United States

Department of

Agriculture

Economic

Research

Service

Rural Development

Research Report

Number 52

Characteristics of

Poverty in Nonmetro

Counties

Elizabeth S. Morrissey
CHARACTERISTICS OF POVERTY IN NONMETRO COUNTIES. By Elizabeth 8.

Morrissey, Economic Development Division, Economic Research Service, U.S.

Department of Agriculture. Rural Development Research Report No. 52.

Abstract

America's nonmetro counties with the highest poverty rates—compared with nonmetro counties with the lowest poverty rates—exhibit major differences and

unexpected similarities. For example, In counties with high poverty rates,

families headed by women are almost three times as likely to be living at or

below the poverty level as they are In counties with low poverty rates. On the

other hand, property Is a source of personal income at similar rates in both

groups of counties. This report identifies the unique characteristics of nonmetro

counties with large proportions of persons living in poverty. Knowing these

characteristics can help public officials develop successful antipoverty programs.

Keywords: Nonmetro areas, poverty rates, dependent populations, employment.

Acknowledgments

The author wishes to thank Thomas A. Carlin, Robert A. Hoppe, Peggy J. Ross,

and James D. Schaub of the Economic Development Division, ERS, USDA, for

critical review and comments. The author also wishes to thank Sharon Black for

her assistance with typing the tables.

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Washington, DC 20005-4788 July 1985

til
Contents

Page

Summary . - tv

introduction .........,........ = . 1

Progress Against Poverty 1

Earlier Studies 2

Data and Methodology 3

Results ,........, = 3

Population Growth and Distribution 3

Population Composition 5

Income and Employment .6

Farm Characteristics 8

Implications ^

Bibliography 10

Summary

America's nonmetro counties with the highest poverty rates—compared with nonmetro counties with the lowest poverty rates—exhibit major differences and

unexpected similarities. For example, in counties with high poverty rates,

families headed by women are almost three times as likely to be living at or

below the poverty level as they are in counties with low poverty rates. On the

other hand, property is a source of personal income at similar rates in both

groups of counties. This report identifies the unique characteristics of nonmetro

counties with large proportions of persons living in poverty. Knowing these characteristics can help public officials develop successful antlpoverty programs.

Counties with the highest poverty rates in 1980 also had higher proportions of

most poverty-prone populations, higher proportions of ethnic populations, higher

proportions of participation in income-assistance programs, and a much lower

level of formal education when compared with counties with the lowest poverty

rates.

But, both county types were similar in several ways: the proportion of elderly persons, participation in Social Security, organization of farm business, and principal occupation of farm operators.

Nonmetro counties with the highest poverty rates have many poor elderly and

disabled individuals and families headed by women, those groups most dependent on Government income-assistance programs. Economic growth without antipoverty programs may not help these groups escape poverty. Also, these groups

will feel the effects of any changes in these income-assistance programs much

more than any other poverty-prone groups.

The author based her study on 1980 data for all 2,443 nonmetro counties in the

United States. From that number, she identified and compared the 100 nonmetro

counties with the highest poverty rates with the 100 nonmetro counties with the

lowest poverty rates.

IV
Characteristics of Poverty in Nonmetro Counties

Elizabeth S. Morrissey'

Introduction

The U.S. poverty rate declined dramaticaily in the

sixties and remained relatively low through the late

seventies (7).^ However, not all rural areas shared in the

reduced poverty.^ Some areas that have been impoverished for decades continue to have high poverty rates.

This study examines some of the demographic, socioeconomic, and employment differences between rural

areas with high poverty rates and low poverty rates.^

Knowing the incidence, location, and composition of

poverty is essential for targeting Federal assistance to

the neediest.

Progress Against Poverty

In 1959, the nonmetro poverty rate was 33.2 percent,

considerably higher than the metro poverty rate of 15.3

percent (3). From 1960 to 1980, the nonmetro poverty

rate dropped to 15.4 percent while the metro rate fell to

11.9 percent (1). The incidence of poverty was still

greater in nonmetro areas, but the metro-nonmetro dif-

*The author is a staff social science analyst with the

Economic Development Division, Economic Research Service,

U.S. Department of Agriculture.

italicized numbers In parentheses refer to literature cited in

the Bibliography at the end of this report.

^Nonmetro refers to places outside a Standard Metropolitan

Statistical Area (SMSA) as defined by the Office of Management and Budget in 1974. An SMSA is a county containing

one or more cities with a population of at least 25,000 as well

as 50,000 persons or more in the contiguous area as defined

by the U.S. Department of Commerce's Bureau of the Census.

The Office of Management and Budget recently revised the

definition of metropolitan areas. The new definition, called a

Metropolitan Statistical Area (MSA), eliminates the minimum

size requirement for the central city and the contiguous area.

The author used SMSA rather than the newer MSA definition

so that a consistent definition of nonmetro could be used for

comparison of change between 1969 and 1979.

3The poverty thresholds used in this report are those established by the Bureau of the Census, U.S. Department of Commerce. For a family of four, the 1980 poverty level was an

annual income of $8,414 or less.

ference narrowed to less than 4 percentage points."^

Economic growth and Federal income security and

assistance programs were largely responsible for reducing nonmetro poverty over the 20-year period. The

economic growth of the sixties and seventies enabled

many of the poor to escape poverty through more jobs

and higher wages. However, economic growth bypassed some areas. Although the number of jobs increased

in nonmetro areas during the sixties and seventies,

almost 75 percent of these new jobs were service jobs,

many of which were part-time and paid low wages.

Often manufacturing firms with high-paying jobs did

not go to nonmetro areas with large minority populations. In areas where they did go, the highly skilled,

highly paid jobs frequently went to outsiders, not to

local people (10).

The enactment of several new Federal income security

programs and changes in others in the last two

decades have played a major part in reducing nonmetro

poverty. The Food Stamp Program, launched in

selected counties in 1964 and nationalized in 1974, was

designed to provide a better diet for the poor. While not

considered an income-transfer program, the Food

Stamp Program makes a substantial contribution to the

well-being of its recipients. The Supplemental Security

Income (SSi) program, authorized in 1972 to replace the

Old Age Assistance, Aid to the Blind, and Aid to the

Permanently and Totally Disabled programs, assists the

disabled and the elderly poor through income transfer.

SSI has p}ayed an important role in reducing poverty

among the elderly and disabled (11). Social Security

benefits and coverage were also expanded, raising the

incomes of many of the elderly above the poverty level.

In addition, supplementary payments and allowances

were permitted for mothers who received Aid to

Families with Dependent Children (AFDC) and who

worked part-time (9).^

"^The nonmetro poverty rate increased to 18.3 percent in

1983 because of the slow down of the national economy and

other factors.

^The working allowance was repealed in the Omnibus

Budget Reconciliation Act of 1981.
These nationwide program enactments and changes

helped 44 percent of persons with pretransfer income

below the poverty level to escape poverty between 1965

and 1976 (9). Among the elderly alone, the poverty rate

dropped more than 20 percentage points (11). During

this period, Federal expenditures for cash transfer payments and in-kind programs such as Food Stamps and

Medicare increased by $147 billion and the proportion

of pretransfer poor households receiving transfer

payments increased from 69 percent to 81 percent (9).

!n addition to having larger proportions of some of the

groups which are especially vulnerable to poverty, the

poverty rate for all of the at-risk .groups is higher in

nonmetro areas. For example, only 20 percent of the

black population resides in nonmetro areas, yet the in=

cidence of poverty among nonmetro blacks is 39.5 percent, 11 percentage points higher than that for metro

blacks. Knowing the geographic distribution of poverty

is important to public officials in order to target funds

to the neediest.

However, these programs may not have been as effective in nonmetro as metro areas. Hoppe has shown that

nonmetro areas receive a slightly higher percentage of

their total personal income from transfer payments. But

nonmetro residents receive lower per capita transfers

than their metro counterparts (6). Ghelfi has shown that

transfers (AFDC, SSI, and Food Stamps) are going to

nonmetro counties in need, but that eligible nonmetro

households do not participate to the extent that metro

eligible households do (4). For example, AFDC participation compared with need is low in many rural

areas (8). Benefits are lowest and coverage more

limited in States containing some of the poorest nonmetro areas (14), A 1975 study conducted by the National Rural Center showed that 21.5 percent of poor

nonmetro families received all or some part of their

income from some assistance program based on need

compared with 33.5 percent of poor metro families (8).

In addition to these program limitations, inflation,

slowed economic growth, and the emigration of the

younger, healthier, better educated poor during the

forties, fifties, and sixties have resulted in prolonged and

intensified poverty for some nonmetro areas (4, 9, 11).

The uneven distribution of the benefits of economic

growth and social programs has contributed to the high

poverty rate in populations with distinctive demographic,

socioeconomic, and employment characteristics. The

elderly, persons in families headed by women, blacks,

other racial minorities, persons with low levels of

education, and the disabled are more likely to be poor

than are other population groups. Some of these

groups are more likely to be in nonmetro areas than in

metro areas. For instance, although the metro population contains slightly higher proportions of blacks and

families headed by women, nonmetro areas contain

higher proportions of the disabled and persons v^ith

less than a high school education. This comparison

suggests that the mix of policies used to alleviate

poverty might differ between metro and nonmetro areas

because of differences in the composition of the poor.

Earlier Studies

This report focuses on nonmetro areas that contain a

relatively large proportion of poor people. The concept

of poverty areas was introduced in the sixties as a way

of identifying areas where the Office of Economic Opportunity (OEO) programs could be aimed. The Census

Bureau .identified poverty areas within major cities

using minor civil divisions.^ However, there have been

few studies identifying and characterizing nonmetro

places with severe poverty problems. Two earlier

studies (1, 6) examined the geography of nonmetro

poverty. These studies focused on the demographic and

socioeconomic characteristics of still-persistent lowincome (SPLI) counties, that is, those nonmetro counties consistently in the lowest per capita personal income quintile of all counties over several decades beginning in the fifties. The results of the most recent

study show that, when compared with all nonmetro

counties, the SPLI counties have some distinguishing

socioeconomic and employment characteristics (6).

Overall, the population of the SPLI counties was smaller

than in all nonmetro counties due more to the small size

of the counties rather than low population density, SPLI

counties also had a high percentage of rural persons/

Blacks and individuals dependent on Government transfer programs were a higher proportion of the SPLI

population than the all-nonmetro population (6).

The economic structure of the SPLI counties differed

from other nonmetro areas. In the SPLI group, like the

rest of the nonmetro America, the largest share of earnings (32.4 percent) came from service industry jobs that

tended to be Government jobs. However, farming was

more important as a source of earnings in SPLI coun-

^Minor civil divisions are primary divisions of counties

established under State law. They may be designated as

towns, townships, precincts, districts, Indian reservations,

boroughs, gores, purchases, or locations. They are only

defined in 29 States.

''Urban people live in urban places or places with 2,500

population or more outside urbanized areas. The remaining

population is rural.
ties than in all nonmetro areas. In 1979, 13.2 percent of

earnings canne from farming in SPLI counties compared

with 8.7 percent for all nonmetro counties.

By definition, per capita personal income and median

family income are much lower in these counties than in

all nonmetro counties. The per capita income in 1979

for SPLI counties was $4,971 versus $7,220 for all nonmetro counties. Income from property (dividends, interest, and rent) was a smaller proportion of personal

income in SPLI counties than in all nonmetro areas,

while income from transfer payments was a larger

share of personal income. In SPLI counties, 21.6 percent of personal income came from transfer payments

in 1979 compared with only 15.4 percent in all nonmetro

counties.

Those studies measured poverty by focusing on rural

areas with persistently low per capita personal income

over several decades. This study uses the incidence of

poverty rather than per capita income as the measure

of poverty.

Data and Methodology

Data for this study came from several sources. The income data are from the Bureau of Economic Analysis,

U.S. Department of Commerce; the Social Security (Old

Age Survivors Disability Income) and Supplemental

Security Income (SSI) data are from the Social Security

Administration; the Aid to Families with Dependent

Children (AFDC) data from the Office of Family Assistance, U.S. Department of Health and Human Services;

and the Food Stamp Program data from the Food and

Nutrition Service, U.S. Department of Agriculture. The

other data are from the 1980 U.S. Census of Population

and the 1978 U.S. Census of Agriculture.

The unit of analysis of this study is the nonmetro

county. A county's poverty rate is measured as the proportion of its noninstitutionalized population with income below the poverty level.^ To contrast the high and

low poverty counties, the author ranked each of the

2,443 nonmetro counties according to its 1980 poverty

rate and chose 200 for analysis. High poverty incidence

(HPI) counties refers to the group of 100 counties with

the highest poverty rates, and low poverty incidence

(LPI) counties refers to the group of 100 counties with

the lowest poverty rates.

^The definition of persons below the poverty level excludes

inmates of institutions, members of the Armed Forces living

in barracks or on military ships, college students living in dormitories, and unrelated individuals under 15 years of age.

The author computed the statistics expressed as

percentages in this paper at the county group (HPi, LPI,

and nonmetro) level. For example, to compute the proportion of the HPI population that Is poor, the sum of

persons with income below the poverty level in all the

HPI counties was divided by the total noninstitutionalized population of the HPI counties.

For the HPI group, the overall poverty rate was 37.6 percent, more than double the rate of all nonmetro areas,

15.2 percent. The poverty rates in this group ranged

from 31.7 percent to 52.7 percent by county. In the LPI

group, the poverty rates ranged from 0 to 7.7 percent,

while the overall poverty rate was 6.6 percent.

Of the HPI counties, 46 percent were neither SPLI counties nor former SPLI counties. Most of these HPI counties that were neither SPLI nor former SPLI counties

were located in the Mississippi Delta (7 counties), the

Rio Grande River Valley (10 counties), and the Indian

reservations and agricultural areas in South Dakota

(9 counties). The designation of these counties as poor

with the poverty measure but not with the per capita

personal income measure probably reflects unevenly

distributed income. Conversely, 54 percent of the HPI

counties were also SPLI counties that have been poor

for over two decades. Thus, the importance of this comparison is that more than half of the HPI counties have

been poor over time and are poor regardless of which

measure of poverty is used. During this study time

period, only two HPI counties escaped the persistent

poverty group.

Results

Of the 100 HPI counties, 81 were located in the Mississippi Delta, the Appalachian Mountains, and the Black

Belt of Alabama. The remainder were located in the

North Central region near Indian reservations and in

the Rio Grande River Valley in Texas and New Mexico.

Mississippi, Kentucky, Texas, and Georgia accounted

for 58 of the HPI counties. More than half of the LPI

counties were in the Great Lakes (adjacent to SMSA's)

region and in the Great Plains region. The Rocky Mountain, New England, and Northeastern Metro Belt areas

accounted for the rest of the LPI counties (fig. 1).

Population Growth and Distribution

During the seventies, HPI counties were more likely to

lose population than LPI counties were (table 1). Most

of the HPI counties which lost population were in the

South—in the Rio Grande Valley in Texas, the Mississippi Delta, and the Alabama Black Belt. In all, 45 of
Figure 1

Counties with High Poverty Incidence

and Low Poverty Incidence

^l£Ít>fel Low poverty

High poverty
the HPI counties and 60 of the LPI counties grew at a

moderate rate. Finally, only 12 HPI counties grew rapidly

compared with 34 LPI counties. Six rapid=growth HPI

counties were in Kentucky, while the remainder were in

Southwestern States and South Dakota.

Growing, even rapidly growing, areas are not immune to

poverty. Population in over half of the HPI counties

grew during the seventies. Several HPI counties that

grew because of increased industrialization have high

percentages of dependent populations or working poor.

Other HPI counties have population groups which

historically have had high birth rates.

square mile in the HPI group compared with 32.1 persons per square mile in the LPI group, and population

size was more than twice as large in LPI counties

(38,000) as in HPI counties (15,000). Low population size

and density may make service development and delivery

difficult because of the accompanying small tax base

and extensive service areas.

Population Composition

The two groups of counties differed markedly with

respect to the proportions of poverty-prone populations,

except for the elderly populations (table 2). HPI counHPI counties also had a higher percentage of rural persons (67.7 percent) than the LPI group (51.9 percent),

smaller total population, and lower population density

than did the LPI counties. There were 16.2 persons per

Table 1—Demographic characteristics of HPI and LPI

counties, 1980

High Low

poverty poverty Non metro

Characteristic incidence incidence counties

Gountjes counties (N = 2,443)

(N = 100) (N = 100)

Table 2—Selected population characteristics, 1980

Proportion of counties per

region:

Northeast 0

North Central 14.0

South 81.0

West 5.0

Proportion of counties by

degree of population

change, 1970-80:

Decrease and no change 43.0

Moderate increase^ 45.0

Rapid increase^ 12.0

Proportion c íhe population

which is in rural

areas 67J

Average population 15,310

Persons per square mile 16.2

Percent

8.0

59.0

6.0

27.0

4.7^

35.7

45.3

14.0

6.0 19.0

60.0 57.6

34.0 23.4

51.9 59.6

Number

37,955 25,613

32.1 25.3

"Nonmetro counties by region do not add to 100 percent

due to rounding.

^Moderate increase - less than or equal to 22.79 percent.

^Rapid increase = greater than 22.79 percent. Cut-offs are

based on national averages of population change.

Source: U.S. Department of Commerce, Bureau of the

Census, 1980.

Characteristic

High

poverty

Incidence

counties

(N = 100)

Low

poverty

incidence

counties

(N = 100)

Nonmetro

counties

(N = 2,443)

Population group:""

Black 37.6

Indian 7.3

Hispanic 8.4

White 53.3

Elderly 12.1

Disabled 12.6

Families headed by

women 19.3

Education—persons age 25

and older:

Completed elementary

school only 42.3

Completed high school

only 40.9

Population age 16 and older

employed 30.6

Workers employed in:

Extractive industries 13.6

Durable goods manufacturing 9.2

Nondurable goods

manufacturing 10.2

Service 18.6

Trade 17.0

Other 31.4

Average family size 3.2

Percent

1.1

.4

1.8

97.5

11.4

7.0

8.5

16.7

69.1

44.8

8.2

18.3

8.7

14.3

18.7

31.8

Number

2.9

8.7

1.7

3.3

88.5

12.8

10.0

10.9

23.6

59.6

39.6

9.7

12.9

10.7

16.2

19.1

31.4

2.8

''Population by race adds to more than 100 percent because

Híspanles may belong to more than one racial group.

Source: U.S. Department of Commerce, Bureau of the Census, 1980.
ties contained a much larger proportion of nonwhites

(almost 50 percent) than did LPI counties (2.5 percent).

Blacks made up almost 38 percent of the population in

the HPI group, while Indians and Híspanles accounted

for more than 7 and 8 percent of the population,

respectively. In contrast, only 1.1 percent of the LPI

population was black, and 0.4 percent and 1,8 percent

were Indian and Hispanic, respectively. Most of the

HPI's with large black populations were located in the

South, particularly in the Mississippi Delta and the

Alabama Black Belt. Large Indian populations were in

HPI counties in Colorado and North and South Dakota.

Areas with large Hispanic populations were in HPI

counties in Texas and New Mexico.

Families headed by women were twice as prevalent in

HP! counties (table 2). Women headed over 50 percent

of families in two HPI counties in South Dakota.

Several HPI counties had over 40 percent of their

families headed by women. However, not all HPI counties had a large proportion of families headed by

women. HPI counties in Kentucky and Montana had

relatively low proportions of families headed by women.

Families in HPI counties also tended to be somewhat

larger; the average family had 3.2 members in the HPÍ

group and 2.9 members in the LPI group (table 3).

The work-disabled population was greater In the HPI

counties (12.6 percent) than In the LPI counties (7 percent). HPI counties with the largest proportions of disabled persons were in Georgia, Kentucky, Missouri, and

Mississippi.

rates for nonwhites were more than double the LPI

rates. Although the proportion of elderly persons was

about the same in the two groups (table 2), the poverty

rate for the elderly in the HPI's (41.3 percent) was more

than three times greater than the LPl's (11 percent). The

higher elderly poverty rate in the HPI's might be explained by the presence of many elderly who are also

minorities. The higher poverty rate in HPI counties may

also reflect low Social Security benefits because of

smaller lifetime contributions to the Social Security

system, more employment in jobs not covered by Social

Security in the HPI group, or more income from savings

or private pensions in the LPI group. The poverty rate

for families headed by women was also higher in the

HPI group than in the LPI group.

HPI counties also had the lowest levels of formal

educational attainment (table 2). Only 40.9 percent of

those persons over the age of 25 in HPI counties had

completed high school compared with 69.1 percent In

LPI counties.

income and Employment

Both per capita personal income and the average

median family income were lower in the HPI group

(table 4). Per capita persona! income for the HPI group,

at $5,085 is substantially less than per capita personal

income for the LPI group, $8,785.

Because per capita income includes extreme values, it

says little about the distribution of income. The distriNot only do HPI counties have a generally higher proportion of poverty-prone persons, but the poverty rates

for all of the demographic groups were higher in the

HPI group (table 3, fig. 2). For example, the HPI poverty

Table â^Proportîon of selected populations living at or

below poverty level, 1980

High Low

poverty poverty Nonmetro

Type of population incidence incidence counties

counties counties (N = 2,443)

(N = 100) (N^iOO)

Percent

Black 53.9 20.2 38.4

Indian 48.6 15.4 34.0

Hispanic 43.5 12.4 27.2

White 25.2 6.4 12.5

Elderly 41.3 11.0 20.3

Families headed by women 55.6 20.0 33.5

Source: U.S. Department of Commerce, Bureau of the Census, 1980.

Figure 2

Selected Poverty Rates

Black White Elderly FHF^

1 Families Headed by a Female.

Source: U.S. Department of Commerce, Bureau of the Census. 1980.
bution of family income underscores the poverty of HPi

counties. The 1980 Census shows that 35.7 percent of

families in HPI counties had annual income below

$7,500 compared with 8.2 percent of families in LPI

counties. Only 14.7 percent of families in HPI counties

Table 4—Income characteristics, 1980

High Low

poverty poverty Nonmetro

Characteristic incidence incidence counties

counties counties (N = 2,443)

(N = 100) (N = 100)

Dollars

Per capita personal income"' 5,085 8,785 7,206

Average county median

family income^ 10,707 21,370

Percent

15,786

Personal income from:

Earnings 68.2 74.2 69.7

Property 11.0 14.8 14.9

Transfer payments 20.8 11.0 15.4

Earnings:

Wages and salaries 80.1 87.9 84.9

Self-employment 19.9 12.1 15.1

County labor and proprietary

income by industry:^'^

Farming 15.5 6.1 8.7

Mining 5.7 6.1 4.4

Manufacturing 16.6 31.9 26.4

Service 12.1 11.9 12.1

Government 18.6 13.2 16.9

Trade (wholesale) 4.8 4.6 4.4

Finance, insurance, real

estate 2.7 3.1 3.3

Transportation, communication, and public utilities 6.3 6.6 6.5

Other 17.7 16.5 17.3

Proportion of population

receiving income from:"^

Aid to Families with

Dependent Chidren

Food Stamps^

OASD!

Supplemental Security

Income

10.7

31.4

7.5

6.3

1.8

3.9

8.6

.8

3.6

9.7

9.3

2.3

""Source: U.S. Department of Commerce, Bureau of

Economic Analysis, 1980.

^Source: U.S. Department of Commerce, Bureau of the

Census, 1980.

bounty labor and proprietary income by place of

employment.

'^Source: U.S. Department of Health and Human Services,

unpublished transfer payment computer tapes, 1979.

Source: U.S. Department of Agriculture, Food and Nutrition

Service, unpublished computer tapes, 1980.

had incomes over $25,000 compared with 43.4 percent

of families in LPI counties. The average family median

income for the LPI group ($21,370) was almost twice

that of the HPI group ($10,707). The major source of

personal income was earnings for both groups with

similar proportions from property (rents, dividends, and

interest). When earnings were broken down Into wages

and salaries and self-employment, wages and salaries

made up most of the earnings in both groups. However,

the HPI group had a larger share of self-employment

(19.9 percent of personal income) than did the LPI

group (12.1 percent of personal income).

White earnings were the predominant source of personal income for both groups, the industries in which

they were earned differed between the two groups (fig.

3). In the HPI group, the largest sources of earnings

were Government (18.6 percent), manufacturing (16.6

percent), and farming (15.5 percent). In the LPI group,

earnings from manufacturing (31.9 percent), government

(13.2 percent), and services (11.9 percent) predominated.

Only 30.6 percent of workers 16 years and older in the

HPI group were employed compared with 44.8 percent

in the LPI group (table 2). Given the larger average

family size in HPI counties, each worker in HPI counties had more dependents than did workers in LPI counties. The HPI group had more employment In service

and extractive industries (largely farming) and almost

the same proportion of employment in trade. There

were major differences in manufacturing employment

Figur© 3

Sources of Personal Income

High poverty

counties

Low poverty

counties

All nonmetro

counties

Earnings

Property

^\N^^\;^ Transfer payments

0 20 40 60

Percent

Source: US. Department of Commerce. Bureau of the Census. 1980.

80
between the county types. HPI counties' manufacturing

employnnent was most likely to be in nondurable goods,

a sector characterized by low wages. LPÎ counties'

manufacturing employment was more likely to be in

durable goods (table 2). For instance, HPI counties had

19.4 percent of employment in manufacturing but only

16.6 percent of labor and proprietary income from

manufacturing. These figures suggest low-wage jobs

predominate in HPI counties. The LPI counties had 27

percent of their employment in manufacturing but 31.9

percent of their labor and proprietary income came

from manufacturing.

Geographically, most of the 46 HPI counties that were

dominated by agriculture (over 25 percent employed)

were in Kentucky, scattered throughout the Coastal

Plains, in the Mississippi Delta, Texas, and South

Dakota. Only one HPI county, in Kentucky, had mining

as its major employment industry. Only five LPI counties were dominated by agriculture, and six in Wyoming,

Colorado, and Texas were dominated by mining. Furthermore, only 8 HPI counties, mostly in the Alabama Black

Belt, were primarily manufacturing counties, compared

with 30 LPI counties. These LPI counties were mostly

located in the Great Lakes region.

One major distinction between HPI and LPI counties is

their dependence on Government assistance programs,

reflecting the higher proportion of dependent populations in HPI counties. Higher proportions of the HP!

population received benefits from means-tested Government assistance programs (table 4). For example, 10.7

percent of the HPI population received AFDC benefits

compared with 1.8 percent of the LPI population. Over

30 percent of the HPI population received Food Stamps

compared with 3.9 percent of the LPI population.

Although the proportion of elderly receiving OASDi did

not differ substantially for the groups, the groups

differed considerably on the percentage of Supplemental Security Income recipients (6.3 percent of the HP!

population compared with 0.8 percent of the LPI population), indicating a larger proportion of poor aged,

blind, and disabled people in the HPt group.

In summary, the results show that the HPI counties

have a sparser population than the LPI counties. HPI

counties also have larger proportions of minorities,

poor elderly, disabled, and families which are headed

by women and which are poor. Lower educational

levels and employment in low wage jobs also differentiate the HPI counties from the LPI counties. Although

personal income is derived primarily from wages and

salaries in both groups, the HPI population relies

significantly more on Government assistance programs

than the LPI population does. Furthermore, in HPI

counties a considerably greater share of earnings came

from farming and Government employment than in LPI

counties, while in LPI counties a much greater share of

earnings came from manufacturing employment, and

manufacturing in LPI counties was of a higher wage

variety. Poverty rates within the vulnerable population

groups were higher in HPI counties than either LPI

counties or all nonmetro counties.

Farm Characteristics

Because income from farming is one of the significant

income differences between the two groups of counties, contrasting the farm characteristics of both

groups may provide insight into the economic structure

of HP! counties. For example, the average farm size in

HPI counties was 1,072 acres—almost twice the average

size in LPI counties (568 acres) and more than twice the

nonmetro average (507 acres). This figure reflects the

predominance of large farms and ranches in the Mississippi Delta, Texas, New Mexico, and South Dakota.

When farm size is examined more closely, HPI counties

had a slightly higher percentage of farms under 50

acres (22.7 percent) than did the LPI group (18.1 percent). This figure probably includes the small farm

structure in Kentucky. HPI counties had a smaller

percentage of 50- to 499-acre farms (table 5).

Although overall farm sales were higher in LPI counties, the percentage of farms with sales of $100,000 or

more varied within HPI counties. For example, six HPI

counties in the Mississippi Delta had more than 40 percent of their farms in the $100,000 and over sales class.

By contrast, no LPI county had more than 32 percent of

farms in the over $100,000 sales category.

Furthermore, the average market value of sales per

farm exceeded $100,000 in 17 HPI counties compared

with 5 counties in the LPI group. Two HPI counties with

poverty rates exceeding 40 percent had average market

value of per farm sales of over $200,000. A closer look

at the HPI counties with the highest market value of

sales per farm shows that all of them had relatively

high poverty rates, over 35 percent, and a fairly large

proportion of minorities. Most of these counties were in

Mississippi or Texas. The existence of these large

farms with high average sales in several HPI counties

suggests that the distribution of income and wealth

among the population is highly skewed. In other words,

some HPI counties with extremely high poverty rates

contain persons with considerable income and assets

derived from ownership of large farms even though a

fairly large proportion of persons have income below

the poverty level.
Table 5—Farm structure characteristics, 1978

High Low

poverty poverty Nonmetro

Characteristic incidence incidence counties

counties counties (N = 2,443)

(N = 100) (N = 100)

Number

Total farms 42,744 83,258

Acres

1,717,485

Average farm size 1,072 568

Dollars

507

Average market value of

annual sales per farm 4,832 5,393

Percent

4,702

Farms by number of acres:

Under 50^ 22.7 18.1 20.3

50-179 30.1 34.4 33.2

180-499 20.0 31.4 27.6

500-999 11.0 9.3 10.6

1,000 and over 16.2 6.8 8.3

Farms by annual sales:^

Less than $40,000 65.5 49.2 63.1

$40,000-$99,999 22.9 37.5 26.9

$100,000 and over 11.6 13.3 10.0

Type of farm ownership:^

Full ownership 53.6 49.6 56.3

Part ownership 31.7 34.2 31.2

Tenants 14.7 16.2 12.5

Type of farm organization:^

Individual or family 85.9 85.7 87.3

Partnership 10.6 11.4 10.3

Corporation 2.9 2.5 2.0

Other .6 .4 .4

Principal occupation of

farm operator:"i

Farming 58.2 64.0 58,7

Other 41.9 36.0 41.3

Major farm crops:^

Cash grains 68.7 42.9 58.6

Tobacco, cotton, potatoes,

hay, and field seed 29.2 29.1 26.3

Vegetables, fruits, and

nuts .8 14.0 7.3

Greenhouse and nursery

products .1 0 .1

Other crops 1.0 13.9 7.7

^Data unavailable for three LPI counties.

^Data unavailable for two LPI counties.

Source: Department of Commerce, Bureau of the Census.

7978 Census of Agriculture.

There was little difference between the groups on farm

ownership, legal organization, principal occupation of

operator, or land value. However, farms in HP! counties

grew more cash grain crops and fewer vegetables, nuts,

and other crops than the farms in LPI counties.

Implications

Impoverished areas where many people depend on

Government assistance programs may have difficulty

improving their economic status. Aside from transfer

payments, there are few ways to reduce this type of

poverty because it is less responsive to improvements

in employment and the general economy. Many of the

poor in such areas are elderly, disabled, or not in the

work force. Hence, changes in Government assistance

programs will have a disproportionate effect on these

areas.

However, economic development can benefit high

poverty areas. Job opportunities and wage levels in

high poverty areas may improve as the general economy does, and more higher wage jobs may develop in

some of the poorer areas. The Increased tax base that

accompanies this economic improvement might go to

improving schools and encouraging young people to remain in school longer. Given the low skill levels in HPI

counties, job programs that teach skills needed in local

industries could be made available to the rural working

poor in order to increase their income. But, because

high wage jobs are not evenly distributed, some supplement to income will probably continue to be necessary

to help raise the working poor out of poverty in high

poverty areas.

In conclusion, poverty in certain areas of nonmetro

America is acute and in some cases persistent. Knowing if there is something unique about these areas

beyond who lives and works there, such as a geographical situation or economic structure, that explains

their high poverty rates is important. Areas with high

poverty rates have small, sparsely settled populations,

a concentration of low-wage industries, and in some

agriculture-dominated areas there is evidence of a dual

economy. These same areas also have a concentration

of minority, dependent, and other poverty-prone populations. Because of their unique economic and demographic attributes, these areas have poverty rates

which are higher for all of the groups which are especially vulnerable to poverty. Hence, programs to assist

high-poverty counties should consider economic and

demographic structures in addition to individual attributes of the counties' population.
Bibliography

(1) Davis, Thomas F. Persistent Low-income Counties

in Nonmetro America. RDRR-12. U.S. Dept. of Agr.,

Econ. Stat. Coop. Serv., May 1979.

(2) Etuk, Efiong. "Structural Correlates of Poverty:

Implications for Public Policy." Ph.D. thesis. Penn.

State Univ., 1976.

No. AGES820827. U.S. Dept. of Agr., Econ. Res.

Serv., Sept. 1982.

(8) National Rural Center. Tlie Rurai Stake in Public

Assistance. Publication Series Vol. 10, 1978.

(9) Plotnik, Robert, and Timothy Smeeding. "Poverty

and Income Transfers: Past Trends and Future

Prospects," Pubiic Policy, Vol. 27, No. 3 (Summer

1979), pp. 255-72.

(3) Getz, Virginia, and Robert A. Hoppe. "The Changing Characteristics of the Nonmetro Poor," Social

Development Issues, Vol. 17, No. 1 (Spring 1983),

p. 5.

(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) Holden, Karen C. "Attaining More Complete Coverage Under Social Security: The 1983 Amendments,"

IRP Focus, Vol. 6, No. 3 (Summer and Fail 1983),

pp. 22-25.

(6) Hoppe, Robert A. Economic Structure and Change

in Persistently Low-Income Nonmetro Counties in

the Seventies. U.S. Dept. of Agr., Econ. Res. Serv.,

forthcoming 1985.

(7) _, and William E. Saupe. Transfer Payments in Nonmetropolitan Areas. ERS Staff Report

(10) Sechler, Susan. Rural Poverty: Progress and Prospects. Unpublished working paper, U.S. Dept. of

Agr., Econ. Res. Serv., Sept. 1981.

(11) Shipp, P. Royal. "Poverty: Trends, Causes and

Cures," Congressional Research Service Review,

Committee Print WMCP: 98-15. (1983).

(12) Tweeten, Luther. "Employment," Rural Society in

the U.S.: Issues for the 1980's. Ed. Don Dillman and

baryl Hobbs. Boulder, CO: Westview Press, 1982.

(13) U.S. Department of Commerce, Bureau of the Census. "Money Income and Poverty Status of Families

and Persons In the United States: 1981 (Advance

Data From the March 1982 Current Population Survey)," Current Population Reports. Series p-60,

No. 134. July 1982.

(14) U.S. Department of Health, Education, and Welfare,

Office of Family Assistance. Characteristics of

State Plans for the Aid to Families with Dependent

Children Program. Sept. 1976.

10
other Reports of Interest on Rural Issues

Physicians in Nonmetro Areas During the Seventies shows that the gap between the number of

physicians in nonmetro and metro areas widened

during the seventies, with nonmetro areas lagging by almost 100 physicians per 100,000 population. This report describes availability of physicians in nonmetro areas In light of population

changes and demand for medical care. RDRR-46.

March 1985. 28 pp. $1.50. Order SN: 001-019-00380-4.

Housing of the Rural Elderly finds that the number of rural elderly households rose 16 percent

b'etween 1974 and 1979 compared with a 10-percent

increase for all U.S. households, based on the

1979 Annual Housing Survey. Most of the U.S.

elderly live in adequate housing, but 27 percent

of the elderly renters and 18 percent of all elderly

living in the South have inadequate housing. In

1979, 15 percent of the rural elderly lived in adequate housing compared with 8 percent of the

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

Order SN: 001-019-00335-9.

Patterns of Change in the Metro and Nonmetro

Labor Force, 1976-82 reveals that nonmetro areas,

particularly farm areas, lagged behind metro

areas in employment growth during the 1976-82

period. This reversed a pattern of faster nonmetro

growth occurring in the late sixties and early

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

Order SN: 001-019-00358-8.

Distribution of Employment Growth in Nine Kentucky Counties: A Case Study shows that people

moving to a nonmetro area held a disproportionate

share of jobs in growing business establishments

and of better paying executive jobs. Manufacturing was the study area's major economic driving

force, but the private service sector (which provided services to the manufacturing sector and to

the area's growing population) was an important

contributor to job growth between 1974 and 1979.

RDRR-41. August 1984. 44 pp. $2.25. Order SN:

001-019-00337-5.

A Profile of Female Farmers in America discusses social and economic characteristics of

female farmers, including age, race, size of

household, farm and off-farm Income, types of

farms female farmers most frequently run, and

value of agricultural products sold. Although the

number of U.S. farms is dropping, the number of

female farmers is rising. They tend to run smaller

farms and earn less than their male counterparts.

RDRR-45. January 1985. 32 pp. $1.50. Order SN:

001-019-00378-2.

Chartbook of Nonmetro-Metro Trends is a quick

check on metro and nonmetro socioeconomic

trends. It presents colorful charts, tables, maps,

and text tracing differences in population,

employment, income, poverty, housing, and government between nonmetro and metro America.

RDRR-43. September 1984. 48 pp. $2.50. Order SN:

001-019-0035M.

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