 0.014

7 Alabama Bibb 0.006 Georgia Walker 0.014

8 North Carolina Cherokee 0.007 Virginia Newport News 0.018

9 Virginia Page 0.007 Virginia King and Queen 0.018

10 Georgia Tattnall 0.007 Ohio Gallia 0.020

11 Georgia Gilmer 0.007 North Carolina Chowan 0.020

12 Virginia Brunswick 0.007 Kentucky Adair 0.020

13 New York Schuyler 0.007 North Carolina Ashe 0.020

14 North Carolina Rutherford 0.007 Kentucky Casey 0.020

15 Georgia Hancock 0.007 North Carolina McDowell 0.020

16 Georgia Greene 0.008 Mississippi Montgomery 0.021

17 Georgia Baldwin 0.008 North Carolina Anson 0.021

18 Virginia Prince Edward 0.008 Georgia Johnson 0.021

19 Alabama Conecuh 0.008 Ohio Champaign 0.021

20 Mississippi Itawamba 0.008 Georgia Hart 0.021

21 Alabama Lamar 0.008 Virginia Harrisonburg 0.021

22 Kentucky Grayson 0.009 Virginia Caroline 0.021

23 Georgia Grady 0.009 Ohio Hocking 0.021

24 South Carolina Pickens 0.009 Georgia Henry 0.022

25 Alabama Geneva 0.010 Ohio Madison 0.022

26 Tennessee Dickson 0.010 Virginia Page 0.022

27 New York Delaware 0.010 Tennessee Tipton 0.023

28 Virginia New Kent 0.010 Ohio Putnam 0.023

29 Ohio Trumbull 0.010 Georgia Gordon 0.023

30 Pennsylvania Beaver 0.010 Maryland Kent 0.023

1967 1992

Note: ARC counties are in bold. 

In Table 9, shown in an appendix, the five most and least unequal counties in each 

state, again for 1967 and for 1992, are shown. There is no systematic relationship 

between being an ARC county and the level of inequality. 

44
4. Contribution of the Appalachian Region to US Inequality 

In this section we look at the contribution of the ARC region as whole to the level of US 

inequality. Dividing all counties in the US into those that are members of the 

Appalachian Regional Commission and those that are not, we can create an intermediate 

sub-national level of aggregation. Thus, following the same strategy as in section 2, we 

can divide US inequality into six components. 

The first component is associated with the differences across the ARC and the 

non-ARC counties, when counties in each division are considered as a whole. This 

component, between ARC and non-ARC, measures the contribution of differences in 

average wages across the two divisions. Each of the two divisions can then be 

decomposed into between counties and within counties components. The sixth and final 

component is the contribution to US inequality of differences across states. 

Figure 8 shows the results of this decomposition. Naturally, the between states 

component remains the same as in section 2, but the within states component is now 

divided into five contributions. The share with which each component contributes to the 

US Theil is given in Table 7, complementing the information in the chart.

The contribution of the between ARC counties remains small, decreasing from

almost 3% in 1967 to slightly more than half that percentage in 1992. The contribution of 

the within ARC counties is equally small, contributing between 5.5% and 6.5% 

throughout the period, with the highest contribution, of 6.5% having been reached in 

1982. 

Clearly, the increase in US inequality is accounted for by the non-ARC counties, 

which is not surprising, given the structure of the Theil index in which the contributions 

of the groups are weighted by each group’s wage share. Given that the ARC’s wage share 

is relatively small, the small contribution of the ARC counties to US inequality measured 

by the Theil index is not surprising. 

45
Figure 8. Contribution of Inequality within and between ARC and Non-ARC counties to the US

0

0.02

0.04

0.06

0.08

0.1

0.12

1963 1967 1972 1977 1982 1987 1992

Between States Between ARC and Non-ARC Between ARC Counties

Between Non-ARC Counties Within ARC Counties Within Non-ARC Counties

Theil 

Table 7. Share of the Contribution to US Inequality of Each of the Five Components 

1963 1967 1972 1977 1982 1987 1992

Between ARC and Non-ARC 0.39% 0.41% 0.38% 0.36% 0.30% 0.39% 0.48%

Between ARC Counties 2.90% 2.63% 2.39% 2.50% 2.18% 1.67% 1.67%

Between Non-ARC Counties 11.50% 11.55% 11.50% 13.01% 11.62% 12.26% 13.16%

Within ARC Counties 5.59% 5.62% 6.06% 6.18% 6.46% 5.95% 5.98%

Within Non-ARC Counties 63.69% 63.34% 64.35% 63.62% 69.40% 70.96% 70.25%

Between States 15.93% 16.45% 15.31% 14.33% 10.05% 8.77% 8.45%

Is it worthwhile to consider the dynamics of the non-ARC and of the ARC 

components of US inequality? The difference in the levels of these components is such 

that a comparison of the contributions is almost meaningless. Still, we can ask: Are there 

differences in the evolution of inequality components between and within both the ARC 

and the non-ARC counties?

Figure 9 shows that the dynamics of the within and between components of the 

non-ARC counties are highly similar. Both components decrease from 1963 to 1967, with 

a steady increase from 1967 to 1992, in the usual “check mark” pattern of overall US 

inequality. 

46
Figure 9. Contributions to Inequality: Between Non-ARC Counties and Within Non-ARC Counties 

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

1963 1967 1972 1977 1982 1987 1992

Within Counties

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Between Counties

Between Non-ARC Counties Within Non-ARC Counties

If we consider the same two components (across counties and within counties) for 

the ARC counties (Figure 10) the dynamics of the two series are again similar, but they 

are not as close as the dynamics of the non-ARC components. For the ARC counties 

components there is a decrease from 1963 to 1967, with both increasing from 1967 to 

1982. However, from 1982 to 1987 there was a decrease in the component associated 

with inequality across ARC counties, while the within ARC counties component 

continued to increase. From 1987 to 1992 both components increased. 

Jensen (1998) found that there has been a persistence in the gap in manufacturing 

between Appalachia and the rest of the country from 1967 to 1992, with Appalachia 

showing consistently lower wage and lower productivity than the national average. Our 

results suggest, though, that in the 1980s, while the difference has remained small, it

increased considerably in relative terms. Jensen also found a high reliance in Appalachia 

of branch plants, as opposed to single plants. Additionally, Jensen concluded that branch 

plants pay higher wages than single unit plants, and that this premium has increased over 

time. This effect may offer a possible explanation for the dynamics of within county 

inequality in Appalachia.

47
Figure 10- Dynamics of the Between ARC Counties, of the Within ARC Counties and of the Between 

ARC and Non-ARC Counties Components of US Inequality 

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002

1963 1967 1972 1977 1982 1987 1992

Between Components

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

Within Component

Between ARC and Non-ARC Between ARC Counties Within ARC Counties

Perhaps the most interesting finding in Figure 10 is the increase in the component 

of US inequality measuring the dispersion between the ARC and the non-ARC groups of 

counties. This component remains small throughout the period and is constant up to 

1982. However, from 1982 to 1992 the gap between the ARC and non-ARC counties 

starts to increase. This indicates that there has been a widening gap between the ARC and 

the non-ARC counties considered as whole beginning in the early 1980s, possibly due to 

slower wage growth in Appalachia than in the non-ARC counties as a whole. 

48
5. Dynamics of Pay Inequality: A Representation in Maps 

In this section, we present highly summarized information on the evolution of inequality 

at the county level. This information is most readily conveyed in map form. To produce 

the maps in Figures 11A through 11G, we first the measured inequality levels for 1992 

in gray-scale so that equal numbers of counties would fall into each bin, ranging from

white for low-inequality counties to black for high-inequality counties. We then froze 

the bin boundaries at their 1992 values, and asked the question: to what degree did the 

distribution of counties across bins shift in earlier years? Thus, as the maps progress 

over time, a movement toward the black indicates a rising proportion of counties that 

would be considered high-inequality in 1992; a shift toward white indicates a falling 

proportion of such counties. Figures 11A through 11G present a sequence of maps for 

the ARC counties. 

Figure 11A- Inequality within ARC Counties in 1963

 Inequality in 1963 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

0.082 - 0.191

49
Figure 11B- Inequality within ARC Counties in 1967

 Inequality in 1967 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

0.082 - 0.191

Figure 11C- Inequality within ARC Counties in 1972

 

0.082 - 0.191

 Inequality in 1972 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

50
Figure 11D- Inequality within ARC Counties in 1977

 Inequality in 1977 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

0.082 - 0.191

Figure 11E- Inequality within ARC Counties in 1982

 Inequality in 1982 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

0.082 - 0.191

51
Figure 11F- Inequality within ARC Counties in 1987

 Inequality in 1987 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

Figure 11G- Inequality within ARC Counties in 1992

 Inequality in 1992 

0.002 - 0.039

0.039 - 0.051

0.051 - 0.064

0.064 - 0.082

0.082 - 0.191

52
While some counties manage to escape, the large increase in within-county 

inequality in general is a nationwide phenomenon of the 1980s. The national pattern is, 

essentially, the local pattern in this instance. 

53
6. Macroeconomic Factors and Inequality Dynamics 

We now turn briefly to consider the role of macroeconomic factors in the evolution of 

inequality across geographic regions. 

We already know that there is a macroeconomic pattern: this is the “check-mark” 

pattern of rising inequality at the national level in the years following 1967. The fact that 

this pattern is replicated in so many instances throughout this study at the regional, state 

and within-state levels tells us that we need not seek too far for regionally-specific 

sources of the movement of inequality; the affair was a national phenomenon. 

Clearly, a reasonable inference from this data might be that the national rate of 

unemployment – a measure of national economic performance that declined from 1963 to 

1967 and then increased dramatically until the early 1990s – played an important role in 

driving up the dispersion of pay rates among those who remained employed in the 

manufacturing sector. The dramatic increase in pay inequality nearly everywhere in the 

early 1980s, when unemployment reached 10 percent, lends further weight to this 

supposition. However, with only six distinct time-series data points, a statistical test of 

this proposition seemed unlikely to be highly persuasive. Table 8 summarizes 

unemployment rates over this time. 

Table 8. Unemployment rates overall and in manufacturing 

US Manufacturing Only

1963-1967 4.6 4.3

1967-1972 4.6 4.7

1972-1977 6.6 6.9

1977-1982 7.2 7.8

1982-1987 7.9 8.6

1987-1992 6.1 6.2

Unemployment Rates

Can unemployment at the local level explain the change in inequality at the local 

level? The answer to this question is clearly, no. There is in fact no correlation whatever 

between changes in unemployment at the county level and changes in the county measure 

of pay dispersions. The force of the effect of national economic conditions, in other 

words, appears to be firmly from national causes to local consequences, at least in this 

data. This can result from the way in which local perceptions are tied to national 

economic conditions. For example, a context of recession or slower growth at the 

national level can influence the sentiment of economic actors and affect their decisions 

and their interactions; the reaction to national conditions will be played out at changes at 

the local level. 

We also examined the relationship between average wage changes in each county 

and the change in inequality in that county. 

In our view, a correct characterization of this relationship is that changes in local 

unemployment and local wages are conditioned by national economic conditions. Figure 

54
12 illustrates this point, showing the change in inequality and the change in average 

wages for each of the counties in our data set over each time interval. 

The figure illustrates the absence of local cross-section correlation in any time 

period: the counties form a shapeless ball in the space of inequality changes and wage 

changes. But the position of the shapeless ball in the space as a whole differs from period 

to period. Most notably, in 1963-1967 the center of gravity of the ball lies in the upperleft quadrant, indicating that a preponderance of counties experience rising wages and 

falling inequality during this prosperous time. In the 1977-1982 period, in contrast, the 

ball moves squarely to the lower right quadrant: falling average wages and rising 

inequality predominate in this period—even though the scale of one effect does not 

predict the scale of the other for any particular county. In the other periods, the force of 

national economic performance, either positive or negative, does not appear sufficient to 

impart a systematic pattern to the relationship between wage change and inequality 

change. 

Figure 12. Changes in Average Wages and Changes in Inequality by County 1963-1992

1963-1967

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

Change in the Theil Index

Change in the Average Wage

Non-ARC Counties

ARC Counties

1967-1972

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

Change in the Theil Index

Change in the Average Wage

Non-ARC Counties

ARC Counties

1972-1977

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

Change in the Theil Index

Change in the Average Wage

Non-ARC Counties

ARC Counties

1977-1982

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

Change in the Theil Index

Change in the Average Wage

Non-ARC Counties

ARC Counties

1982-1987

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

Change in the Theil Index

Change in the Average Wage

Non-ARC Counties

ARC Counties

1987-1992

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

Change in the Theil Index

Change in the Average Wage

Non-ARC Counties

ARC Counties

55
Table 9, finally, summarizes this information. In the strongly prosperous and 

strongly recessionary periods of the mid-1960s and early 1980s, respectively, there is a 

broadly negative relationship between wage growth and inequality change. In the 

remaining periods, the relationship is more evenly balanced; we suggest that there is not a 

statistically significant difference between the proportion of counties for whom the 

relationship is positive and those for whom it is negative. 

Table 9. Share of Counties (points in the graphs of 

Figure 3) that are either in quadrants where the relationship between wage changes and inequality

changes is positive or negative 

Positive Negative

1967 32% 68%

1972 63% 37%

1977 53% 47%

1982 28% 72%

1987 58% 42%

1992 53% 47%

In short, periods of exceptional prosperity—full employment over a sustained 

time—are required to markedly reduce inequality in American manufacturing pay. 

Periods of crisis—a steep recession, even if relatively short—are sufficient to alter the 

profile of inequality in the country for the worse, and for a generation. This is true inside 

and outside the Appalachian region, though we also find evidence that suggest that in the 

absence of great and sustained national prosperity, there is a tendency for a low-income

region like Appalachia to slip behind the rest of the country on average. This conclusion 

results from the comparison of the difference between ARC and non-ARC counties in the 

1980s, when the gap between the averages in the two groups of counties widened. We 

would expect, of course, if this view is correct, that some narrowing of differentials 

would have occurred in the late 1990s. Unfortunately, while this trend may have started 

and prove detectable in the 1997 Census, it will take until the 2002 Census becomes 

available before it can be tested definitively by the methods developed here. By that 

time, moreover, it may be that a slowdown or even a recession will have undone any 

progress achieved. 

A final inference thus seems in order. Our evidence gives no reason to suppose 

that rising inequality within and between the counties of Appalachia and elsewhere has 

served any positive social or economic purpose. In particular, it is not associated with 

rising productivity or increasing living standards. And so, if inequality in American 

society is itself a problem—as we believe it is—it may be time to address that problem by 

concerted efforts of policy. Such policies would have to aim to reduce the pay gaps we 

observe prominently in plant-based geographic data: not inequalities across states, 

regions, and large distances, but inequalities that are socially conspicuous precisely 

because they are increasing in small neighborhoods. What measures can achieve this 

aim? In our view, stronger social minimums—a rising minimum wage—would be a 

constructive first step. Support for stronger labor organizations, to recapture some of the 

56
solidarity that characterized American communities, comparatively speaking, in the late 

1960s, would be a second step and, for manufacturing in particular, perhaps an even more 

important measure. 

57
References 

Bernard, A. B., Bradford Jensen, J. (1998). Understanding Increasing and Decreasing 

Wage Inequality, NBER Working Paper No. W6571. 

Conceição, P., Galbraith, J. K. (2000), “Constructing Long and Dense Time-Series of 

Inequality Using the Theil Index,” Eastern Economic Journal, 26(1): 61-74. 

Jensen, Bradford J. (1998). Birth and Death of Manufacturing Plants and Restructuring 

of Appalachia’s Industrial Economy, 1963-1992, Washington DC: Appalachian Regional 

Commission. 

McGuckin and Pascoe (1988) “The Longitudinal Research Database (LRD): Status and 

Research Possibilities.” Survey of Current Business, Vol. 68, No. 11, pp. 31-37. 

Shannon, C. E. A Mathematical Theory of Communication, Bell System Technical 

Journal, vol. 27, 1948, 379-423. 

Theil, H. Economics and Information Theory. Chicago: Rand McNally and Company, 

1967. 

Topel, R. H. (1994). “Regional Labor Markets and the Determinants of Wage 

Inequality,” American Economic Review, 84(2), 17-22. 

58
Appendix 1. Notes on the Theil Index*

Henri Theil (1967) first noted the possibility of using Claude Shannon’s (1948) 

information theory to produce measures of income inequality. Shannon’s theory was 

motivated by the need to measure the value of information. Shannon argued that the more 

unexpected an event is, the higher the yield of information it would produce. To 

formalize this idea, Shannon proposed to measure the information content of an event as 

a decreasing function of the probability of its occurrence. Adding some axiomatic 

principles, most importantly that independent events should yield information 

corresponding to the sum of the individual events’ information, Shannon chose the 

logarithm of the inverse of the probability as the way to translate probabilities into 

information. The logarithm allows the decomposition of the multiplicative probabilities 

into additive information content. 

If we have a set of n events, one of which we are certain is going to occur, and 

each with a probability xi of occurring, then and the expected information 

content is given by Shannon’s measure: 

xi

i

n

=

∑ = 1

1

[1] H x

x

i

i

n

i

= =

∑

1

1 log

The information content is zero when one of the events has probability 1; we draw no 

information from the occurrence of an event we are sure is going to happen. The 

information content is maximum when x

n

i i = = 1 , 1,...,n ; in this case H = log n. In other 

words, maximum information is derived from the occurrence of one event in a context of 

maximum uncertainty. To borrow from thermodynamics, maximum information is 

derived from a state of maximum disorder, or maximum entropy. This is the reason why 

entropy is used as a synonym of expected information. 

Theil was attracted to information theory because it might lead toward a general 

partitioning theory. Beyond dividing certainty (probability 1) into various uncertain 

probabilities, information theory presented an opportunity to devise measures for the way 

in which some set is divided into subsets. Theil considered it natural to apply information 

theory to the partitioning of overall income throughout the taxpayers of a country. If we 

were to apply Shannon’s measure directly to individual shares of income, we would have 

a measure of equality (recall that the maximum of Shannon’s measure occurs when all 

the shares are equal). Therefore, Theil proposed to subtract Shannon’s measure from log 

n, leading to his well-known measure of inequality: 

[2] T

n

r r i i

i

n

= =

∑

1

1

.log

 

*

 Adapted from Conceição and Galbraith (2000). 

59
Where ri is the ratio between individual income (yi) and average income (μY):

r

y

y

n

i

i

Y

Y

i

i

n

= = =

∑

μ , μ 1 . The value of the Theil index (T index) is a monotonically 

increasing measure of inequality in the distribution of income, bounded by T n ∈[ ] 0,log .

Theil argues that the fact that T does not have an upper bound but depends always 

on population size is desirable. Consider a society with only two individuals in which one 

earns all the income. In this case, T = log 2. Next, consider another society in which all 

the income is again concentrated in one person, but the overall population is now one 

thousand. In this case, T = log 1000, a much higher value as desired in a much more 

unequal society. 

Consider now a different situation: if the division of income in this larger society 

were in the same proportion as in the first (half of the population having all the income), 

then we would have again T = log 2 for the larger society, as is to be expected. In 

general, Theil showed that T , in which θ is the proportion of the population 

having all the income (1/2 in our last example). This is independent of the size of the 

population. 

= log 1

θ

This feature of the Theil index is important in the context of the report because 

the value of the Theil index depends on the size of the population. Since we are 

considering counties with fairly different number of plants, the comparison of the Theil 

values should be made having in mind that the values of the Theil index that we are 

reporting are not relative, in the sense that the upper bound differs according to the 

number of plants being considered. We chose not to normalize the Theil values to include 

in the inequality measure the added information associated with the size of the 

population, in the context described in the previous two paragraphs. 

Theil’s measure has all of the desirable properties of an inequality measure: it is

symmetric (invariance under permutations of individuals), replication invariant

(independent of population replications), mean independent (invariant under scalar 

multiplication of income), and satisfies the Pigou-Dalton property (inequality increases as

a result of a regressive transfer). It is also Lorenz-consistent, meaning that it agrees with 

the quasi-ordering that can be derived from comparing Lorenz curves. 

An important characteristic of entropy-based indexes such as the Theil index is 

that they are decomposable. If individuals are grouped in a mutually exclusive, 

completely exhaustive way, overall inequality can be separated into a between-group

component and a within-group component. If we consider that the population is divided 

into m groups, g1, g2, ..., gm, each with nj individuals, j=1, ..., m, then the decomposition 

takes the self-similar form of a fractal: 

[3] 

T p R R p R

T

n

r r

j j

j

m

j j

j

m

j j

j

j

i

i g

i

j

= +

=















= =

∈

∑ ∑

∑

1 1

1

log

log

T

60
The population proportion in each group is represented by p

n

n j

j = and the ratio of

average group income to overall average income by Rj

j

Y

= μ

μ . 

There are several reasons why it may be of interest to have a decomposable 

measure of inequality. One might be interested in analyzing the functional distribution of 

income according to some criterion that divides the overall population into groups. 

Examples are race, gender (both of which were explored by Theil in 1967), education 

level, economic sector, age, to name a few. Another reason might be associated with

geography (different regions, like, say, states or countries, which were explored also by 

Theil in 1967). Another possibility is study differences in urban vs. rural populations. Yet 

another reason may be related to the differentiation of sources of income.

A further important motivation, again recognized by Theil himself, is associated 

with data. Data on income is often reported in income brackets, which do not give 

information on what is the distribution of income within the income bracket. Theil 

explored how the decomposition properties of the T index might help in devising 

measures of inequality not based on percentiles. 

61
Appendix 2. Extremes of Equality and Inequality at the County Level in 

the US by State 

Table 9- Most Unequal (Left) and Most Equal (Right) Counties in the US by State

Marengo 0.09 Shelby 0.19 Bibb 0.006 Geneva 0.025

Escambia 0.08 Russell 0.15 Conecuh 0.008 Pike 0.036

Russell 0.08 Marengo 0.15 Lamar 0.008 Winston 0.042

NonARC Counties 0.07 NonARC Counties 0.14 Geneva 0.010 Pickens 0.042

Dallas 0.07 Clarke 0.14 Elmore 0.012 Coffee 0.045

NonARC Counties 0.11 Whitfield 0.18 Tattnall 0.007 Hancock 0.005

Haralson 0.09 Washington 0.14 Gilmer 0.007 Randolph 0.014

Glynn 0.07 Thomas 0.13 Hancock 0.007 Walker 0.014

Lowndes 0.07 Greene 0.13 Greene 0.008 Johnson 0.021

Jackson 0.06 Barrow 0.13 Baldwin 0.008 Hart 0.021

ARC Counties 0.09 Muhlenberg 0.17 Grayson 0.009 Adair 0.020

NonARC Counties 0.09 ARC Counties 0.13 Adair 0.011 Casey 0.020

Boone 0.07 Warren 0.12 Monroe 0.012 Laurel 0.027

Christian 0.07 Franklin 0.12 Wayne 0.013 Monroe 0.028

Pike 0.06 NonARC Counties 0.11 Simpson 0.013 Nelson 0.029

Somerset 0.17 Allegany 0.09 Baltimore 0.014 Kent 0.023

Talbot 0.11 Baltimore 0.08 Howard 0.022 Carroll 0.034

Dorchester 0.08 Montgomery 0.08 Allegany 0.026 Dorchester 0.036

Charles 0.06 Prince George's 0.08 Anne Arundel 0.026 Garrett 0.036

Cecil 0.06 Harford 0.07 Carroll 0.027 Talbot 0.037

NonARC Counties 0.09 Franklin 0.16 Scott 0.005 Montgomery 0.021

ARC Counties 0.06 Marion 0.14 Itawamba 0.008 Panola 0.025

Jones 0.06 Monroe 0.13 Leflore 0.013 Leflore 0.028

Forrest 0.06 Pike 0.11 Washington 0.013 Prentiss 0.036

Harrison 0.05 Copiah 0.10 Noxubee 0.014 Grenada 0.037

New York 0.09 New York 0.26 Schuyler 0.007 Schoharie 0.002

Schoharie 0.07 Westchester 0.18 Delaware 0.010 Wyoming 0.025

Suffolk 0.07 Richmond 0.11 Tompkins 0.013 Putnam 0.029

Bronx 0.07 Kings 0.11 Cortland 0.018 Lewis 0.034

Saratoga 0.06 Queens 0.11 Genesee 0.018 Franklin 0.035

Lenoir 0.10 Columbus 0.16 Cabarrus 0.005 Rutherford 0.014

Columbus 0.08 Craven 0.12 Macon 0.005 Chowan 0.020

Pamlico 0.08 Cumberland 0.11 Cherokee 0.007 Ashe 0.020

ARC Counties 0.07 NonARC Counties 0.11 Rutherford 0.007 McDowell 0.020

Beaufort 0.06 Pamlico 0.11 Ashe 0.011 Anson 0.021

Clermont 0.06 Richland 0.10 Trumbull 0.010 Gallia 0.020

Athens 0.06 Clermont 0.10 Shelby 0.011 Champaign 0.021

Gallia 0.05 Summit 0.09 NonARC Counties 0.012 Hocking 0.021

Preble 0.05 Cuyahoga 0.09 Defiance 0.013 Madison 0.022

Hancock 0.05 Lawrence 0.09 Holmes 0.014 Putnam 0.023

North Carolina

1967 1992 1967 1992

Ohio Ohio

North Carolina

1967 1992 1967 1992

New York New York

1967 1992 1967 1992

Mississippi Mississippi

1967 1992 1967 1992

Maryland Maryland

1967 1992 1967 1992

Kentucky Kentucky

1967 1992 1967 1992

Georgia

1967 1992

Alabama

1967 1992

Georgia

1967 1992

1967 1992

Alabama

Pennsylvania Pennsylvania

Schuylkill 0.06 Allegheny 0.12 Beaver 0.010 Elk 0.028

Bedford 0.06 Susquehanna 0.10 Elk 0.011 Mifflin 0.034

Carbon 0.06 Lehigh 0.10 Sullivan 0.017 Sullivan 0.034

Philadelphia 0.06 Greene 0.10 Juniata 0.018 Union 0.035

Snyder 0.06 Northampton 0.10 Mercer 0.018 Mc Kean 0.037

Clarendon 0.07 Saluda 0.19 Pickens 0.009 Chesterfield 0.025

Charleston 0.07 Dorchester 0.13 Horry 0.012 Laurens 0.028

Darlington 0.06 Darlington 0.12 Newberry 0.014 Cherokee 0.038

Dorchester 0.06 Bamberg 0.11 Allendale 0.014 Sumter 0.038

Florence 0.06 NonARC Counties 0.10 Cherokee 0.015 Oconee 0.043

ARC Counties 0.09 NonARC Counties 0.13 Carroll 0.006 Marshall 0.013

McMinn 0.08 Campbell 0.10 Dickson 0.010 Tipton 0.023

NonARC Counties 0.07 ARC Counties 0.10 Warren 0.010 Lauderdale 0.025

Madison 0.05 Carroll 0.10 White 0.010 Dyer 0.025

Putnam 0.05 Unicoi 0.10 Campbell 0.014 Madison 0.038

Northumberland 0.12 Fairfax 0.15 Floyd 0.003 Essex 0.012

Westmoreland 0.09 Prince Edward 0.14 Louisa 0.006 Newport News 0.018

Arlington 0.08 Amherst 0.14 Page 0.007 King and Queen 0.018

Hampton 0.07 Pulaski 0.13 Brunswick 0.007 Harrisonburg 0.021

Lancaster 0.07 Arlington 0.11 Prince Edward 0.008 Caroline 0.021

Barbour 0.06 Barbour 0.11 Marion 0.012 Ritchie 0.031

ARC Counties 0.06 Logan 0.11 Lewis 0.012 Cabell 0.036

Berkeley 0.05 ARC Counties 0.10 Logan 0.013 Harrison 0.038

Randolph 0.05 Ohio 0.09 Kanawha 0.014 Webster 0.039

Wayne 0.04 Wood 0.09 Mineral 0.014 Marion 0.040

Virginia Virginia

1967 1992 1967 1992

West Virginia West Virginia

1967 1992 1967 1992

1967 1992

Tennessee Tennessee

1967 1992 1967 1992

South Carolina South Carolina

1967 1992 1967 1992

1967 1992

Note: ARC counties are in bold.

62
Appendix 3. Data Sources and Description 

The data used in this paper are from the Longitudinal Research Database (LRD). The 

LRD is composed of the Census of Manufactures from each year it was collected 

starting in 1963 (63, 67, 72, 77, 82, 87, and 92). The data items available on the LRD 

include employment, wages, output, capital, material inputs, among other. The plants on 

the LRD are assigned unique identifiers that allow the plants to be tracked over time, 

creating a panel data set. For more information on the LRD, see McGuckin and Pascoe 

(1988). 

LRD Coverage by Two-digit Major Industry Groups based on the Standard Industrial 

Classification of 1987 

SIC 20 Food and kindred products 

SIC 21 Tobacco products 

SIC 22 Textile mill products 

SIC 23 Apparel and other finished fabric products 

SIC 24 Lumber and wood products 

SIC 25 Furniture and fixtures 

SIC 26 Paper and allied products 

SIC 27 Printing, publishing and allied industries 

SIC 28 Chemicals and allied products 

SIC 29 Petroleum refining and related industries 

SIC 30 Rubber and miscellaneous plastic products 

SIC 31 Leather and leather products 

SIC 32 Stone, clay, glass and concrete products 

SIC 33 Primary metal industries 

SIC 34 Fabricated metal products, except machinery and transportation equipment 

SIC 35 Industrial and commercial machinery and computer equipment 

SIC 36 Electronic and other electrical equipment & components 

SIC 37 Transportation equipment 

SIC 38 Measuring, analyzing and controlling instruments; photographic, medical 

and optical goods; watches and clocks 

SIC 39 Miscellaneous manufacturing industries 

63