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Economic

Research

Service

Rural

Development

Research Report

Number 50

Economic Structure

and Change in

Persistently LowIncome Nonmetro

Counties

Robert A. Hoppe
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The Economic Research Service has no copies for free mailing.
Economic Structure and Change in Persistently Low-Income Nonmetro Counties.

By Robert A. Hoppe, Agriculture and Rural Economics Division, Economic Research

Service, U.S. Department of Agriculture. Rural Development Research Report No. 50.

Abstract

Some nonmetro areas, largely in the South, remained as severely depressed during

the seventies as they had been for decades, despite nonmetro America's general

economic gains during the period. These counties differed from other nonmetro

counties in location, population characteristics, economic structure, and farm structure. However, some severely depressed counties improved their incomes in the

seventies, primarily through nonfarm industries such as services and manufacturing.

Mining also provided a large share of the growth in some of the counties with the

largest income improvements. Farming helped some counties, but farm income has

been too erratic recently to be reliable. Nothing guarantees a county's escape from

low-income status; some of those that escaped earlier in the decade returned to lowincome status by 1979.

Keywords: Economic growth, low-income areas, nonmetro income, per capita income,

poverty, rural poverty

Acknowledgments

Dorothy H. Atkins, Sharon Black, and Blenda Gately helped prepare this report.

Calvin Beale, Herman Bluestone, Thomas A. Carlin, Linda M. Ghelfi, Bemal L. Green,

Peggy J. Ross, and Leslie Whitener all made useful suggestions.

Washington, DC 20005-4788 October 1985
Contents

Page

Glossary v

Summary vii

Introduction 1

County Classification and Methodology 1

Data Sources 2

SPLI Counties Versus Nonmetro Counties 2

Location 2

Population Characterisiics 2

Economic Structure 5

Farm Ciiaracteristics . 7

Results from Discriminant Analysis 9

Economic Change 9

Income Relatives 10

Earnings: Largest Source of Growth 10

SPLI and LPLI Counties 10

The 1975 and 1979 Escapees 13

Summary and Implications 14

SPLI Counties: A Summary 14

Differences Among Low-Income Groups 14

Escaping Low-Income Status 14

Income Growth: A Panacea? 15

Bibliography 16

Appendix A—Southern SPLI Counties Versus Other Southern Counties 17

Demographic Characteristics 17

Economic Characteristics 17

Summary for the South 19

Appendix B—Using Discriminant Analysis to Differentiate

Between Groups 20

Procedures 20

Results: PL! and SPLI Functions 21

Results: Improvement Functions 23

Conclusions 25

IV
Glossary

College counties. Counties that contain a senior State

college.

Disabled. Noninstitutional population between 16 and

64 years that reported a work disability. A disability is a

physical or mental health condition lasting as least

6 months that limits the kind and amount of work one can

perform. An area's disabled population is measured by the

proportion of the people between 16 and 64 years old

reporting a work disability.

Earnings (earned income). The sum of wages and

salaries, other labor income, and proprietors' income.

Elderly. Anyone 65 years old or older.

Employed. Employed workers at least 16 years old. An

area's employed population is measured by employed

workers at least 16 years old as a percentage of the total

population.

1975 escapees. Persistently low-income counties that

escaped their low per capita income status in 1975 but

returned to it in 1979.

1979 escapees. Persistently low-income counties that

retained their low per capita income status in 1975 but

shed it in 1979.

Nonmetropolitan (nonmetro) areas. Counties that are not

part of a Standard Metropolitan Statistical Area. (See

Standard Metropolitan Statistical Area.)

Nonwhite population. Blacks, Indians, Eskimos, Aleuts,

Asians, Pacific Islanders, and members of other nonwhite

races not elsewhere classified.

Other labor Income. Largely employer contributions to

private pension and welfare funds.

Persistently low-Income (PLI) counties. Counties with

low per capita income in 1950, 1959, and 1969.

Personal income. Total income individuals receive from

wages and salaries, other labor income, proprietors'

income, property income, and transfer payments.

Poverty rate. Percentage of the population that is poor.

Poverty status was determined by comparing 1979 income

to an income cutoff that varies with family size and composition. For example, a family of four with two children

was poor if its income was below $7,356. Both the count

of the poor and the count of the population exclude

inmates of institutions, members of the Armed Forces in

barracks, college students in dormitories, and unrelated

individuals less than 15 years old.

Property Income. Dividends, interest, and rent.

Proprietors' income. Self-employment income of sole proprietors and partners.

High scliool graduates. Population 25 years old or older

that graduated from high school. An area's high school

graduates are measured as the percentage of the population at least 25 years old that graduated from high school.

Income relative. An area's per capita personal income as

a percentage of the U.S. average. An area's income

relative is calculated by dividing its per capita personal

income by the national per capita income.

Lost persistently low-income (LPLI) status counties.

Persistently low-income counties that lost their low per

capita income status in both 1975 and 1979.

Low-income status. Counties are low-income in a given

year if they are in the bottom fifth when ranked by per

capita personal income.

Metropolitan (metro) areas. Counties that are part of a

Standard Metropolitan Statistical Area. (See Standard

Metropolitan Statistical Area.)

Retirement counties. Counties with net inmigration of

whites 60 years old or older of at least 10 percent between

1960 and 1970.

Rural. See urban.

Services. Transportation, communications, public utilities,

wholesale and retail trade, finance, insurance, real estate,

business services, personal services, and agricultural

services.

Spanish population. People of Spanish origin, as determined by a self-identification question on the 1980 Census

questionnaire. May be of any race, white or nonwhite.

Standard Metropolitan Statistical Area (SMSA). A county

or group of counties containing at least one central city

with a population of 50,000 or more, or a central city with

a population of at least 25,000 if the city's population plus

the population of contiguous, thickly populated places

equals 50,000 or more. Additional contiguous counties are
considered part of an SMSA if they are economically and

socially integrated with the central city.

Still persistently low-income (SPLI) counties. Persistently low-income counties that retained their low per

capita income status in both 1975 and 1979.

Transfer payments. Payments for which no work was

done in the current time period. Transfers are largely from

government programs, such as Social Security, Unemployment Compensation, Medicare, and public assistance

(welfare).

Urban. The urban population lives in urbanized areas or in

places with populations of 2,500 or more outside urbanized areas. (See urbanized area.) The remaining population is rural. Metro is not synonymous with urban, and

nonmetro is not synonymous with rural. (See metro, nonmetro, and SMSA.) One can be both nonmetro and urban.

For example, a person living in a city of 2,500 inhabitants

or more in a county outside an SMSA is both nonmetro

and urban. Similarly, a person living on the fringes of an

SMSA in the open country or in a small city with less than

2,500 inhabitants is both metro and rural.

Urbanized area. An area that has a total population of at

least 50,000 and a population density of at least 1,000 per

square mile. An urbanized area generally consists of a

central city and its surrounding, densely settled suburbs.

Urbanized and metro are not synonymous. Not all parts of

all metropolitan counties are densely populated enough to

be classified as urbanized.
Summary

Some nonmetro areas, largely in the South, remained as

severely depressed during the seventies as they had been

for decades, despite nonmetro America's economic gains

during the period. These counties differed from other

nonmetro counties in location, population characteristics,

economic structure, and farm structure.

However, some severely depressed counties improved

their incomes in the seventies, primarily through nonfarm

industries such as services and manufacturing. Mining

also provided a large share of the growth in some of the

counties with the largest income improvements. Farming

helped some counties, but farm income has been too erratic recently to be reliable. Nothing guarantees a county's

escape from low income; some of those that escaped

earlier in the decade returned to low-income status by

1979.

This report builds on an earlier study that identified

298 nonmetro counties that had low incomes in 1950,

1959, and 1969, and examines what happened to those

persistently low-income (PLI) counties in the seventies.

This study divides the original 298 PLI counties into four

classifications:

• Still persistently low-income (SPLI) counties. The

231 PLI counties that retained their low-income status

in both 1975 and 1979.

• Lost persistently low-income (LPLI) status counties. PLI counties that lost their low-income status in

both 1975 and 1979.

• 1975 escapees. PLI counties that lost their lowincome status in 1975 but returned to it in 1979.

• 1979 escapees. PLI counties that retained their lowincome status in 1975 but lost it in 1979.

SPLI counties differed markedly from all nonmetro counties in location, population characteristics, economic structure, and farm structure. All except 18 of the counties

were located in the South. SPLI counties had higher rates

of work-limiting disabilities, larger nonwhite populations,

more female-headed families with no husband present,

fewer high school graduates, and fewer workers. The SPLI

group depended heavily on transfer payments (such as

Social Security and Unemployment Compensation) for

income and had a poverty rate much above the nonmetro

average. Almost 30 percent of the SPLI population lived in

poverty. Farms tended to be small in the SPLI group, with

acreages averaging slightly over half the nonmetro

average.

The remaining 67 counties lost their low-income status in

1975, in 1979, or in both 1975 and 1979. Most of the

economic growth that enabled these counties to lose their

low-income status came from nonfarm sources. However,

economic growth alone is not a panacea for all of a lowincome county's problems. Personal income in a county

may grow rapidly but not be evenly distributed. In such a

county, groups of people could remain in poverty, in spite

of impressive income growth. For example, three LPLI

counties had white poverty rates less than 10 percent and

black poverty rates above 30 percent. LPLI counties still

had high disability rates and low educational and employment levels.

In addition, loss of low-income status is not necessarily

permanent. The 1975 escapees returned to low-income

status in 1979, in part because of falling farm income.

And, LPLI counties that lost their low-income status

because of a revival of the coal mining industry may

regain it during recessions severe enough to reduce the

demand for coal.

VII
Economic Structure and Change

in Persistently Low-income

Nonmetro Counties

by Robert A. Hoppe'

Introduction

Some nonmetro areas remained as severely depressed

during the seventies as they had been for decades,

despite economic growth and development in rural

America during the period. Other depressed areas

improved their incomes. Davis ranked nonmetropolitan

counties by per capita personal income in 1950, 1959, and

1969 (3).^'2 He identified 298 nonmetropolitan counties that

ranked in the lowest per capita income quintile in all 3

years, and analyzed economic changes in these persistently low-income (PL!) counties between 1969 and

1975. He found that improved earnings from mining and

farming helped some counties escape the bottom quintile

by 1975. This report builds on the Davis study by examining changes in his PLI counties throughout the seventies.

Understanding economic growth in these counties may

contribute to developing more effective policies for lowincome rural areas.

County Classification and IVIethodology

This report divides Davis' original 298 PL! counties into

the following four low-income groups based on their 1975

and 1979 per capita income ranks:

Classification

Ranked in bottom quintile in: Number

of

1950, 1959, 1969 :: 1975 : 1979 counties

SPLI (still PLI) Yes Yes Yes 231

LPLI (lost PLI status) Yes No No 27

1975 escapees Yes No Yes 11

1979 escapees Yes Yes No 29

Total NA NA NA 298

Using 2 nonconsecutive years helps distinguish between

counties with fluctuating income and counties with truly

improving income. Both 1969 and 1979 were near the

peak of a business cycle (7 7). During 1975, however, the

economy contracted for the first quarter of the year and

expanded for the last three quarters. Using years from different parts of business cycles allows us to see how lowincome counties performed relative to other counties under

differing business conditions.

An original PLI county was classified as a still persistently

low-income (SPLI) county if it remained in the bottom quintile in both 1975 and 1979. Of the 298 original PL! counties, 231 were SPLI counties.

A county was deemed to have lost its persistently lowincome (LPLI) status if it escaped the bottom quintile in

both 1975 and 1979. Only 27 of the original PL! counties

lost their low-income status during the seventies.

The remaining original PLI counties were more difficult to

classify. Twenty-nine counties (labeled 1979 escapees)

ranked in the bottom quintile in 1975 but not 1979; 11

counties (designated 1975 escapees) ranked in the bottom

quintile in 1979 but not 1975.

The classification deals with counties that left or remained

in the bottom income quintile after a prolonged stay. If

NA = Not applicable.

*The author is an economist with the Economic Research Service, U.S. Department of Agriculture, Washington, D.C.

^Italicized numbers in parentheses refer to references in the

bibliography at the end of the report.

2This study classified a county as nonmetropolitan if it was outside a Standard Metropolitan Statistical Area (SMSA), as defined

in the 1974 Office of Management and Budget designations. For

a more exact definition, see "Standard Metropolitan Statistical

Area" in the glossary or (22). Because Davis (3) used the 1974

SMSA designations when selecting the original persistently lowincome counties, this report also used the 1974 designations

rather than the more current Metropolitan Statistical Areas (21).
Data Sources

Much of the county data used in this report comes

from the Bureau of Economic Analysis (BEA), U.S.

Department of Commerce. BEA annually estimates

employment and personal income in each county

(78, 79). Personal income consists of wages and

salaries, other labor income, proprietors' income,

property income, and transfer payments. For more

information about BEA's income and employment

data, see (20). For definitions of various sources of

income, see (20) or the glossary.

Wages and salaries, other labor income, and proprietors' income together make up earnings. Earnings are largely estimated by place of work and are

assigned to the county where workers work, not

where they live (20). BEA provides computer tapes

with estimates of earnings by industry (such as farming and mining) and by type (wages and salaries,

other labor income, and proprietors' income) for

each county. BEA also estimates employment by

place of work.

Personal income for each county is derived from

total earnings by place of work. Earnings are adjusted to reflect commuting after personal contributions for social insurance (PCSI), such as Social

Security, are subtracted from them. The commuting

adjustment converts earnings from a place-of-work

basis to a place-of-residence basis. Finally, transfer

payments and property income are added to adjusted earnings to get personal income by place of

residence. No adjustments to transfers or property

income are necessary, for both are estimated by

place of residence.

Thus, an estimate of earnings adjusted for both

PCSI and commuting was available only for total

earnings. In this report, estimates of earnings by

type and by industry are restricted to earnings

before the commuting and PCSI adjustments.

When total earnings are compared to transfer and

property income, however, earnings are adjusted

for commuting and PCSI. The adjustments put

earnings, transfers, and property income on the

same measurement basis and allow them to sum to

total personal income.

In addition to using BEA data, this report draws

heavily from the censuses of population and

agriculture (14, 15). The most current demographic

data available are from the 1980 Census of Population. The income data from the census are for

1979, the year before the census was conducted.

Therefore, 1979 BEA income and employment data

are also used rather than data from a later year.

Similarly, data from the 1978 Census of Agriculture,

the agricultural census closest to 1979, are used.

some counties left the bottom quintile, others have

entered. This report says nothing about these new entrants; it only examines the chronically low-income counties and attempts to show how some of them managed to

improve their income relative to other counties.

The remainder of this report is divided into two parts. The

first part compares the SPLI counties with all nonmetro

counties; the second part shows how some low-income

counties improved their income status during the

seventies.

SPLI Counties Versus Nonmetro

Counties

Focusing on the SPLI counties helps us understand how

low-income areas differ from other nonmetro areas. The

SPLI counties differ markedly from all nonmetro counties

in location, population characteristics, economic structure,

and farm structure. Data for the other low-income groups

are included in the tables for readers interested in making

their own comparisons. Much of the other groups' data is

also discussed later in the report.

Location

All except 18 SPLI counties were located in the South (fig.

1). Four Southern States—Georgia, Kentucky, Mississippi,

and Tennessee—each had more than 20 SPLI counties

(table 1).3

Population Characteristics

Over 3.5 million people, or about 6 percent of the

nonmetro population, lived in SPLI counties in 1980. Nonwhites made up a much larger share of the population in

30ne may wonder if southern SPLI counties were only

marginally different from other nonmetro southern counties, in

most respects, however, the characteristics of other nonmetro

southern counties were much closer to those for all nonmetro

counties than to those for southern SPLI counties. Appendix A

compares selected characteristics of southern SPLI and other

nonmetro southern counties.
the SPLI group than in nonmetro counties as a whole

(table 2). However, the racial composition of the population

among the SPLI counties varied widely. Of the 231 SPLI

counties, 93 were over 90 percent white in 1980 and 48

were over 50 percent black. Indians accounted for 65

percent of the population in Sioux County, ND; 78 percent

in Todd County, SD; and 93 percent in Shannon County,

SD. People of Spanish origin made up 80 percent of the

population in the six SPLI counties near the Rio Grande in

Colorado, New Mexico, and Texas. Almost all SPLI counties were either southern highland counties with largely

white populations or counties with sizable black, Indian, or

Spanish populations.

The SPLI population per county was about 10,000 less

than the 25,500 nonmetro average (table 2). This resulted

from small county size rather than low population density,

for the SPLI population density actually exceeded the

Figure 1

Persistently low-income counties are mainly in the South

SPLI counties

LPLI counties

D 1975 escapees

1979 escapees
nonmetro average.'* A few SPLI counties had extremely

low population densities. For example, all eight of the SPLI

counties in the Northern and Western States of Minnesota,

the Dakotas, Colorado, and New Mexico had fewer than

10 people per square mile. Rural people represented over

80 percent of the population in the SPLI group, with 125 of

the 231 SPLI counties being entirely rural (table 2).^

People in SPLI counties were more likely to report a worklimiting health disability than the nonmetro population in

general (table 2). The highest disability incidence was in

Searcy County, AR, where 22.3 percent of the population

between 16 and 64 years of age reported a work-limiting

disability. Ten other SPLI counties also had disability incidences of at least 20 percent.

Table 1—Geographic distribution of low-income

nonmetro counties

Region and State SPLI LPLI 1975 1979

counties counties escapees escapees

Number

United States 231 27 11 29

Northeast 0 0 0 0

Midwest 14 0 1 0

East North Central 2 0 1 0

Illinois 1 0 1 0

Ohio 1 0 0 0

West North Central 12 0 0 0

Minnesota 1 0 0 0

Missouri 8 0 0 0

North Dakota 1 0 0 0

South Dakota 2 0 0 0

South 213 27 10 29

South Atlantic 62 9 5 6

Florida 5 1 1 0

Georgia 25 5 2 5

North Carolina 14 0 0 0

South Carolina 8 1 1 1

Virginia 4 2 1 0

West Virginia 6 0 0 0

East South Central 117 10 2 11

Alabama 17 1 1 2

Kentucky 33 7 1 1

Mississippi 40 2 0 5

Tennessee 27 0 0 3

West South Central 34 8 3 12

Arkansas 10 5 3 3

Louisiana 9 0 0 4

Oklahoma 10 0 0 3

Texas 5 3 0 2

West 4 0 0 0

Mountain 4 0 0 0

Colorado 1 0 0 0

New Mexico 3 0 0 0

The elderly made up about the same percentage of the

population in the SPLI group as in all nonmetro counties

(table 2). The percentage of adults at least 25 years old

who graduated from high school was substantially less in

the SPLI group than in all nonmetro counties.

The SPLI poverty rate was almost double the nonmetro

average (table 2). Almost 30 percent of the SPLI population lived in poverty. Two SPLI counties—Starr, TX, and

Tunica, MS—each had poverty rates above 50 percent;

they were the poorest counties in the Nation in 1979.

Another 21 SPLI counties had poverty rates of at least 40

percent. The rate for SPLI blacks was particularly high,

double the rate for SPLI whites, triple the rate for the total

nonmetro population, and 9 percentage points higher than

the rate for the total nonmetro black population.

People in families with a female head of household and no

husband present have one of the highest poverty rates in

the Nation (16). Concentrations of these female-headed

families partially explain the low-income status of individual

counties. The SPLI group had a higher incidence of these

families than all nonmetro counties (table 2). Two-thirds of

the SPLI counties had a greater percentage of femaleheaded families than the 10.9 percent nonmetro average.

Employed workers at least 16 years old expressed as a

percentage of total population is a dependent population

measure. The percentage employed varies inversely with

the number of dependents. When the percentage

employed is small, the population that must depend on the

earnings of others tends to be large. These dependents include children, many elderly, the disabled, and the

unemployed. Only one-third of the SPLI population worked,

compared with two-fifths of the total nonmetro population

(table 2).

Retirees, colleges, and proximity to an SMSA can contribute to economic well-being (1). Retirees receive Social

Security and other pensions, students bring money from

home, and local people can commute to a nearby SMSA

to work. A smaller share of the SPLI counties was

classified as college counties or adjacent to an SMSA than

the nonmetro average. A slightly larger share of the SPLI

counties, however, was classified as retirement counties.

Source: (79).

^Excluding Alaska increases the nonmetro population density to

25.3 people per square mile. After this adjustment, the SPLI

group's density about equals the nonmetro average.

^Urban people live in urbanized areas or in places with populations of 2,500 or more outside urbanized areas. (73). (Urbanized

areas have total populations of 50,000 or more and population

densities of at least 1,000 per square mile.) The remaining

population is rural. One can simultaneously be both nonmetro and

urban. People living in a city of 2,500 inhabitants or more in a

county outside an SMSA are both nonmetro and urban.
Economic Structure

Earned income, or earnings, formed a smaller share of

total personal income in 1969, 1975, and 1979 in the SPLI

group than In nonmetro counties in general (table 3). The

same was true for property income from dividends, interest, and rent. Transfer payments were a larger portion

of personal income in the SPLI group. However, this does

not mean that per capita transfers were higher in the SPLI

group. The 1979 per capita transfer payment in the SPLI

group was $1,074, slightly less than the $1,106 nonmetro

average. In low-income areas, even a small transfer payment can be a large share of total income.

Public assistance formed a relatively small portion of SPLI

transfer payments. It comprised 20 percent of total

transfers in SPLI counties, while retirement programs, including Social Security, made up 59 percent. NeverTable 2—Selected population characteristics of low-income and all nonmetro counties

Itemi Unit SPLI

counties

LPLI

counties

1975

escapees

1979

escapees

All

nonmetro

counties

Counties Number 231 27 11 29 2,469

1970 population

1980 population

Population growth

Thousands

do.

Percent

3,106

3,524

13.5

483

581

20.3

130

148

13.8

413

487

17.9

54,424

63,002

15.8

Nonwhite (1980)

Black

Indian

Other

do.

do.

do.

do.

27.9

25.1

1.7

1.1

16.8

15.4

.7

.7

24.4

23.9

.3

.2

26.6

24.0

2.3

.3

11.8

8.7

1.2

1.9

Spanish (1980) do. 3.2 1.3 .7 .9 3.3

Population per county

(1980)

Population per square

mile (1980)

Number

do.

15,257

25.7

21,512

38.7

13,447

28.6

16,804

30.6

25,517

20.7

Rural (1980) Percent 83.1 71.6 81.1 75.8 59.6

Disabled (1980) do. 14.4 14.7 14.8 13.1 10.0

Elderly (1980) do, 13.1 12.4 13.9 13.4 12.8

High school graduates

(1980) do. 42.2 44.9 44.0 45.5 59.6

Poverty rate (1979);

Total

White

Black

do.

do.

do.

28.5

22.0

47.2

23.0

19.6

40.6

23.7

17.9

42.7

23.5

17.1

42.9

15.2

12.5

38.4

Female-headed families

(1980) do. 14.2 12.3 13.5 13.4 10.9

Employed population

(1980) do. 33.3 33.5 35.3 36.0 39.6

Counties classified as:

Retirement

College

Adjacent to SMSA

do.

do.

do.

16.0

2.2

31.2

7.4

7.4

37.0

9.1

0.0

18.2

24.1

10.3

55.2

14.6

7.2

39.2

iSee the glossary for definitions of terms. More detailed definitions of demographic terms, such as nonwhite, Spanish, and rural, can be

found in (13). More detailed explanations of retirement and college counties can be found in (1).

Source: (15).
theless, public assistance was a much larger share of total

transfer payments in the SPLI group than in all nonmetro

counties. Only 9 percent of all nonmetro transfers, or less

than 50 percent of the percentage in the SPLI group,

came from public assistance, and about 70 percent came

from retirement programs.

The composition of income changed over time in the SPLI

group (table 3). Property's share of total income grew

gradually in the group, following the nonmetro trend.

Transfer payments' share of SPLI income, however, peaked in 1975, which contained the trough, or tow point, of a

business cycle. By 1979, when the Nation was near the

peak of a business cycle, transfers still provided a larger

share of income than in 1969, another business cycle

peak. Earnings' share of income was lowest in the SPLI

group in 1975 and did not return to its 1969 level by 1979.

Wage and salary jobs made up an increasing share of the

SPLI group's employment during the decade (table 3).

Wages and salaries' share of earnings, however, peaked

in 1975. Although farm proprietors' share of employment

declined, farming continued to provide a larger proportion

of employment and earnings in the SPLI group than in

nonmetro areas in general. Earnings per farmer, however,

were low. Only 40 of the 231 SPLI counties had earnings

Table 3—Selected sources of personal income, employment, and earnings in low-income and all nonmetro counties

At!

Source SPLI LPLI 1975 1979 nonmetro

counties counties escapees escapees counties

PercenV

Personal income:

1969—

Transfer payments 16.6 16.4 15.2 15.8 11.3

Property income 9.4 9.7 9.6 10.5 12.9

Earnings2 74.0 73.9 75.1 73.7 75.9

Total 100.0 100.0 100.0 100.0 100.0

1975—

Transfer payments 24.1 20.1 20.7 21.9 16.6

Property income 10.5 10.1 10.4 11.7 14.0

Earnings^ 65.4 69.8 68.8 66.3 69.4

Total 100.0 100.0 100.0 100.0 100.0

1979—

Transfer payments 21.6 19.1 20.8 18.1 15.3

Property income 11.0 10.8 11.7 12.2 14.8

Earnings2 67.4 70.1 67.5 69.7 69.9

Total 100.0 100.0 100.0 100.0 100.0

Employment:

1969—

Wage and salary jobs 69.4 77.9 72.2 72.0 80.3

Farm proprietors 22.3 13.8 18.9 19.1 11.4

Nonfarm proprietors 8.3 8.3 8.8 8.9 8.3

Total 100.0 100.0 100.0 100.0 100.0

1975—

Wage and salary jobs 73.1 82.1 74.9 75.4 82.3

Farm proprietors 18.4 10.4 16.4 15.8 9.5

Nonfarm proprietors 8.5 7.5 8.7 8.8 8.2

Total 100.0 100.0 100.0 100.0 100.0

1979-

Wage and salary jobs 75.1 83.2 75.3 79.2 83.5

Farm proprietors 15.8 8.8 15.1 12.4 8.0

Nonfarm proprietors 9.1 8.0 9.5 8.4 8.6

Total 100.0 100.0 100.0 100.0 100.0

See footnotes at end of tabie. Continued—
per farm proprietor greater than the nonmetro average in

1979. Of the 40 counties, 14 were in Mississippi and

another 8 were in Georgia.

The source of earnings by industry in the SPLI group did

not change dramatically (table 4). Services comprised

about one-third of earnings in each year; manufacturing,

about one-quarter; and government, about one-fifth. The

SPLI group generally had more earnings from farming

than nonmetro counties in general, although the percentage from farming in the group fell after 1969.

Farm Characteristics

Farms tended to be small in the SPLI group, with an

average acreage slightly over half as large as the

nonmetro average (table 5). This small farm size is

reflected in the small percentage of the group's farm

operators who reported farming as their principal occupation. Operators of small farms may choose to combine

farm with off-farm work.

Average farm size among the SPLI counties varied widely,

ranging from 52 acres in Graham County, NO, to 7,761

Table 3—Selected sources of personal Income, employment, and earnings in low-income and all nonmetro

counties—continued

All

Source SPLI LPLI 1975 1979 nonmetro

counties counties escapees escapees counties

Percent ^

Earnings:^

1969-

Wages and salaries 70.8 76.9 68.4 70.4 76.1

Other labor income 3.7 5.5 3.6 3.7 4.3

Farm proprietors'

income 13.2 6.6 15.9 12.8 8.9

Nonfarm proprietors'

income 12.2 10.9 12.1 13.0 10.7

Total 100.0 100.0 100.0 100.0 100.0

1975—

Wages and salaries 73.0 75.9 65.1 74.2 76.3

Other labor income 6.0 8.6 5.1 5.8 6.3

Farm proprietors'

income 9.9 5.8 20.9 9.1 8.8

Nonfarm proprietors'

income 11.2 9.7 8.9 10.8 8.5

Total 100.0 100.0 100.0 100.0 100.0

1979—

Wages and salaries 71.7 76.6 69.2 73.7 77.0

Other labor income 7.2 10.5 7.1 6.7 7.9

Farm proprietors'

income 10.7 6.0 13.8 10.9 6.9

Nonfarm proprietors'

income 10.3 6.9 9.9 8.7 8.3

Total 100.0 100.0 100.0 100.0 100.0

^Totals may not add due to rounding.

^Adjusted for commuting and personal contributions to social insurance.

3Not adjusted for commuting and personal contributions to social insurance.

Sources: {18, 19).
acres in Shannon County, SD. Only 31 of the SPLI counties, however, had an average farm size greater than the

508-acre nonmetro average.

Sales per farm in the SPLI group were about half the

nonmetro average (table 5), although sales varied within

the group. Of the 231 SPLI counties, 38 had sales per

farm that exceeded the nonmetro average. Sales per farm

in the SPLI group were highest in Sharkey County, MS,

where they reached $228,300. Sharkey County, located in

the Mississippi Delta, also had $32,605 in earnings per

farm proprietor in 1979, more than three times the

nonmetro average.

Why did Sharkey County remain poor with such high

average earnings per farm proprietor? Because prosperous

farmers were only a small proportion of the total working

population. Of the 2,258 employed people reported in the

county in the 1980 Census of Population, only 174, or 7.7

percent, were farm operators or managers (72). Lower

paid farmworkers and related occupations were much

more common, making up 17.1 percent of all workers.

Racial disparities also existed in the county. Of farm

operators and managers, 93.7 percent were white, but

81.1 percent of farmworkers and workers in related occupations were black. Blacks also had a much higher

unemployment rate, 11.8 percent compared with only 2.3

percent for whites. The poverty rate was 44 percent for the

county, 55.7 percent for blacks, and 21.6 percent for

whites.

Woodlands were a much larger share of farmland in the

SPLI group than in all nonmetro counties (table 5). This

suggests that a large portion of land in the SPLI group

may not be well suited for agriculture. Woodlands were

less than 20 percent of farmland in only 37 SPLI counties,

13 of which were in Colorado, New Mexico, North Dakota,

Oklahoma, South Dakota, and Texas. These western

counties had little harvested cropland; most of their

farmland was used for pastureland or grazing. Another 12

Table 4—Source of earnings by major industry in low-income and all nonmetro counties^

All

Year and major SPLI LPLI 1975 1979 nonmetro

industry counties counties escapees escapees counties

Percent^

1969:

Farming 16.7 8.2 19.8 15.8 10.9

Mining 2.1 12.9 .5 1.7 2.7

Construction 5.3 5.8 4.0 5.1 5.9

Manufacturing 24.1 18.6 26.6 25.3 26.9

Services 32.2 33.7 31.4 32.9 35.6

Government 19.6 20.8 17.7 19.2 18.0

Total 100.0 100.0 100.0 100.0 100.0

1975:

Farming 12.7 6.7 23.6 11.7 10.7

Mining 4.3 24.4 .7 2.3 3.9

Construction 4.8 5.0 5.7 7.1 6.0

Manufacturing 23.3 15.3 24.1 25.5 24.2

Services 33.4 29.3 27.9 33.0 36.1

Government 21.6 19.2 18.0 20.3 19.1

Total 100.0 100.0 100.0 100.0 100.0

1979:

Farming 13.2 6.8 16.5 12.8 8.7

Mining 4.4 25.3 .7 2.4 4.4

Construction 5.4 4.3 4.0 12.8 6.4

Manufacturing 25.0 17.9 30.5 21.4 26.2'

Services 32.9 30.1 31.4 29.0 37.3

Government 19.1 15.6 16.9 19.1 17.0

Total 100.0 100.0 100.0 100.0 100.0

^Not adjusted for commuting and personal contributions to social insurance.

^Totals may not add due to rounding.

Source: (19).
counties with less than 20 percent of their farmland in

woodlands were in the Mississippi Delta. At least 70 percent of the farmland in each of these counties was in

harvested crops.

Results From Discriminant Analysis

Discriminant analysis was used to further explore the

differences between SPLI counties and other nonmetro

counties. Discriminant analysis is a statistical technique

that helps researchers identify characteristics that

combine to distinguish between two or more groups (6).

A characteristic that appears by itself to be useful in

differentiating among groups of counties may not be

useful after adjusting for interactions with other

characteristics. The information presented in this section

is condensed. Appendix B presents a more detailed

discussion of discriminant analysis.

Discriminant analysis generally supported the findings

discussed earlier. The more important characteristics

were percentage of income from transfer payments;

percentage of the population that was elderly, nonwhite,

or disabled; and percentage of the population that

graduated from high school.^ SPLI counties tended to

receive a larger share of their income from transfer

payments and have larger percentages of nonwhite or

disabled population. The other nonmetro counties had

larger percentages of elderly or high school graduates.

Other variables were less effective in distinguishing

between SPLI and other counties. These less important

variables included percentage of earnings from farming

and percentage of the population that lived in rural

areas. SPLI counties were more rural and received a

larger share of earnings from farming.

Economic Change

The discussion so far shows the chronically low-income

SPLI group differed from nonmetro counties in general.

Other original PLi counties, however, left the bottom quintile during the seventies. Examining economic data will

help us understand how income growth differed among the

four low-income county groups. Patterns may emerge to

explain how counties escaped their low-income status.

sQne would not expect percentage elderly to be an important

distinguishing characteristic after examining table 2. For an explanation of why it was, see Appendix B.

Table 5—-Selected farm characteristics of low-income and all nonmetro counties, 1978

Characteristic Unit SPLI

counties

LPLI

counties

1975

escapees

1979

escapees

All

nonmetro

counties

Average farm size Acres 283 240 242 272 508

Average harvested

cropland per farm do. 76 72 85 82 151

Farmland in:

Harvested cropland

Woodland

Percent

do.

26.9

26.9

30.1

29.6

35.1

28.5

30.0

26.9

29.8

8.7

Market value of sales

per farm

1,000

dollars 23.8 24.8 34.1 26.1 47.1

Farms with sales

greater than $40,000 Percent 12.0 14.2 20.0 12.9 26.8

Tenure of farm operators:

Full owners

Part owners

Tenants

do.

do.

do.

66.7

25.2

8.1

66.9

25.4

7.8

63.4

25.6

11.0

63.9

27.8

8-3

56.2

31.2

12.6

Farm operators reporting

farming as their

principal occupation do. 46.3 42.0 52.9 43.8 58.6

Source: (14).
Per capita income grew in all groups (table 6). Per capita

income by itself, however, does not tell how a particular

group fared when compared with the rest of the Nation. To

make such comparisons, income relatives were calculated

by dividing each group's per capita personal income by

that for the Nation. Each income relative expresses a

group's per capita personal income as a percentage of the

U.S. average.

Income Relatives

The LPLI group made the most progress in the seventies

(table 6). Its income relative grew by 17.9 percentage

points between 1969 and 1979, with most of the increase

between 1969 and 1975. By 1979, its relative was only 9.4

percentage points less than that for all nonmetro counties.

The 1979 escapees also progressed during the seventies.

The group ended the decade with a relative of 68.5, or only 13.9 points behind the nonmetro average.

The 1975 escapee group showed an unusual pattern; its

relative rose between 1969 and 1975 and fell between

1975 and 1979. By 1979, its relative was only 5.2 points

higher than in 1969.

Finally, the SPLI counties also improved. The group began

the decade with an income relative of 48.6 percent and

finished with 56.8 percent. The gap between the nonmetro

and SPLI income relatives steadily narrowed during the

seventies. Nevertheless, the SPLI income relative was still

substantially less than those for the LPLI group or

nonmetro counties in general.''

Earnings: Largest Source of Growth

Examining the source of growth can help explain changes

in income relatives in the four low-income county groups.

There are three sources of personal income growth:

transfer payments, property income, and earnings.

Transfers generally provided a larger share of growth in

the low-income groups than in all nonmetro counties (table

7). Transfer payments were particularly important in the

SPLI group between 1969 and 1975. In that period,

transfer payments provided almost a third of the group's

income growth. During the second period, transfers provided 21 percent of the 1975 escapees' income growth.

Retirement programs (including Social Security and

Medicare), not public assistance, were the largest source

(about 60 percent) of transfer payment growth between

1969 and 1979 in each low-income group.

Property provided less income growth than the nonmetro

average in all the low-income groups. Earnings, however,

were the largest source of income growth in all four lowincome groups. About two-thirds of each group's total income growth from 1969 to 1979 came from earnings.

SPLI and LPLI Counties

Thus, changes in earnings show why the income relative

improved so much more in the LPLI than in the SPLI

''All groups improved markedly since the fifties. In 1959, the

SPLI group had an income relative of 40.5 percent, the other lowincome groups had relatives between 44 and 45 percent, and

nonmetro counties had a relative of 72 percent.

Table 6—Per capita personal income in low-income and all nonmetro counties

All

Item SPLI LPLI 1975 1979 nonmetro

counties counties escapees escapees counties

Dollars

Per capita personal

income:

1969 1,783 2,020 2,069 1,989 2,840

1975 3,219 4,230 3,982 3,567 4,833

1979 4,971 6,394 5,395

Percent

6,000 7,220

Relative per capita

personal income:

1969 48.6 55.1 56.4 54.2 77.4

1975 54.9 72.2 67.9 60.9 82.5

1979 56.8 73.0 61.6 68.5 82.4

Source: (79).

10
group. Large changes in earnings are reflected in large

changes in personal income.

Earnings Growth. Earnings per worker grew by 85 percent between 1969 and 1975 in the LPLI group (table 8).

This rate was far greater than the SPLI and nonmetro

rates. Earnings per worker in the group were actually

higher than the nonmetro average by 1979. Mining, largely

coal mining, provided about 34 percent of the group's

earnings increase between 1969 and 1975 and 26.8 percent between 1975 and 1979. Mining's rapid growth

changed the rank of industries within the LPLI group.

When industries were ranked by share of total earnings,

mining was the fourth largest LPLI sector in 1969 and the

second largest sector in 1975 and 1979 (table 4). Mining

was still fourth in the LPLI group when ranked by wage

and salary employment in 1975 and 1979. Mining apparently pays well but employs relatively few people.

SPLI earnings per worker also grew faster than the

nonmetro average in both the 1969-75 and 1975-79

periods (table 8). The growth was not enough for the SPLI

counties to lose their low-income status, however. The

largest sources of earnings growth were services, government, and manufacturing during the earlier period. From

1975 to 1979, the largest sources of growth were services

and manufacturing followed by government and farming.

Mining was a much smaller source of earnings growth in

the SPLI than LPLI group.

Mining played an important role in raising the LPLI group's

income, but characterizing all LPLI counties as mining

counties is misleading. Mining was the largest source of

earnings growth in only eight LPLI counties from 1969 to

1975 and in only six counties from 1975 to 1979 (table 9).

It was the second largest source of earnings growth in an

additional two counties in each period. Ten LPLI counties

had mining as their largest or second largest source of

earnings growth from 1969 to 1975 and/or 1975 to 1979.

Kentucky had seven of these counties, Virginia two, and

Alabama one. Bituminous coal was the major mineral

mined. Other important sources of earnings growth in the

LPLI group were services in both periods; manufacturing,

especially in the second period; and government in the

first period (table 8).

Demographic Differences. The LPLI and SPLI groups

also differed in racial composition; percentage of the

population classified as rural; population density; percentage of counties classified as retirement, college, or adjacent to an SMSA; population growth; and poverty rate

(table 2). A smaller portion of the population was nonwhite

or Spanish in the LPLI group. Population composition,

however, varied considerably in the LPLI group. Of the 27

LPLI counties, 11 were at least 97 percent white, 5 were

at least 37 percent black, and Calhoun County, SC, was

about 55 percent black.

Table 7—Percentage of personal income growth from transfer payments, property income, and earnings In lowincome and all nonmetro counties

Source and period

All

SPLI LPLI 1975 1979 nonmetro

counties counties escapees escapees counties

Percent

Transfer payments;

1969-75

1975-79

1969-79

Property income;

1969-75

1975-79

1969-79

Earnings:^

1969-75

1975-79

1969-79

32.6

17.4

24.1

11.6

11.9

11.8

55.7

70.7

64.1

23.1

17.2

20.1

10.5

12.0

11.2

66.4

70.8

68.6

26.1

21.0

23.8

11.2

14,9

12,9

62.7

64.1

63.3

29.0

13.1

19.2

13.1

12.7

12.9

58.0

74.2

68.0

23.1

13.0

17.5

15.4

16.2

15.9

61.5

70.8

66.6

^Adjusted for commuting and personal contributions to social insurance.

Source; (19).

11
The LPLI group was less rural and more densely

populated than the SPLI group (table 2), and no LPLI

counties had fewer than 10 people per square mile. The

LPLI group had about 10 more people per square mile in

1970 than the other group, which may have contributed to

its economic growth. Trade and service firms in sparsely

settled areas may have trouble reaching scattered

households and businesses. Business expansion is easier

where potential customers are closer together. Some

businesses may also require a market or a specialized

labor force of a minimum size before they can start.

The LPLI group also had proportionately more college

counties and counties adjacent to an S M SA and fewer

retirement counties than the SPLI group.

Some demographic differences may reflect the LPLI

group's recent economic growth. For example, the LPLI

group had higher population growth during the seventies

and lower poverty rates than the SPLI group (table 2). On

the other hand, poverty rates actually declined slightly

more in the SPLI group since 1969.

The LPLI and SPLI groups were similar in other respects

(table 2). The two group's measures of elderly, disabled,

and employed populations; high school graduates; and

female-headed households differed by only 2 or 3 percentage points at the most. The LPLI group's high disability

rate, low educational levels, and low employment ratio,

when compared with the nonmetro averages, suggest that

the group has lasting social problems despite imTable 8—Earnings per worker and source of earnings growth by major industry in low-income and all nonmetro

counties^

All

Item SPLI LPLI 1975 1979 nonmetro

counties counties escapees escapees counties

Dollars

Eamings per worker:

1969 3,723 4,519 3,806 3,871 5,406

1975 5,910 8,362 6,612 6,110 8,384

1979 8,791 11,873 8,588

Percent

10,136 11,627

Growth in earnings per

worker:

1969-75 58.7 85.0 73.7 57.8 55.1

1975-79 48.7 42.0 29.9 65.9 38.7

Source of growth in

total earnings by

major industry:

1969-75—2

Farming 6.6 5.5 27.8 6.2 10.5

Mining 7.7 34.0 1.0 3.1 5.6

Construction 3.9 4.4 7.5 9.8 6.2

Manufacturing 22.0 12.6 21.4 25.9 20.3

Services 35.2 25.6 24.0 33.1 36.8

Government 24.6 17.9 18.3 21.8 20.7

Total 100.0 100.0 100.0 100.0 100.0

1975-79—2

Farming 14.0 7.0 -3.9 13.8 5.0

Mining 4.6 26.8 .7 2.4 5.3

Construction 6.5 3.1 -.9 18.5 6.9

Manufacturing 27.6 22.1 48.7 22.6 29.8

Services 32.2 31.6 41.4 24.9 39.5

Government 15.1 9.4 13.9 17.8 13.4

Total 100.0 100.0 100.0 100.0 100.0

^Not adjusted for commuting and personal contributions to social insurance.

^Totals may not add due to rounding.

Sources: (7Ö, 79).

12
provements in these measures and impressive economic

growth in recent years.

Disability may be particularly severe in mining counties. In

1979, Social Security Disabiiity, Worker's Compensation,

and Black Lung programs together paid $394 per capita in

the five LPLI counties with mining as their largest source

of growth from both 1969 to 1975 and 1975 to 1979. The

same programs paid only $82 per capita in nonmetro

counties as a whole. Economic growth from mining has a

price: the disability rate in the five counties ranged from

14.4 to 19.3 percent compared with the 10 percent

nonmetro average.

Discriminant Analysis. The discriminant analyses in Appendix B support the indications that SPLI and LPLI

groups are quite similar in some demographic

characteristics. The discriminant analyses show that few

factors, other than economic growth from various sources,

are useful in identifying PLI counties that left the bottom

quintile in either the 1969-75 or 1975-79 periods.

Although demographic characteristics may be useful in

identifying low-income counties, they are less important in

determining which counties improved their income.

The 1975 and 1979 Escapees

The SPLI and LPLI counties either left the bottom quintile

in both 1975 and 1979 or remained in the bottom quintile

both years. The 1975 and 1979 escapees behaved more

erratically; they escaped in one year but not the other.

Changes in farm earnings largely explain the 1975

escapees' erratic behavior and their falling income relative

between 1975 and 1979. Between 1969 and 1975, farming

provided about 28 percent of the group's total earnings

growth (table 8). Farming also was the largest source of

earnings growth in 6 of the 11 counties from 1969 to 1975

(table 9). Other large sources of earnings growth during

the same period were services, manufacturing, and

government (table 8).

However, farm earnings in the county group fell between

1975 and 1979. Other sectors also contributed to the

group's slow earnings growth between 1975 and 1979.

Earnings from construction fell, and earnings from mining,

services, and government grew more slowly in the

1975-79 period than in the 1969-75 period (not shown).

Growth in the second period was largely concentrated in

services and manufacturing (table 8).

Table 9—Number of low-income and all nonmetro counties with a particular industry as their largest source of

earnings growth^

Period of industry

AH

SPLI LPLI 1975 1979 nonmetro

counties counties escapees escapees counties

Number

1969-75:

Farming

Mining

Construction

Manufacturing

Services

Government

Total

1975-79:

Farming

Mining

Construction

Manufacturing

Services

Government

Total

24

19

2

42

115

29

231

39

9

2

69

102

10

231

1

5

6

4

27

4

6

0

7

9

1

27

6

0

1

3

1

0

11

3

0

0

3

5

0

11

3

0

1

7

14

4

29

8

1

3

8

7

2

29

423

125

45

378

1,255

243

2,469

312

134

44

612

1,285

82

2,469

"INot adjusted for commuting and personal contributions to social insurance.

Source (79).

13
The relatively large number of 1979 escapees is puzzling.

Why did so many counties (29) escape the bottom quintile

in 1979 after failing to do so in 1975? The business cycle

may provide part of the answer. The year 1975 contained

a business cycle trough, while 1979 was near a business

cycle peak (17). Although local or regional business cycles

may not coincide with national business cycles, a strong

national economy may have helped to pull the 1979

escapees out of the bottom quintile.

Construction, manufacturing, services, and government all

provided substantial earnings growth in the 1979 escapee

group (table 8). The growth of some sectors was highly

concentrated in specific counties. For example, Claiborne

County, MS, the construction site of a large nuclear power

plant (2), accounted for over three-fifths of the group's

gains in construction earnings between 1969 and 1979.

Farming was the fifth largest source of earnings growth in

both periods for the 1979 escapees (table 8). Nevertheless, farming was the largest source of earnings growth

in 8 of the 29 counties from 1975 to 1979 (table 9).

A larger share of the 1979 escapees was classified as

retirement counties, college counties, or adjacent to an

SMSA than any other low-income group or nonmetro counties in general (table 2). Perhaps these factors contributed

to the group's improved income status.

Summary and Implications

Economic and demographic characteristics varied among

groups and even among counties within groups. Despite

these variations, some generalizations hold true.

SPLi Counties: A Summary

The SPLI group depended heavily upon transfer payments

for income. Over one-fifth of the group's income came

from transfers in 1979 (table 3), and almost one-quarter of

the group's personal income growth between 1969 and

1979 came from transfer payments (table 7). The group's

poverty rate remained far above the nonmetro average.

Demographic characteristics also set the SPLI group apart

from other nonmetro areas (table 2). Larger shares of the

SPLI population were nonwhite or disabled. SPLI tamiles

were more likely to have a female head, and smaller

percentages of the SPLI population graduated from high

school or worked. Over four-fifths of the SPLI population

was rural.

Differences Among Low-Income Groups

Income relatives for the four low-income groups best summarize differences in income growth and escape from lowincome status.

The SPLI group's income relative grew from 48.6 percent

to 56.8 percent between 1969 and 1979. Personal income

grew in the SPLI counties during the seventies, narrowing

the income gap between the SPLI group and nonmetro

counties in general. Yet, this growth was not enough to

move the SPLI counties out of the bottom income quintile.

The LPLI group ended the decade with an income relative

of 73 percent, or 17.9 percentage points more than in

1969, with most of the increase coming in the 1969-75

period. The LPLI group was the only low-income group

where mining was a major source of growth.

The 1979 escapees' income relative grew to 68.5 percent

by 1979. Construction, manufacturing, services, and

government were large sources of growth. A strong national economy may also have helped the counties leave

the bottom quintile in 1979.

Only the 1975 escapees' income relative grew between

1969 and 1975 and fell between 1975 and 1979. By 1979,

the group's income was 61.6 percent, or only 5.2 percentage points higher than in 1969. Decreases in farm earnings between 1975 and 1979 largely explain the group's

falling income relative and its return to the bottom quintile.

Earnings from construction also decreased, however, and

other sectors grew more slowly in the second period than

in the first.

Escaping Low-Income Status

Income growth enabled 67 of the 298 original PLI counties

to escape their low-income status, although the escape

was only temporary for some. Leading sources of growth

varied among groups and within groups; both farm and

nonfarm earnings contributed to counties leaving the bottom quintile.

Farming as an Option. Some counties escaped the

bottom quintile in either 1975 or 1979, helped at least in

part by farm income. Farming was the largest source of

earnings growth in over half of the 1975 escapees during

the first period and in over one-quarter of the 1979

escapees during the second (table 9). It was also the

largest source of growth In three or four LPLI counties in

each period. Bad weather, poor agricultural prices, or cost

increases (such as interest) could reduce farm income in

these counties. Note that the 1975 escapee counties

dependent on farming returned to the bottom quintile in

1979, and some of the LPLI counties and 1979 escapees

may have already gone back into the bottom quintile

because of falling farm income since 1979. Farm income

fluctuated after 1979. U.S. net farm income was $32.3

billion in 1979, the highest since 1973, when it was $34.4

billion (11). In 1980, net farm income fell to $21.2 billion.

14
By 1981, it grew to $31 billion, fell to $22,3 billion in 1982,

and fell again to $16.1 billion in 1983.

The importance of farming as a means to leave the bottom

quintile should not be overstated. Only 7.5 percent of the

SPLI population lived on farms in 1980. Farming alone

cannot lift many counties out of the bottom quintile.

Nonfarm Industries. Most earnings growth in all the

groups came from nonfarm sources. Services and

manufacturing generally were major sources of nonfarm

growth in all four groups in both periods. In the LPLI

group, where income improved the most, the major

sources of earnings growth were as follows: services in

both periods; manufacturing, particularly in the second

period; government in the first period; and mining in both

periods.

Counties with large income increases from coal mining

could fall back into the bottom quintile during a recession

severe enough to affect the demand for coal. For example,

a national recession, an oil glut, and energy conservation

measures reduced the demand for coal in the early

eighties (8). The unemployment rate in eastern coal mining

counties reached 13.5 percent in 1982 compared with only

9.7 for the Nation. Depending on coal mining to escape

the bottom quintile poses a problem other than business

cycles. As discussed earlier, economic growth from mining

has a price: high disability rates.

Potential for Permanent Escape. Counties need substantial growth over a long period of time to escape the bottom

quintile, but unfortunately, economic growth is often erratic. For example, both the LPLI and 1975 escapee

groups had much larger earnings growth rates than the

nonmetro average between 1969 and 1975 (table 8). Between 1975 and 1979, however, the 1975 escapees' rate

fell to 8.8 percentage points less than the nonmetro

average, and the group went back into the bottom quintile.

During the same period, the LPLI group's growth rate fell

to only 3.3 percentage points above the nonmetro

average.

A county must have ranked more than 490th from the bottom in per capita personal income to escape the bottom

quintile. The median rank in 1979 for the 1979 escapees

was 610, or only 120 positions above the cutoff. Of the 29

1979 escapees, 12 were within 100 positions of the cutoff.

The median LPLI rank was 761 in 1979, much higher than

the median 1979 escapee rank. Seven LPLI counties,

however, had lower ranks in 1979 than in 1975. Six of

these seven counties ranked in the 500's or low 600's in

1979. Some of the LPLI counties and 1979 escapees near

the cutoff in 1979 may fall back into the bottom quintile

during future recessions.

Income Growth: A Panacea?

Although most people agree that higher per capita income

is desirable, it is not a panacea for all problems in lowincome counties. Personal income in a county may grow

rapidly but not be evenly distributed. In such a county,

groups of people could remain in poverty, in spite of impressive income growth. For example, three LPLI counties

had white poverty rates less than 10 percent and black

poverty rates above 30 percent (15).

Many people in the LPLI group remained poor despite a

decade of growth and high average earnings per worker.

Blacks in LPLI counties had a poverty rate substantially

higher than the 15.2-percent average for the total

nonmetro population (table 2). Some whites also failed to

benefit fully from the LPLI group's growth. The group's

white poverty rate was 19.6 percent, much less than the

rate for blacks in the group but more than the average

nonmetro rate.

The elderly, the ill or disabled, female heads with small

children, and people with little education or few jobs skills

are among the people who are less likely to work and

benefit directly from local earnings growth. Many lowincome counties have concentrations of all of these people. Two-fifths of both the LPLI and SPLI counties

simultaneously had a percentage of high school graduates

less than the nonmetro average and percentages of the

elderly, the disabled, and female-headed families greater

than the nonmetro average.

By themselves, such counties would be hard pressed to

take care of all these people simultaneously. Even counties with rapid economic growth may still need income

support. National and State changes in program benefits

and eligibility criteria are important to the people and

economies of low-income counties.

15
Bibliography

(1) Beale, Calvin L. "A Further Look at Nonmetro

Population Growth Since 1970," American Journal of

Agricultural Economics, Vol. 58, No. 5 (Dec. 1976),

953-8.

(2) Crecink, John C. Sharing in Economic Growth, The

Mississippi Experience. Agricultural Economics

Research Report No. 136. Mississippi State University, Mississippi Agricultural and Forestry Experiment

Station, July 1982.

(3) Davis, Thomas F. Persistent Low-Income Counties in

Nonmetro America. RDRR-12. U.S. Dept. Agr., Econ.

Stat. Coop. Ser., May 1979.

(4) Ghelfi, Linda M. "Major Public Assistance Programs:

Dependency of Nonmetropolitan Counties, 1980."

Paper presented at Rural Sociological Society annual

meeting, San Francisco, CA, Sept. 2, 1982.

(5) Kiecka, William R. "Discriminant Analysis," SPSS.-

Statistical Package for the Social Sciences, Chapter

23. New York, NY: McGraw-Hill Book Co., 1975.

(6) . Discriminant Analysis. A Sage University

Paper: Quantitative Applications in the Social

Sciences, No. 19. Beverly Hills and London: Sage

Publications, 1980.

(7) Morrison, Donald G. "On the Interpretation of

Discriminant Analysis," Journal of Marketing

Research, Vol. VI (May 1969), 156-63.

(8) Myers, Paul R. Regional Population and Employment

Adjustments to Rising Coal Production. RDRR-37.

U.S. Dept. of Agr., Econ. Res. Serv., Nov. 1983.

(9) Ross, Peggy J., Bernai L. Green, and Robert A.

Hoppe. "Classifying Major Socioeconomic Patterns

Among U.S. Nonmetropolitan Counties." Paper

presented at Rural Sociological Society annual

meeting, Texas A&M University, Aug. 1984.

(10) Tatsuaka, Maurice M. Discriminant Analysis: The

Study of Group Differences. Selected Topics in Advanced Statistics: An Elementary Approach, No. 6.

Champaign, IL: Institute for Personality and Ability

Testing, 1970.

(11) U.S. Department of Agriculture, Economic Research

Service. Economic Indicators of the Farm Sector: Income and Balance Sheet Statistics, 1983. ECIFS 3-3.

Sept. 1984.

(12)

(13)

(14)

(15)

(16)

(17)

(18)

U.S. Department of Commerce, Bureau of the Census. 1980 Census of Population: General Social and

Economic Characteristics. Vol. 1, Chapter C, Part 26,

Mississippi, PC 80-1-C26, July 1983.

, Bureau of the Census. Census of Population

and Housing 1980: Summary Tape File 3, Technical

Documentation. 1982.

, Bureau of the Census. Computer tapes,

Census of Agriculture, 1969, 1974, and 1978.,

, Bureau of the Census. Computer tapes,

Census of Population, 1970 and 1980.

, Bureau of the Census. "Characteristics of

the Population Below the Poverty Level, 1982," Current Population Reports. Series P-60, No. 144, Mar.

1984.

, Bureau of Economic Analysis. "Business

Cycle Expansions and Contractions in the United

States," Business Conditions Digest. July 1984.

, Bureau of Economic Analysis. Computer

tapes of local area employment, 1969-79.

(19) , Bureau of Economic Analysis. Computer

tapes of local area personal income, 1959, 1969-70.

(20) , Bureau of Economic Analysis. Local Area

Personal Income. Reprinted from Local Area Personal

Income, 1976-81, Vol. 1, Aug. 1983.

(21) U.S. Executive Office of the President, Office of

Management and Budget, Public Affairs. OMB-83-20.

June 27, 1983.

(22) , Office of Management and Budget,

Statistical Policy Division. Standard Metropolitan

Statistical Areas. Revised edition, 1975.

16
Appendix A—Southern SPLI Counties

Versus Other Southern Counties

Most of the SPLI counties are in the South, so how do

southern SPLI counties compare with other southern

nonnnetro counties? Are the characteristics of other

southern nonnnetro counties similar to those for southern

SPLI counties? Or are they closer to the averages for all

nonmetro counties?

population grew 18.4 percent in the other southern

counties, 2.6 percentage points higher than the nonmetro

average and 5.1 percentage points higher than the

southern SPLI average. The population density for

southern counties was double the nonmetro average and

substantially above that for southern SPLI counties. A

larger percentage of the other southern counties was

classified as retirement, college, or adjacent to an SMSA

than either the southern SPLI's or all nonmetro counties.

Appendix A answers these questions by examining

differences between southern SPLI counties and other

southern nonmetro counties. For brevity, comparisons are

restricted to three groups:

• Southern SPLI counties;

• Other southern nonmetro counties that do not belong

to any low-income groups; and

• All nonmetro counties.

This appendix examines differences in demographic and

economic characteristics among the three groups. In this

discussion, the phrase "other southern nonmetro counties

that do not belong to any low-income groups" is

abbreviated to "other southern counties."

Demographic Characteristics

Population characteristics of other southern counties are in

some respects closer to those of all nonmetro counties

than those of southern SPLI counties. Percentage of the

population that was rural, disabled, or employed was

almost identical in the other southern and all nonmetro

groups (app. table 1). The rural and disabled percentages

were substantially higher in the southern SPLI group than

in the other two groups. Percentage employed was about

6 percentage points lower in the southern SPLI group than

in the other groups.

Percentage of the population that was nonwhite or poor,

and percentage of families with a female head were

highest in the southern SPLI counties and lowest in all

nonmetro counties (app. table 1). Other southern counties

fell between the two extremes, generally closer to all

nonmetro counties than to the southern SPLI counties.

Economic Characteristics

Some economic characteristics of other southern counties

and all nonmetro counties were quite similar. Southern

SPLI counties, however, were distinct from either of the

other two groups.

Per Capita Income. Southern SPLI per capita income was

less than per capita income in both all nonmetro and other

southern groups (app. table 2). Other southern counties'

per capita income was much closer to the nonmetro

average than that for the southern SPLI counties each

year. By 1979, per capita personal income in other

southern counties reached 96 percent of the nonmetro

average compared with only 69.1 percent in southern SPLI

counties.

Economic Structure. Percentage of income from

earnings, property income, and transfer payments were

about the same in the other southern counties and all

nonmetro counties in each year examined (app. table 3).

Southern SPLI counties, on the other hand, received more

of their income from transfers and less from property

income and earnings than the other groups.

The southern SPLI counties received a smaller share of

their employment and earnings from wages and more from

proprietors than either of the other groups (app. table 3).

Although southern SPLI counties received a particularly

large share of their earnings and employment from farm

proprietors, the share declined by 1979. Again, statistics

for the other southern counties and for all nonmetro

counties were similar each year.

A smaller portion of the population 25 years old and older

graduated from high school in other southern counties

than in all nonmetro counties. However, the percentage in

other southern counties was closer to the nonmetro

average than to the still lower southern SPLI percentage.

Other southern counties surpassed both southern SPLI

and all nonmetro counties in population growth. The

Southern SPLI counties and the other two groups also

differed in earnings by industry (app. table 4). Farming

paid a larger percentage of earnings in southern SPLI

counties than in the other groups. The percentage of

earnings from farming was smallest in the other southern

counties in each year. The nonmetro average was closer

to the percentage for other southern counties than for

southern SPLI counties, except in 1975.

17
Other southern counties had the largest share of earnings

from manufacturing each year, followed by all nonmetro

counties. Southern SPLI counties had the smallest share

from manufacturing. The nonmetro average was actually

closer to the percentage for southern SPLI counties than

to other southern counties.

Services paid about one-third of total earnings each year

in all three groups. Services paid less in the SPLI counties, however, than in the other groups. Percentage of

earnings from services in the other southern counties was

slightly closer to the alt nonmetro percentage than to the

southern SPLI percentage.

Southern SPLI counties received a slightly larger portion of

their income from government than the two other groups

in each year. The percentage for the other southern counties and all nonmetro counties were within 0=6 percentage

point of each other each year.

Appendix table 1—Selected population characteristics of selected southern and all nonmetro counties

ltemi Unit

Southern SPLI

counties

Other southern

counties

All nonmetro

counties

Counties

1970 population

1980 population

Population growth

Nonwhite (1980):

Black

Indian

Other

Spanish (1980)

Population per county

(1980)

Population per square

mile (1980)

Rural (1980)

Disabled (1980)

Elderly (1980)

High school graduates:

(1980)

Poverty rate (1979):

Total

White

Black

Female-headed families

(1980)

Employed population

(1980)

Counties classified as:

Retirement

College

Adjacent to SMSA

Number

Thousands

do.

Percent

do.

do.

do.

do.

do.

Number

do.

Percent

do.

do,

do.

do,

do,

do.

do.

do.

do.

do.

do.

213

2,948

3,340

13.3

27.9

26.5

1,1

.3

2.1

15,681

29.4

82.6

14.5

13.1

41.8

28.5

21.9

47.2

14.3

33.4

15.4

2.3

32.9

827

19,497

23,093

18.4

18.8

16.7

.7

1.4

3.3

27,924

41.8

59.1

11.2

12.7

51.8

17.4

13.1

37.2

12.5

39.5

20.3

8.0

49.2

2,469

54,424

63,002

15.8

11.8

8.7

1.2

1.9

3.3

25,517

20.7

59.6

10.0

12.8

59.6

15.2

12.5

38.4

10.9

39.6

14.6

7.2

39.2

^See the glossary for definitions of terms. More detailed definitions of demographic terms, such as nonwhite, Spanish, and rural, can be

found in (73). More detailed explanations of retirement and college counties can be found in (7).

Source: (75).

18
Appendix table 2—Per capita personal Income In

selected southern and all nonmetro counties

Year

Southern

SPLI

counties

Other

southern

counties

All

nonmetro

counties

1969

1975

1979

1,786

3,231

4,989

Dollars

2,619

4,520

6,934

2,840

4,833

7,220

Source: (79).

Summary for the South

Other southern counties are more like the nonmetro

average in many respects than the southern SPLI counties. The nonmetro South, therefore, is made up of two

groups of counties. One group contains the SPLI counties,

which continue to rank near the bottom in per capita personal income. The other group contains more fortunate

counties that approach the nonmetro average in per capita

personal income and several demographic and economic

measures.

Appendix table 3—Selected sources of personal income, employment, and earnings In selected southern and all

nonmetro counties

Southern Other All Southern Other All

Source SPLI southern nonmetro Source SPLI southern nonmetro

counties counties counties counties counties counties

Percent'^ Percent^

Personal income: 1979—

1969- Wage and salary jobs 75.3 84.8 83.5

Transfer payments 16.3 11,3 11.3 Farm proprietors 15.7 7.2 8.0

Property income 9.4 12.2 12.9 Nonfarm proprietors 9.0 7.9 8.6

Earnings^ 74.3 76.5 75.9 Total 100.0 100.0 100.0

Total 100.0 100.0 100.0

1975-

Earnings:^

1969— Transfer payments 23.9 17.1 16.6 Wages and salaries 70.7 78.3 76.1 Property income 10.4 13.4 14.0 Other labor income 3.7 4.5 4.3 Earnings^

Total

65.7

100.0

69.5

100.0

69.4

100.0 Farm proprietors'

income 13.4 7.0 8.9

1979- Nonfarm proprietors'

income 12.2 10.1 10.7 Transfer payments 21.4 15.6 15.3 Total 100.0 100.0 100.0 Property income 11.0 14.3 14.8

Earnings2 67.6 70.1 69.9 -|975__

Total 100.0 100.0 100.0 Wages and salaries 72.9 78.6 76.3

Other labor income 6.0 6.7 6.3 Employment:

1969— Farm proprietors'

income 9.9 6.5 8.8 Wage and salary jobs 69.5 82.0 80.3 Nonfarm proprietors'

Farm proprietors 22.3 10.5 11.4 income 11.2 8.1 8.5 Nonfarm proprietors 8.2 7.5 8.3 Total 100.0 100.0 100.0 Total 100.0 100.0 100.0

1979— 1975— Wages and salaries

Other labor income

71.7 78.5 77.0 Wage and salary jobs 73.3 83.6 82.3 7.3 8.1 7.9 Farm proprietors 18.3 8.7 9.5 Farm proprietors' Nonfarm proprietors 8.4 7.7 8.2 income 10.7 5.7 6.9 Total 100.0 100.0 100.0 Nonfarm proprietors'

income 10.3 7.7 8.3

Total 100.0 100.0 100.0

^Totals may not add due to rounding.

^Adjusted for commuting and personal contributions to social insurance.

3Not adjusted for commuting and personal contributions to social insurance.

Sources: (18, 19).

19
Southern Other All

SPLI southern nonmetro

counties counties counties

Appendix table 4—Sources of earnings by major

industry in selected southern and all

nonmetro counties^

Year and

major industry

1969:

Farming

Mining

Construction

Manufacturing

Services

Government

Total

1975:

Farming

Mining

Construction

Manufacturing

Services

Government

Total

1979:

Farming

Mining

Construction

Manufacturing

Services

Government

Total

16.9

2.0

5.2

24.7

32.1

19.2

100.0

12.7

4.3

4.8

23.9

33.2

21.0

100.0

13.2

4.4

5.5

25.6

32.7

18.7

100.0

Percent^

9.0

3.7

5.9

29.4

34.6

17.6

100.0

8.0

5.2

5.9

27.0

35.4

18.5

100.0

7.1

5.6

6.5

28.6

35.8

16.5

100.0

10.9

2.7

5.9

26.9

35.6

18.0

100.0

10.7

3.9

6.0

24.2

36.1

19.1

100.0

8.7

4.4

6.4

26.2

37.3

17.0

100.0

^Not adjusted for commuting or personal contributions to social

insurance.

2Totals may not add due to rounding.

Source: (79).

Appendix B—Using Discriminant Analysis

to Differentiate Between Groups

Discrinninate analysis was used to provide more information about the characteristics of low-income counties.^

Discriminant analysis is a statistical technique that helps

researchers examine the differences between two or

more groups (6). The technique produces functions that

BRoss, Green, and Hoppe also used discriminant analysis to

show how various types of counties differed from other

nonmetro counties (9). Low-income counties were one of the

types they considered. Results from (9) are consistent with

those in this appendix. However, more details for low-income

counties are presented here.

identify which linear combinations of independent

variables best distinguish among groups {10). A variable

that appears by itself to differentiate between groups may

not be a good discriminator after adjusting for interactions with other variables. The goal of discriminant

analysis is to classify cases into groups using the independent variables. The effectiveness of the function is

most clearly shown by the percentage of the cases it correctly classifies by group (4).

This appendix uses discriminant analysis to answer the

following questions:

• Which characteristics differentiate between the

original PLI counties and other nonmetro counties in

1969?

• Are the same characteristics useful in identifying

SPLI counties in 1979?

• Which characteristics identify low-income counties

that improved their income status?

• Do the same characteristics identify improving counties in different time periods?

Procedures

Two groups of counties were used to answer the first

question: the original 298 PLI counties and all other

nonmetro counties in the 48 contiguous States. The 231

SPLI counties and other nonmetro counties were used in

a second analysis to see how SPLI counties differed from

nonmetro counties in general.

Klecka argues that deriving and testing functions with the

same cases overstates the predictive power of the

classification procedure (6). He recommends that each

case be randomly assigned to an analysis or test

subgroup. The analysis subgroup is used to build the functions; the test subgroup checks the ability of the function

to classify counties. Morrison, on the other hand, argues

that all cases should be used when determining which

variables are good discriminators (7). Because finding

good discriminators rather than making predictions is the

major focus of this appendix, all counties were used for

both analysis and classification.

Two additional analyses examined low-income groups that

improved during the seventies. The first function compared

counties that left the low-income quintile between 1969

and 1975 (the 1975 escapees and the LPLI counties) with

the remaining original PLI counties. This function showed

which characteristics were associated with a county improving its income status during the early seventies. The

second function compared counties that escaped the low20
income quintile between 1975 and 1979 (the 1979

escapees and the LPLI counties) with the remaining

original PLI counties. This function showed which

characteristics were associated with an improved income

status during the late seventies.

Variables used in each function (app. table 5) were chosen

after consulting the text and tables in the body of the

report. Most variables are self-explanatory. "Prior," the

last variable in the 1975-79 improvement function, should

be explained, however. Economic change between 1969

and 1975 helps explain a county's status in 1975, but a

county's status in 1979 depends on changes in both the

1969-75 and 1975-79 periods. "Prior," a dummy variable,

summarizes change during the earlier period.^ The value

of "prior" is 1 for counties that escaped the bottom quintile in 1975 and 0 for counties that remained in the bottom

quintile in 1975.

A southern dummy variable and percentage of families

with female heads were not used in the analyses because

of high correlations in the 0.7000's with other variables.

The southern dummy variable was highly correlated with

percentage that graduated from high school; percentage of

families with a female head was highly correlated with

percentage of the population that was nonwhite. Percentage of earnings and change in earnings from wages and

salaries, other labor income, farm proprietors' income, and

nonfarm proprietors' income also were omitted from the

analyses. Some of these variables were highly correlated

with other economic variables. Similarly, percentage

disabled and percentage employed were omitted from the

PLI and SPLI functions. Both of these variables were

highly correlated with transfer payments in 1979.''o After

these adjustments, the highest correlations were in the

0.5000's and low 0.6000's for the PLI and SPLI functions

and in the 0.5000's for the improvement functions.

A stepwise procedure selected variables for inclusion in

each function; a variable was selected only if its contribution to discrimination between groups was significant at

the 0.10 level.11 Variables failing to enter a function may

have been good discriminators alone, but in combination

9The same economic change variables for both periods cannot

be used in the same function because the variables are highly

correlated. For example, the simple correlation between change in

mining earnings in the two periods is 0.7587 for the 298 PLI

counties.

loSimple correlations, 1979 SPLI model: (1) Percentage of personal income from transfers with percentage disabled = 0.6905;

(2) Percentage of personal income from transfers with percentage

of population employed = -0.6761.

i^The criterion for inclusion was Wilk's lambda. For more information about stepwise criteria, see (6) and (5).

with other variables, their unique contributions were insufficient (6).

Results: PLI and SPLI Functions

Results from the PLI and SPLI functions appear in app.

table 6. Group 1 consists of the 298 PLI counties in the

first function and the 231 SPLI counties in the second

function. In both functions, group 2 consists of the 2,144

counties that were not PLI in 1969. The 67 original PL!

counties that avoided SPLI status in 1979 were excluded

from the second analysis; they were not added to group 2.

Thus, differences in variables that enter the first and

second functions are due to economic and social changes

in the original groups rather than shifting counties from

one group to another.

The canonical correlation summarizes the degree of

relatedness between the groups and the function and

ranges from 0 (no relationship) to 1 (6). The functions in

app. table 6 have canonical correlations of about 0.6000,

indicating high relatedness. The canonical correlation

squared shows the portion of variation in a function

explained by the groups (6). The groups explain about

one-third of the variation in the PLI and SPLI functions.

The group centroids are the average function value for

each group. If group centroids are well separated and

distinct relative to dispersion in the groups, Wilk's lambda

is near zero. Higher values, up to a maximum of 1,

indicate less discrimination (6). The lambdas in the two

functions are both acceptable, in the 0.6000's. Wilk's

lambda can be converted to chi-square to test for

significance, and both functions are significant at the

0.001 level.

Standardized Coefficients. The coefficients for each

variable are standardized: the size of a variable's

coefficient shows the relative importance of the variable

(6). For example, the most important variable in the PLI

and SPLI functions is percentage of personal income from

transfer payments (app. table 6). The coefficients are

analogous to multiple regression coefficients (10). Each

coefficient shows the importance of a variable after

adjusting for the effects of other variables.

The signs of the standardized coefficient tell whether a

variable contributes in a positive or negative direction to

the value of the function (10). The positive direction in the

PLI and SPLI functions is more descriptive of the PLI or

SPLI groups because both of these groups have positive

centroids. The negative direction is more descriptive of the

other counties in the functions. For example, one would

expect a higher portion of personal income to come from

21
Appendix table 5—Variables used in the discriminant analyses

Function

Variable 1969

PL!

1979

SPLI

1969-75

improvement

Economic structure:

Percentage of earnings

from—1

Farming

Mining

Construction

Manufacturing

Services

Government

Percentage of personal

income from—^

Transfer payments

Property income

Per capita change in earnings

from—2, 3

Farming

Mining

Construction

Manufacturing

Services

Government

Per capita change in personal

income from—2. 3

Transfer payments

Property income

Farm structure:'^

Percentage of farms with sales

of at least $40,000

Demographic characteristics:^

Percentage nonwhite

Population per square mile

Percentage disabled

Percentage elderly

Percentage graduated from

high school

Percentage of population

employed

Percentage rural

NA

NA

NA

NA

NA

NA

NA

NA

X

X

NA

X

NA

X

X

X

X

X

X

X

X

X

X

NA

NA

NA

NA

NA

NA

NA

NA

X

X

NA

X

X

NA

X

NA

NA

NA

NA

NA

NA

NA

NA

NA

X

X

X

X

X

X

NA

NA

NA

NA

NA

NA

NA

NA

NA

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Location (0, 1 dummy

variables):

Adjacent to an SMSA

Retirement county

College county

Earlier classification (0, 1

dummy variable):

Prior^

X

X

X

NA

X

X

X

NA

X

X

X

NA

NA = Not applicable. Variable not used in function.

X = Used in function.

iThe 1969 PLI function used 1969 data, and the 1979 SPLI function used 1979 data.

2The 1969-75 function used 1969-75 change data, and the 1975-79 function used 1975-79 change data.

3Per capita change in source i, 1969-75 = ^i, 1975 "^i, 1969

Population, 1975

Per capita change in source i, 1975-79 = ^1, 1979~^i, 1975

Population, 1979

where X

I. y

Earnings or personal income from source i in year y.

4The 1969 PLI and 1969-75 functions used 1969 Agricultural Census data. The 1975-79 function used 1974 Agricultural Census data

whil iile the 1979 SPLI function used 1978 Agricultural Census data.

5The 1969 PLI and both improvement functions used 1970 Census of Population data. The 1979 SPLI function used 1980 Census of

Population data.

eprior = 0 if a county was in the bottom quintile in 1975. Prior

escapees, and prior = 0 for SPLI counties and 1979 escapees.

1 if it was not. In other words, prior = 1 for LPLI counties and 1975

22
transfers in low-income counties than in other counties.

Percentage of personal income from transfer payments

and the group 1 centroids, therefore, both have positive

signs (app. table 6).

Some variables entered both functions (app. table 6). One

can conclude that low-income counties tended to depend

more on farm earnings and transfers in both 1969 and

1979. In both functions, low-income counties tended to

have more farms with small sales, a larger percentage

nonwhite population, a smaller percentage elderly, a

smaller percentage that graduated from high school, and a

larger percentage rural population. Earnings from farming

had a comparatively large or important coefficient in 1969,

but by 1979, it had one of the least important coefficients.

Other variables, including transfers, percentage nonwhite,

percentage of elderly, and percentage that graduated from

high school, were relatively important in both functions.

When compared with other variables, percentage of large

farms had fairly low coefficients in both functions.

The signs on most of these variables were as expected

from data in the body of the report. The exception was

percentage elderly, which had a negative sign, even

though the SPLI group had a slightly larger percentage

elderly in table 2 than all nonmetro counties. The sign on

percentage elderly, however, is correct. Because the

statistics in table 2 were calculated after aggregating all

the counties in a group, they are essentially weighted

averages. Discriminant analysis, on the other hand, gives

each .county equal weight. The unweighted average

percentage elderly was lower in PLI and SPLI counties

than in other nonmetro counties. The negative sign on the

elderly coefficient does not mean that the elderly are unimportant in low-income areas but that percentage elderly is

a better indicator of other nonmetro counties.

Percentage of earnings from manufacturing and percentage of personal income from property entered the PLI

function but not the SPLI function. The coefficients for

both of these variables were small when compared with

other coefficients in the function. One might expect a

negative sign on manufacturing because a larger share of

earnings came from manufacturing in all nonmetro counties than in SPLI counties in all years (table 4). The

unweighted percentage of earnings from manufacturing,

however, was slightly higher in SPLI and PLI counties than

in other nonmetro counties.

Population density, adjacency to an SMSA, and retirement

county status entered the SPLI function after failing to

enter the PL! function. These were minor discriminators

with coefficients only in the 0.1000's at the most. A

positive sign was expected on population density, since

the SPLI group had 25.7 people per square mile compared

with only 20.7 for all nonmetro counties (table 2). The

unweighted population density, excluding Alaska and

Hawaii, was only 29.9 people in SPLI counties and 43.3 in

other nonmetro counties. This is consistent with the

negative sign in app. table 6.

The retirement county variable is more closely associated

with other nonmetro counties, although a larger percentage of SPLI counties were retirement counties. This suggests that retirees do make a contribution to local

economies after adjusting for the effects of other variables.

Percentage disabled and percentage employed were omitted, as explained earlier, because of high correlations with

percentage of income from transfer payments. A second

set of PLI and SPLI functions was derived by substituting

percentage disabled and percentage employed for the

transfer variable. The new functions yielded results similar

to those in app. table 6. Both of the new variables entered

the PLI and SPLI functions with the expected signs.

Percentage employed was the largest coefficient in the PL!

function, and percentage disabled was the largest coefficient in the SPLI function.

The Classification Test. The ultimate test of a discriminant function is how well it classifies cases. Although both

functions correctly classified about 87 percent of all cases

(app. table 6), some cases may have been correctly

classified by chance. Tau shows how much a function improves over chance (6). Both functions produced 74 percent fewer errors than expected from chance. However,

the functions did a much better job of picking low-income

counties than other nonmetro counties.

Results: Improvement Functions

Results from the improvement functions appear in app.

table 7. Group 1 in both functions consists of PLI counties

that remained in the low-income quintile, while group 2

consists of counties that escaped the bottom quintile.

Thus, the SPLI counties and the 1979 escapees made up

group 1 in the 1969-75 function; LPLI counties and 1975

escapees made up group 2. In the 1975-79 function, the

SPLI counties and 1975 escapees made up group 1, while

the LPLI counties and 1979 escapees made up group 2.

Association between the groups and the discriminant functions was slightly higher than in the PLI and SPLI functions (app. tables 6 and 7). Canonical correlations reached

the 0.6000's compared with the high 0.5000's in the

earlier functions. The groups in the improvement functions

explained between one-third and one-half of the variation

23
Appendix tabte 6—PU and SPLI counties versus other

nonmetro counties

Appendix table 7—Improvement functions

Item 1969

PLI

1979

SPLi

.64150 .67422

1,081.20(9) 933.46(10)

.001 .001

2.11682

- .22807

.15320

Canonical correlation 0.59875 0.57077

Squared .35850 .32578

Wilk's lambda

Chi-square (d.f.)

Significance

Group centroids;!

Group 1 (low-income counties) 2.00434

Group 2 (other nonmetro counties) - .27859

Standardized coefficients:

Economic structure—2

Percentage of earnings from:

Farming .42178

Mining ** **

Construction

Manufacturing .10307

Services

Government

Percentage of personal

income from:

Transfer payments .79226 .65545

Property income -.20688

Farm structure—3

Percentage of farms with

sales of at least $40,000 - .12332 - .09104

Demographic characteristics—^

Percentage nonwhite .41183 .30676

Population per

square mile * *

-.10378

Percentage elderly - .35033 - .40439

Percentage graduated

from high school - .23475 - .36924

Percentage rural .10126 .21394

Location (0, 1 dummy

variables):

Adjacent to an SMSA ** -.09017

Retirement county ** -.09134

College county * * * *

Percentage of classes correctly

classified^ 87.1 87.0

Group 1 (low-income

counties) 91.6 95.2

Group 2 (other nonmetro

counties) 86.5 86.1

Tau 74.3 74.1

** = Variable did not meet minimum criteria for inclusion in

function.

^Group 1 is the PLI group in the 1969 PLI function and the

SPLI group in the 1979 SPLI function.

2The 1969 PLl function used 1969 data, and the 1979 SPLi

function used 1979 data.

3The 1969 PLl function used 1969 Agricultural Census data,

and the 1979 SPLI function used 1978 Agricultural Census data.

4The 1969 PLl function used 1970 Census of Population data,

and the 1979 SPLI function used 1980 Census of Population data.

Item 1969-75

improvement

1975-79

improvement

Canonical correlation 0.60283

Squared .36340

Wilk's lambda .63659

Chi-square (d.f.) 131.65(9)

Significance .001

Group centroids:"'

Group 1 (remained in bottom

quintile) -.28788

Group 2 (left bottom quintile) 1.96968

Standardized coefficients:

Economic structure—2

Per capita change in earnings from:

Farming

Mining

Construction

Manufacturing

Services

Government

Per capita change in

personal income from:

Transfer payments

Property income

Farm structure—^

Percentage of farms with

sales of least at $40,000

Demographic characteristics—^

Percentage nonwhite

Population per square mile **

Percentage disabled

Percentage elderly

Percentage graduated from

high school

Percentage of population

employed

Percentage rural -.32161

Location (0,1 dummy variables):

Adjacent to an SMSA

Retirement county -.28087

College county

Earlier classification (0,1

dummy variable):

Priors NA

Percentage of cases correctly

classified:^ 88.9

Group 1 (remained in bottom

quintile) 90.4

Group 2 (left bottom quintile) 78.9

Tau 77.9

0.68812

.47351

.52649

186.68(10)

.001

- .45467

1.96482

.62121 .46042

.64282 .22940

.36514 **

.32490 .20311 * * .31574

.24887 .29041

,37241 • *r

40118 .36904

.28044

.26306

.24115

.77458

88.3

91.7

73.2

76.5

** = Variable did not meet minimum criteria for inclusion.

NA = Not applicable. Variable not used in this analysis.

^Group 1 consists of SPLI counties and 1979 escapees in the

1969-75 function and SPLI counties and 1975 escapees in the

1975-79 function. Group 2 is made up of LPLI counties and 1975

escapees in the 1969-75 function and LPLI counties and 1979

escapees in the 1975-79 function.

2The 1969-75 function used 1969-75 change data, and the

1975-79 function used 1975-79 change data.

3The 1969-75 function used 1969 Agricultural Census data, and

the 1975-79 function used 1974 Agricultural Census data.

^Both functions used 1970 Census of Population data.

sprior = 0 if a county was in the bottom quintile in 1975.

Prior = 1 if it was not. In other words, prior = 1 for LPLI counties and 1975 escapees, and prior = 0 for SPLI counties and

1979 escapees.

24
in the discriminant functions. (See the canonical correlation squared.)

The improvement functions correctly classified about 90

percent of the observations. According to tau, they improved over random assignment by more than 75 percent.

The functions classified group 1 counties more successfully than group 2 counties.

Standardized Coefficients, 1969-75. Change in earnings

from mining and farming had the highest coefficients in

the 1969-75 function (app. table 7). This is hardly surprising, given the growth in earnings from mining in the LPLI

counties and the growth from farming in the 1975

escapees (table 9). Most of the other economic variables

entered the function, all with positive signs and all but one

with relatively large coefficients in the 0.3000's or

0.4000's.

Counties remaining in the bottom quintile tended to be

more rural (app. table 7). No other demographic variables

entered the 1969-75 function.

sign in app. table 7. Coefficients in discriminant functions

imply association, not necessarily causality. One interpretation of the positive disability coefficient is that group 2

improved its income status despite its higher disability

rate, not because of it. Alternatively, higher disability levels

could increase income from disability programs.

Adjacency to an SMSA entered the function with the anticipated sign. Both the 1979 escapee and LPLI groups

had a higher proportion of adjacent counties (table 2).

Finally, one of the striking differences between the PLI or

SPLI functions (app. table 6) and the improvement functions (app. table 7) is the variables selected for entry.

Demographic variables frequently entered the PLI and

SPLI functions. Most of the variables in the improvement

functions, however, were economic. This suggests that

although demographic characteristics may be useful in

identifying low-income counties, they are less important in

determining which counties lose their low-income status.

Conclusions

The negative coefficient for retirement county status may

simply reflect a greater concentration of these counties in

group 1. Approximately 16.9 percent of the group 1 counties were also retirement counties compared with only 7.9

percent of group 2 counties. Thus, retirement county

status is more closely associated with group 1 than 2.

Standardized Coefficients, 1975-79. Prior had the most

important coefficient in the 1975-79 function (0.77458),

followed by change in earnings from farming (0.46042). In

other words, many counties escaping the bottom quintile

during the second period also escaped during the first

period. The only other variables with coefficients as high

as the 0.3000's were growth in property income and

growth in earnings from services.

Only two demographic variables, percentage disabled and

percentage employed, entered the 1975-79 function. The

positive sign on percentage disabled was somewhat surprising, since disability and improving incomes are not normally linked, at least at the individual level. Group 2 had a

higher average disability rate, which is consistent with the

The questions posed at the beginning of this appendix can

now be answered. Discriminant analysis identified which

variables differentiated between the original PLI counties

and other nonmetro counties in 1969. Because the same

variables generally entered the SPLI function, the same

characteristics distinguished low-income counties in both

1969 and 1979.

Both PLI and SPLI counties depended more on transfer

payments and farming than other nonmetro counties. PLI

and SPLI counties also tended to have larger percentages

nonwhite, disabled, and rural. Other nonmetro counties

had larger percentages of the population aged, employed,

or graduated from high school, and a larger percentage of

farms with sales over $40,000.

Discriminant analysis also determined which factors were

associated with an improving income status in the

1969-75 and 1975-79 periods. Again, the variables entering the two functions were similar. Most of the economic

change variables, but few demographic variables, entered

the improvement functions.

25
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these characteristics can help public officials develop successful antipoverty programs. In counties with high poverty

rates, families headed by women are almost three times

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Distribution of Employment Growth in Nine Kentucky

Counties: A Case Study shows that people moving to a

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economic driving force, but the private service sector (which

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job growth between 1974 and 1979. RDRR-41. August

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Counting Hired Farmworkers: Some Points To Consider

concludes that as many as two-thirds of the Nation's hired

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more likely to describe workers employed in hired farmwork year round. AER-524. December 1984. 16 pp. $1.00.

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early seventies. RDRR-44. December 1984. 28 pp. $2.00.

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Chartbook of Nonmetro-Metro Trends is a quick check

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Housing of the Rural Elderly finds that the number of

rural elderly households rose 16 percent between 1974

and 1979 compared with a 10-percent increase for all U.S.

households, according to this study based on the 1979

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adequate housing, but 27 percent of the elderly renters

and 18 percent of all the elderly living in the South have

inadequate housing. In 1979, 15 percent of the rural

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of the urban elderly. RDRR-42. July 1984. 20 pp. $1.50.

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