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Rural Development

Research Report

Number 68

Local Farm Structure

and Community Ties

Thomas A. Carlin

Bernai L Green

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Local Farm Structure and Community Ties. Thomas A. Carlin and Bernai L.

Green. Agriculture and Rural Economy Division, Economic Research Service;

US. Department of Agriculture. Rural Development Researcfi Report No. 68.

Abstract

A county's farm structure—whether large-scale or small farms dominate—is influenced not only by changes within farming but also by key factors such as nonfarm

economic activity, geography, and population growth. This report identifies which

counties are dominated by small- or large-farm agriculture as well as a large group,

termed unclassified, which could go either way. Large-farm counties where agriculture dominates the economy are concentrated in the Plains, Midwest, and Mississippi Delta. Small-farm counties, with larger, more diversified economic bases, are

concentrated in the Southeast. However, more than 50 percent of the U.S. counties

are unclassified, presenting local planners special challenges in charting development either toward agriculture or to a more diversified economic base. Unclassified counties that tend to have small-farm characteristics are found mostly in New

England and scattered parts of the Southeast and West. Those tending to have

large-farm characteristics are usually located adjacent to large-farm counties.

Keywords: Farm structure, rural development, large farms, small farms.

Acknowledgments

The authors acknowledge the helpful assistance and comments from Susan

Bentley, Nora Brooks, Mary Ahearn, Leslie Whitener, and Greg Hanson. Enid

Hodes provided editorial assistance. The authors also thank Sharon Davis who

typed the manuscript.

Cover photo: Courtesy of Soil Conservation Service, U.S. Department of

Agriculture.

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Washington, DC 20005-4788 March 1988
Contents

Page

Summary iv

Introduction 1

Background 1

Community Link 1

Changes in Farming 1

Changes in Rural America 2

Shifts in Local Planning Approaches 2

Assessing Local Attributes that Drive Farm Structure Change 3

Research Objectives 3

Conceptual Framework 3

Defining Farm Structure 5

Profiling Small- and Large-Farm Counties 5

Using a Model to Differentiate County lypes 9

Method of Analysis 9

Variables Considered 10

Results of Discriminant Analysis 10

Categorizing the Unclassified Counties 13

Implications for Rural America 13

References 16

Appendix A—Developing Subregions 17

Appendix B—Alternative Discriminant Models 20

m
Summary

A county's farm structure is influenced not only by changes within farming but

also by key factors such as nonfarm economic activity, geography, and population

growth. This report identifies which counties are dominated by small- or largefarm agriculture as well as a large group, termed unclassified, which could go

either way. Rural development practitioners and county officials can use this information to examine their jurisdictions and decide whether to diversify their economy or to strengthen but restructure their farm sector, possibly going to larger

farms.

Large- and small-farm counties are typed according to the portion of farms with

1982 gross sales of less than $40,000. Large-farm counties are those with the lowest

percentage, while small-farm counties are those with the highest percentage. Agriculture dominates the economy of large-farm counties. Such counties have small

employment bases and few nonfarm job opportunities and are concentrated in the

Plains, Midwest, and Mississippi Delta. Their leaders will decide whether they can

effectively compete in global agricultural markets. Small-farm counties have larger,

more diversified economic bases and are concentrated in the Southeast. Their leaders will focus on finding a niche for farming in a basically nonfarm economy. Unclassified counties, which make up 50 percent of all counties in the continental

United States, fall in the middle range.

Since 1980, State and local governments have been asked to play a stronger role in

guiding their own destinies. The more importance of farming in the local economy,

the more critical it is that local leaders understand the factors that influence the

farm sector's structure. Leaders in large-farm counties thus face stronger challenges in charting a course for their local economies. Some local leaders will be

evaluating whether their farm sector has enough comparative advantage to emerge

as a winner among international competitors, while others will want to follow a

strategy of building a diversified local economy. Diversifying, however, will likely

also change the organization of the local farming sector.

Leaders of unclassified counties, or those that fall in the middle range, stand to

learn the most from this study's findings because it is in these jurisdictions that

controlled change can best be carried out. This report gives guidance on which way

the farming sectors in such counties are headed. Unclassified counties with characteristics that portend small-farm structure are found mostly in New England and

scattered parts of the Southeast and West. Those that are moving more toward

large-farm structure are concentrated in areas adjacent to large-farm counties.

Based on analytic findings, the authors provide brief scenarios of how all three

county groups—large-farm, unclassified, and small-farm—will respond to impending changes in U.S. agriculture.

IV
Local Farm Structure

and Community Ties

Thomas A. Carlin and

Bernai L. Green*

Introduction

Americans realize that sustained financial problems in

the farm sector influence whole communities and regions whose economies are based on agriculture. The

strong interdependence could translate into rapid restructuring of the farm sector and farm-dependent communities. Concerned groups throughout the country's

farm regions are consequently exploring ways to cope

with the restructuring process.*

This report suggests that the structure of a local farm

sector is closely related to the basic economic, human

capital, and geographic attributes of the local community. We identify the areas of the country dominated by

small-farm and large-farm agriculture and examine the

factors historically associated with the two types.

Knowing about these factors will help local officials to

interpret changes in their local farm sector influenced by

national trends. The information can be valuable to

them as they weigh options that could alter the course of

development in their communities.

Background

Our investigation into the question of local farm structure draws primarily on two models often cited in rural

social science literature. The first concerns the emerging

structure of the U.S. food and fiber system [17, 18]? Our

attention to the food and fiber system focuses on its

emerging dualistic nature. Proponents of the first model

argue that the farm sector is moving toward a structure

in which a few large producers grow most of the U.S.

food and fiber and a large number of small farms pro-

* The authors are agricultural economists with the Agriculture and

Rural Economy Division, Economic Research Service, U.S. Department of Agriculture.

' A notable example is the conference "Coping with Change: Rural

America in Transition^' sponsored by the Foundation for Future

Choices and the Hubert H. Humphrey Institute, at the University of

Minnesota, December 2-3, 1986.

^ Italicized numbers in parentheses refer to items cited in References

at the end of this report.

duce relatively little. The second model centers on community development.

Community Link

Our attention to community development focuses on the

emerging economic and social diversity of nonmetropolitan (nonmetro) communities and the changing role of

farming in most local communities [5]. Both themes,

change in the food and fiber system and community

development, dominate current pubUc discussion in the

rural social sciences. Often, but not always, the two

models are explored separately. We believe that important relationships exist between the structure of agriculture and other economic activity in the local community.

Our purpose is to investigate those relationships.

Changes in Farming

The U.S. farm sector has had a persistent problem of

longrun excess production capacity since at least World

War II [5]. As wartime price supports were Ufted, it

became evident that the farm labor force needed to be

pared if farming was to become viable without extensive

Government support. In postwar America, infusion of

laborsaving technology to the farm had a dramatic effect. Labor, in fact, has been leaving agriculture since

the early 1950's. Visible signs of this 1950's phenomenon

included rapid declines in the farm population, net outmigration from rural areas, absolute declines in population and employment in many rural communities, and

the near disappearance of southern sharecropper agriculture.

During the mid-1970's, economic conditions and

weather combined to bring a unique set of circumstances

to the farm sector. Product prices increased, farm incomes rose, and real interest rates were low. And, with

the expectations that U.S. agriculture would reach new

heights, land prices were bid up. The farm sector experienced a new period of prospferity.

Conditions sharply reversed themselves in the early

1980's, leading to a farm financial crisis. The value of

farm capital, primarily land, was too high in view of
income growth prospects and higher real interest rates.

The value of the production plant dropped, primarily

through falling land prices. Declining land prices coupled with low commodity prices have created cash flow

problems for some farmers. Unable to obtain credit,

some are being forced to liquidate part or all of their

farming operation. Perhaps 10-15 percent of the Nation's farmers who were in business before 1980 will

cease operating before the financial adjustment is complete [10, 23], Political debates focus on whether this

phenomenon is a slight divergence from past trends or a

massive shakeout of the sector. Considerable interest has

arisen over what the farm sector and farm-dependent

communities will look like when the crisis has run its

course [10, 21, 22],

One outcome of the combined effect of technology,

public poUcy, and other factors throughout the postWorld War II period is the movement towards a dual

farm structure: a large number of small farming establishments which produce relatively little of total U.S.

food and fiber and a few large establishments which

provide a major portion of total output [15, 24], There

has been considerable periodic concern about the disappearing middle, what many consider to be the traditional family farm [6],

Communities in the western Corn Belt and Northern

Plains have been hurt by the recent farm financial situation because farming dominates the economies of these

communities [22]. There has been a general feeling that

farming communities in areas dominated by large-scale

agriculture are less viable than those dominated by

small- and family-sized farms [16], Research has provided mixed answers about how nonfarm industrial

development affects the local farm sector. Some studies

suggest that development of this kind results in farms

shifting to less labor-intensive commodities as the opportunity cost of labor increases. If an area is dominated by smaller scale marginal farms, then nonfarm

industrial development may complement the farming

sector by providing jobs for underemployed farmers and

family members.

Changes in Rural America

The economic environment of postwar rural America

has changed considerably [3]. Rural areas became more

economically diverse. During the 1950's and early

1960's, the decUne in the number of farmers exceeded

any growth in nonfarm employment in most rural communities [4]. This massive exit from farming resulted in

a precipitous decline in rural population as millions of

people migrated to urban centers to find work. By the

late 1960's, the farm population had shrunk so much

that growth in nonfarm employment outpaced further

declines in farming. This uptick in the nonfarm economy resulted in net employment and population growth

primarily in southern rural areas. Such growth more

recently took place in the rural Southwest.

Manufacturing and services such as those associated

with retirement and recreation activities began to dominate the economy of some communities during the postwar period. A key feature of this transformation was the

drop in the number of areas dependent on farming as a

major economic activity [3, 11], For example, the number of counties with 20 percent or more of earned income from farming fell sharply from about 2,000 in the

early 1950's to 700 or fewer by the late 1970*s.

Farmers faced with adjustment problems now have a

different set of options than might have been available

in the 1950's and 1960's. There is a reduced need, for

example, to migrate to urban centers because of more

plentiful off-farm job opportunities nearby. However,

there is a definite geographic aspect to how farming's

structure is changing. Rural development experts recognize that the quantity and quality of natural and human

resources vary considerably across the country.

Shifts in Local Planning Approaches

For 7 years, the Nation has followed a public policy

course of "New FederaUsm" in which State and local

governments play a stronger role in charting their future.

This policy has important implications for how local

leaders approach problems. Community leaders, especially in areas where farming still dominates the local

economy, are interested in and need to know what might

happen to their local farm sector. They are raising vital

questions. In view of what is known about factors that

influence the local farm structure, what is the future for

farming in the community? How will farming's future

affect local decisions about economic development and

local public infrastructure?

Researchers are interested in answering those questions

in a systematic way. By analyzing local farm structure,

identifying variables associated with local farm structure, and interpreting those variables in light of current

conditions, they are discovering vital new information.

This information will be useful to local government

policymakers as they consider options that might alter

the course of development in their communities.

Today's situation merits attention for two key reasons.

First, the pace and extent of the restructuring process in

the domestic farm sector is not necessarily subject to the

control of American pohcymakers. Global developments, such as emergence of foreign competitors in the

world farm product market, constitute unstable forces

that will share in reshaping American agriculture.
Second, more population declines in the U.S. farm belt

can cause precipitous declines in vital public- and

private-sector goods and services. For example, continuing loss of residents in the Northern Great Plains will

result in downtown business failures. Thus, the sustained crisis in farming is causing public concern about

what the farm sector and farm-dependent communities

will look Uke once the crisis ends [10, 21, 22],

Assessing Local Attributes that

Drive Farm Structure Change

Our research examines forces impinging on local farm

structure. We define counties by the relative proportion

of large-scale farms. That information is then used to

suggest prospective trends in the structure of the local

farming sector and impUcations for the community.

Research Objectives

We first formulate a conceptual model that identifies the

major factors believed to be influencing the structure of

U.S. agriculture. We measure farm structure by farm

product sales. The importance of the model lies in providing a rationale for choosing the independent variables

to be used in the analysis.

We define counties in which farming is decidedly small

scale and large scale. We then use discriminant analysis

to select from a wide group of variables only those

which are best able to identify counties characterized by

small-scale and large-scale farming. Finally, we use this

information to suggest the general direction in which

counties in the middle of the continuum are moving. It

is in these counties that local leaders may best be able to

influence the direction of the local farming sector.

Conceptual Framework

Babb developed a conceptual approach to identifying

factors that affect farm structure to provide decisionmakers "a basis for making informed choice among alternative (farm) structures" [7]. Babb's model contained

most of the critical elements relevant to our inquiry.

However, he did not attempt to quantify and test his

hypothesized relationships between farm structure and

characteristics of farm operators, the farm sector, rural

communities. Government, and rural people.

Our objective was to quantify and test the relationships

between farm structure and the local farming sector,

community economic structure, local geography, local

human capital, and the public sector, starting with

Babb's conceptual approach. The conceptual niodel

guided our efforts in selecting appropriate data.

We based our research on the premise that the national

farm sector is influenced by international and national

policies and events, called macro events, as well as the

sum of local events across the United States.^

The effects of macro events on the farming sector are

well known and have been chronicled [23], Macro events

may influence local community structure, but the linkages are complex. Unraveling these relationships is beyond the scope of our analysis. We essentially treat them

as given or fixed.

Our interest is with the other sets of factors representing

the local community. We maintain that these variables

play an important role in influencing local farm structure, and, that depending on the particular combination

present at any one time, their role may overshadow the

effects of macro events. If we are correct, State and

local officials need to think about solutions to the farm

crisis not in terms of national agricultural policy but

rather in terms of broader development poUcy. Our

groups of local attributes include: farming sector, community economic structure, geography, human capital,

and population size and the public sector.^

Local Farming Sector. Components of the local farming

sector include enterprise combinations (specialization in

one or more types of crops or livestock), level of

technology adopted by local producers, land

characteristics (the extent to which the land is productive

and adaptable to crop and/or livestock production),

level of capital investment in the local farming plant,

and returns to land, labor, and other capital. The factors

are distributed differently across the United States, and

they influence the way the local farming sector evolves

over time. As we will later show, large-scale commercial

farming has developed in different parts of the United

States than has small-scale farming.

^ These ideas can be expressed mathematically as:

(1) National farm structure = f[(macro events)

+ ¿j (local farm structurci)]

1 = 1

where:

(2) Macro events = g(national public poUcy, national economic

conditions, international economic conditions)

(3) Local farm structure; = h(macro events, FSj, ESi, Q, HC;, PS;)

where:

FSi is attributes of the local farming sector, ESi is the local

community economic structure, Gj is local geography, HC; is

local human capital, PS; is population size and the pubhc

sector, and i is the local community.

* Local is defined, for this report, as county.
Community Economic Structure. A local community's

nonfarm economic activities, such as durable goods

manufacturing, compete with farming because they

provide alternative uses of labor, land, and other capital.

In economic theory, these nonfarm economic activities

estabhsh the opportunity cost for farm labor and

capital. If the opportunity cost of labor, for example, is

high compared with what can be earned with local farm

resources, farmers might reduce the size of their

operations and work off-farm. Alternative scenarios can

be hypothesized, but we contend that community

economic structure has a major influence on local farm

structure.

Geography. Our notion of geography encompasses a

wide variety of variables including topography, climate,

settlement patterns, and the availability of water. For

example, irrigating from surface and ground water

sources has allowed large-scale farming to become

viable, especially in semiarid parts of the country such

as California and the Texas High Plains. The large scale

was dictated by the need to spread high fixed costs for

irrigation equipment over a large number of acres. In

the United States, land irrigated with ground water

reached 32 million acres in 1977, but ground water levels

are in chronic decline for 15 milHon of these acres [19],

Drops in ground water levels mean rising energy costs to

lift water from deep aquifers. Thus, even if other costs

remain constant, farm structure will be affected by

escalating pumping costs and, in some instances, by

economic exhaustion of the water supply. An example of

areas facing exhaustion of the water supply are the

central and southern Great Plains, which are depleting

the Ogallala Aquifer. Costs may increase rapidly in

semiarid areas that contain metropolitan centers

competing with farms for scarce water.

Human Capital. The study of human capital gained

prominence during the 1950's when conventional

explanations for growth of gross national product fell

short of the mark. Schultz, in a pioneering article in

1956, reported that greater use of conventional inputs

accounted for little of the increased output in farm and

nonfarm sectors [75], This mystery caused him to

emphasize the improved quality of people as productive

agents and the emergence of the productive arts. Martin

explained that human capital results from investments in

formal and informal education, health, and aesthetic

and recreational experiences. Human capital is an

integral part of the individual, and is therefore as mobile

as the individual [14].

Two important aspects of human capital come into play.

First, humans have a dual role. They are a factor or

means of production and they are the end consumers of

the product. The need for large capital investments in

farming coupled with technological revolution led many

to agree that farming now requires relatively high levels

of education. Thus, the 1960's signaled the beginning of

a trend toward more farmers and farm managers who

have 4 years or more of college education [7]. The

second aspect is that mobility or migration, which is

governed mainly by wage and earnings differences

among industries, continues to characterize the US.

farm sector. When f^rm regions are close to large

nonfarm labor markets, mobility is enhanced.

Productivity gains in the farm sector have been so great

that millions of farmers and farm managers have been

released to seek nonfarm employment. There were, for

example, 6.3 million farms in 1940, compared with 2.3

million in 1985 [24]. In the same period, off-farm

earnings rose. For instance, off-farm income was

negligible in 1940, but by the 1980's it ranged from

54-72 percent of income of farm operators [24],

Population Size and the Public Sector. Population size is

an important indicator of what size private and pubhc

service sectors a community can maintain. At the local

level, population increases translate into new patterns of

demand for land formerly used for agricultural purposes

and open space. Growth was especially strong in urban

fringe areas and rural areas in the 1970's. Population

decreases, on the other hand, can signal difficulty in

maintaining tax revenues to support current service levels

[2. 12].

Communities depend on revenues from taxes and other

sources to fund vital public services such asschools,

roads, fire protection, and waste disposal. A recent

Senate study points to eroded local tax bases in

midwestern farm counties resulting from recent declines

in farmland values [22]. Crumbling tax bases, combined

with higher tax delinquency rates and reduced Federal

and State aid, spell hard times for local governments

trying to manage local services. At the other extreme,

fast-growing communities may be faced with rising

demands for public services, pushing up landownership

costs. Population change and pubHc-sector actions

consequently can be expected to cause important

adjustments in farm structure.

The Federal Government distributes funds to State and

local areas. These funds are for a variety of purposes

and take the form of direct transfers (cash), grants, and

loans to local governments, businesses, and individuals.

For example, the Federal Government distributed $28.1

bilUon in 1980 to businesses and local governments for

economic development [9]. Economic development

programs include revenue sharing, small business loans

and grants, and loans for constructing water and sewer

facilities. It distributed another $16 billion in 1980 for
agriculture. The monies went toward Commodity Credit

Corporation loans for commodities, direct Government

farm program payments, farm ownership and operating

loans (direct and insured), and emergency loans. As the

farm financial crises deepened after 1980, Federal

expenditures for agriculture grew rapidly. These funds

influence both the local farming sector and the

community.

The five components of the conceptual model of local

farm structure we described raise some key questions

which we address:

* Which areas of the country are dominated by

small-farm and large-farm agriculture?

* What factors are most associated with local farm

structure?

We defined localfarm structure in the following way:

For each county in the continental United States, we

calculated the percentage of farms with gross sales of

less than $40,000 in 1982. This yielded a continuum of

percentages that could be reduced to a small number of

county groups by setting cutoff points or percentage

threshold levels (fig. 1). To make our key analytical

model which used multiple discriminant analysis more

manageable, we selected cutoff points on the continuum

of percentages that would allocate the 3,069 counties

into fourths.

A small-farm county was thus defined as a county in

which 88 percent or more of farms had 1982 gross sales

of less than $40,000. A large-farm county was defined

as a county in which fewer than 59 percent of farms had

1982 gross sales of less than $40,000.^ A middle group of

1,569 counties was temporarily unclassified and held

aside until later in the analysis.

* What do the identified functional associations

portend for local farm structure?

Defining Farm Structure

A guiding principal in choosing an operational definition of small-farm and large-farm areas was complementarity with current Economic Research Service research

on the same theme [15]. Gross farm product sales of

$40,000 or more has been used often in recent years to

distinguish between commercial farms and small noncommercial farms [10],

Profiling Small- and Large-Farm Counties

Small-farm counties are concentrated in eastern Texas

with extensions into Louisiana and the Ozarks region

and in the Appalachian region. They are also scattered

^ The exact percentages used in the computer sorting were 88.05 for

small-farm counties and 59.27 for large-farm counties. We have

rounded the percentages to the nearest whole number for ease of

presentation. Six counties containing major cities had no farms in

1982.

Figure 1

Categorizing U.S. counties by farm structure1.2

/ 1/4 1/2 1/4 \

/ (N=750) (N = 1.569) (N =750) \

/ Large-farm oriented Temporarily unclassified Small-farm oriented \

59 88

Percentage of farms with sales under $40,000

100

1/ Includes all counties in the continental United States. Counties are arrayed and sorted into quartiles based on percentage of farms

which had gross farm sales of less than $40,000 in 1982.

2/ N = 3,069 in our classification scheme.
through the Southeast, the upper parts of the Lakes

States, and parts of the West (fig. 2). Large-farm counties are heavily concentrated in the northern and southern Great Plains (with eastward extensions into the Corn

Belt, lower parts of the Lakes States, and west into

Montana) and in the Mississippi River Delta region.

They are also scattered through parts of the Eastern and

Western United States (fig. 2).

Large-farm counties are less likely to be part of a metro

area, and nonmetro large-farm counties are less likely to

be adjacent to a metro area (table 1). Greater distance

from an urban center affects the options available to

farmers in large-farm counties who are striving to adjust

to current farm financial stress.

Table 1—Residential areas in whieb larg&-farm and small-farm

connttes are found

Item

Largefarm

counties

Smallfarm

counties

All

counties

Number

Total counties 750 750 3,069

Residence:

Metro area'

Nonmetro area

Adjacent to a metro area'

Not adjacent to a metro

area

78

672

193

479

202

548

240

308

712

2,357

906

1,451

' Variable was used in the di^ariininant analysis.

Using data available from secondary sources, we examined the characteristics of small-farm and large-farm

counties. We used the 1978 arid 1982 Censuses ofAgriculture to obtain information on key characteristics of

the local farming sector, the 1980 Census ofPopulation

to obtain information on the characteristics of the local

population, and Bureau of Economic Analysis, U.S.

Department of Commerce, county income and employment data to obtain information on the structure of the

local economy. Tables 2-5 present means for selected

variables for large-farm and small-farm counties. We

also calculated t-scores to help determine whether group

means differ significantly from one another.

Farm sector data reflect expected basic differences in

farm characteristics between the two county groups

(table 2), For example, the average value of land and

buildings per farm is about three times greater in the

large-farm counties, reflecting the higher proportion of

larger commercial farms. Large-farm counties depend

less on livestock production, reflecting their concentration in the major US. grain belt. The proportion of area

in farms for large-farm counties is over twice that of

small-farm counties, reflecting in part geographic differences between the two county types. The higher proportion of land in farnas in large-farm counties also shows

up as higher total farm sales per county. Large-farm

counties have more farms per county but, on average,

the number of farms increased between 1978 and 1982

Table 2—Selected characteristics of the local farm sector, large-farm counties, and small-farm counties

Item

Coimties

Average:

Total farms, 1982^

Farms with sales of —

less than $40,000

$40,000-$249,999

$250,000 or more

Change in number of farms, 1978-82^

Total farm sales, 1982^

Livestock sales, 1982^

Land area in farms, 1982^

Value of land and buildings per farm, 1982^

Operators in 1982:

Not working off-farm

Working off-farm;

1-99 days

100-199 days

200 or more days

Unit

Number

do.

Percent

do.

do.

do.

Large-farm

counties

750

786

56.7

13.9

7.0

22.4

Small-farm

counties

750

667

30.5

9.0

10.9

49.5

T-value

NA

4.12

do. 370 620 -11.99

do. 360 41 31.72

do. 55 6 21.22

Percent -5.5 8.0 -24.77

,000 dol. 84,394 11,124 19.19

Percent 48.1 62.8 -10.22^

do. 79.2 35.7 36.67

Dollars 600,014 202,608 25.50

79.20

25.55

-27.52

-70.63^

NA = Not applicable.

' Excludes abnormal farms. This category includes institutional farms, experimental and research farms, and Indian reservations.

^ Variable was used in the discriminant analysis.

^ T-test based on separate variance estimates because the populations have different variances for that variable.

Sources: Censuses ofAgriculture^ 1978 and 1982.
Figure 2

County farm structure, 1982^

I Large-farm counties

Unclassified counties tending

toward a large-farm structure

Unclassified counties tending

toward a small-farm structure

^H Small-farm counties

1/ See text for definition of county types.
for small-farm counties while farm numbers declined in

the large-farm counties.

About half of all U.S. farms with farm sales of $40,000

or more are located in large-farm counties. Thus, any

economic conditions or farm policy actions that affect

larger farms disproportionately will be felt the most in

large-farm counties.

The economic structure of large-farm and small-farm

counties differs in major ways (table 3). Large-farm

counties have about half the total employment when

measured against small-farm counties. Employment

growth between 1975 and 1982 (approximately trough to

trough in the business cycle) in large-farm counties is

about one-third that in small-farm counties. In addition,

relatively fewer nonfarm jobs exist in large-farm counties

than in small-farm counties. Manufacturing jobs constitute a smaller part of the local economy of large-farm

counties. Large-farm counties depend more on farming

as a source of income. This greater dependency is reflected in the distribution of labor and proprietor's income according to industry. Farming accounted for 20

percent of 1982 earned income in large-farm counties,

compared with less than 5 percent in small-farm counties. Thus, a large-farm community's overall economic

performance is more likely to be tied to agriculture than

is a small-farm community's. Per capita personal income

is significantly higher in large-farm counties, although

per capita transfers are significantly lower.

Large-farm counties have had smaller population bases

than small-farm counties since 1970 or earlier (table 4).

And, they have been characterized by lower population

growth since 1970. They are therefore less likely to have

participated in the diverse economic changes in nonmetro America over the last 20 years. Large-farm counties

have higher per capita total Federal outlays than do

small-farm counties. This spending advantage appears to

be influenced by per capita agriculture outlays which are

15 times higher in large-farm counties.

The quality of the human capital in the large-farm

county population is high as shown by the significantly

higher proportion of the 1980 working-age population

with a high school education and the significantly lower

incidence of disability (table 5). However, large-farm

counties do have a significantly higher proportion of

Table 3—Selected characteristics of the local economic structure, large-farm counties, and small-farm counties

Item Unit Large-farm

counties

Small-farm

counties T-value

Counties Number 750 750 NA

Avcraßc

Total employment, 1982

Nonfarm employment, 1982

Total 1982 employmentManufacturing industries

Service-producing industries'

Government

do.

do.

do.

do.

do.

13,947

12,356

2,133

5,322

2,389

29,077

28,179

4,818

12,368

5,305

-4.17

-4.38

-4.82

-3.82

-5.26

Total employment, 1975^

Nonfarm employment, 1975

Total 1975 employmentManufacturing industries

Service-producing industries^

Government

do.

do.

do.

do.

do.

12,701

11,005

2,107

4,429

2,363

23,878

22,934

4,487

9,251

4,760

-3.99

-4.28

-4.69

-3.59

-4.70

Change from 1975-82 in:

Total employment

Nonfarm employment^

Manufacturing employment^

Service employment* ^

Percent

do.

do.

do.

6.1

10.2

23.6

12.9

17.4

20.8

22.7

28.1

-11.28

-9.31

.23

-12.16

Total labor and proprietor income, 1982

Labor and proprietor income derived from:

Farming

Manufacturing industries^

Service-producing industries' ^

1,000 dol.

Percent

do.

do

210,805

21.0

15.1

31.0

489,349

4.3

23.5

29.2

-3.78

26.09

-10.75

3.41

Per capita personal income, 1982^

Per capita transfer payments, 1982^

Dollars

do.

9,807

1,505

8,261

1,560

14.85

-3.34

NA = Not applicable.

'Includes wholesale and retail trade; finance, insurance, and real estate; and personal and business service firms.

^Variable was used in the discriminant analysis.
Table 4—Selected characteristics of the local population and public-sector support

Item

Counties

Average population:

1984

1980

1970

Counties where population:

Grew 1970-80, grew 1980-84

Declined 1970-80, grew 1980-84'

Grew 1970-80, declined 1980-84'

Declined 1970-80, declined 1980-84'

Rural population, 1980'

Population change, 1970-80'

Per capita Federal outlays:

Total'

Agriculture'

Human capital'

Economic development'

Unit

Number

Large-farm

counties

750

Small-farm

counties

750

T-value

NA

do.

do.

do.

31,901

30,946

28,268

67,264

63,152

52,269

-5.21

-5.13

-4.44

Percent

do.

do.

do.

40.4

12.7

20.0

26.9

80.2

1.5

15.9

2.4

-17.27

8.67

2.09

14.31

do. 69.2 66.5 1.91

do. 4.8 25.6 -19.91

Dollars

do.

do.

do.

2,830

812

84

178

2,256

55

126

182

6.19

24.46

-1.47

-.17

NA = Not applicable.

^Variable was used in the discriminant analysis.

Table 5—Selected human capital characteristics

Large- SmallItem Unit farm farm T-value

counties counties

Counties Number 750 750 NA

1980 population:

Poor' Percent 15.5 17.4 -4.24

65 years old or older' do. 14.2 12.7 7.40

25 years old or older

and high school

education' do. 63.0 54.2 14.65

16-64 years old and

disabled' do. 8.3 11.8 -23.14

NA = Not applicable.

' Variable was used in the discriminant analysis.

that many of the variables are highly correlated. Thus, a

smaller set of key variables may significantly differentiate the county types.

Knowing which variables best differentiate between

county types enables researchers to successfully classify

counties into groups. Local community leaders can then

use that classification scheme to type their own counties. That capability would help them recognize the implications of trends in their areas and identify factors

that can be used to shape new directions for their local

farm sectors. Planning decisions would necessarily be

made in the context of the county population and economic characteristics. The following section elaborates

on methodology.

aged people who are typically heavy consumers of

health care services. The relatively higher income levels

are reflected in a lower incidence of poverty in the largefarm counties.

Using a Model to Differentiate County Types

We have shown that large-farm and small-farm counties

differ in many respects. Yet this information does not

suffice to identify which of the variables considered,

functioning as a group, best differentiate the two county

types. The univaríate analytical approach which was

taken to arrive at large-farm and small-farm county

differences does not take into account the possibility

Method of Analysis

We used stepwise discriminant analysis as our primary

analytical technique.* We used the Statistical Package for

the Social Sciences (SPSSX) computer program to make

the computations [20],

Discriminant analysis permits us to identify those variables most important in differentiating between our two

groups of counties. Variables are entered and removed

one at a time so that the overall Wilks' lambda is minimized. Discriminant analysis accomplishes this by form-

* For a more detailed discussion of discriminant analysis, see [13] in

the References section.
ing a linear combination of discriminating variables of

the form:

D = d,F, + d2F2+ ...... +d„F„

where D is the score on the discriminant function, the

d's are weighting coefficients, and the F's are standardized values of the n discriminating variables included in

the analysis. In the two-case approach, the D's can be

viewed as scores arrayed along a line. The scores will

tend to cluster around two points called centroids. The

farther apart the centroids, the better the discriminating

function can differentiate between the two types of

counties. The weights, or d's, can be interpreted much

like regression coefficients: we can identify the variables

that contribute most to differentiation along the plane.

We can use the results of the analysis both to identify

the precise variables associated with farm structure and

to classify counties in the middle. Classifying counties in

the middle would enable planners to determine if those

counties possess characteristics that tend to move them

toward large-farm or small-farm counties.

Variables Considered

Tables 1-5 present the total set of variables originally

considered for analysis. Considerably fewer variables

were actually included in the model for two reasons. If

two or more variables were highly correlated, only one

could be used in the model because of the technique's

mathematical properties. The variables which tended to

be the most highly correlated were the set related to

economic structure. For example, total employment in

1982 was highly correlated with total employment in

1975, nonfarm employment in 1982 and 1975, and 1982

labor and proprietor income from manufacturing and

services. Thus, the number of variables pertaining to

economic structure actually used in the analysis was

markedly smaller than the original set.

To simplify the use of geographic variables in our analysis, we divided the United States into five regions using a

county classification system reported by Beale and

Fuguitt (fig. 3).^ Large-farm counties are distributed

among the five regions in a distinctly different pattern

from that of small-farm counties (table 6). For example,

69 percent of the large-farm counties are located in the

Central U.S. Agriculture region, compared with only 4

percent of the small-farm counties. As we point out

later, the uneven distribution has important implications

for public efforts to alter Federal farm programs.

Results of Discriminant Analysis

The discriminant function differentiated between largefarm and small-farm counties quite well as both the

difference in the group centroids and the relatively high

canonical correlation suggest (table 7).* In our analysis,

we adopted the convention of splitting the counties in

each group in half using randomized procedures, developing the function using one-half the group, and then

testing the model using the other half. Over 96 percent

of the counties included in the analysis were correctly

classified using the discriminant function. A similar

proportion of the counties not included in the original

analysis was also correctly classified using the discriminant function. This result further testifies to the strength

of the discriminant function.

The variables significant in discriminating between

large-farm and small-farm counties included not only

farm-related variables, but also variables denoting location, economic structure, human capital, and population. This result is consistent with our hypothesis.

Variables loading in favor of large-farm counties include:

* having a relatively high value of land and buildings

per farm,

* having a high total farm sales per county,

* having a high percentage of land in farms, and

* having a high proportion of county-earned income

from farming.

This result supports the earlier univariate analysis finding that large-farm counties have a less diverse economy

than do small-farm counties.

* We examined two additional discriminant functions which are

discussed in Appendix B.

Table 6—Regions in wliich large-farm and small-farm counties

are found

Large- Small- All

counties Item farm farm

counties counties

Number

Total counties 750 750 3,069

Region:

Urban Northeast' 104 45 525

Eastern Mountains and

Uplands' 6 402 697

Southeastern Coastal Plains' 55 180 547

Central U.S. Agriculture 517 31 900

West' 68 92 400

^ For a fuller discussion, see Appendix A. Variable was used in the discriminant analysis.

10
Figures

Subregions used in farm structure analysis

Urban Northeast

Eastern Mountains and Uplands

Southeast Coastal Plains

Central U.S. Agriculture

West
Table 7—Results of discriminant analysis

Item Standardized function

coefficients

County means'

Large-farm Small-farm

Total counties

Counties in analysis

Farm-related variables:

Value land and buildings per farm, 1982

Total farm sales, 1982

Percent land area in farms, 1982

Percent change in number of farms, 1978-82

Percent livestock sales, 1982

Location variables:

Urban Northeast

Eastern Mountains and Uplands

Southern Coastal Plains

West

Metro, 1983

Nonmetro adjacent to a metro area, 1983

Human capital variables:

Percent 1980 population 65 + years old

Percent 1980 population disabled

Percent 1980 population 25 + years with

high school education

Percent 1980 population poor

Economic structure variables:

Total employment, 1982

Percent employment change, 1975-82—

Total nonfarm

Manufacturing

Services

Per capita income, 1982

Percent earned income, 1982—

Farming

Manufacturing

Services

Per capita transfer payments, 1982

Population and public sector:

Percent population change, 1970-80

1970-80, 1980-84 population changeGrew 1970-80, declined 1980-84

Declined 1970-80, grew 1980-84

Declined 1970-80, declined 1980-84

Percent rural population, 1980

Per capita Federal outlaysTotal

Agriculture

Human capital

Economic development

Group centroids

Canonical correlation

Wilks' lambda

1,500

750

-.31

-.30

-.16

.54

.15

.72

.51

.20

.11

.22

.20

-.26

.19

.90

.19

750

381

Number

Dollars

Unit

-2.00

750

369

586,278

97,684

198,774

10,725

79.6

-5.8

48,0

Percent

35.7

1,1

62.0

13.6

1.1

7.9

8.7

6.0

51.7

25.2

12.4

10.0

27.6

29.5

29.8

14.3

8.3

12.7

11.8

62.4

15.8

54.3

17.3

10,811

10.2

21.1

12.3

Number

Percent

24,770

21.0

24.7

28.4

9,704

Dollars

8,180

20.9

14.7

3L3

Percent

4.2

23.6

29.2

1,504

Dollars

1,550

4.2

Percent

26.3

12.9

17.8

29.1

70.0

19.0

14.4

3.0

66.5

2,847

826

66

167

Dollars

2,136

55

99

196

2.07

' The means apply only to those counties used in the analysis.

12
Variables loading in favor of small-farm counties include:

* having a relatively high percentage change in the

number of farms%

* having a higher proportion of farm sales from livestock products,

* being in the eastern Mountain and Upland regions,

southern Coastal Plains, and West relative to the

Central US. Agriculture region,

* having a higher total employment base,

* having higher population growth, and

* having higher aged and disabled populations.

Small-farm counties are characterized by a larger and

more diverse economic base.

Categorizing the Unclassified Counties

The discriminant function was developed using the top

and bottom quartile of counties arrayed by proportion

of total farms with 1982 gross farm sales of less than

$40,000. This leaves 1,563 counties, approximately the

middle half, unclassified.^° Our analysis suggests that

large-farm structure in a county is associated with a

county typology featuring a relatively small nonfarm

economic base and slow population growth or population decline. Some of the unclassified counties have

characteristics which portend a drift toward a large-farm

county structure. Identifying which direction unclassified counties might go enables local policymakers and

private-sector entrepreneurs considering tradeoffs between farm and nonfarm economic development options

to make informed decisions.

The first step in classifying counties was to generate

classification functions for the model presented in table

7. The overall function scores for each county were then

generated, using classification functions and variables

previously identified as being best able to discriminate

between large- and small-farm counties. All counties

were then denoted as large-farm oriented or small-farm

oriented.

^ Because most counties lost farms between 1978-82, this variable

can also be interpreted to mean that the decline in the number of

farms was relatively small.

'° Six counties containing major cities had no farms in 1982.

Results show that the model correctly classified 728 of

the original large-farm counties and 717 of the original

small-farm counties (table 8).. The model grouped 740 of

the 1,563 unclassified counties into the large-farm category and 823 into the small-farm category. We have

summarized the results of the county classification by

State (table 9).

Unclassified counties possessing characteristics that lean

toward small-farm structure are concentrated in New

England, the Southeast, and the West (see fig. 2). They

typically lie adjacent to small-farm counties. Farm structure in these unclassified counties will probably gravitate

toward that of the small-farm counties, and local community and farm leaders will likely focus on finding a

niche for farming in a basically nonfarm economy.

Unclassified counties possessing characteristics that

portend large-farm structure are concentrated in areas

adjacent to large-farm counties in the Central U.S. Agriculture and Urban Northeast regions (see fig. 2). Community leaders in the unclassified counties tending

toward large-farm counties will likely face difficult decisions about the role of farming in their communities.

They may need to take steps to increase nonfarm employment growth, perhaps at some loss to the farming

sector. Policy options might range from making land use

decisions that favor more urban development to setting

up transportation programs to ease commutes to nonfarm jobs outside the area. Regardless of the solutions

being considered, tensions between farm and nonfarm

constituencies are likely to be higher in these counties.

Implications for Rural America

The structure of the local farming sector is influenced

not only by conditions within that sector but also by

conditions in the nonfarm sector. Analysis presented

here suggests which factors are likely to be associated

with the changing farm structure. The local driving

forces considered include the local farming sector, community economic structure, geography, human capital,

and population size and the public sector.

These forces will tend to push some county economies

toward specialization in farming, possibly with only a

few super farms remaining. Farming counties dominated

by super farms imply an intent to compete on a global

basis. Counties may alternatively shift increasingly toward a diversified economic base which includes farming, but is not dependent upon it. The analytical

approach used in this report is designed to identify three

groups of counties (measured by the proportion of small

farms they contain) and to set up a procedure to assess

which way the middle group will shift. Thus, counties

on the verge of major economic change will be able to

13
anticipate if they are becoming large-farm counties or

small-farm counties.

Large-Farm Region. This region includes the Northern

Great Plains, Southern Great Plains, western part of the

Corn Belt, and Mississippi River Delta (see fig. 2). This

is the same area where local economies are dominated

by farming [3], The vast plains portion is characterized

by a very low population density, specialization in grain

farming, and a culture of affluence as evidenced by high

per capita income.

Unclassified Region. This region is composed of broad

bands of counties located throughout the United States.

They are typically buffer or hybrid areas lying between

concentrated areas of large-farm and small-farm

counties. They have more control over their economic

destiny, compared with large-farm counties. Community

leaders of the unclassified counties will need to know

how much their blend of resources predisposes them to

become either large-farm or small-farm counties.

Community leaders can then be actively involved in

influencing changes at the local level.

Global competitive pressures and output-increasing

technology will move the agricultural sector of the Great

Plains toward greater consolidation of farms, releasing

farm labor to the nonfarm job market. Nonfarm jobs

are lacking in many farm communities, suggesting that

farmers will have to migrate. Some will move

intraregionally to magnet counties with diversified

economies healthy enough to maintain basic public

services such as health care and transportation. Others

may relocate to medium-to-large metro areas at the

periphery of the Great Plains. This migration likely will

be selective, with the younger, better educated residents

moving first and furthest. It is in these areas that land

prices have declined the most recently due to increased

uncertainty about farm profits. Lower land prices will

result in a reduced flow of tax revenues to local and

State governments. Also, there has been discussion

about lowering per farmer payment limits on Federal

farm commodity programs. Because this region is

dominated by large-farm agriculture and because the

crop mix includes most commodities covered by Federal

farm programs, this region will likely be affected the

most by such limitations if enacted.

Public pressure is being exerted on all levels of

government to maintain a high enough level of public

and private services to keep the giant region from

continuing to decline economically. We anticipate that

community leaders will be evaluating whether or not

their farming sector has enough advantage to emerge as

global victors among farm sector competitors. Some will

want to follow a strategy designed to build a diversified

economy. Establishing a knowledge base of their

resources and of how those resources mesh with the

broader economy will be important to this

decisionmaking process.

Table 8—Allocating 3,063 U.S. counties into large-farm and small

Our discriminant analysis provides some benchmarks for

making this determination. For example, data in table 9

show that Alabama, with 67 counties, had none

categorized as large-farm counties, 21 categorized as

small-farm counties, and 46 unclassified. Of the 46

unclassified counties, 3 tend to be large-farm counties

and 43 tend to be small-farm counties according to the

patterns of variables identified by the discriminant

function. Because most unclassified counties tend

toward small-farm agriculture, Alabama State and local

officials may wish to give more attention to nonfarm

options as a way to strengthen rural economies.

Small-Farm Region. This region is located primarily in

the Southeast. Its diversified farming system will act to

insulate its farm economy from the effect of global

competition. In fact, reduced feed grain prices will lower

costs for its livestock, poultry, and fish producers. The

trend toward increased poultry consumption will benefit

large poultry producing areas such as Georgia and

Virginia and the Ozark mountain sections of Arkansas,

Missouri, and Oklahoma. The region is probably best

characterized by its medium-sized metro centers which

offer extensive blends of goods and services. These

centers are easily reached by most farm families. Such

proximity to nonfarm employment opportunities will

foster part-time farming with the cash flow eased by

off-farm paychecks. It will also promote competition for

labor with existing larger farms in the region. However,

an offsetting factor is that the region's nonfarm jobs,

especially those in shoe, clothing, and textile

manufacturing, are experiencing strong competitive

pressures from foreign exporters. Thus, nonfarm

industries in small-farm counties are not necessarily

immune from the effects of strong competitors in an

increasingly interdependent world economy.

-farm categories using the discriminant function

County definitions Classified by discriminant functions as being a

Large-farm county Small-farm county Total

Large-farm

Unclassified

Small-farm

Total

728 97

740 47

33 4

1,501 49

Number

22

823

717

1,562

Percent

3

53

96

51

Number

750

1,563

750

3,063

14
Table 9—County categorization by State'

Counties

State

Alabama

Arkansas

Arizona

California

Colorado

Connecticut

Delaware

Florida

Georgia

Iowa

Idaho

Illinois

Indiana

Kansas

Kentucky

Louisiana

Massachusetts

Maryland

Maine

Michigan

Minnesota

Missouri

Mississippi

Montana

North Carolina

North Dakota

Nebraska

New Hampshire*

New Jersey

New Mexico

Nevada

New York

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Virginia

Vermont

Washington

Wisconsin

West Virginia

Wyoming

Total

Unclassified leaning toward^—

^^^^^ ^farm" ^farm" Unclassified Large-farm Small-farm

structure _x—X— structure

Number

67 0 21 46 4 42

75 15 18 42 6 36

14 2 2 10 6 4

58 11 9 38 15 23

63 16 10 37 16 21

8 0 0 8 1 7

3 1 0 2 1 1

67 3 13 51 7 44

159 24 43 92 9 83

99 82 0 17 17 0

44 18 3 23 10 13

102 60 3 39 28 11

92 17 12 63 52 11

105 45 2 58 57 1

120 1 69 50 1 49

64 10 19 35 18 17

14 1 1 12 0 12

23 8 3 12 5 7

16 1 3 12 1 11

83 2 17 64 44 20

87 47 8 32 26 6

114 6 28 80 59 21

82 10 31 41 4 37

56 32 4 20 12 8

100 19 26 55 19 36

53 36 0 17 17 0

93 83 0 10 10 0

10 0 1 9 0 9

20 0 0 20 12 8

32 4 12 16 8 8

17 4 4 9 2 7

60 18 3 39 24 15

88 5 20 63 43 20

77 5 36 36 27 9

36 6 9 21 3 18

67 6 8 53 19 34

5 0 1 4 2 2

46 1 21 24 9 15

66 54 0 12 11 1

95 1 70 24 2 22

254 35 110 109 82 27

29 1 8 20 4 16

98 6 41 51 3 48

14 6 0 8 0 8

39 9 13 17 2 15

71 36 0 35 29 6

55 0 48 7 0 7

23 3 0 20 13 7

,063 750 750 1,563 740 823

' County categorization was based on the percent of total farms with 1982 gross farm sales of less than $40,000.

^ This determination was made using the classification function obtained from the discriminant analysis.

15
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(22) US. Congress, Senate Committee on Government

Affairs. Governing the Heartland: Can Rural Governments Survive the Farm Crisis? 99th Congress, 2nd Session. Senate Print 99-176. July 1986.

(23) U.S. Department of Agriculture, Economic Research Service. Agricultural Finance: Outlook and Situation Report, AFO-26. Mar. 1986.

(12) Hrubovcak, James M., and Frances A. Burke.

Farm Real Estate Taxes, 1981, SB-701. US. Dept. Agr.,

Econ. Res. Serv., Nov. 1983.

(24) . Economic Indicators ofthe Farm Sector:

National Financial Summary, 1984. ECIFS 5-2. Nov.

1986.

16
Appendix A—Developing Subregions

Subregions used for this study were derived from those

developed by Fuguitt and Beale [8]. Their subregions

reflect rather homogeneous combinations of counties

having similar economic activity, history, physical setting, settlement patterns, and culture (app. fig. 1). Fuguitt and Beale developed these areas for use primarily

in demographic studies. However, the subregions had

intuitive appeal as independent variables for our study

of farm structure.

Geography affects the type of farming in a region

through such factors as terrain, rainfall, and soil quality.

The Northern Appalachian Coal Fields, for example, are

much less conducive to large-scale crop farming than are

other regions.

Settlement patterns affect the set of options a farm family has for dividing its labor between farm and nonfarm

activities. For example, farm families living in the

Northeast Metropolitan Belt are more likely to have

better access to nonfarm jobs than those living in the

Northern Great Plains. Thus, the opportunity cost of

labor is much higher in the Northeast.

Nonfarm industry in a region also affects farmland values. Recent declines in farmland values have been less

severe in the Lower Great Lakes Industrial area than

further west.

Some combination of the Fuguitt and Beale subregions

also appeared to be superior to other regional delineations such as the Census Bureau's divisions or USDA's

farm production regions. For example, the Mississippi

Delta farm production region contains Mississippi, Louisiana, and Arkansas. Yet the true Mississippi Delta contains only a small band of counties adjacent to the

Mississippi river. Other areas of Arkansas, for example,

are mountainous and unsuited to large-scale agriculture.

Regions designated by Fuguitt and Beale differentiate

between the true Delta and the Ozark-Ouachita mountains further west.

We used the following regions in our analysis (app. fig.

2):

Urban Northeast. This area is dominated by the

contiguous metropolitan areas stretching from Boston to

Washington, DC, and the manufacturing and

metropolitan belt that extends from Albany, New York,

to Milwaukee, Wisconsin, along the southern shores of

the Great Lakes. We also included Northern New

England, the Upper Great Lakes, and the Dairy Belt.

This broad region contains 525 counties and 422,565

farms. About two-thirds of the farms reported sales of

less than $40,000 in 1982. Over 89 million people lived

in the area in 1984, making it the most populous region

of the five.

Eastern Mountains and Uplands. This area includes the

Northern and Southern Appalachian Coal Fields; the

Blue Ridge, Great Smokies, and Great Valley areas of

the Southern Appalachians; the Southern Piedmont; the

Southern Interior Uplands lying between the Ohio and

Tennessee Rivers; and the Ozark-Ouachita Uplands.

This area generally contains major pockets of rural

poverty, has historically been dominated by small-farm

agriculture, and has the textile industry as an important

part of the manufacturing economy. There are 697

counties and 515,069 farms in the area. Over 87 percent

of the farms reported sales under $40,000 in 1982. This

is the largest incidence of small farms among the regions

we defined. About 35 million people lived in the area in

1984.

Southeast Coastal Plains. This area includes the

Southern Coastal Plains from Virginia to the Mississippi

Delta; Florida and the low-lying coastal areas from

Charleston, South Carolina, to Houston, Texas; and the

East Texas Coastal Plain. It includes the old southern

Cotton Belt and peanut and tobacco farming areas;

areas with a large rural black population; and resorts,

retirement, and the semitropical agriculture of Florida

and Texas. The area still contains several counties

characterized by large-farm agriculture. It included 547

counties and 329,842 farms as of 1982. About 80

percent of the farms had sales of less than $40,000 in

1982. About 38 million people lived in the area in 1984.

We were unable to use the original Fuguitt and Beale

regions for our analysis. Our unit of observation is the

county and the dependent variable is a function of the

proportion of farms with sales under $40,000. Some

regions had few counties of any one type. For example,

the Ozark-Ouachita subregion had no counties with

fewer than 58 percent of farms with sales under $40,000.

Other regions had similar distributions of counties

across our county types. These factors suggested to us

that some regional aggregation was appropriate.

Central U.S. Agriculture. This region contains the major

U.S. agricultural producing areas. It covers the

Mississippi Delta counties, the Corn Belt from Central

Indiana to eastern Nebraska and Kansas, and the Plains

counties from Texas to Canada. It has 900 counties and

697,355 farms. Fewer than 60 percent of the farms

reported sales under $40,000 in 1982, making it the

region with the highest concentration of large farms

among the five regions. It is also the least populated

region with about 27 miUion people in 1984.

17
Appendix figure 1

Original subregions of the United States

1. Northern New England - St. Lawrence

2. Northeastern Metropolitan Belt

3. Mohawk Valley and New York - Pennsylvania Border

4. Northern Appalachian Coal Fields

5. Lower Great Lakes Industrial

6. Upper Great Lakes

7. Dairy Belt

8. Central Corn Belt

9. Southern Corn Belt

10. Southern Interior Uplands

11. Southern Appalachian Coal Fields

12. Blue Ridge, Great Smokies, and Great Valley

13. Southern Piedmont

14. Coastal Plain Tobacco and Peanut Belt

15. Old Coastal Plain Cotton Belt

16. Mississippi Delta

17. Gulf of Mexico and South Atlantic Coast

18. Florida Peninsula

19. East Texas and Adjoining Coastal Plain

20. Ozark-Ouachita Uplands

21. Rio Grande

22. Southern Great Plains

23. Northern Great Plains

24. Rocky Mountains, Mormon Valleys, and Columbia Basin

25. North Pacific Coast (including Alaska)

26. The Southwest (including Hawaii)
Appendix figure 2

Subrogions used in farm structure analysis

Urban Northeast

Eastern Mountains and Uplands

Southeast Coastal Plains

Central U.S. Agriculture

West

vo
West. This region contains the sparsely settled Rio

Grande valley from south Texas up to Colorado; the

Rocky Mountains, Mormon Valleys, and Columbia

Basin where irrigated agriculture is important; the

Southwest; and the Pacific Northwest. The region has

400 counties, the smallest number of counties in any

region. Only 269,890 farms were in the area in 1982,

three-fourths of which reported sales under $40,000.

The region has extensive Federal lands and, with some

exception, limited agricultural potential. In 1984, about

46 miUion people lived in the area.

Appendix B—Alternative Discriminant Models

Selection of variables was discussed regularly throughout the course of our research. We tended to focus our

attention on two groups of variables, farm related and

location. We asked ourselves, "What would happen if

we deleted farm-related and location variables?" We

therefore reran the discriminant function models with

these variables deleted. The results appear in appendix

tables 1 and 2.

Deleting the farm-related variables caused a modest

drop in separation of group centroids (-1.53 versus 1.58)

compared with the initial model whose groups centroids

were -2.00 versus 2.07. But, the main result of the deletion was that the human capital variables made a robust

appearance in behalf of the small-farm counties.

Deleting the group of location variables constructed

from the Fuguitt and Beale regions resulted in a slight

drop in the separation of group centroids to -1.86 versus

1.80 from -2.00 versus 2.07. The coefficients for the

farm-related variables tended to increase in behalf of the

large-farm counties. One human capital variable, percent of 1980 population aged 25 and over with at least a

high school education, maintained its appearance in

behalf of the large-farm counties. But, in the model

with farm-related variables deleted, the education variable appeared in behalf of small-farm counties.

20
Appendix table 1—Results of discriminant analysis that excluded farm-related variables

Item Standardized function County means ^

coefficients Large-farm iSmall-farm

1,500 750

Number

750

750 381

Dollars

369

NA

NA

NA

NA

NA

586,278

97,684

79.6

-5.8

48.0

Percent

198,774

10,725

35.7

1.1

62.0

.96

.62

.30

13.6

1.1

7.9

8.7

6.0

51.7

25.2

12.4

.15 10.0

27.6

29.5

29.8

.35

.29

14.3

8.3

12.7

11.8

.29

.16

62.4

15.8

Number

54.3

17.3

.24 10,811

Percent

24,770

.20

10.2

21.1

12.3

9,704

Dollars

Percent

21.0

24.7

28.4

8,180

-.50

-.15

-.31

20.9

14.7

31.3

1,504

Dollars

Percent

4.2

23.6

29.2

1,550

.27 4.2 26.3

12.9

17.8

29.1

70.0

Dollars

19.0

14.4

3.0

66.5

-.14

2,847

826

66

167

2,136

55

99

196

Total counties

Counties in analysis

Farm-related variables:

Value land and buildings per farm, 1982

Total farm sales, 1982

Percent land area in farms, 1982

Percent change in number of farms, 1978-82

Percent livestock sales, 1982

Location variables:

Urban Northeast

Eastern Mountains and Uplands

Southern Coastal Plains

West

Metro, 1983

Nonmetro adjacent to a metro area, 1983

Human capital variables:

Percent 1980 population 65 + years old

Percent 1980 population disabled

Percent 1980 population 25 + years with

high school education

Percent 1980 population poor

Economic structure variables:

Total employment, 1982

Percent employment change, 1975-82—

Total nonfarm

Manufacturing

Services

Per capita income, 1982

Percent earned income, 1982—

Farming

Manufacturing

Services

Per capita transfer payments, 1982

Population and public sector:

Percent population change, 1970-80

1970-80, 1980-84 population changeGrew 1970-80, declined 1980-84

Declined 1970-80, grew 1980-84

DecUned 1970-80, declined 1980-84

Percent rural population, 1980

Per capita Federal outlays—

Total

Agriculture

Human capital

Economic development

Group centroids

Canonical correlation

Wilks' lambda

Unit

-1.53 1.58

.84

.29

NA = Not applicable.

' The means apply only to those counties used in the analysis.

21
Appendix table 2—Results of discriminant analysis that excluded location variables

Item Standardized function County means^

coefficients Large-farm Small-farm

1,500 750

Number

750

750 381

Dollars

369

.39

.35

.26

-.54

-.20

586,278

97,684

79.6

-5.8

48.0

Percent

198,774

10,725

35.7

7.7

62.0

NA

NA

NA

NA

13.6

1.1

7.9

8.7

6.0

51.7

25.2

12.4

10.0

27.6

29.5

29.8

-.29

14.3

8.3

12.7

11.8

.34 62.4

15.8

Number

54.3

17.3

-.19 10,811

Percent

24,770

10.2

21.1

12.3

9,704

Dollars

Percent

21.0

24.7

28.4

8,180

.27

.15

20.9

14.7

31.3

1,504

Dollars

Percent

4.2

23.6

29.2

1,550

-.29 4.2 26.3

.14

12.9

17.8

29.1

70.0

Dollars

19.0

14.4

3.0

66.5

2,847

826

66

167

2,136

55

99

196

Total counties

Counties in analysis

Farm-related variables:

Value land and buildings per farm, 1982

Total farm sales, 1982

Percent land area in farms, 1982

Percent change in number of farms, 1978-82

Percent livestock sales, 1982

Location variables:

Urban Northeast

Eastern Mountains and Uplands

Southern Coastal Plains

West

Metro, 1983

Nonmetro adjacent to a metro area, 1983

Human capital variables:

Percent 1980 population 65 + years old

Percent 1980 population disabled

Percent 1980 population 25 + years with

high school education

Percent 1980 population poor

Economic structure variables:

Total employment, 1982

Percent employment change, 1975-82—

Total nonfarm

Manufacturing

Services

Per capita income, 1982

Percent earned income, 1982—

Farming

Manufacturing

Services

Per capita transfer payments, 1982

Population and pubHc sector:

Percent population change, 1970-80

1970-80, 1980-84 population changeGrew 1970-80, declined 1980-84

Declined 1970-80, grew 1980-84

Dechned 1970-80, declined 1980-84

Percent rural population, 1980

Per capita Federal outlays—

Total

Agriculture

Human capital

Economic development

Group centroids

Canonical correlation

Wilks' lambda

Unit

1.80 -1.86

.23

NA = Not applicable.

' The means apply only to those counties used in the analysis.

22
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