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DOCUKENT RESUME 



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RC 018 076 



AUTHOR 
TITLE 



Goreham, Gary A*; And Others 
Distribution of Personal Income in 
Agriculture-Dependent Counties of Midwestern States: 
A Policy Variables Approach, 

North Central Regional Center for Rural Development, 
Ames, Iowa, 
ISBN-0-936913-03-7 
Oct 90 

loop. 

Reports - Research/Technical (143) 



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REPORT NO 
PUB DATE 
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IDENTIFIERS 



KF01/PC04 Plus Post£.ge. 

♦Economic Change; Economic Factors; Educational 
Attainment; Females; * Income; »Labor Force; Models; 
Path Analysis; *Public Policy; Regression 
(Statistics) ; *Rural Areas; Social Distribution; 
Social Structure; Theori-^s 

Countxes; Gini Coefficient; ^Income Distribution; 
•United States (North Central) 



ABSTRACT 



Significa't social, demographic, and economic changes 



have occurred in the North Central states since 1950. This document 
examines structural and policy variables related to distribution of 
income, during the years 1960^80 in the 397 counties defined as 
agriculture-dependent in 13 North Central states. Personal income 
distribution has been explained by four types of theories: 
stochastic, personal characteristic, regional endowment, and 
development • These theories were integrated into a single wor)cing 
model for empirical analysis* A regression model was developed that 
included five structural variables and five policy variables. 
Correlation coefficients were calculated to determine the degree to 
which variables were related to income distribution (Gini-ratio) in 
1960, 1970, and 1980, The relative influence ox county structural 
endowments and social and economic policies on income inequalities 
differed in each census year, shifting from policy variables as most 
influential in 1960 to structural variables as slightly more 
influential in 1980. Income distribution was related to retirement 
benefits and education levels (see pages 37-42) in all three census 
years; and uo unemployment benefits, county government expenditures, 
proportion of commercial farms, and manufacturing employment in two 
out of three years* This report: contains 70 references and 21 data 
tables and graphs. (SV) 



n Reproductions supplied by EDRS are the best that can be made ^ 
* from the original document. * 



o Distribution of Personal Income 
^ in Agriculture-dependent 

o Counties of Midwestern States: 

m 

A Policy Variables Approach 



Gary A. Goreham 
Richard W. Rathge 
Glenn D. Pederson 



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■PERMISSION TO REPRODUCE THIS 
MATERIAL HAS BEEN GRANTED BY 

TO THE EDUCATIONAL RESOURCES 
INFORMATION CENTER (ERIC)." 



NORTH CENTRAL REGIONAL CENTER FOR RURAL DEVELOPMENT 




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2 



iowa State University 
216 East Hall 
Ames. Iowa 50011 
(515) 294-8321 



DISTRIBUTION OF PERSONAL INCOME 

IN AGRICULTURE-DEPENDENT 
COUNTIES OF MIDWESTERN STATES: 
A POLICY VARIABLES APPROACH 



o 

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3 



II 



DISTRIBUTION OF PERSONAL INCOME 

IN AGRICULTURE-DEPENDENT 
COUNTIES OF MTDWESTERN STATES: 
A POLICY VARIABLES APPROACH 



by 

Gary A. Goreham 
Department of Agricultural Economics 
North Dakota State University, Fargo 

Richard W. Rathgc 
Department of Agricultural Economics 
North Dakota State University, Fargo 

Glenn D. Pederson 
Department of Agricultural Economics 
University of Minnesota, St. Paul 



North Central Regional Center for Rural Development 
Iowa State University 
Peter Korsching, Director 

J 

October 1990 



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4 



Printed by University Publications 
Iowa State University 

Library of Congress Catalog Card Number: 90-63213 
ISBN 0-936913-03-7 



CONTENTS 



listed Tables vii 

list oi AppeiHfix Tables viii 

list <tf Figures ix 

Ouptar One: Income Distribntioa IVdky 1 

Social and Economic Change in Rural America 1 

Income-Enhancement Policies 2 

Agricultural Policies 3 

Chapter Two: Tlieorks of Personal Income Distribution 9 

Stochastic Theories 9 

Personal Characteristics Theories 10 

Personal Ability Theories 10 

Family Background and life Cycle Theories 10 

Human Capital Theories 11 

Personal Characteristic Theories in Perspective 12 

Regional Endowment Theories 12 

Urban-industrial Impact Hypothesis 13 

Economic Base Theories 13 

Ecological Theories 14 

Development Theories 14 

Neoclassical Economic Development Tiieories 15 

Income-employment Growth Theories 16 

An Empirical Approach ... 16 

Chapter Three: Methodology for Analyzing Income Dstribution 21 

Scope of the Study 21 



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Measure of Income Inequality 21 

Variables Explaining Income Inequality 24 

Data 24 

Chapto' Four Detennimnts of Income DistribiitkHi at the County Levd 27 

Correlation Analysis 27 

Structural Variables 27 

Policy Variables 30 

Regression Analysis 30 

1960 Regr^on Model 32 

1970 Regression Model 32 

1980 Regression Model 34 

Summary 34 

Chapter Five: Structunil Factors as Determinants of Incooie Distribution 37 

Level of Education 37 

Manufijcturing Labor ForD? 43 

ji Female Labor Force 44 

Commercial Farms 44 

Services Labor Force 46 

Chafiter She Policies as Detcnninants of Income IHstribiition 51 

Population Change 51 

Retirement Transfer Payments 5?. 

Income Maintenance Transfer Payments 53 

Total County Government Expenditures 54 

Unemployment Transfer Payments 55 

Chapter Seven: Summary and Coodnsions 57 

Chapter- Researdi Needs 59 

Appendix A: list of Counties m Study 63 

Appendix B: GhU-ratios and Related Data 67 

B&Iiogniphy 89 



Er|c vi 



LIST OF TABLES 



Table 1. List of Variables and Definitions Included in Analysis 



Table 3. Estimated Coefficients for Selected Structural and Policy 
Variables Regressed on Gini-ratios, 1960 

Table 4. Estimated Coefficients for Selected Str.ctanJ and Policy 
Variables Regressed on Gini-ratios, IS '0 

Table 5. Estimated Coefficients for Selected Structural and Policy 
Variables Regressed on Gini-ratios, 1980 



25 



Table 2. Correlational Coefficients of Structural and Policy 

Variables with Gini-ratios 



33 



35 



36 



8 

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LIST OF APPENDIX TABLES 



Appendix Table B.l. Gini-ratios for Income Distribution 
in Agriculture-dependent Counties of the North 

Central Region, 1960, 1970 and 1980 67 

Appendix Table B.2. Correlation Coefficient Matrix *br 
Income Distribution (Gmi-ratio) and Predictor 

Variables, North Central Region, 1960 80 

Appendix Table B.3. Correlation Coefficient Matrix for 
Income Distribution (Gini-ratio) and Predictor 

Variables, North Central Region, 1970 80 

Appendix Table B.4. Correlation Coefficient Matrix for 
Income Distribution (Gini-ratio) and Predictor 

Variables, North Central Region, 1980 ?2 

Appendix Table B.5. Student Enrollments, Expenditures 
for Education, and Education Expenditures per 

Student, United States, 1950-1982 84 

Appendix Table B.6. Number of Farms, Farm Income and 
Government Payments, by Value of Sales Class, 

United States, 1960-1982 86 



viii 



LIST OF FIGURES 



Figure 1. Atkinson's explanation of earnings 12 

Figure 2. Composite theory of income distribution 19 

Figure 3. Agriculture-dependent counties in the 

North Central region 22 

Figure 4. The Loienz-curve of income concentration 23 

Figure 5. Path analysis displaying determinants of 

income distribution, 1960 38 

Figure 6. Path analysis displaying determinants of 

income distribution, 1970 41 

Figure 7. Path analysis displaying determinants of 

income distribution, 1980 42 

Figures. Expenditures per student by level of school, U.S., 1950-1982 47 

Figure 9. Percent of total gross farm income by value 

of sales class farms, 1960-1982 48 

Figure 10. Percent of direct government payments by 

value of sales class farms, 1960-1982 49 

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CEAPTER ONE 

INCOME DISTRIBUTION POLICY 



Social and Economic Chan^ in Rural America 

Significant social, demographic and economic changes have occurred in the North Central 
region of the United States since 1960. As patterns of population growth shifted in favor of rural 
areas (Long and DeAre 1982), residential growth in nonmetropolitan counties outpaced that in 
metropolitan areas by more than three to one (8.7 percent and 2.6 percent, respectively) 
(Adamchak et al. 1985). The most notable aspect of this reversal was that the largest population 
gains were recorded in unincorporated rural areas. 

The income gap between persons living in urban and rural areas of the U.S. is noticeably 
high. In 1980, the median income of urban American families was $20,653, while the median 
income of families in rural America was $17,995, a difference of S2,658 (Bureau of the Census 
1983). Economic growth in nonmetropolitan areas of the North Central region surpassed that of 
the region's metropolitan centers. Nonfarm wage and salary employment in nonmetropolitan 
counties increased by 2.0 percent between 1973 and 1979, while the comparable rate in 
metropolitan counties was 1.7 percent (Bluestone 1982). Interestingly, the highest rates of 
increased employment occurred in metropolitan fringe counties, in less-urbanized counties not 
adjacent to metropolitan centers, and in totally rural counties adjacent to metropolitan centers. 

Structural changes accompanied these lesidential and employment shifts. Foi example, 
women entered the labor force in record numbers during the 1970s. Less than 41 percent of all 
women over age 15 were in the labor force at the beginning of the decade. However, by 1980, 
more than one-half had joined the labor force (Bureau of the Census 1983). 

Farm size continued to increase during the i960 to 1980 period, reflecting the process of 
structural change in agriculture. As farm structure changed, farm family and household income 
distribution also changed. Net nonfarm income (as a percent of net, before- tax earnings reported 
by farmers) varied considerably by size, type and location of the farm unit. A substantial 



11 



proportion of income reported by farm recipients was derived from off-farm sources. 
Government farm program payments and numerous other factors affected farm income 
distribution during the period. A study conducted by the U.S. Department of Agriculture 
indicated that a substantial percentage of benefits went to larger farms (Reinsel et al. 1987). 

These general indicators of change in rural and urban areas of the region provide useful 
information to policymakers at the local, state and national levels. They do not, however, 
provide information on how the benefits of economic growth were distributed among 
socioeconomic groups (urban, rural farm, and rural nonfarm families and households) or within 
socioeconomic groups. Furthermore, these general indicators do not provide information on how 
policies and programs redistribute the benefits over time. Following is a description of some of 
the historic and contemporary social and economic policies that have been intended to have an 
impact on income levels of select groups of Americans. 

Income-Enhancement Polides 

Since the 1930s, numerous pieces of legislation have been enacted to enhance the livelihoods 
of rural residents. This legislation is divided into the areas of social insurance, welfare programs, 
and employment and training programs. 

The Social Security Act of 1935 was one of the first sodal msurance programs in this 
country. The act was initially targeted for elderiy workers who might not otherwise have had 
sufficient retirement income. The act was later amended to include the survivors of elderly 
workers (1939) and the disabled (1956). Medical coverage for hospital and physician 
reimbursement for those aged 65 and over was added to these social insurance programs in 1965 
in the form of Medicare. Legislation revising the benefits, coverage and target audience of social 
insurance has been passed periodically. 

Legislation affecting welfare programs is a second income-enhancement policy. Major 
components of this legislation can be traced to the days of the Great Depression. As part of the 
Social Security Act of 1935, Aid to Dependent Children (ADC) was initiated. During the 1960s, 
additional welfare programs were created as part of the federal "war on poverty." Aid to 
Families with Dependent Children (AFDC) resulted from the Public Welfare Amendment of 
1962. This amendment provides for rehabilitation, services and income for parents or relatives 
caring for a child. While many of the social insurance programs were targeted largely toward 
the elderiy poor, ADC and AFDC were targeted toward the younger poor. 

Under the tiUe programs of the 1965 Social Security Act, Medicaid was made available to 
participants of AFDC programs. The state and federal funds used in this program were intended 



Er|c 2 



to provide medical payment assistance to the poor. Additionally, food stamp programs for those 
at or below the poverty level were instituted by the Food Stamp Act of 1964 under the auspices 
of the Federal Surplus Commodities Corporation. 

Income-enhancement legislation continued to be enacted during the decade of the 1970s. 
Women, Infants and Children (WIC) programs were initiated in 1973. The following year, 
Supplementary Security Income (SSI) combined and federalized three programs that were 
originally components of the 1935 Social Security Act: Old Age Assistance, Aid to the Blind, 
and Aid to the Permanently and Totally Disabled. Additionally, low-income energy assistance 
programs were initiated in 1975. Thus, welfare programs and policies startai during the 1960s 
that were intended to enhance the incomes of the poor and the elderly, were continued through 
the 1970s. In addition, programs were either added or enhanced to assist women, children and 
the disabled. 

Whereas social insurance and welfare programs were established to enhance incomes directly 
through monetary or food assistance, anploymeiit and training {Hvgnuns offered educational 
assistance. The Fair Labor Standards Act (FLSA) of 1938 set minimum wages for certain groups 
of workers. Between the 1938 FLSA and the 1960s "war on poverty" legislation, very few 
employment and training programs were initiated. Three key programs were started during the 
early 1960s: the Manpower Development and Training Act of 1962, the Job Corps of 1964, and 
the Neighborhood Youth Corps of 1964. Two additional key enactments of the 1970s included 
the Comprehensive Employment and Training Act (CETA) of 1974 and the Public Service 
Employment Program of 1974. 

Billions of dollars have been spent on various social insurance, welfare, and employment and 
training programs since their inceptions. However, relatively little is known about how the 
infusion of this money into the economy has affected the overall distribution of income. Which 
policies and programs are most effective in the distribution of income? Further, by what means 
does the redistribution take place? 

Agricuitiinil Fdlides 

Along with income-enhancement policies, the impact of agricultural policies on income 
distribution must be analyzed because of the role that agriculture plays in these areas. Surpluses 
of agricultural products have kept the prices of these products low, thus keeping farmers' 
incomes low. Throughout the past half century various policies served as experiments to 
determine their impact on farm income by adjusting the surplus supply of agricultural production 
either through voluntary or mandatory production controls or by stimulating foreign demand. 
Following is a brief summary of five of these experimental programs. 



13 



3 



The Depression of the early 1930s brought to the nation's awareness the plight of the poor 
in rural areas. As a result, the Agricultural Adjustment Act of 1933 was enacted to enhance farm 
income by reducing crop acreage through voluntary production limitation agreements. It was 
oelieved that voluntary acreage reductions would occur if farm operators were assured of 
receiving a profitable price for their commodities. Parity levels were set to provide farmers with 
purchasing power equivalent to what existed during the prosperous period of 1900 to 1914. 

Other acts followed shortly thereafter with the objective of improving both the income and 
quality of life in rural America. The Emergency Farm Mortgage Act and the Farm Credit Act 
of 1933 established emergency and long-term crwlit programs to assist farmers. The Soil 
Conservation Act of 1935 and the Soil Conservation and Domestic Allotment Act of 1936 sought 
to reduce production of smplus crops by paying fiarmers for improved land use and for 
conservation practices. 

The Agricultural Adjustment Act of 1938, the basis of today's agricultural price support and 
adjustment laws, maintained the voluntary conservation and acreage allotments of earlier 
legislation. However, ±e act made marketing quotas for basic crops mandatory. Attempts were 
made to reduce agricultural product surpluses by stimulating foreign trade. A decade later, the 
Commodity Credit Corporation (CCC) Charter Act of 1948 made nonrecourse loans available. 
That is, producers were able to obtain loans from Uie federal government using the grain they 
produced as collateral. 

The Agricultural Trade Development and Assistance Act of 1954 (PL 480) allowed the 
government to make agreements for sale of farm products for foreign currency, to make 
shipments for emergency relief and other aid, and to barter farm products o^vned by tiie 
government for needed materials. The objective of the act was to stimulate foreign trade of 
agricultural products, and was extended into the 1980s. 

The Agricultural Act of 1956 increased mandatory marketing quotas or allotments and 
established the Soil Bank, the first voluntary land retirement program. Acreage reserves and 
conservation reserves wv^q developed. Acreage reserves aimed at short-term withdrawal of land 
from production, whereas conservation reserves witiidrew land from production for up to 10 
years. This program proved to be very cosUy to tiie fedeial government. 

With tiie goal of improving income for tiie average farmer, much of the agricultural 
legislation in tiie 1930s, 1940s and 1950s made conservation and acreage allotments voluntary, 
but made production-control marketing quotas for "basic" crops mandatory. Quotas were not the 
amount of produce farmers were allowed to market. Ratiier, they were the number of acres 



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farmers were allowed to plant in order to produce a given amount of commodities. Farmers were 
offered inducements to voluntarily reduce their acreage. Some of these inducements included 
access to nonrecourse loans, and cash and in-kind payments at a percentage of parity (Rasmussen 
1985; Bowers 1987). 

Despite these programs, surpluses of agricultural products mounted through the 19S0s and 
the govemment*s expense grew. Controls placed on certain commodities led to overproduction 
of uncontrolled commodities. Decreases in acreage allotments were accompanied by an increase 
in farming intensity-heavier use of fertilizers, pesticides, machinery and better varieties of seeds. 
This, in turn, led to increased production. Meanwhile, demand for American farm commodities 
did not improve enough to compensate for this increased production despite increased exports 
under the PL 480 programs. 

As a result of excess production and lower price supports, farmers received lower prices for 
their products during the 1950s. Additionally, federal outlays for farm programs grew. Total 
federal expenditures for farm price and income programs ranged from $1.7 billion in 1950 to 
$2.9 billion in 1959 (Budget of the United States Government, FY J 952-1987), 

A major shift occurred in American agricultural poHcy in the early 1960s, resulting in a 
second agriculture policy era that lasted from 1964 to 1973. Under the 1961 Food and 
Agriculture Act, farmers were offerui a proposal to switch from acreage controls to true quotas 
on the amount of produce that they could market. They were to receive higher price supports in 
return for reducing their production. The proposal was expected to raife farm income and, at the 
same time, reduce government storage costs. During the 1963 referendum, farmers soundly 
defeated the proposal, bringing an end to an era of mandatory controls and ushering in an era 
of voluntary production and acreage r«luction programs. Voluntary diversion continued with the 
Food and Agricultural Act of 1965. This act differentiated between the income-enhancement 
features of farm programs for basic crops and stability-enhancement features. 

The agricultural policies implementea during this time period were of minimal success in 
reducing acreage and total supply of agricultural products. They were even less successful in 
reducing costs to the federal government. A decrease in acreage allotments was met by an 
increase in the intensity of farming, and thus, an increase in total production. Federal outlays for 
farm price and income programs grew from $3.3 billion in 1964 to $3.7 billion in 1973 (Budget 
of (he United States Govemmem, FY 1952-1987). 

The types of policies and programs enacted during this era continued to play a role in farm 
income well beyond 1973. However, international events had an even more significant impact 



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5 

I 



on farmers' incomes and resulted in a third agriculture policy era that lasted from 1973 through 
1976. American farm products reached high demand starting in 1973 as a result of world crop 
shortages and a worldwide inflation. Because of world demand (exemplified by the historic 
purchases of American grain by the Russians), export subsidies, and the devaluation of the 
dollar, stocks of American grain declined. The result was an increase in the market price farmers 
received for their produce. By 1976, however, farm prices began to sag as production exceeded 
demand, ending the 1974 to 1976 period of relative prosperity. 

While much of the pre- 1973 legislation emphasized reJucing agricultural production, the 
Agriculture and Consumer Act of 1973 emphasized increasing production to respond to the 
growing demand for U.S. farm products. Farmers were assured of target prices through 
defideiicy payments, or direct payments on crops made when target prices were higher than 
loan rates or market prices. Loan rates were set below market prices to move farm products into 
markets rather than into government storage. Between 1973 and 1977 target prices were generally 
lower than market prices. Fueled by inflation, the cost of production and the value of land was 
pushed higher. Farm price and income programs cost the federal government $1.1 billion, $.7 
billion and $1.1 billion, in 1974, 1975 and 1976, respoitively (Budget of the United States 
Government, FY 1952-198'/). Even with inflation, diese costs were lower than those of the 
pre- 1964 policy era. 

A fourth agriculture policy era, from 1977 to 1981, was marked by a continuation of 
voluntary production and acreage reduction controls as well as by high inflation. Although prices 
were relatively high, land values and production costs were also high. 

Because of all-out production, farm prices were sagging by the time of the Food and 
Agriculture Act of 1977. This legislation established a farmer-owned reserve program for wheat 
that allowed farmers to hold their grain for three to five years rather than sell it to government 
stocks. Target prices and loan rates were increased, and cost-of-production figures were used to 
escalate target prices. In addition, historic acreage allotments were replaced with set-aside 
procedures. These programs cost the federal government $3.8 billion in 1977, $5.7 billion in 
1978, $3.6 billion in 1979, and 2.8 billion in 1980 {Budget of the United Stat^ • Government, FY 
1952-1987). 

The enactment of the 1981 Agriculture and Food Act served as the beginning of the fifth 
agriculture policy era. The Agriculture and Food Act of 1981 continued voluntary control 
policies established through the 1970s. The Reagan administration's goals for agriculture policy 
and programs were to give them a market orientation, simplify their operation, and reduce their 
costs to the federal government. 



6 



Between 1981 and 1984, agricultural exports fell, depressing prices and raising government 
costs. As a result, surpluses accumulated and price support costs were driven higher. Farmers 
continued their high levels of production under the voluntary programs, increasing 
government-held surpluses and government costs. To counter this problem, the U.S.D.A.'s 
payment-in-kind (PIK) program was established to offer surplus agricultural commodities owned 
by Uie government for agreements to reduce production by cutting crop acreage. The objective 
was to simultaneously reduce both production and government surpluses and to increase farm 
income. 

The Food Security Act of 1985 set marketing quotas, loan rates, target prices, deficiency 
payments and acreage limitations. In addition, it provided for "sodbuster" and "swampbuster" 
programs, conservation reserve programs, and the dairy herd buy out program. Federal 
expenditures for farm price and inrome programs escalated from $4.0 billion in 1981 to more 
than $19 billion in 1986 (Budget of the united States Government, FY 1952-1987). With the 
decline in land values and market prices and the increase in production costs, this policy period 
has been called the era of the farm crisis. 

In eacij of these five agriculture policy eras, overproduction led to decreased prices for farm 
commodities. Numerous policy approaches attempted to reduce production. Some of these 
approaches included voluntary acreage reduction programs, mandatory acreage reduction 
progiams, market quotas of commodities, and long-term conservation reserves. Although each 
approach had some successes and some failures that had an impact on production and, in turn, 
the prices paid to producers, we have yet to determine their overall impact on income distribution 
in agriculturally-dependent areas. 

What accounts for the variation in the level and distribution of income found in our nation's 
rural areas? Which of the policies described above potentially redistribute income? This report 
attempts to answer these kinds of questions. The primary objective is to investigate the 
relationship between the distribution of household income in agriculture-dependent counties of 
the North Central region and selected social, demographic and economic determinants. This 
investigation will provide insight into the process of income distribution. It will identify the 
effects of policies that potentially influence income distribution through a detailed analysis of the 
interrelated system of social and economic change and income distribution. 

Chapter 2 reviews the theories offered to explain income distribution. Based on these 
theories, we derive a model to analyze income distribution in the region's agricultural-dependent 
counties. The third chapter describes the methods used to study the impact of selected policy and 
structural variables on income distribution. Chapter 4 describes the findings of the study and 



17 



7 



i 



includes an analysis of the variables that significantly and consistently impact income distribution 
across time. The fift chapter is a discussion of structural factors found to determine income 
distribution, while Chapter 6 discusses policies that determine income distribution. The final two 
chapters are a summary of this study and suggestions for additional research. 



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CHAPTER TWO 



THEORIES OF PERSONAL 
INCOME DISTRIBUTION 

Various theories have been proposed to explain observed characteristics of personal income 
distribution J These theories can be logically grouped into four general categories based on their 
similarities. This section reviews these four categories and illustrates how they may be integrated 
into a single working model for empirical analysis. They are: stochastic, personal characteristic, 
regional endowment and development. One characteristic of this literature is clear-there is no 
single, unified theory of distribution of personal income. 



Stochastic Theories 

Theories of the stochastic, or theoretic-statistical type (Bjerke 1961), hold that incomes are 
lognormally distributed and that income levels are random (i.e., due to chance). Stochastic 
theories relate income distribution to the workings of an indefinite number of small, unidentifi- 
able influences. These thwries attempt to show how income distribution is affected primarily by 
the opportunities people derive from chance events (Jencks 1972). For example, an acquaintance 
may steer an individual from one line of work to another. Or, a new exit is built on the interstate 
near one's restaurant. 



Stochastic theories of income distribution have been developed largely by British economists 
over the past several do:ades. American alternatives have been formulated by Friedman (1953) 
as part of the "theory of choice under uncertainty," and by Thurow (1975) as the "random walk 
theory." These theories argue that even if a free-enterprise economy could begin with complete 



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^Most theorists acknowledge that the observed distribution of personal income is skewed. Hiis assumption has 
been challenged by economists such as Stanley Lebergott (1939). He pointed out that when only males aged 25 
through 64 and credit availability are considered, the distribution becomes remarkably normal. Kuznets (1974) 
concurs that the income distribution for this group shows appreciably narrow inequality. Nevertheless, he also points 
out that other subgroups, such as family units with youth, old and female heads are increasing rapidly and are 
conc^trated in lower income brackets. 



19 



equality in income and wealth, inequalities would be evident within one generation. Friedman, 
a proponent of the stochastic theory, has sharply criticized other paradigms that focus on 
determinants of income distribution. His contention as a neoclassicist is that income distribution 
is entirely market driven and arises from job competition. 

Personal Characteristics Hieories 

A second theoretical orientation used to explain differences in income distribution focuses 
on personal characteristics, such as differences in individual or family traits. This perspective 
holds that inequalities are not due to chance or mere market forces as suggested by stochastic 
theorists. Rather, the distribution of income is rooted in the value society places on various 
personal traits. Three of the more common approaches in this field include persvnal ability 
theories, family background theories and human capital theories. 

Personal Ability Theories 

Ability, usually measured as Intelligence Quotient (IQ), is commonly cited as a determinant 
of earnings. Bowles and Gintis (1973), however, have shown that the independent influence of 
IQ on earnings is fairly small. When education and social class are held constant, a person in the 
higher IQ categories stands only a slightly higher chance of increased earnings than does the 
person with an average IQ. The relationship between IQ and economic success is likely derived 
from the common relationship of these two variables with family background and level of 
education. 



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Family Bacdiground and Life Cyde Theories 

Bowles (1972) found that, when taken together, both years of education and socioeconomic 
status based on family background had a significant effect on level of earning:. Socioeconomic 
background was positively related to earnings only through its relationship with educational level. 
Similarly, years of education had a relatively minor influence on earnings independent of social 
background. A shortcoming of Bowles' work was his exclusion of various human capital 
variables. As a result, his conclusions have not been universally accepted. 

One variation of family background theory is the life cycle theory. Research in both the U.S. 
(Kuznets 1953; Blinder 1974) and Great Britain (Prest and Stark 1967; Polandyi and Wood 1974) 
indicate that age, abilities, savings and spending, and work habits account for the bulk of income 
distribution. Each of these variables is affected by the stage of life in which workers find 
themselves. Thus, earning inequalities measured at any point in time may be overstated. 
According to life cycle theorists, a life cycle income rather than a point-in-time income would 
more accurately measure income inequalities. 

10 20 



Human Capital Theori^ 

Human capital theorists contend that expected lifetime incomes rise as individuals invest in 
themselves. The more marketable and competitive they become, the greater their lifetime 
earnings increase. For example, individuals with advanced education and training are often in 
better positions to compete for higher paying jobs (Chiswick 1974). 

An individual's investment is rewarded over the future period of employment in the form 
of higher earnings. In a short-run conceptual context, human capital investment translates into 
an upward shift of the individual's marginal value product of labor and a correspondingly higher 
return on the investment in education. This implies an increase in income for those who invest 
in human capital. 

While numerous research efforts have been devoted to testing human capital theories, 
Becker's (1967) work is especially noteworthy. He maintains that personal income is primarily 
a function of an individual's "learning, skills,. . . acquired through belonging to a particular 
family and culture" (Becker and Tomes 1979: 1158). Unfortunately, the impacts of education 
on income distribution are not yet clearly understood. Stiglitz (1975) argues that education serves 
as a device to "screen" individuals with respect to their employment assets. Thus, it may not be 
the added knowledge base education supplies to individuals, but rather the adaptability and 
flexibilitv it instills. 

Individuals may make other investments to increase their earning abilities. Some of these 
include migration (Sjaastad 1962), health (Grossman 1972), on-the-job training (Mincer 1962), 
job search (Spence 1974), information evaluation (Stigler 1962), preschool investment in one's 
children (Leibowitz 1977), and family (Nerlove 1974). 

Human capital theories of the Chicago School are often counterposed against the inheritance 
theories of the Cambridge School (e.g. Sahota 1978). Unfortunately, such comparisons often 
confuse cunent income with total wealth. Thurow (1975) distinguishes between income and 
wealth by calling for a "random- walk" theory to account for inherited wealth and a 
"job-competition" theory to account for earnings and income. Atkinson (1975) observes that 
wealth is a function of saved earnings plus the accumulation of all income from capital, including 
capital gains. 

In his critique of human capital theories, Lydall (1976) lists five implicit assumptions made 
by human capital theorists. First, everyone has equal ability. Second, labor, education and capital 
markets are perfect and always in equilibrium, both instantaneously and over time. Third, people 
have perfect knowledge of the future and make fully rational decision^. Fourth, there is no 

ERIC 2 1 



on-the-job training, no leaming-by-doing, and no effect of age on ability. Finally, there are no 
"hierarchy" effects on earnings. He notes that human capital theories explain the level of earnings 
by people with different levels of education rather than the distribution of these earnings because 
it does not explain why some people invest more in themselves than other people do. 



Persom^ Characteristic Tlieories in Perspective 

In an analysis of the effects of variables ranging from ability to education, family 
background and employment status, Taubman (1976) found that nearly all variables change 
during a person's life cycle. When other variables are held constant, e;Iucation leads to 
significant differences in earnings. Nevertheless, these differences are small in comparison to 
those that rise from a conglomeration of family background, attitudes and nonpecuniary 
preferences, and are no larger than those due to ability. 

Atkinson (19" 5) illustrated the lelationship among the various personal characteristics 
theories of ability theory, family background theory and h'jman capital theory (Figure 1). 
According to Atkinson, measured childhood IQ is a function of pen. lypic IQ, whereas years of 
schooling is a function of one's family socioeconomic background. As a result, earnings is a 
function of measured childhood IQ, years of schooHng, family socic economic background and 
chance. Lydall (1976) devised a model similar to Atkinson's. Lydoli's model added the 
psychological "D-factor" (drive, dynamism, determination, energy, industry and self-discipline), 
occupation, and age (a proxy for experience and on-the-job training). 




Figure 1. Atkinson's explanation of earnings. 



Regtonal Endowment Theories 

A third general category of theories pertaining to income distribution are endowment 
theories. These theories center on a regional as opposed to an individual unit of analysis. As a 
result, tlie data are usually aggregate statistics that encompass a specified geographic unit such 
as a city, township, county or state. The basic premise of the endowment perspective is that 



o 12 

ERIC 



geographic areas differ in the characteristics and attributes that directly affect income 
distributions. The three most prominent approaches include the urban-industrial impact 
hypothesis, economic base theories and ecological theories. 

Urban-industrial Impact Hypothesis 

The urban-industrial impact hypothesis suggests that the presence of an urban center, 
industrial and retail trade employment, and availability of services is positively related to rural 
residents' incomes. T. W. Schultz (1951; 1953) observed that farm incomes and productivity are 
highest near centers of urban-industrial development. He suggested that land and labor costs rise 
as a result of being geographically close to a city. This, in turn, induces farmers to mechanize, 
increasing their labor productivity. Competition for excess labor from rural areas is found in the 
cities and farm products receiving a higher price in the cities make a capital investment in 
fanning more profitable. 

In addition, Schultz found that cities are able to open new job opportunities to absorb a 
larger number of people. Because of the education and training levels required by these new 
urban jobs, the level of education in urban centers is raised. Furthermore, urban living breaks 
down traditional, ascriptive components of rural migrants, which increases their potential for 
social mobility. 

Tauriainen and Young (1976) sought to determine the impact of a set of urban-industrial 
variables on income and productivity of agricultural workers. Using Finnish communes in their 
analysis, Tauriainen and Young found general support for Schultz's hypothesis. They concluded 
that his hypothesis may be more useful on a regional rather than local level and that measures 
sensitive to urban aspects of central places should be included in future analyses rather than 
including only industrial development aspects. 

Economic Base Theories 

Economic base theories suggest tliat any activities serving to bring money into a community 
may be said to represent that community's economic base (Henry, Drabenstott and Gibson 1986). 
Tnese theories are founded on the notion that growth is the preeminent aspect of an economic 
system's health. They believe that shocks, stimuli or influxes to the economic system will have 
repercussions on the economy. Money brought into the community will be recirculated as goods 
and services are bought and sold, creating a multiplier effect. Various industries have differing 
multipliers; that is, each category of industry has a different ability to bring new money into a 
community and to have that money recirculated. An input-output (I-O) matrix is frequently used 
to display the multipliers. 



?3 



13 



One variation of this type of theory is the sectoral or dual-economy theory. This theory 
argues that labor markets are neither homogeneous nor fully competitive because many workei-s 
are confined to the peripheral sector of the economy. In the peripheral sector, firms are small, 
have lower market power and lower profits. As a result, this sector is unable to pay moderate 
wages compared with core or monopoly firms. This theory has received support from a number 
of researchers such as Jacobs (1982). He notes that states with a greater proportion of the work 
force in small establishments are likely to have a comparatively unequal income distribution. 

Ecological Theories 

Wilkinson (1973) describes the course of adaptation to ecological change. He theorizes that 
population growth and environmental change produce ecological disequilibrium that leads to 
resource scarcity. At this point, two alternatives are available. The first involves a breakdown 
in local self-sufficiency, forcing imports to cover deficiencies and specialized production for 
exports. The second alternative is to change to new resources or to more intensive methods to 
exploit current resources. If the latter alternative is chosen, productive processes arc involved. 
The result is a need for more tools and equipment for increasingly complex tasks, the application 
of additional energy for productive processes, and an increased emphasis on labor saving methods 
(which may include additional division of labor). The impact of ecological variables on income 
distribution remains to be tested. 

Regional endowment theories appear to address most directly the issue of differentials in the 
level of income across regional or state units. However, in their current form these theories do 
not explicitly attempt to relate endowment differentials to observed differences in income 
distribution across regions. To the extent that the level and distribution of income are 
interrelated, regional endowment theories potentially add to a composite theory of income 
distribution. 

Development Theories 

A final category of theories focusing on income distributions are the development theories. 
In general, development theories address socioeconomic change. They are grounded on the 
assumption that alterations in social structures allow institutions and organizations to cope better 
with the environment, thereby enhancing the opportunity to reach desired goals. Development 
theories imply change and dynamics. 

A number of development theories have been offered that deal with the impacts of various 
interventions on income distribution. Two examples are neoclassical economic development 
theories and income-employment growth theories. 



Er|c 14 



It should be noted that the terms "economic development" and "economic growth" are not 
synonymous, although they are similar concepts and imply processes that result in similar 
observed outcomes, such as higher incomes. Each region has varying quantities and qualities of 
natural resources, labor, private and public capital, institutions, t^hnology, and innovation. 
Economic development alters the mix or combination of these basic factors and changes their 
quality and, hence, their productivity. 

Economic growth, on the other hand, results from an increase in scale and not necessarily 
a change in the mix of the basic factors. Economic growth is the process of advancing aggregate 
productivity by expanding the resource base. The difference in these two concepts is best 
illustrated by Edwards and Coltrane, who stated, "discovering natural resources, inventing 
techniques, changing the input mix, creating products, innovating organizational arrangements, 
and tapping markets are associated more with new ways of doing things than with expanding the 
volume of things done, more with development than with growth" (1972:230). 

Neoclassical Kconomlc Development Theories 

Depressed areas are frequently characterized by job scarcity and low wage rates. Opposite 
characteristics may be found in prosperous areas. According to neoclassical econom.ic theories, 
two areas grow more similar over time as labor migrates from a depressed area to a prosperous 
area, and as capital moves from a prosperous area to a depressed area. Unfortunately, in some 
instances, labor and capital both move out of a depressed area. The quality of capital and labor 
that are sufficiently mobile is of critical concern to the distribution of income. 

To improve wage rates in a depressed area, capital resources must be increased and 
unemployment reduced. This may be accomplished by improving human capital (through 
education) or by expanding material capital (through infusion of private and public capital and 
use of local natural resources). Neoclassical theories, or marginal revenue product theories 
(Gordon 1972; Bluestone et al. 1973), have been criticized by the sectoral theorists for failing 
to account for the heterogeneity in the labor market and the confmement of many workers to the 
peripheral sector in the economy (Jacobs 1982), 

Tweeten and Brinkman (1976) refer to neoclassical theories as the "resource efficiency" 
approach because it emphasizes technical training and employment services to fill existing jobs. 
Advocates of this perspective recommend improving the labor market and subsidizing labor 
mobility to align private and social costs/benefits. 



'5 



15 



Income-employineflt Growth Theories 

Income-employment growth theories usually stress aggregate savings, investments, exports, 
and social/economic engineering. Three illustrations are described by Tweeten and Brinkman 
(1976). The first one they label the "local community improvement approach." It requires 
minimum outside assistance and is directed at improving decision-making processes that focus 
community resources on solving local problems. The major issues it confronts include quality 
of life, services and housing needs. Unfortunately, this approach does not deal directly with the 
income distribution issue. 

A second strategy is that of "equity and fairness. " This approach places emphasis on state 
and federal aid and tax reform to bring about programs aimed at redistributing income to the 
disadvantaged. By organizing the disadvantaged and offering them remedial education and 
training, those who use this theory hope to overcome discrimination and poverty. 

The third technique described by Tw^ten and Brinkman is that of "place prosperity." This 
very popular approach attempts to attract industry and jobs by offering low local wage scales and 
by improving services. Ironically, development methods aimed at attracting new, urban industries 
do not always insure higher or more equitable incomes. 

Kuznets (1955) suggested that during early periods of industrialization, when nonagricultural 
populations are relatively small in comparison to the total population, income distribution may 
be more unequal than that of the agricultural population. That is, a U-shaped relationship exists 
between level of economic development and income equality. Economic development initially 
depresses income equality, but later causes it to improve. He notes that rapid urbanization and 
industrialization are frequently accompanied by an influx of low-income immigrants either from 
the region's agricultural areas or from abroad. 

Both the equity and fairness and place prosperity growth approaches deal with increased 
growth in the socioeconomic system resulting from change in input quality. Changes are not 
necessarily expected in the quantity or addition of inputs. It is believed that technical progress 
results from a blending of scientific knowledge and entrepreneurial innovation, economic 
flexibility, and mobility. This, however, cannot happen unless the system is appreciably 
modernized or developed. For development to occur, the social system must be in the process 
of becoming differentiated and integrated (Spengler 1965). 

An Empirical Approach 

Not all regions are affected equally during periods of economic change. During times of 
depression or stagnation, some places experience acute hardship while others appear to enjoy 

ERIC 16 rb 



relative prosperity. Numerous economic factors undoubtedly serve as determinants of income 
distribution. Moreover, these economic factors interact with several social and demographic 
forces in the processes of economic change and income generation. The previous discussion 
highlighted the diversity of theories that purpoit to explain the level and distribution of personal 
income. As illustrated by that discussion, the theories are varied and often conflicting. Clearly, 
distribution of income is a complex and dynamic process that deserves much additional 
theoretical and empirical work. Efforts to develop empirical models that test these separate 
theories, or attempt to integrate them, have been confionted with significant data problems at 
several levels of aggregation. Therefore, it is useful to consider how previous empirical studies 
have been developed. 

Gardner (1969) hypothesized "immediate* and "ultimate" determinanu of income inequality. 
Immediate determinants include factors of production and rates of return to these factors, such 
as population change, net migration, income levels from types of income sources, and earnings 
type. Ultimate determinants are the factors that influence rates of return and the distribution of 
factor ownership, such as disequilibrium in the labor force, location, population size, long-term 
migration trends, and county expenditure patterns. The ultimate determinants include 
policy-oriented variables. Gardner's empirical analysis involved estimates of a state-level, 
cross-sectional model using census data. Consistent with a cross-sectional approach, Gardner 
redefined the income measure as equilibrium Oong-run) income with the use of an income-gener- 
ating function. He found that labor market adjustments influence a reduction in short-run, but 
not long-run, inequality. Further, Gardner observed that increases in the capital/labor ratio, 
average level of education, and research/extension were associated with increases in both farm 
incomes and dispersion of farm incomes. 

Thurow (1970) conducted an empirical analysis of both longitudinal and cross-sectional 
variations in income distribution at the state level. Census income data for households were used 
to estimate the parameters of the beta distribution. Two beta distribution parameters that capture 
the "concentrating" impacts of determinants were analyzed in the cross-sectional model. 
Variables such as race, proportion of families living on farms, labor force participation, level 
of education, employment, and industrial base were foun -> be significant determinants of 
income distribution. 

Pederson (1975) developed an empirical analysis of county-level size distribution of census 
income for rural farm households in Minnesota. The study focused on the variance of the logs 
of income (as Gardner's study proposed) without the use of an income-generating function, due 
to limited data available at the county level of analysis. Results of the study indicate that no 
consistent and uniform set of variables explained variation in income dispersion across counties 

ERIC ? 7 



in cross-section for the years 1950, 1960 and 1970. When census year data were pooled, the 
distribution of educational attainment, off-farm work, number of earners per family, government 
payments to farmers, and mobility of labor were found to be significant determinants of income 
distribution. The pooled model accounted for 44 percent of variation in income dispersion across 
counties in the state. 

Foley (1977) completed an analysis of income inequality in 30O counties utilizing census 
income and the Gini coefficient as a measure of income distribution. Foley's results indicate that 
population growth rate, race, median family income population density, and proportion of the 
labor force employed in manufacturing, cross-tabulated by type of county, were key determinants 
of income distribution. Foley's estimated model accounted for 50 percent of the variation in 
income inequality across counties. Additionally, when considering various categories of counties, 
the model accounted for 72 percent of income inequality in standard metropolitan statistical area 
(SMSA) counties and 58 percent in rural counties. 

A synthesis of the existing theories and previous empirical studies of size distribution of 
income into a single empirical model is not possible. The alternative strategy employed here is 
to specify a reduced model that draws selectively on the findings of previous studies. Thurow 
and Gardner found structural (resource base/endowment) differences between states to be 
significant sources of variation in income distribution. Thurow, Gardner and Foley found that 
socioeconomic policy factors were also significant determinants of income distribution at the state 
and county levels. 

Resource endowment variables indicate how characteristics of income recipient units such 
as level of resource productivity, distribution cf resources between recipient units, and 
participation and mobility of labor and capital resources serve as determinants of expected 
income distribution. Various hypotheses may be developed regarding the expected relationships 
between the distribution of educational attainment, labor force participation rates, wage rates and 
labor mobility, and the selected measure of income distribution. 

Socioeconomic policy variables capture the effects of direct and indirect relationships 
b-etween two classes of government activity ^transfer payments and government expenditures) and 
measures of income distribution. Government transfer payments of various types result in direct 
income increases to selected recipient units, and are the basis for potential income redistribution 
among recipient units. For example, income maintenance and unemployment transfer payments 
are intended to improve the income position of recipients with lower incomes. To the extent that 
these programs achieve that fundamental objective, the policy is redistributive toward more equal 
income distribution. 



Er|c 18 



Government expenditures generate direct and indirect benefits that are potentially 
redistributive in nature. For example, government expenditures on education influence incomes 
of recipients during the expenditure period and indirectly the future incomes of those receiving 
the educational service. Since the latter effect lags the actual expenditures, the indirect 
redistributive effects of policy variables are not adequately captured in cross-sectional analyses. 
Gardner's analysis of the relationship between the distribution of long run income and ultimate 
(policy) determinants provides a more direct test of the significance of policy variables in a 
cross-sectional study. The analysis reported in this paper captures only the "first-round," direct 
effects of government activity and policy variables. These effects may or may not indicate that 
a more equal distribution of income has occurred through transfers or expenditures. 

Social and economic policies may have an impact on income distribution in a number of 
ways. First, they may have a direct effect by injecting money into a local economy. For 
example, income transfer payments are intended to sustain families at the lower end of the 
distribution. As a result, income is redistributed. Second, such policies may interact directly with 
structural endowment variables. Government assistance for education of those in poverty may 
increase the level of education in an area. This allows recipients to move into better-paying jobs. 
Redistribution of income results. 

How endowment and redistribution variables may relate to income distribution and to each 
other at a cross-section in time is illustrated in Figure 2. Given the nature of systemic change, 
single-direction arrows could become two-direction arrows longitudinally. 



Social and 
Economic N 
Redistribution 
Policies 





Distribution 
of Income 



Structural 
and Market 
Endowments 



Figure 2, Composite theory of income distribution. 



19 



CHAPTER THREE 



METHODOLOGY FOR ANALYZING 

INCOME DISTRIBUTION 

Scope of the Study 

The project's scope was to determine policy-relevant variables that affect income distribution 
in those counties primarily dependent on agriculture. Thus, in this analysis, only agriculture- 
dependent counties were considered. CountiK were used as the unit of analysis because they 
form the smallest jurisdictional base common to most states where structural endowment 
dynamics and income redistribution policies are most clearly operative. 

Only agriculture-dependent counties were selected in order to reduce the range of variables 
that could be of potential importan(;e in the analysis. This was accomplished by selecting 
relatively homogeneous counties where agricultural resources and employment potentially played 
the dominant role in the process of income generation. Bender et al. (1985) and Ross and Green 
(1985) developed a typology of counties using economic base. They defined "nonmetropolitan 
agriculture counties" as those counties with 20 percent or more of total labor and proprietor 
income produced from farming/ranching during 1975 to 1979 (Figure 3). Their definition is used 
in this analysis. 

Only the 13 states in the North Central region of the U.S. have been included. Of the 1,175 
counties in these states, 397 (33.8 percent) were defined as agriculture-dependent. All counties 
included in this analysis are listed by state in Appendix A. 

Measure of Income Inequality 

The Gini-ratio wzi selected as the measure of income inequality. It may be illustrated as a 
ratio of the area between the Lorenz-curve and diagonal divided by the total area under the 
diagonal (Figure 4). 



ERIC 



100 



Aggregate 
Income 

(cumulative 
percent) 



0 



Diagonal of X / 




equality X / ^^^^^^ 


Lorenz 




^•Ny. curve 




or income 




curve 














fi+1 100 


Recipient units (cumulative percent) 





Figure 4. The Lorenz-airve of income concentration. 

The Gini-ratio ranges from 0 (when incomes are equal and the cumulative frequency equals 
the diagonal line) to 1 (at which point all income is received by a single income recipient unit). 
As a measure of income inequality, the Gini-ratio is computed as: 

G = A = 1/2 - Area under the Lorenz-curve 
A+B 1/2 

G = 1 - 2 (Area under the Lorenz-curve) 

Assuming that the curve can be approximated by a collection of straight line segments, the area 
under any given segment is: 

2 

where f^ is the frequency of observations in the i-th income class and Xj is the midpoint (either 
arithmetic or geometric) of the i-th income class. The area under the entire curve is: 

£(fi^i-fi) * ( Xj 4- Xj^t)- 



By substitution, the Gini-ratio is: 

G = 1 - Si (fi^i -fi)(Xi + Xi^j). 

Gini-ratios of the counties vary widely from each other and across time. Appendix Table B. 1 
lists the 1960, 1970 and 1980 Gini-ratios of counties included in the analysis. 



.^2 



23 



Variables Explainmg Income Inequality 

In this analysis, independent variables were term^ "structural variables'* and "policy varia- 
bles." Within the structural grouping were variables that serve either as definitions of develop- 
ment or as key indicators of development (Eberts and Young 1971; Gunther and Ellis 1977). 

Structural variables included in the analysis are level of education, manufacturing and 
services employment, urbanization, proportion of commercial farms, women in the labor force, 
and political participation. Other structural variables include income from the manufacturing, 
government, wholesale trade, agriculture, retail trade and service sectors. Additionally, net 
earnings from dividends, interest and rent were analyzed. 

Policy variables include percentage change in population, transfer payments for retirement, 
income maintenance, unemployment, farm programs and veterans' benefits. Other policy 
variables include total county government expenditures and county government expenditures for 
highways, education, welfare and health. 

Percentage change in population was very high when correlated with various sources of 
income, government expenditures and transfer payments. That is, as population increased, 
incomes, expenditures and transfer payments increased at an almost identical rate. This problem 
of variables being very highly correlated, or collinear, created difficulties in data analysis. The 
problem was solved by transforming all aggregate dollar amounts to per capita dollar amounts. 

Data 

Data for this project were collected from a number of sources. Data on population, income, 
industry and occupation, demographic characteristics, and geography were provided by the 
Census of Population and from the City and County Data Book. Bureau of Economic Analysis 
reports contained data on employment, transfer payments, farms and income. All data were at 
the county level of aggregation. 

Data points used in the rescrarch were uie census years 1960, 1970 and 1980. Social and 
demographic variables based on census data were measured for each of these years. Economic 
variables were measured as of the previous year (1959, 1969 and 1979) to maintain consistency 
with census data. 

Operational definitions for each of ti-icse explanatory variables are listed in Table 1. In 
addition, the hypothesized sign of the relationship between each independent variable and th«^ 
Gini-ratio is indicated in the right-hand column. These hypotheses are based on a review of 
research literature pertaining to income distribution. 



24 



?3 



Tabk 1. List of Variables and Definitions loduded in Analysis 



Variable 




Hypolhfsmd 
Rdatkwsliips 


Structural Varabhs 


High School Graduates 


Persons age 25 and over having con^leted a high 

cpnf>r\1 i\/^4TTw^ qc a tya rt*^n tn Oft €\¥ oil rv^f^cr^ti c acTA '7 ^ 

and over. 




Manufac tilling 
Employment 


Persons employed in roanuiacturing as a percentage of 
all persons in the labor force. 




Services En^ioyment 


Persons employed i services as a percentage of all 
persons in the labor force. 




Urban Population 


Persons ^ in places of at least 2,500 residents as a 
percentage of all p^wns m the county. 


-1- 


Commercial Farms 


Dummy variable where: 1 » 40 percent or more of the 
ranns m the county had farm mcomes or $40,000 or 
more in 1980; and 0 » less than 40 percent of the 

femvi in thft cruintv haH farm incv^TViAfi nf t^lO 000 nr 
more in 1980. A farm with farm income of $40,000 is 
defined as a "commercial farm. " 




Women in Labor Force 


Women in the labor force as a percentage of all per- 
sons in the labor force. 




Political Participation 


Persons voting in presidential election as a percentage 
of all persons of voting age. 




Manufacturing Income 


Per capita manufacturing income. 


4- 


Government Income 


Per capita government income. 




Wholesale Trade Income 


Per capita wholesale trade income. 




Agriculture Incon^ 


Per capita agriculture income. 




Retail Trade Income 


Per capita retail trade income. 




Services Income 


Per capita services industry income. 




Net Earnings 


Per capita net'-eamings. 




Dividends, Interest, Rent 


Per capita income from dividends, interest and rent. 





Table 1, cont. List of Variables and Definitions Included in Analy^ 



Variable 


Offfititiftrt 


Hypotiieazed 
ReUtiooship 






Population Change 


Pefceat change in total county pc^ation during the 
previous decade. 




Retirement Transfers 


Per capita retirement transfer payments. 




Income Maintenance 
Transfers 


Per capita mcome mamtenance transfer 
paymfflts. 




Unemployment Transfers 


Per capita unemployment transfer payments. 


+ 


Farm Program Transfers 


Per capita farm program transfer payments 




Veteran Bwiefit Transfers 


Pa* capita veteran b^iefit transfer payments. 




Total County 
Government Expenditures 


Per capita county government total expenditures. 




Coimty Highway 
Expenditiires 


Per capita county govemn^t expenditures for high- 
ways. 




County Education 
Expenditures 


Per capita county government expraditures for educa- 
tion. 




County Welfare 
Expenditures 


Per capita cotmty government expenditures for welfare 
programs. 




County Health 
Expenditures 


Per capita county government expenditures for health 
programs. 





Er|c 26 



CHAPTER FOUR 



DETERMINANTS OF INCOME 
DISTRIBUTION AT THE COUNTY LEVEL 

Correlation Analysis 

Correlation coefficients were calculated to determine the degree to which structural and 
policy variables relate to income distribution (Gini-ratio). Table 2 lists the correlation coefficients 
for census years 1960, 1970 and 1980. In addition, the table lists the correlation's probability 
level and the percentage of variance (r^) in Gini-ratio explained by each predictor variable in 
each of the three years that were analyzed. 

Structural Variables 

A number of similarities were noted among the three years when comparing the structural 
variables correlation coefficients. In 1960, tliC scale of farming was inversely related to income 
distribution (1960 Gini-ratio) (r=-.320). The greater the proportion of commercial farms in a 
county, the more equal was the county's distribution of income and the lower the Gini-ratio. This 
relationship remained significant in 1970 (r=-.257) and in 1980 (r=-.279). 

Although significantly related in all thr^ census years, the correlation coefficient for 
percentage of population living in urban places was higher in 1980 (r=-.389) than in either 1960 
(r=-.129) or 1970 (r=-.166). This indicates that the greater the percentage of residents living 
in urban places, the more equal was the county's distribution of income (that is, the lower the 
Gini-ratio). 

The percentage of the population age 25 and over with a high school degree was a third 
structural variable significantly related to Gini-ratios in all three years. Although the relationship 
was strong in 1960, 1970 and 1980, it diminished slightly over time (r=-.512, r=-.310 and 
r— -.271, respectively). 



Other structural variables were significantly related with income distribution at only two 
points in time. The percentage of the county's labor force comprised of women was relaied to 
income distribution in 1970 (r=-.180) and in 1980 (r=-.340), but not in 1960 (r=-.048). 

The relationship between the percentage of the labor force employed in manufacturing 
underwent directional change between 1960 and 1980. In 1960, a weak but significant positive 
relationship was found between percentage of the labor force employed in manufecturing and the 
Gini-ratio (r=.169). In 1970, the rel' ionship remained weak, but the sign reversed (r=-.155). 
In 1980, however, the relationship between manufacturing employment and the Gini-ratio 
remained inverse but had considerably more strength (r=-.363). 

Thus, the relationship between manufacturing employment and the Gini-ratio made a 
transition during the two decades. The greater the percentege of the labor force employed in 
manufacturing in 1960, the less equal was the distribution of income. However, by 1980, the 
greater the percentage of the labor force employed in manufacturing, the more equal was the 
distribution of income. By comparison, the percentage of the labor force employed in services 
was inversely related to the Gini-ratio, while the strength of the relationship declined over the 
three years. 

Overall, stronger relationships were found between structural variables and income 
distribution in 1980 than in either 1960 or 1970. The strongest structural variables that served 
as predictors of income distribution across time included percentage of high school graduates, 
manufacturing employment, percentage of urban population, and scale of fanning. 

The structural variable that displayed the most consistent relationship with the Gini-ratio was 
retail trade income. Moderate but significant correlation coefficients were found for retail trade 
income in 1960, 1970 and 1980 (r=-.276, r=-.226 and r=-.280, respectively). Three other 
variables were significantly correlated with the Gini-ratio in 1960 and 1970, but not in 1980. In 
1960, sendee industry income, net earnings, and earnings from dividends, interest and rent had 
correlation coefficients with the Gini-ratio of -.196, -.286 and -.458, respectively. In 1970, the 
correlation coefficients of services income, net earnings, and income from dividends, interest and 
rent were -.191, -.265 and -.262, respectively. 

Several structural variables were significantiy correlated with the Gini-ratio in only one year. 
Those variables included government income in 1960 (r=-.297), manufacturing income in 1980 
(r=.,394) and wholesale income in 1980 (r=-.207). Agricultural income was not significantly 
correlated wiUi Uie Gini-ratio in any of the years analyzed. 

ERIC 28 



Table 2. Correlation CoeiliciKils Structural and Folky Variables witb Ginl-ratios.'* 





1960 




19S0 


Variable 


r 


I* 


P 


r 




P 


r 




P 


Stnictml Variables 


nign dCuOQi vnmiiiiics 


1^ 






- 110 


no6 




- 271 


071 


0*0 


iVlHXlUlli^rlUXUI^ CXUpitljluCUi 




020 






024 


** 


- 363 


132 


000 




- 142 


020 




- 124 


015 


** 


- 054 


.003 






- 129 


017 




- 166 


028 




- 389 


151 


000 






102 




- 257 


066 




- 279 


078 


000 


WOXDCii lU JLrfiUUi FUIVC 




002 




- 180 


032 




- 340 


1 16 

. k Aw 


000 


r UilUwiil I^UiivlpcUlUIl 




010 




no 


OIQ 




279 


078 


000 


iVlaiiUlttVlUIiJl^ iuwvajc 




000 




- 161 


027 




* 394 


155 


00m 


Government Income 


-.297 


.088 




-.033 


.001 




-.057 


.003 




Wholesale Trade Income 


-.184 


.034 


4i4i4i 


-.129 


.017 




-.207 


.043 


000 


Agriculture Income 


.003 


.000 




-.098 


.010 


* 


-.053 


.003 




Retail Trade Income 


-.276 


.076 




-.226 


.051 




-.280 


.078 


000 


Services Income 


-.196 


.038 




-.191 


.036 


0000 


-.146 


.021 


00 


Net Earnings 


-.286 


.082 




-.265 


,070 


0000 


-.116 


.013 


0 


Dividends, Interest, Rent 


-.458 


.210 


0m*m 


-.262 


.069 


0000 


-.117 


.014 


0 

1 



* p LE .05 
♦* p LE .01 
♦*♦ p LE .001 
p LE ,0001 



(Table 2 ooot. oo page 31) 



*As used above, *r" is the zero-order correlation coefficient; "r^" is the square of the zero-order correlation 
coefficient and provides an estimate of the percentage of the variance in the Gini-ratio explained by the independent 
variables; *p' is the sq>proximated probability of r. 




r!8 2' 



Policy Variables 

Policy variables used in the analysis were significantly correlated with the Gini-ratio more 
frequendy in 1960 than in cither 1970 or 1980. Three variables-percentage change in population, 
retirement transfer payments, and income maintenance transfer payments-were correlated with 
the Gini-ratio in each of the three years. The correlation cocfRcients of population change with 
the I960, 1970 and 1980 Gini-ratios were -.101, -.151 and -.312, respectively. The correlation 
coefficients of retirement transfer payments with the Gini-ratios in 1960, 1970 and 1980 were 
-.162, -.157 and -.280, respectively. The negative signs on the correlation coefficients between 
population change and retirement transfer wments with ; Gini-ratios indicate that as the level 
of any of these predictor variables increased, income inequality decreased. Income maintenance 
transfer payments had correlation coefficients with the Gini-ratios for 1960, 1970 and 1980 of 
.319, .188 and .256, respectively. The positive signs of these correlation coefficients and the 
Gini-ratio indicate that increases in income maintenance transfer payments are associated with 
higher levels of income distribution inequality. 

Three policy variables were significanUy related to income distribution in 1960 and 1970, 
but not in 1980. The first was highway expenditures (r=-.396, r=-.156 and r-.034, 
respectively). The second was education expenditures (r«-.369, r=-.204 and r=-.039, 
respectively). The third, total county government expenditures, was significantly correlated with 
Gini-ratios in 1960 and 1970, but not in 1980. The correlation coefficients for these were 
r=-.521, r=-.257 and r=-.057, respectively. 

Three policy variables were significantly related to income distribution in 1960 and 1980, 
but not in 1970. The first was unemployment transfers (r-.142, r=-.172 and r=-.056, 
respectively). Unemployment transfer payments were positively related to the 1960 Gini-ratio, 
although the 1980 unemployment transfer payments were inversely related to that year's Gini- 
ratio. Second, veteran benefit transfer payments were positively related to the Gini-ratios of 
1960, 1970 and 1980 (r=.295, r=.053 and r=.162, respectively). Finally, county welfare 
program expenditures were inversely related to Gini-ratios in 1960, 1970 and 1980 (r=-.416, 
r=.095 and r--.120, respectively). 

Regression Analysis 

Regression analysis was used to determine how structural variables and policy variables were 
related to income inequality at times when other variables were controlled. Three criteria were 
used to select explanatory variables from the complete set (Table 1). Explanatory variables 
needed to: (1) be of particular theoretical import; (2) maintain statistical significance in a 
regression with all other predictor variables with a minimum probability of .05 for at least two 
of the three years analyzed; and/or (3) be noncollinear with other variables at any given year. 

Er|c 30 



Table 2, coot. Correlation Coefficients of Structural and Policy Variables »rith Gini-ratios. 





1960 


1970 


1980 


Vttuble 


r 




P 


r 


r» 


P 


r 


p» 


P 


Ftolky Vuubles 


Populatioa Change 


-.101 


.010 




-.151 


.023 




-.312 


.097 


**** 


Retirement Transfers 


-.162 


.026 




-.157 


.025 




-.280 


.078 


**** 


Income Maintenance 
Transfers 


.319 


.102 




.188 


.035 




.256 


.066 


♦♦♦♦ 


Unen^Ioyment Transfers 


.142 


.020 




-.056 


.003 




-.172 


.030 


*4i4i 










-.023 


.001 




.294 


.086 


**** 


Veteran Benefit Transfers 


.295 


.087 


**** 


.053 


.001 




.162 


.026 


** 


Total County Govenin^t 


-.521 


.271 


**** 


-.257 


.066 




-.057 


.003 




County Highway 
Expenditures 


-.396 


.157 


**** 


-.156 


.024 


** 


.034 


.001 




County Education 
Expenditures 


-.369 


.136 


mm** 


-.204 


.042 


**** 


-.039 


.001 




County Welfare 
Expenditures 


-.416 


.173 


mm** 


.095 


.009 




-.120 


.014 


* 


County Health Expenditures 


-.125 


.016 


** 


-.056 


.003 




-.005 


.000 




N 


397 


397 


397 



♦ p LE .05 
p LE .01 
p LE .001 
♦♦♦♦ p LE .0001 




4U 



31 



Collinearity was defined as occurring between two variables if the correlation coefficient was 
equal to or greater than 0.75 (see Appendix Tables B.2, B.3 and B.4). While this may appear 
conservative, it seemed prudent to use this cutoff given the number of counties (N=397). 

Using these criteria, 10 explanatory variables were selected to be regressed against the Gini- 
ratius for 1960, 1970 and 1980. Five structural variables were used in the model. These include 
the percentage of the population age 25 and over having completed at least a high school degree, 
percentage of the labor force employed in manufacturing, percentage of the labor force employed 
in services, percentage of the labor force mads up of women, and commercial farms as a 
percentage of all farms in 1978. Five policy variables were also used in the model. These include 
percentage change in county population since the last census; retirement income maintenance 
and unemployment transfer payments; and total county government c'^oo ditures. 

1960 Regression Modd 

When taken as individual sets of variables, policy variables were slightly better predictors 
of income distribution in 1960 than were structural variables (Table 3). When decomposed into 
independent variable sets, the statistics for structural and policy variables were .283 and .33 1 , 
respectively. In the decomposed sets, three policy variables were significantly related to income 
distribution. These include retirement and income maintenance transfer payments and total county 
government expenditures (beta=-.200, beta=.202 and beta=-.438, respectively). Income 
maintenance transfer payments were positively related to the Gini-ratio, although the other two 
variables were inversely related with it. In the decomposed set, structural variables that were 
significantly related to the Gini-ratio include the proportion of high school graduates and the 
scale of farming (beta=-.452 and beta=-.n6, respectively). The full 10-variable model 
explained more than one-third of the Gini-ratio variance (adjusted-R^=.364). The beta- weights 
for services employment and unemployment transfer payments changed signs when included in 
the 10-variable model, although neither variable was statistically significant. In addition, the 
proportion of commercial farms dropped from statistical significance in the full model. 

1970 Regression Modei 

When taken independe ntly, structural variables and policy variables were weak predictors 
of income distribution in 1970 (Table 4). These sets of variables achieved R^s of only . 171 and 
.148, respectively. Three structural variables (proportion of high school graduates, manufacturing 
employment, and proportion of commercial farms) were significant predictors of the Gini-ratio 
(beta=-.301, beta=-.247 and beta=-.141, respectively). As in 1960, the proportion of women 
in the labor force was not a statistically significant variable and maintained a positive sign. 

41 

ErJc 32 



Table 3. Estimated Co^Tirieiits for Sheeted Structural and Policy Variables Regresr%d on 
Gini-ratios, 1960 



Varuibks 


StnictiMml 
Var«M« 


bets 


AO 


Sinictmi Vnisbks 


High School Graduates 






-.238*** 


Maaufacturing Employment 


.092 




.107 


Services Employment 


.000 




-.014 


Women in Labor Force 


.020 




.021 


Commercial Farms 


-.116* 




-.080 


lUky Variables 


Population Change 




.031 


.055 


Retirement Transfers 




-.200*** 


-.152** 


Income Maintenance 
Transfers 




.202*** 


.155** 


Unemployment Transfers 




.066 


-.055 


Total County Government 
Expenditures 




-.438*** 


-.294*** 






.283 


.331 


.380 


Adjusted-R^ 


(.274) 


(.322, ( (.364) 


N 


397 


397 


397 



^ less than or equal to .05 
"^^p less than or equal to .01 
***p less than or equal to .001 



.12 



All policy variables served as significant predictors of income distribution wh-.n decomposed 
into an independent set of variables. The beta-weights for population change; retirement, income 
maintenance and unemployment transfer payments; and total county government expenditures 
were -.180, -.189, .160, -.122 and -.211, respectively. Each variable was inversely related to 
the Gini-ratio with the exception of income maintenance transfer payments. 

The full 10-variable model accounted for 19 percent of the Gini-ratio variance. When 
structural and policy variables were merged, three structural variables (proportion of high school 
graduates, manufacturing employment and proportion of commercipi farms) remained as signi- 
ficant variables. Additionally, only three of the policy variables remained significant (retirement 
and unemployment transfer payments and total county government expenditures). The beta- 
w t for each of these variables fell appreciably from their levels in the independent model. 

1980 Regression Model 

When regressed individually on income distribution, structural variables explained a greater 
proportion of income distribution in 1980 than did policy variables (Table 5). Whereas structural 
variables achieved an of .328, the of policy variables was only .225. 

Four of the five variables in the structural set were significantly related to income 
distribution. The beta-weights for proportion of high school graduates, manufacturing 
employment, proportion of women in the labor force, and proportion of commercial farms were 
-.314, -.407, 140 and -. 168, respectively. Four of the five policy variables (population change 
and retirement, income maintenance and unemployment transfer payments) were significantly 
related totheGim-ratio(beta=-.357,beta=-.258,beta=.342andbeta=-.127, respectively). The 
positive sign on income maintenance transfer payments indicates that it was directly related to 
income distribution inequality. 

The 10-variable model explained nearly 39 percent of the Gini-ratio variance. Each of the 
variables significantly related to income distribution in their decomposed models was also 
statistically significant in the full model. 

Siunmary 

Based on correlation and regression analysis, it appears that the role of tJie identified 
determinants of income distribution has changed over the past three decades. Policy variables 
were slightly more significant determinants of income distribution in 1960. However, structural 
variables proved to be the most significant predictors in 1980. Structural and policy variables 
were equally important in 1970, suggesting that this was a transition period. 

ERIC 3^ 



Table 4. F-gH'^^f^ CoefTicieiits for Selected Stractmal and Fdlky Variabtes R^ressed on 
Gini-ratios, 1970 



Variables 


Stnidml 
Variable 


Volicj 
Variables 
beta 


An 

Variables 
bcCn 


SfnKtnral Variables 


High School Graduates 


-.301*** 




-.216** 


Maou&cturing Employment 


-.247*** 




~.204** 


Services Employment 


-.051 




-.049 


Women in Labor Force 


.019 




.068 


Conunercia! Farms 


-.141** 




-.129* 


Folky Variables 


Population Qiange 




-,180*** 


-.055 


Retirement T/ansfers 




-.189*** 


-.118* 


Income Maintoiance 
Transfers 




.160** 


.079 


Unemployment Transfers 




-.122* 


-.132* 


Total County Government 
Expenditures 




-.211'"** 


-.136* 






.171 


.148 


.210 


Adjusted-R^ 


(.160) 


(.137) 


(.190) 


N 


397 


397 


397 



less than or equal lo .05 
♦*p less than or equal to ,01 
♦**p less than or equal to .001 



35 



Tabk 5. Estimated CoeflldNits for Selected Structural and Policy Variables Regressed on 
Gini-ratios, 1980 



Variables 


Structml 


Polkj 


AU 
Variables 






bc«a 


bete 


Sinidnnl Variables 




High School Graduates 


-.314'"** 




-.262*** 


Manufiactunng EcE^loyment 


-.407*** 




-.264*** 


Services Eoqiloyineat 


-.018 




.012 


Womoa in Labor Force 


-.140* 




-.150** 


Commercial Farms 


-.168*** 




-.158*** 


FoGqf Variables 


Population Change 




-.357*** 


-.196*** 


Retirement Transfers 




-.258*** 


-.151** 


Income Maintenance 
Transfers 




.342*** 


,204*** 


Unemployment Transfers 




-.127** 


-.139** 


Total County Govenmient 
Expenditures 




-.047 


-.031 






.328 


.225 


.402 


Adjusted-R' 


(.320) 


(.215) 


(.386) 


N 


397 


397 


397 



less than or equal to ,05 
less than or equal to .01 
♦**p less than or equal to ,001 



ERLC 



STRUCTURAL FACTORS AS 
DETERMINANTS OF 
INCOME DISTRIBUTION 

Five structural variables were selected for inclusion in a regression model as determinants 
of county-level income distribution. These variables were considered potential determinants of 
income distribution, and in some cases, of each other. To analyze the systemic relationships of 
structural variables and income distribution, patli analysis was used. Path analysis is a 
multivariate statistical procedure that illustrates linear causal relationships in a closed system of 
variables. It is used to determine both direct and indirect relationships. 

Level of Education 

The percentage of population age 25 and over having completed a high school degree was 
significantly related to the Gini-ratio in all three years. Figure 5 depicts the path analysis results 
for resource variables in 1960. For simplicity, only direct effects of 0.15 and above are 
displayed. In 1960, the proportion of high school graduates was related significantly to the Gini- 
ratio (beta=-.24). Endowment variables most strongly related to the proportion of high school 
graduates were the proportion of commercial farms (beta=.21) and manufacturing employment 
(beta=-.15). 

Path analysis revealed the 1970 proportion of high school graduates was also a significant 
predictor of the Gini-ratio 05eta=-.22) (Figure 6). In addition, the proportion of high school 
graduates contributed to the 1970 Gini-ratio through its relationship with manufacturing 
employment (beta=-.16), which was also a significant predictor of the Gini-ratio. Commercial 
farming served as the only endowment variable predictor of the 1970 proportion of high school 
graduates (beta =.21). 



37 



PCP0P55 



PCRETIR6 



.re: 



PCMAIN6 



PCUHEM6 



.34, 



PCG0VX6 



LGFARM 



.20 



.33, 



.25, 



■ 32; 



PHS60 



PMANEMP6 



PSEREMP6 



PVI0MLF6 



.21 



.15 




-.29 



Re£stributioa 
Variables 



Endowmmi 
Variables 



IKstribution 
Variables 



Figure 5. Fith analysis displaying detenninants of income distribution, 1960. 



er|c 



47 



The percentage of persons age 25 and over with a high school degree increased from 37.1 
percent in 1960 to 47.6 percent in 1970. Part of this increase may be explained by the larger 
number of veterans using their educational benefits and the increased appropriations available for 
education from President Johnson's "Great Society" and "War on Poverty" programs. 

As illustrated in Figure 7, the 1980 proportion of high school graduates was a significant 
predictor of the Gini-iatio in 1980 (beta=-.26). As in both 1960 and 1970, the proportion of 
commercial farms was an important predictor of education level (beta=.24). By 1980, more than 
one-half of the "baby boom" generation was over age 25. This helps account for the increase in 
high school educated people, which went from 47.6 percent in 1970 to 61.7 percnt in 1980. As 
the educated "baby boom" generation aged to 25 years old and over, they com^,. M a sizeable 
portion of the entire 25-years-old-and-over group. 

Given these findings, what policy implications might be suggested? If we take these findings 
at face value, efforts aimed at increasing the educational level of residents should have the effect 
of redistributing incomes more equitably. This has been the belief of both conservative and 
liberal policymakers for the past several decades (Thurow 1975). The advantage of this approach 
as perceived by policymakers is that increasing education leads to a measure of income 
redistribution without requiring any major redistribution of capital. The fmdings support the 
human capital theory in that increased education enhances income, at least its equivalent 
distribution. Since our dependent variable has been income distribution rather than income levels, 
our findings would qualify human capital theory by noting that an increase in aggregate education 
level is associated with income equality. 

As indicated in Table 2, the proportion of high school graduates was strongly correlated with 
the Gini-ratio for each of the three years. The strength of that relationship, however, diminished 
over time. Additionally, the amount of money spent by local governments on education was 
highly correlated with the Gini-ratio, and the strength of that relationship also diminished 
appreciably over time. 

The amount of money expended by all governments on education nationwide increa^ 
dramatically during the last 30 years. It rose from $8.7 billion in 1950 to $164.5 billion in 1980. 
The three-decade rise was dramatic even when inflation wa , considered. When calculated in 1967 
constant dollars, total government expenditures for education nationwide jumped from $12.2 
billion in 1950 to $66.7 billion in 1980 (Appendix Table B.5). 

However, when the increase in number of school enrollments as a result of the "baby boom" 
is considered, a different picture emerges. Educational expenditures per student (in 1967 constant 



48 



39 



dollars) rose from $390 in 1950 to $617 in 1960, a jump of more than 58 percent (Figure 8). 
Between 1960 and 1970, constant dollar expenditures per student increased from $617 to $1 ,038, 
a rise of more than 68 percent. Between 1970 and 1980, constant dollar expenditures per student 
began to level. It rose from $1,038 to $1,144, an increase of only 10 percent. Prior to 1970, 
expenditures per student rose for the kindergarten through 12th-gr3de level (K-12) and for the 
college level. After 1970, expenditures per student continued to rise modestly for the K-12 level 
(21.67 percent), but dropped for the college level (-28.01 percent). 

Most researchers have concluded that a relationship exists between education and income 
distribution, but many are reluctant to infer a causal link. For example, Bowles (1972) notes 
interrelationships between the social class from which people come and their educational and 
income levels. He concluded that neither social class nor education determines income directly. 
Instead, these factors determine the number of occupational opportunities from which people are 
able to choose. Thus, those with higher education and from higher social status backgrounds have 
greater opportunities to choose jobs with greater monetary and nonmonetary rewards. 

Similarly, Thurow stated that "instead of people looking for jobs, there are jobs looking for 
people-for 'suitable' people* (1975:68). The first approach Thurow labeled "wage competiticn" 
theory, while the second he referred, to as "job competition" theory. He argued that the increas- 
ing supply of more-educated workers forces them to accept less favorable jobs that would have 
been taken by those with lower education levels. Programs aimed at increasing educational levels 
have served to change the supply of more-educated workers, not necessarily the demand. 

Thurow also notes that education becomes a good mvestment at the individual level, not 
simply because it raises one's income, but rather because it raises one's income relative to others. 
Thus, "education becomes a defensive measure necessary to protect one's 'market share'" 
(1975:79). On the national level, Thurow believes massive educational investments as a means 
of redistributing income may be wasted. Rather, he opts to make a frontal attack on wage 
differentials through technical progress, guaranteed government jobs, policies designed to create 
labor shortages, public wage scales to exert pressure on low-wage employers, and incentives to 
encourage employers to compress wage differentials. 

Programs aimed at increasing educational levels appear lo have been effective in the 1950s 
and 1960s. They may have been of less value in the 1970s. A growing proportion of the labor 
force presently holds educational degrees compared with the number only a few decades ago. If 
education is to continue to serve as a means of redistributing income in agriculture-dependent 
areas, policies need to be adopted that will increase occupational opportunities as well as enhance 
educational opportunities. 

ERIC *0 



PCP0P57 



PCRETIR7 



PCMAIN7 



PCUNEM7 



PCG0VX7 




PW0MLF7 



-.22 



.20. 



> GIN170 



Redtstributioa 
Variables 



Eadowment 
Variables 



Distributioa 
Variable 



figure 6. Path analysis displaying determinants of income distribution, 1970. 



ERIC 



50 



41 



-.19. 
.34> 



PCP0P78 



PCRETIR8 



.43. 



-.3! 



PCMAiNS 



PCUNEMS 



PCG0VX8 



-.16 

-■is: 



.23. 



C.31 



LGFARM 



PHS80 



PHANEMP8 



PSEREMP8 



PW0WLF8 



.3:. 



24 



.52 



.56 




RedistrQMitHMi 
Variables 



Eodowmeni 

Variables 



Distributioa 
Variable 



Figure 7. analysis delaying determinants of income distribution, 1980. 



ERIC 



51 



42 



Manufacturing Labor Force 

Neither regression analysis nor path analysis show the percentage of the labor force 
employed in manufacturing as a significant predictor of the Gini-ratio in 1960. The variable was 
indirectly related to income inequality through its relationships with the 1960 percentage of high 
school graduates (beta=-.15) (Figure 5). However, the negative correlation suggests increased 
rural manufacturing is associated with lower educational levels. The earlier discussion of the 
impact of education on income distribution implies that rural manufacturing may have important 
negative consequences for rural areas. For example, it is possible to conclude from the inverse 
relationship that rural manufacturing is ineffective in helping a community retain its educated 
population, an endowment that facilitates income equality. 

It is interesting to note that in botii ly70 and 1980, the percentage of the labor force 
employed in manufacturing was a strong predictor of the Gini-ratio (beta --.20 and -.26, 
respectively). Additionally, services employment was inversely related to the manufacturing 
employment in 1980 (beta =-.38), while tiie proportion of women in the labor force in 1980 
revealed a positive association (beta=.49) (Figure 7). 

The emphasis on manufacturing as a facet of community and economic development during 
the 1970s and 1980s was in part based on tiie assumption that manufacturing equalizes income 
distribution. Some support for this notion has beea ('ocumented. For example, in their study of 
rural Texas communities, Reinschmiedt ar J Jones (1977) found that locating a new industry in 
a community has a positive effect on incomes of those employed by the industries. Other 
researchers, however, have been skeptical of the relationship between industrial development and 
improved incomes. Summers and Clemente (1976) found that industrial development failed to 
significantiy impact economic status (total annual income) in their sample of counties in Illinof s. 
Additionally, Rogers et al. (1978) found mixed support for the relationship between change in 
manufacturing and change in levels of income in their sample of Iowa counties. 

The fact that manufacturing employment was significantly related (negative sign) to the Gini- 
ratio inequality in 1970 and 1980, but not in 1960, supports the findings of Kuznets (1955). He 
held that early periods of industrialization are associated with greater income inequality while 
later periods are associated wiUi greater income equality. The stage of industrialization (Murdock 
and Schriner 1978) may account for this disparity. 

Policymakers need to be aware that increasing manufacturing employment in agriculture- 
dependent counties may not have an immediate effect of redistributing income. It may, however, 
have the potential to do so over tiie long term. Additionally, our findings suggest that those 
counties developing their manufacturing industries may need to anticipate additional economic 



52 



43 



changes. For example, we found that retirement income in counties may increase with expanding 
manufacturing. This may reflect additional payments to social insurance and pension programs 
for employees retiring in the county. Alternatively, it may imply the loss of the young and more 
mobile residents of the community that inflates the number of elderly per capita. Finally, an 
increase in unemployment payments may be associated with rising manufacturing employment. 
This may result firom the selective nature of employment within the manufacturing industry as 
illustrated by the high proportion of employed women. 

Female Labor Force 

Although the proportion of women in the labor force contributes to manufacturing 
employment (beta =.30), the variable did not play a major role in determining the distribution 
of income until 1980. In 1980, the proportion of women in the labor force was a significant 
predictor of the Gini-ratio (beta=-.15). In addition, the proportion of women in the labor force 
in 1980 made a strong contribution to manufacturing employment (beta =.49) (Figure 7). 

In all three years, the proportion of women in the labor force was positively related to both 
manufacturing and services employment (Appendix Tables B.2, B.3 and B.4). As the proportion 
of the labor force composed of women grew, so too did the number of manufacturing and service 
employees as a percentage of all employees. The proportion of women in the labor force was not 
significantly related to the Gini-ratio until 1980. This may suggest that women's pay schedules 
did little to improve the relative distribution of income in the region. Also, it probably reflects 
the sharp increase of women entering the labor force in the 1970s. On average, 25 percent and 
32 percent of the labor force was composed of women in 1960 and 1970, respectively. That 
number reached 37 percent in 1980. 

The proportion of women workers is expected to continue growing throughout the 1980s. 
Although gains will likely be made in the proportion of women employed in a variety of 
traditionally male-dominated profi^ions, women will continue to provide the bulk of employees 
in the low-wage manufacturing and service sectors. Policymakers need to consider means to 
improve women's employment opportunities as well as pay schedules to have a significant impact 
on the distribution of wage income in rural counties. 

Commerdal Farms 

Large, commercial farms as a proportion of all farms in a county was not a significant 
predictor of the Gini-ratio in either 1960 or 1970, but it was in 1980. The proportion of 
commercial farms was indirectly related to income inequality in all three of the years analyzed 



ERIC ^ 



through a curious association with education levels. Counties with a higher percentage of 
commercial farms had a higher proportion of high school educated adults in 1960 (beta=.21), 
1970 (beta=.21) and 1980 (beta=.24). 

Although this association is difficult to explain, policymakers should consider its 
ramifications. For example, it may imply that counties with large commercial farms may also 
have a significant pool of unemployed or underemployed workers. In particular, women are often 
available for off-farm employment. This is an important human resource that should be tapped. 

According to Schultz's (1951; 1953) urban-industrial impact hypothesis, farms located near 
cities will be more profitable than those located farther away from cities. Higher profitability 
results from the farmers' need to mechanize in order to compete with the city for labor and land. 
Because counties with no urban centers are more likely to be agriculture-dependent, it is 
anticipated that farms located in these counties would be relatively less profitable than 
nonagriculture-dependent counties. 

AlUiough the total number of farms in the U.S. has declined since the mid- 1930s, differences 
in rates of change are evident in size of farms. The number of farms with annual gross cash 
incomes of less than $10,000 fell from 3,126,000 in 1960 to 1,239,000 in 1980, a drop of 60 
percent. However, the number of farms with annual gross cash incomes of $40,000 and over 
grew from 113,000 in 1960 to 625,000 in 1980, an increase of more than 450 percent. While 
the number of farms with income of $40,000 and over comprise only 2.9 percent of all farms 
in 1960, they comprise nearly 26 percent of all farms in 1980. On the other hand, farms with 
incomes of less than $10,000 fell from 78.9 percent of all farms in 1960 to 50.9 percent of all 
farms in 1980 (Figure 9; Appendix Table B.6). 

Large farms have been found to receive a disproportionately larger share of government 
commodity program payments (Cochrane 1986; Reinsel etal. 1987). In 1980, commercial farms 
were 22.9 percent of the nation's farms, brought in 82.9 percent of the nation's farm income, 
and accepted 73 percent of government payments designated for tiie nation's farms (Figure 10; 
Appendix Table B.6). Proponents of farm program i>ayments point to tiie benefit of Uiese 
payments to all residents of a community or trade area as the result of a multiplier effect, which 
occurs as money injected into a local economy is recirculated through buying and selling goods 
and services. 

However, our data show tiiat counties receiving larger government farm payments have less 
equitable income distributions. (Table 2 indicates the positive correlation coefficient between the 
1980 Gini-ratio and farm program transfer payments is .294.) While Uiis finding may reflect the 



r.4 



45 



relationship of larger farm payments going to larger farms (the correlation coefficient between 
the proportion of large, commercial farms in a county and the 1980 farm program transfer 
payments was .202), it may also reflect the disparity of county-aggregated government farm 
payments across the North Central region. Counties dominated by ranching receive a relatively 
small amount of farm payments compared with counties that are dominated by feed-grain or 
wheat farms. 

Medium- and large-sized farms add to a county's total income, but add less to its population 
base than do smaller farms. Small farms are unable to adopt size-dependent practices and their 
net incomes are small. Large farms, on the other hand, are able to realize economies of size and 
enjoy larger net incomes as a result of their higher volume (Cook and Knutson 1987). 

As indicated by the inverse relationship between the proportion of large, commercial farms 
^ and the 1980 Gini-ratio, counties with a greater proportion of commercial farms had a more 
equal distribution of income in 1980. Commercial-sized farms add to a county's total income, 
but add less to its population base than do smaller farms. In counties with smaller proportions 
of commercial farms, a wider range of farm sizes exist; there is greater heterogeneity. Income 
distribution may have less to do with the scale of agriculture than it does with homogeneity of 
farm size. The more homogeneous a county's farms are, the more equally distributed their 
incomes may be. 

Policymakers need to be aware that policies related to agriculture may have an impact on 
county-level income distribution. The more their policies equalize the income among farmers, 
the more equal the county's distribution of income as a whole will be. One suggestion for 
equalizing farm incomes has been to eliminate direct government payments and subsidies to 
large, commercial farm operations (Office of Technology Assessment 1986). 

Seirices Labor Force 

The proportion of th^ labor force employed in services was not a significant predictor of 
income inequality for any of the thice years. This is not surprising, considering the relatively low 
wages paid to employees in this sector of a local economy. If anything, this sector may help 
maintain the social and economic structures that currently exist. Policymakers and development 
specialists should attempt to enhance service industries in their counties. However, they need to 
be aware that their efforts may not be effective in equalizing the distribution of income, at least 
in the short run. 



rr ft 



ERIC 46 



Expendi tures 
Per Student 



S3 .000 
2,800 
2.6001- 
2,400 
2.2001- 
2.000 - 

1.800 - 

1.600 

1.400 

1,200 

1,000 
800 
600 
400 
200 




1950 1952 1954 1956 195^ lU6 lUt 1964 1966 1968 1*970 1972 1974 1976 1978 1980 1982 



Figure 8. Expenditures per student by level of school (in 1967 constant dollars), United 
States, 1950-1982. 



47 



Percent 



Farns With 
Of 




$40.000-S99,999 



$10,000-$39,999 



less Than $10,000 



974 1976 1978 1980 1982 



Figure 9. Percent of total gross farm income by value of sales diss fanns, 1960-19S2. 



ERIC 



48 



r.7 




1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 



Figure 10. Percent of direct govemmeiit payments by vahie of sales class farms, 1960-15^. 

o f^8 49 

ERJC 



POLICIES AS DETERMINANTS 
OF INCOME DISTRIBUTION 

It is hypothesized that social policies serve to redistribute income both directly and through 
their impact on local structural variables. 

Populatioa Chan^ 

It was expected that population change in a county would affect the county's endowment 
structure over time. Population change serves as an indicator of the nature of market interactions 
that have occurred or are expected to occur. Population affects income distribution in terms of 
the income categories of those who may migrate into or out of the county. Indirect effects on 
income distribution may be more diverse. 

Population change served as a significant predictor of the Gini-ratio only in 1980 
{beta=-.20). This finding may reflect the positive impact of a population turnaround on rural 
areas. During the decade of the 1920s, more than 80 percent of U.S. nonmetropolit'in counties 
experienced residential growth, a situation unparalleled since the turn of the century. This 
"population turnaround" illustrates an important shift from economic concerns to quality of life 
factors as motivation to migrate. 

In each of the years analyzed, population change was related to other variables that affected 
the Gini-ratio (Figures 5, 6 and 7). Population change was consistently associated with level of 
education. The beta-weights in 1960, 1970 and 1980 were .20, . 19 and . 14, respectively. The 
positive sign indicates that ^ucation levels rise with population increases and fall with population 
declines. Residential growth tends to boost education levels. This finding supports the contention 
that population expansion helps revitalize rural communities. The income-equalizing effects of 
education suggests that newcomers to agriculture-dependent counties aid in redistributing income. 

51 



A positive relationship was found between population change and the proportion of 
manufacturing employment in 1980 (beta=.21). This reflects the ability of new or expanding 
rural manufacturing firms to attract new residents. From this standpoint, community development 
specialists may wish to consider rural manufacturing as a useful option for economic development 
in agriculture-dependent counties. This is especially true given that rural manufacturing enhances 
income equality. However, policymakers need to remain mindful of negative consequences 
associated with rural manufacturing, as discussed earlier. 

In addition to the role it plays in affecting structural variables, population change also affects 
other policy variables. In both 1960 and 1980, population change was inversely related to income 
maintefiance transfer payments (beta=-.33 and beta =-.35, respectively). This implies that 
residential growth is an important stimulus to an area's economy which, in turn, is reflected by 
the lower number of individuals in need of public support. 

Retirement Transfer Payments 

Retirement transfer payments are a major source of income in the economies of many 
counties. We expected that the input of retirement transfers would affect lower income 
individuals, thus decreasing the Gini-ratio. On the other hand, high retirement transfer payments 
may be an indicator of a higher number of elderly and/or eldeirly who have the ability to receive 
higher retirement transfer payments. 

In both 1960 and 1980, retirement transfer payments served as a significant predictor of the 
Gini-ratio (beta=-.15 and beta=-.15, respectively). It should be noted that in 1970 the 
beta-weight of retirement transfer payments with the 1970 Gini-ratio was -.12 (p=,037), only 
slightly below the level chosen to represent important priorities. As a result, the value of 
retirement benefits in bolstering rural economies is quite apparent. The emphasis that community 
developers need to place on the elderly is highlighted by the disproportionately high number of 
seniors who reside in our nation's rural farm communities. For example, in 1986, roughly 11.3 
percent of urban residents were over the age of 64 and slightly more seniors lived in rural 
nonfarm areas. However, 13.8 percent of the rural farm residents were elderly. 

In all three years analyzed, retirement transfer payments were significantly related to level 
of «lucation. The beta-weights of retirement transfer payments with proportion of high school 
graduates in 1960, 1970 and 1980 were .33, .25 and .23, respectively. This relationship is not 
intuitively obvious and raises a key issue community developers may wish to address. This 
positive association may represent higher retirement benefits that more educated elderly receive 
relative to their lesser educated counterparts. This finding suggests that an important economic 
stratification exists among rural elderly. Planners should seriously explore whether there are 



er|c 



pockets of disadvantaged elderly residing in an area because of their potential need for services. 
Some support for this notion is found in the association between retirement benefits and income 
maintenance benefits. 

In both 1960 and 1980, retirement transfer payments were positively related to income 
maintenance transfer payments (beta=.16 and beta=.43, respectively). This implies that more 
public assistance is needed in areas with higher concentrations of elderly. Additionally, we found 
that retirement transfers were inversely related to population change between 1970 and 1980 
(beta=-.28). This may signal the inability of rural areas with high concentrations of elderly to 
retain its younger residents, thus potentially leading to economic stagnation. 

Policymakers need to consider the role retirement programs have on county-level income 
distribution. Such payments are made to those who usually nave a reduced income, thus 
increasing their annual e?jnings. Further, counties with a high proportion of retirement transfer 
payments per capita will most likely have a high proportion of elderly residents (Green 1987). 
This may be beneficial in that it helps stabilize the county's income. On the other hand, it can 
be a problem because retirement benefits are often fixed, even during volatile inflationary 
periods. 

Nevertheless, policymakers must also be aware of additional concerns and issues regarding 
shifting elderiy populations. For example, increases in the number of seniors may dramatically 
increase an a'sa*s need for medical services and health facilities. In addition, the critical 
questions of what rural delivery systems should be implemented or maintained needs to be 
addressed. 

Income Maintenance Transfer Payments 

Income maintenance transfer payments are considered a means to provide support for 
low-income individuals and families. By providing additional fimding at the low end of the 
income scale, an inverse relationship with the Gini-ratio is expected. 

Contrary to expectations, higher levels of income maintenance transfer payments were not 
related tj income equality. In both 1960 and 1980, a positive relationship was found between 
income maintenance transfers and the Gini-ratio (beta=.15 and beta=.20, respectively). The 
beta- weight for 1970 was only .08 (p=.164). 

Income maintenance transfers were not significantly related to any structural variables in 
1960. However, it was inversely related to education level (beta=-.22) in 1970. The negative 
sign indicates that higher levels of income maintenance transfer payments were related to lower 



53 



education levels. In 1980, income maintenance transfers were again inversely related to education 
level (beta--.24) and to the proportion of large, commercial farms in the county (beta=-.17). 

Income maintenance transfers were also related to policy variables. In both 1960 and 1980, 
income maintenance transfers were related to retirement transfers (beta=.22 and beta=.41, 
respectively). In 1960, income maintenance transfers were inversely related to county 
government total expenditures (beta=-.34). 

Policymakers need to be cautious about accepting a causal link between income maintenance 
transfers and income distribution. Due to the cross-sectional rather than longitudinal nature of 
this research project, it is not logical to conclude that increases in income maintenance transfers 
lead to a less equitable distribution of income. However, it can be stated that such transfers are 
logically correlated with income inequality. Thus, in those counties where income inequality was 
the highest, income maintenance transfers were the highest. That such payments were made at 
all reflects the existing unequal distribution of income. 

The incomes of nonfarm families, either white or nonwhif*^ and headed by males under age 
65, follow the movements of aggregate income quite closely. However, the incomes of farm 
families, families headed by women, and those headed by an elderly person are far more isolated 
from economic growth (Anderson 1964; Thurow 1969; Treas 1983). The latter groups of 
families are more likely to be in need of income maintenance. While some policymakers have 
recommended that recipients of income maintenance transfers should be enrolled in work or 
training programs, such strategies have met wu*: limited success (Rein 1982; Congressional 
Budget Office 1987). Problems with these programs include reduction in welfare benefits when 
recipients work, lack of consistent employment opportunities, and lack of employment 
marketability. These are all issues for policymakers to address. 

Total County Govemment Expenditures 

County govemment expenditures include money spent on such items as highways, education, 
health, public welfare and police protection. Expenditures for such items are beneficial to those 
at the top as well as those at the lower end of the income scale. It was hypothesized that counties 
with higher govemment expenditures would have more equitable distributions of income. 

Of all the policy and structuial variables in 1960, govemment expenditures was the strongest 
predictor of the Gini-ratio (beta=-.29). It was not, however, a significant predictor of the Gini- 
ratio in either 1970 or 1980. The beta-weights for county govemment total expenditures in 1970 
and 1980 were .14 (p=.017) and -.03 (p=.554), respectively. Thus, its direct role on 
redistributing income diminished appreciably over the time period in question. 



Er|c 54 



On the other hand, the role of county government total expenditures increased over time as 
a means of affecting structural variables. In 1960, county government total expenditures was 
related only to level of education (beta =.32). In 1970, it was related to both level of education 
(beta=.25) and inversely related to manufacturing employment (beta=-.16). In 1980, county 
government total expenditures was related to three structural variables: level of education 
(beta=.23), manufacturing employment (beta=-.18) and the proportion of large, commercial 
farms (beta=.18). 

In addition, county government total expenditures was related to income maintenance transfer 
payments in both 1960 and 1980 (beta=-.27 and beta--.35, respectively). Two other policy 
variables were affected by county government total expenditures in 1980: retirement transfers 
(beta=.27) and the change in population between 1970 and 1980 (beta=-.19). 

County government expenditures can have an impact on income distribution by either 
providing goods and services to the residents or by paying those who provide the goods and 
services. For example, as a county spends funds on public health programs, the level of health 
in the county would be expected to rise. This, in turn, could affect the amount of work and 
income lost due to illness. Furthermore, maintaining a public health staff and highway 
maintenance crews, for example, could provide additional jobs and income. 

Policymakers need to be aware that county government expenditures may not directly 
redistribute income. Rather, it purchases those structures and services that are related to an 
equitable income distribution or are needed to maintain one. Consistently higher county 
government expenditures are directly related to higher levels of education among the population 
and to lower levels of income maintenance tiansfers, both of which are related to income 
distribution. 

Unemploym^ Transfer Payments 

Per capita unemployment transfer payments were not significantly related to the distribution 
of income in any of the three years analyzed. They were, however, inversely related to level of 
education and positively related to income maintenance transfers in both 1960 and 1980. This 
may suggest that unemployment transfers are not so much a determinant of either of inese 
variables, but rather are a correlational indicator. We might expect that workers displaced 
through layoffs and the like would be more prevalent among lower wage earners with less 
education than among their more educated, salaried counterparts. 



er|c 



n3 



55 



CHAFTER SEVEN 

SUMMARY AND CONCLUSIONS 

The purpose of this research was twofold. First, we sought to determine those structural and 
policy variables related to the distribution of income in agriculture-dependent counties. Second, 
based on an analysis of the variables that affect income distribution, implications were suggested 
for policymakers. 

Overall, social and economic policies influence county structural variables in several ways. 
In turn, these structural endowments and policies were influential in determining the distribution 
of income. A model was developed that included five structural variables (commercial farms, 
education level, manufacturing employment, services employment and women in the labor force) 
and five policy variables (population change, retirement transfers, income maintenance transfers, 
unemployment transfers and county government expenditures) (Figure 11). - ; 

Important differences were found in how county structural endowments and selected social 
and economic policies related to the distribution of income in each census year. In 1960, policy 
variables were most influential in determining the distribution of income. An important transition 
occurred during the 1960s that shifted the influence of structural and policy variables on income 
inequality. In 1970, both structural and policy variables were found ■> be significant in 
explaining county-level variations in Gini-ratios. By 1980, structural variables were slightly more 
important predictors of income distribution than were policy variables. 

These findings pose several noteworthy implications for policymakers, planners and 
development specialists. First, social and economic policies deserve continued attention due to 
their various impacts. For example, retirement benefits were found tr. be significant determinants 
of income distribution in all three periods studied. As the proportion of elderiy continues to rise 
in rural America, greater attention needs to be focused on the impact the elderiy will have on 
the economy of agricultural counties. 



ERIC 



Similarly, the redistribution of income via unemployment benefits or various county 
government expenditures was found to reduce the inequality of income during two of the three 
periods studied. Recent economic pressures in rural areas have severely strained many rural 
governments, hampering their ability to aid in the transition of displaced farmers, former 
business owners and other rural residents. The growing gap between available resources and 
needs may be reflected in an increasing disparity among incomes. This is illustrated by our 
finding that income maintenance payments increased in counties where a less equal distribution 
of income existed. 

Second, the growing importance of residential and county structural endowments on income 
distribution indicates that current economic and demographic changes in rural America may 
create serious economic consequences. For example, the shining residential composition of 
agricultural counties due to out-migration may sharply alter the distribution of income as the 
younger, highly educated residents and their famili^ leave. A lower proportion of educated 
residents intensified the disparity of income in all study periods analyzed. 

Third, structural characteristics of the county also were found to be important determinants 
of income distribution. For example, growth in the manufacturing sector appeared to be effective 
in facilitating income equality in two of the three periods investigated. However, these structural 
changes are not without consequence. Rural manufacturing is not noted for high wages, and 
therefore, this industry may underutilize the skills of residents. 

Finally, the number of commercial farms as a percentage of all farms was also significantly 
related to income distribution during two of the three periods analyzed. Farm legislation resulting 
in farm program payments has been particularly beneficial to operators of large, commercial 
farms. The effect of these programs on local, rural economies merits additional future research. 



»0 



ERIC 58 



CHAPTER EIGHT 

RESEARCH NEEDS 



This ai.alysis of county-level distribution of pei^nal income has suggested some important 
continuing research issues. These issues are classified into two areas: conceptual problems and 
measurement problems. Conceptual problems arise due to the diversity of theories (and 
hypotheses) that have been developed as plausible explanations for observed distributions of 
income and wealth, and changes in those distributions over time. This lack of a unified theory 
of personal distribution of income is both desirable and a reason for concern. It is desirable 
because there are undoubtedly many determinants of income and, therefore, many determinants 
of the distribution of income. These diverse factors add to the range of policy questions that can 
be addressed by research on income distribution effects. This paper has selectively reviewed 
those theories and hypotheses. 

The lack of a unified theory is disconcerting for those who would like unequivocal answers 
about the distributional impacts of policy actions. A second reason for concern relates to the 
empirical models researchers might formulate to evaluate the trade-offs between alternative 
policies. Income distribution models are typically qualitative indicators of impacts. Yet, 
alternative models may produce conflicting assessments of the same set of policy actions ba:ause 
of how determinants are chosen for a particular model. 

The second conceptual problem relates to level of analysis. Previous empirical work has been 
done on the impacts of macroeconomic factors on income distribution, and on the effects of 
microeconomic distributions on distribution of income. This latter approach necessarily expands 
the number and range of factors that need to be considered to include various demographic and 
socioeconomic determinants. The important conceptual research issues here are: (1) the 
recognition that income distribution is a process and, therefore, has other distributions (for which 
data is often lacking) as its determinants; and (2) the need to consider that income distributions 
are the result of several cumulative and dynamic forces. 



59 



Empirical models have a great deal of difficulty, for example, in capturing the effects of 
* changing distributions of population age and education due to migration and social investments, 
and of changes in the distribution of ownership of productive resources (such as land) along with 
its changing use. This study focused on county-level units of observation on the distribution of 
income. Other studies have selected state-level units due to the greater availability of data on 
distributions of explanatory variables. The county level would appear to be an appropriate unit 
for analysis of microdistributional impacts of policy variables, but additional work needs to be 
focused on the development of consistent micro-level models and data. This study illustrates 
some of the potentials (and shortcomings) of our current ability to analyze policy impacts at the 
county level. 

Measurement problems arise in conjunction with conceptualization of the income distribution 
process, but they relate more to empirical estimation than to the question of what determinants 
to consider. The measurement issue can be divided into problems with measuring income, and 
problems with measuring income distribution. This study (and earlier ones) used census money 
income as the income metric. There are several problems with using money income if the intent 
is to capture changes in the economic positions of recipients. CHianges in nonmoney, wealth 
positions are not measured. A second problem with measured money income is that it reflects 
both transitory income variations and the level of long-run (or equilibrium) income expectation 
of recipients. It is this latter income measure that is of greatest concern when evaluating the 
impacts of socioeconomic policies in cross-sectional models. To capture long-run income it is 
necessary to estimate that component directly with the use of an income-generating function 
(process), as demonstrated by Gardner (1969). Unfortunately, data required by such a function 
are not readily available for a county-level analysis. This is an area for future research work. 

The second general problem with measuring income is selection of the appropriate recipient 
unit. The choice is among family units, family and household units, and individuals. Families 
and households are different units due to the category of unrelated individuals, which is included 
in the census household data series. A more fundamental difference occurs when comparing 
income data for family/household units with income data expressed on an individual (per capita) 
basis. Several analysts have rejected use of family and household income data in favor of 
analyzing lime series and panel income data for individuals. The reasons are that analysis of 
individual income eliminates the problem of variations in family size, number of workers 
employed per family, and similar issues. Since census data are for family and household units, 
future research on county-level income distribution needs to include factors such as family size 
and number of workers when formulating an appropriate policy analysis model of long-run 
income distribution. 



ErJc 60 



The problem of measuring income distribution is significant for future empirical research, 
since the size distribution of income can be characterized by the use of several statistical tools. 
A majority of past work (including this study) has used the Gini-ratio of income concentration. 
However, the Gini-ratio interprets all shifts in income distribution as equally important. For 
example, a dollar-amount transfer between income classes above the mean income has the same 
impact on the Gini-ratio as the same dollar-amount transfer between income classes below the 
mean. Other measures (such as the variance of the ' of income) are more sensitive to these 
different transfer effects. 

Second, the Gini-ratio is a single-parameter measure of income distribution. Several analysts 
have recognized the need to characterize the changing shape of the size distribution as well as 
shifts in the position of the distribution, and have imposed analytical distributions (gamma, beta) 
on income data. These parametric approaches to measuring the size distribution of income are 
promising directions for future research at the county level. 

This study and these comments on future research needs suggest the following as a partial 
research agenda for extension of income distribution analysis at the county level: 

1. Further development and refinement of the hypothesis concerning "structural" (resource 
endowment) determinants, and their interaction with selected "policy" (social and economic) 
variables. This development needs to consider the dynamic and cumulative nature of the 
distribution process. 

2. Development of an appropriate methodology for extracting the long-run component of 
income from census money income data for distribution analysis. 

3. Application of parametric approaches to characterizing the microdistribution of long-run 
income with several moments that can be analyzed either individually, or jointly, as 
functions of the identified determinants. 



08 



61 



AFPENDTK A: 

LIST OF COUNTIES IN STUDY 



ILLINOIS (29/102)": Alexander, Bond, Brown, Bureau, Calhoun, Christian, Cumberland, 
DeWitt, Douglas, Edgar, Ford, Hancock, Henderson, Iroquois, Jersey, Livingston, 
Marshall, Mason, Mercer, Moultrie, Piatt, Pike, Pulaski, Schuyler, Scott, Shelby, Stark, 
Warren, Washington. 

INDIANA (12/92): Benton, Carroll, Clinton, Franklin, Newton, Parke, Pulaski, Switzerland, 
Tipton, Union, Warren, Wnite. 

IOWA (54/99): Adair, Adams, Audubon, Benton, Buchanan, Buena Vista, Butler, Cedar, 
Cherokee, Clayton, Crawford, Davis, Delaware, Emmet, Fayette, Franklin, Fremont, 
Greene, Grundy, Guthrie, Hamilton, Hancock, Harrison, Howard, Humboldt, Ida, Jones, 
Keokuk, Kossuth, Louisa, Lyon, Madison, Mahaska, Marshall, Mills, Mitchell, Monona, 
Montgomery, O'Brien, Osceola, Palo Alto, Plymouth, Pocahontas, Ringgold, Sac, Shelby, 
Tama, Taylor, Van Buren, Washington, Wayne, Winneshiek, Worth, Wright. 

KANSAS (39/105): Brown, Clark, Decatur, Doniphan, Finney, Ford, Gove, Grant, Greeley, 
Hamilton, Harper, Haskell, Jewell, Kingman, Kiowa, Lane, Lincoln, Marshall, Meade, 
Mitchell, Morris, Morton, Nemaha, Osborne, Ottawa, Pratt, Republic, Rush, Scott, 
Sheridan, Smith, Stafford, Stanton, Stevens, Thom?.s, Wabaunsee, Washington, Witchita. 

KENTUCKY (33/120): Adair, Bath, Bracken, Breckenridge, Butler, Carlisle, Casey, 
Cumberland, Edmonson, Fleming, Garrard, Green, Hart, Henry, Hickman, Jackson, Larue, 
Lewis, Lincoln, Logan, Metcalfe, Monroe, Nicholas, Owen, Owsley, Robertson, 
Rockcastle, Shelby, Spencer, Todd, Trigg, Trimble, Washington. 



The first number in brackets represents the number of agriculture-dependent counties in the state. The 
second number represents the totai number of counties in the state. 

R9 



MICHIGAN (2/83): Huron, Missaukee. 

MINNESOTA (34/87)'': Big Stone, Chippewa, Cottonwood, Dodge, Fairbault, Fillmore, 
Goodhue, Grant, Houston, Jackson, Kittson, Lac qui Par, Lincohi, Lyon, Marshall, Martin, 
Murray, Meeker, Nobles, Norman, Pipestone, Polk, Pope, Red Lake, Redwood, Renville, 
Rock, Roseau, Sibley, Stevens, Swift, Traverse, Waseca, Watonwan. 

MISSOURI {?.2JU5y: Atchison, Caldwell, Carroll, Chariton, Clark, Clinton, Daviess, De 
Kalb, Gentry, Harrison, Hickory, Holt, Howard, Knox, Lafayette, Lewis, Lincoln, Linn, 
Mercer, Mississippi, Monroe, Nodaway, Ozark, Pike, Putnam, Ralls, Saline, Schuyler, 
Scotland, Shelby, Sullivan, Worth. 

NEBRASKA (64/93)**: Antelope, Banner, Blaine, Boone, Boyd, Brown, Burt, BuUer, Cass, 
Cedar, Chase, Cherry, Cheyenne, Clay, Cuming, Custer, Deuel, Dixon, Douglas, Dundy, 
Fillmore, Franklin, Frontier, Furnas, Garden, Gosper, Grant, Greeley, Hamilton, Harlan. 
Hayes, Hitchcock, Holt, Howard, Johnson, Kearney, Keya Paha, Kimball, Knox, Logan, 
McPherson, Morrill, Nance, Nemaha, Nuckolls, Pawnee, Perkins, Pierce, Polk, Richardson, 
Rock, Saunders, Sheridan, Sherman, Sioux, Stanton, Thayer, Thurston, Valley, Washington, 
Wayne, Webster, Wheeler, York. 

NORTH DAKOTA (43/53): Benson, Billings, Bottineau, Burke, Burleigh, Cavalier, Dickey, 
Divide, Dunn, Eddy, Emmons, Foster, Golden Valley, Grant, Griggs, Hettinger, Kidder, 
La Moure, Logan, McHenry, Mcintosh, McKenzie, McLean, Mercer, Morton, Mountrail, 
Nelson, Oliver, Pembina, Ramsey, Ransom, Renville, Richland, Sargent, Sheridan, Sioux, 
Slope, Steele, Towner, Traill, Walsh, Wells. 

OmO (1/88): Paulding. 



*Cook, Lake and Ramsey counties did not list rural farm data in 1960 and were thus drc^ped as agriculture- 
dependent counties. 

•Missouri county did not list rural farm data in 1960 and 1970 and was thus dropped as a& agriculture- 
dependent county. 

^Artour and Hooker counties did not list rural farm data in 1960 and 1970 and were thus dropped as 
agriculture-dependent counties. 

er|c 64 70 



SOUTH DAKOTA (41/66)^ Aurora, Bon Homme, Brule, Buffalo, Campbell, Charles Mix, 
Clark. Corson, Day, Deuel, Douglas, Edmunds, Faulk, Gregory, Haakon, Hamlin, Hand, 
Hanson, Harding, Hutchinson, Hyde, Jerauld, Kingsbury, Lincoln, Lyman, Marshall, 
McCook, McPherson, Mellette, Miner, Minnehaha, Potter, Roberts, Sanborn, Spink, Sully, 
Todd, Tripp, Turner, Union, Ziebach. 

WISCONSIN (13/72)^: Buffalo, Clark, Crawford, Dunn, Grant, Iowa, Lafayette, Marquette, 
Pepin, Richland, Trempealeau, Vernon, Waushara. 



'Jackson county Wi.s eliminated from the data set even though it was an agriculture-dependent county. It 
combined with Wsshabau^h coimty in the 1970s. 

'Shawano county was eliminated ftom the data set even though it was an agriculture-dependent county. 
Menominee county was created out of Shawno and Oconto counties in the 1960s. 



71 



63 



APPENDIX B 

GEST-RATIOS AND RELATED DATA 



Appendix Table B.l. 


Gini-rsitios for Income Distribution in Agriculture-dependent 
Counties of the Nortb Central Region, I960, 1970 and 1980. 


State 


County 


GinKSO 


GmiTO 


GoiiSO 


Illinois 


Alexander 


0.4094 


0.5651 


A A 4 

0.4631 




Bond 


0.3948 


0.4835 


0.4227 




Brown 


0 4312 


i\ A A^a 

0.4498 


0.4232 




Bureau 


0.4326 


0.3917 


0.3689 




Calhoun 


0.4003 


0.3958 


f\ A 4 A 4 

0.4141 




Chnstian 


0.3946 


0.3775 


0.3796 




Cumberland 


0.3974 


0.3777 


U.3oi7 




T\~ W/ift 
UC Will 




U.^ / JO 






Douglas 


0.3879 


0.3716 


0.3647 




Edgar 


0.3834 


0.3718 


0.4128 




Ford 


0.3827 


0.3722 


0.3658 




Hancock 


0.3833 


0.3691 


0.3875 




Hendersen 


0.3847 


0.3706 


0.3910 




Iroquois 


0.3816 


0.3709 


0.3716 




Jersey 


0.3805 


0.3639 


0.3588 




Livingston 


0.3775 


0.3618 


0.3500 




Marshall 


0.3735 


0.3623 


0.3768 




Mason 


0.3765 


0.3635 


0.3835 




Mercer 


0.3793 


0.3616 


0.3687 




Moulirie 


0.3775 


0.3606 


0.3707 




Piatt 


0.3765 


0.3600 


0.3463 




Pike 


0.3752 


0.3588 


0.4116 



72 



67 



Appendix Table B.1 coiit. 







vluiOU 


\9llll/v 


VjrlXUoU 


ljunois cone. 


rUiaSKl 


U.J / /4 


A riAll 
U. jOJI 






Schuyler 


U.jolo 


0.362o 


0.4069 




oCOtt 


u.j/y / 


A liC/fl 

U#3t>43 


0.4330 




oneiDy 




0.3033 


U.3 /03 




Stark 




0*3o4D 


0.3997 




Warren 


UoolJ 


0.303/ 


0.3o43 




Washington 


u.j/yj 


0.3044 


0.3 /o3 


Indiana 


Benton 


0.47ij 


0.4791 


0.3630 




Carroll 


{jA2fb 


A 10A1 

0.3o9i 


0.3491 




Clinton 


0.3767 


0.3624 


0«3662 




Franklin 


U.372D 


A 1C7Q 

0,337o 


0.3831 




Newton 




A K1 1 


0.33o2 




Parke 


0.38/3 


0.3323 


0.3881 




PuiasKi 


0.3OOO 


0.3334 


0.3921 




Switzerland 


0.3 


0.333 / 


A A 1 QQ 

0.418o 




Tipton 


A " 1 

0 y/jl 


0.3336 


A OiCAO 

0.3698 




Union 


0*3749 


0.3353 


0.4328 




Warren 


03768 


0.3517 


0.3941 




White 


0.3742 


A 1C 1 A 

0.3514 


0.3397 


Iowa 


Adair 


0.301S 


0.49 /o 


A A'XfYJ 

0.430/ 




Adams 


A IQQ^ 

o.3oy / 


0.403 / 


A i4lAQ 

0.430o 




AUGUDOn 




A AAAI 


0.3934 




tjenton 






U.3 / /2 




Buchanan 


A 'IAQ'7 


0.3949 


0.3848 




Buena Vista 


A >4A1^ 

0.403O 


U.39oO 


0.3892 




Butler 


0.3970 


0.3928 


0.3848 




Cedar 


0.4030 


A lAlA 

0.3919 


0.3723 




LneroKee 


0.4034 


A 

U.3o3o 


0.3928 




Clayton 


A 1AAA 

0.3990 


U.38/4 


A /<A'71 
0.40/1 




V»>1 oW Iv/i \i 




0 3844 


0 3856 




Davis 


0.4058 


0.3834 


0.4327 




Delaware 


0.4070 


0.3861 


0.3794 




Emmet 


0.4011 


0.3863 


0.3812 



Appendix Table B.l cont. 



Slate 


Coaiity 


GinidO 


GinTZO 


GiniSO 


Iowa cont. 


Favette 


0.3990 


0.3865 


0.3919 






0 3980 


0 3834 


0 3928 




pr^tnont 


0 4049 


0 3838 


0.4281 






0 4027 


0.3846 


0.4025 




finindv 


0 4007 


0.3828 


0.3620 




Guthrie 


0.4009 


0.3799 


0.4106 




Hamilton 


0.3994 


0.3791 


0.3804 




Hancock 


0.3997 


0.3806 


0.3746 




Harrison 


0.3995 


0.3796 


0.4156 








0 3794 


0 4215 




Hiimhnlfit 
n u 1 1 1 i/vii v L 


0 3973 


0 3797 


0 3904 




Ida 


0 4014 


0.3799 


0.3975 






0 4009 


0 3801 


0.3730 




Keokuk 


0.3997 


0.3793 


0.3923 




Kossuth 


0.3977 


0.3797 


0.3936 




T DU!^ 


0 3984 


0.3791 


0.3739 




T von 


0 3985 


0 3788 


0.4150 






0 3987 


0 3768 


0 4004 




X/fahjl^ka 


0 3977 


0 3798 


0.3886 




Marshall 


0.3958 


0.3785 


0.3626 




Mills 


0 3963 


0 3767 


0.3740 




Mitchell 


0.3955 


0.3768 


0.4222 




Monona 


0.3965 


0.3771 


0.4097 




Montfiomerv 


0.3959 


0.3781 


0.3871 




0*Brien 


0 3947 


0.3773 


0.3992 






0 3971 


0 3774 


0 3843 




Pain Alto 


0 3968 


0 3782 


0.4088 




Plymouth 


0 3959 


0.3769 


0.3949 




Pnrjih on < 


0 3954 


0 3774 


0.4042 




Ringgold 


0.3977 


0.3771 


0.4549 




Sac 


0.3975 


0.3780 


0.3888 




Shelby 


0.3984 


0.3779 


0.3762 




Tama 


0.3961 


0.3758 


0.3847 



ERIC 



74 



69 



Appendix Table B.l cont. 


State 


County 


GiEu60 


GiniTO 


GiniSO 


Iowa cont. 


Taylor 


0.3988 


0.3774 


0.4485 




Van Buren 


0.3978 


0.3775 


0.4507 




Washington 


0.3982 


0.3779 


0.3905 




Wayne 


0.3990 


0.3773 


0.4325 




Winneshiek 


0.3978 


0.3783 


0.4076 




Worth 


0.3990 


0.3791 


0.3792 




Wright 


0.3969 


0.3812 


0.3854 


Kansas 


Brown 


0.4446 


0.5050 


0.4014 




Clark 


0.3639 


0.4626 


0.4833 




I>ecatur 


0.3449 


0.4381 


0.4396 




Doniphan 


0.4108 


0.4198 


0.3783 




Finney 


0.3856 


0.4117 


0.3533 




Ford 


0.3861 


0.3900 


0.3862 




Gove 


0.3876 


0.3895 


0.4909 




Grant 


0.3913 


0.3897 


0.3683 




Gray 


0.3745 


0.3907 


0.4279 




Greeley 


0.3737 


0.3884 


0.4821 




Hamilton 


0.3791 


0.3880 


0.4651 




Harper 


0.3737 


0.3862 


0.4218 




Haskell 


0.3766 


0.3902 


0.4518 




Jewell 


0.3761 


0.3845 


0.4506 




Kingman 


0.3858 


0.3831 


0.4128 




Kiowa 


0.3850 


0.3826 


0.4800 




Lane 


0.3756 


0.3844 


0.4222 




Lincoln 


0.3807 


0.3864 


0.4584 




Marshall 


0.3746 


0.3885 


0.4124 




Meade 


0.3719 


0.3919 


0.3984 




Mitchell 


0.3796 


0.3928 


0.4009 




Morris 


0.3794 


0.3950 


0.4169 




Morton 


0.3716 


0.3902 


0.4653 




Nemaha 


0.3856 


0.3927 


0.4446 




Osborne 


0.3834 


0.3934 


0.3980 




Ottawa 


0.3829 


0.3956 


0.4102 



i 

erIc ™ 



Appendix Table B.l cont. 



State 


Countv 


Gini60 


Gini70 


GiniSO 


Kansas cont. 


Pratt 


0.3771 


0.3915 


0.3880 






0 3828 


0 3931 


0.4278 




XVUoii 


0 3771 


0 3953 


0.4438 




Scott 


0 3770 


0.3958 


0.4123 






0 3775 


0 3897 


0.4721 




Smith 


0 3824 


0 3918 


0.3958 




Stafford 


0.3789 


0.3916 


0.4316 




Stanton 


0 3796 


0.3931 


0.4965 




Stevens 


0 3796 


0.3921 


0.4177 




Thoma^j 


0 3807 


0.3906 


0.4288 




^V2)hi)iin<f^ 


0 3850 


0.3913 


0.3842 




^V^^ 1 110 ton 


0 3856 


0 3912 


0.4421 








0 3918 


0 4805 


IVwillUW-Ajr 




0 5129 


0 5409 


0.4396 




Rath 


0 4374 


0.4310 


0.4270 




Rrarken 


0 4572 


0.4546 


0.4264 




Rreclnnridffe 


0,4017 


0.4376 


0.4335 




Rntler 


0 4050 


0.4255 


0.4283 




Carlisle 

WrOA AA^AW 


0.4281 


0.4197 


0.4411 




Casev 


0.4340 


0.4290 


0.4437 




Pumhfirlanri 


0 4459 


0.4331 


0.4530 




F/imnnvin 

i A i vA A^w & > 


0 4444 


0.4264 


0.3975 




Fleminff 

* AWl A1AAA& 


0.4486 


0.4305 


0.4342 




Garrard 

^^SU A UA %A 


0.4413 


0.4309 


0.4145 




Green 


0.4415 


0.4265 


0.4458 




Hart 


0 4544 


0.4258 


0.4472 




Henrv 

A AWIl A y 


0.4482 


0.4176 


0.4148 




Hickman 


0.4533 


0.4240 


0.4536 




Tflckvtn 


0 4600 


0.4255 


0.4580 




Lame 


0.4541 


0.4281 


0.4156 




Lewis 


0.4566 


0.4264 


0.4250 




Lincoln 


0.4487 


0.4226 


0.4230 




Logan 


0.4590 


0.4278 


0.4000 



71 



Appendix Table B.l cont. 


State 


County 


Gini60 


Gini 70 


Gini 80 


Kentucky cont. 


Metcalfe 


0.4515 


0.4325 


0.4564 




Monroe 


0.4576 


0.4318 


0.4587 




Nicholas 


0.461S 


0.4313 


0.4379 




Owen 


0.4593 


0.4284 


0.4174 




Owsley 


0.4609 


0.4296 


0.4385 




Robertson 


0.4061 


0.4311 


0.4685 




Rockcastle 


0.4606 


0.4311 


0.4499 




Shelby 


0.4625 


0.4329 


0.3893 




Spencer 


0.4618 


0.4317 


0.4189 




Todd 


0.4570 


0.4317 


0.4390 




Trigg 


0.4551 


0.4310 


0.4282 




Trimble 


0.4564 


0.4309 


0.4307 




Washington 


0.4551 


0.4292 


0.4585 


Michigan 


Huron 


0.4552 


0.4312 


0.3900 




Missaukee 


0.4329 


0.4106 


0.3876 


Minnesota 


Big Stone 


0.4201 


0.3978 


0.4015 




Chippewa 


0.3215 


0.5363 


0.3972 




Cottonwood 


0.3356 


0.4058 


0.3991 




Dodge 


0.4U8 


0.4325 


0.3782 




Faribault 


0.4105 


0.4133 


0.3967 




Fillmore 


0.3905 


0.3973 


0.4061 




Goodhue 


0.3981 


0.3994 


0.3735 




Grant 


0.3783 


0.3852 


0.4215 




Houston 


0.3914 


0.3852 


0.3869 




Jackson 


0.3900 


0.3832 


0.3940 




Kittson 


0.3983 


0.3867 


0.4068 




Lac qui Parle 


0.3912 


0.3857 


0.4140 




Lincoln 


0.3913 


0.3844 


0.4568 




Lyon 


0.3915 


0.3847 


0.3956 




Marshall 


0.3924 


0.3843 


0.4133 




Martin 


0.3917 


0.3828 


0.3866 




Meeker 


0.3898 


0.3835 


0.3990 




Murray 


0.3905 


0.3849 


0.4144 



® 72 
ERIC ' 



Appendix Table B.l cont. { 










GiniSO 


iViiniicsoiA wuni* 






0 3830 


0 3979 




iNomiHn 




n 1811 


n 4105 






l/.^OO/ 


0 18S0 


0 4279 




PnlV 
4 OIK 




0 3851 


0 3952 




rope 


n 1807 


n 184S 


0 4219 














Keuwooa 


n 1807 


n 1861 


0 1007 




Ken vine 




n ias7 


n 1867 




KOCK 




n 1871 


0 1019 




KOSSeall 




n 186S 


0 1011 








n :^848 


0 3904 








0 3854 


0 4256 




owiii 




0 3880 


0 4223 








0 3880 


0 4605 




Wosecs 


n 180/\ 


f> 1881 


n 1718 




Watonwan 


o lonn 


n 1868 


n 1077 


Missouri 


Aicnison 


U. 


n 1866 


n 4009 




uaiQweii 




n S916 


0 4477 




I^OiTOii 


n 174fi 


0 4110 


0 4351 








0 4153 


0 4390 






n 4178 


0 4088 


0 4431 




v^iinion 


rt 4717 


0 4056 


0 3767 








0 4036 


0 4357 






0 4101 


0 4043 


0 4209 




vjeniiy 


n 4i/>7 


0 3035 


0 4292 




nam sun 


n 47^1 


n 1007 


0 4S74 




tiicKory 


n 4701 


0 lOOO 


0 4352 




Unit 

nun 


0 4776 


0 4131 


0 4445 






n 4714 


0 4018 


0 4461 




Knox 


0.4212 


0.4048 


0.4707 




Lafayette 


0.4288 


0.4097 


0.3853 




Lewis 


0.4295 


0.3962 


0.3930 




Lincoln 


0.4273 


0.3944 


0.3815 



78 



73 



Appendix Table B.l cont. 








State 


County 


GiniSO 


Giiii70 


GiniSO 


Missouri cont. 


Linn 


0.4191 


0.3956 


0.4303 




Mercer 


0.4205 


0.3919 


0.4107 




Mississippi 


0.4173 


0.3930 


0.4475 




Monroe 


0.4217 


0.3975 


0.4069 




Nodaway 


0.4261 


0.3962 


0.4137 




Ozark 


0.4197 


0.3974 


0.4345 




Pike 


0.4247 


0.3984 


0.4181 




Putnam 


0.4236 


0.3984 


0.4193 




RaUs 


0.4306 


0.4009 


0.3804 




Saline 


0.4198 


0.3983 


0.3984 




Schuyler 


0.4225 


0.4000 


0.4397 




Scotland 


0.4173 


0.4020 


0.4344 




Shelby 


0.4187 


0.4038 


0.4126 




Sullivan 


0.4216 


0.4014 


0.4649 




Worth 


0.4187 


0.4039 


0.4970 


Nebraska 


Antelope 


0.3842 


0.3939 


0.4388 




Banner 


0.3771 


0.5579 


0.3800 




Blaine 


0.3539 


0.5630 


0.4110 




Boone 


0.3176 


0.4906 


0.4406 




Boyd 


0.4053 


0.4667 


0.4185 




Brown 


0.3777 


0.4709 


0.4381 




Burt 


0.4403 


0.4290 


0.4006 




Butler 


0.3458 


0.4442 


0.4006 




Cass 


0.4066 


0.4241 


0.3444 




Cedar 


0.3894 


0.4399 


0.4230 




Chase 


0.3933 


0.4061 


0.4096 




Cherry 


0.3864 


0.4138 


0.3912 




Cheyenne 


0.3864 


0.4105 


0.3970 




Clay 


0.3901 


0.4021 


0.3873 




Cuming 


0,3916 


0.3942 


0.3834 




Custer 


0.3849 


0.4055 


0.4154 




Deuel 


0.3936 


0.3964 


0.3823 




Dixon 


0.3925 


0.3997 


0.4163 



ERIC 

5 



Appendix Table B.l cont. 










VrUUillj 


Glni60 


GiniTO 


GiniSO ' 




LAI ug lad 


0.3913 


0.4021 


0.3662 




L/unoy 


0.3888 


0.3958 


0.4197 




C « 1 1 tnt\^€^ 
nililllV/iw 


0.3640 


0.3604 


0.3810 




FlailAilll 


0.3629 


0.3610 


0.4145 




Fiunocr 


0.3639 


0.3606 


0.4057 






0.3667 


0.3627 


0.4131 






0.3649 


0.3627 


0.3838 




oos[)er 


0.3678 


0.3625 


0.3961 






0.3670 


0.3639 


0.3858 






0.367: 


0.3645 


0.4503 




riaiiiiiion 


0.3666 


0.3646 


0.3565 






0.3680 


0.3658 


0.4210 




riayes 


0.3686 


0.3663 


0.4136 




rillCiiCOCK 


0.3689 


0.3665 


0.3905 




null 


0.3678 


0.3669 


0.4033 




riowaru 


0.3686 


0.3681 


0.3870 




jonnson 


0.3714 


0.3681 


0.4105 






0.3739 


0.3681 


0.3714 




fwcya Italia 


0.3741 


0.3687 


0.4275 






0.3745 


0.3696 


0.3770 






0.3753 


0.3699 


0.4251 






0.3724 


0.3686 


0.4219 




ivicr^ncrsuii 


0.3748 


0.3712 


0.4058 




iviurnu 


0.3741 


0.3717 


0.4229 






0.3745 


0.3720 


0.4117 




rNcmalla 


0.3743 


0.3706 


0.3996 




l\UCiv01l9 


0.3756 


0.3710 


0.3833 




r^awncc 


0.3756 


0.3726 


0.4201 




rcriuna 


0.3770 


0.3718 


0.4018 




Pierce 


0.3764 


0.3715 


0.3840 




Polk 


0.3780 


0.3719 


0.3920 




Richardson 


0.3796 


0.3732 


0.4105 




Rock 


0.3787 


0.3727 


0.3880 



so 



75 



Appendix Table B.l cont. 



State 


County 




OIIU/U 


(jrlnloU 


iNeDiESKB cont. 


oaunaers 


V.J f to 


U.J f£y 






Snendan 


0.3794 


0.3735 


0.4204 




Sherman 


0.3792 


0.3739 


A ^ACI 

0.3953 




Sioux 


0.3799 


0.3739 


0.3948 




Stanton 


0.3812 


0.3753 


0.3777 




Thayer 


0.3817 


0.3754 


A ^AAI 

0.3907 




Thurston 


A <^0 1 1 

0.3811 


0.3756 


A il AO^ 

0.4087 




Valley 


0.3814 


0.3754 


f\ A ^ CA 

0.4150 




Washington 


A '^O 1 A 

0.3814 


0.3754 


0.3533 




Wayne 


0.3827 


0.37OO 


A OA 1 C 

0.3915 




WcDSICi 




u.j/jo 






wneeier 




U.J /OU 


U« J/0/ 




York 


U.3ol9 


U.3/Oi 


U.3/40 


Nortii Dakota 


Benson 


U.41 /U 


U.4U30 


A '^QOA 

u.j9yu 




Bilhngs 


A 4 f CA 

0.4559 


A AC%00 

0.4988 


A CAA^ 

0.5442 




Bottineau 


0.4778 


0.5216 


0.3971 




Burke 


A ^ A AQ 

0.3448 


0.4683 


0.3964 




Burleigh 


A j| CA 

0.3459 


A j4 Ai4 

0.4046 


A ^C^A 

0.3534 




Cavalier 


0.3o33 


0.3931 


A >1A>40 

0.4048 




Dickey 


A IQ'^I 

U.3o23 


A 10^*7 

0.3o3 / 


A AAtXl 




Divide 


A ^OAC 

0.3895 


A lAlO 

0.3938 


0.4253 




Dunn 


0,3 /9o 


A 1A'7Q 

0.39 /o 


A AOAQ 




body 


A lOC^ 

0.3oj2 


0.3955 


0.4t)09 




Emmons 


A 'ion 
0.3827 


A OOiCA 

0.3869 


A ;f vf A£ 

0.4406 




Foster 


0.3867 


A /I A^l 

0.4071 


0.4329 




Golden Valley 


0.3877 


A il AA^ 

0.4006 


i\ C A Af\ 

0^5440 




A 

Orant 


A OOA£ 

0.3895 


A >f A/(A 

0.4040 


A il 1 OA 

0.4189 




Griggs 


A IQAA 


A ACQ 


n X7<Q 




Hettinger 


0.3o44 


U.4U10 


A A^CQ 




AJUUCi 




v. J70 i 






La Moure 


0.3842 


0.4071 


0.3830 




Logan 


0.3784 


0.4056 


0.4500 




McHenry 


0.3888 


0.4083 


0.4496 



76 



81 



Appendix Table B.l cont. 












Glni60 


GlniTO 


GiniSO 


INOrui UaKOia voni. 


jVlWilliUdn 


0.3901 


0.4088 


0.4460 




MCxvenzie 


0.3920 


0.4160 


0.4234 






0.3924 


0.4112 


0.3952 




mercer 


0.3852 


0.4068 


0.3928 




Moiton 


0.3915 


0.4105 


0.3753 




Mountrail 


0 3919 


0.4015 


0.4075 






0.3814 


0.4019 


0.4248 




Oliver 


0.3874 


0.4021 


0.4805 




remDina 


0 3904 


0.4025 


0.3889 




PiercL 


0.3852 


0.4010 


0.4577 




K&iusey 


0.3871 


0.3992 


0.3879 




Koiisoni 


0.3892 


0.4035 


0.4290 




Kenviiie 


0.3862 


0.4003 


0.4201 




Kicnianu 


0.3791 


0.3986 


0.3888 




aargeni 


0.3834 


0.4002 


0.3924 




t« A MM >4 n 

anenoan 


0.3832 


0.3979 


0.4965 




oIOUX 


0 3856 


0.3991 


0.5556 




Mope 


0.3852 


0.3973 


0.5630 




oieeie 


0.3850 


0.3976 


0.4932 




i owner 


0.3875 


0.3966 


0.4930 




1 roiii 


0.3850 


0.4015 


0.3940 






0.3818 


0.3969 


0.4059 






0.3854 


0.3976 


0.4259 




JraUluing 


0.3826 


0.3766 


0.3368 




Aurora 


0.3827 


0.3761 


0.3828 




tson Honinie 


0.4143 


0.3916 


0.4292 




Druie 


0.4521 


0.7483 


0.3737 




DUIIaiU 


0.4152 


0.4634 


0.3250 




uanipDeu 


0.4120 


0.4519 


0.5549 




Charles Mix 


0.4073 


0.4752 


0.4170 




Clark 


0.4146 


0.4751 


0.4358 




Carson 


0.4353 


0.4924 


0.5070 




Day 


0.4257 


0.4548 


0.4065 



ERJ.C 



77 



Appeimix Table B.l cont. 


State 


County 


Gini60 


Gini70 


GiniSO 


South Dakota cont. 


I>euel 


0.4487 


0.4553 


0.4411 




Douglas 


0.4121 


0.4408 


0.3665 




Edmonds 


0.4105 


0.4518 


0.4056 




Faulk 


0.4050 


0.4579 


0.4978 




Gregory 


0.4245 


0.4413 


0.4831 




Haakon 


0.4033 


0.4515 


0.4540 




Hamlin 


0.4146 


0.4463 


0.4348 




Hand 


0.4284 


0.4428 


0.4458 




Hansor 


0.4228 


0.4307 


0.5147 




Harding 


0.4308 


0.4409 


0.5547 




Hutchinson 


0.4238 


0.4427 


0.4394 




Hyde 


0.4126 


0.4445 


0.5021 




Jerauld 


0.4271 


0.4485 


0.5031 




Kingsbury 


0.4252 


0.4487 


0.4038 




Lincoln 


0.4224 


0.4461 


0.4012 




Lyman 


0.4222 


0.4376 


0.4995 




McCook 


0 4253 


0.4356 


0.3924 




Mcpherson 


0.4252 


0.4316 


0.3996 




Marshall 


0.4160 


0.4317 


0.4321 




Mellette 


0.4179 


0.4300 


0.6534 




Miner 


0.4223 


0.4325 


0.3985 




Minnehaha 


0.4182 


0.4349 


0.3586 




Potter 


0.4189 


0.4z74 


0.4725 




Roberts 


0.4130 


0.4018 


0.4220 




Sanborn 


0.4111 


0.4027 


0.5129 




Spink 


0.4074 


0.4018 


0.4478 




Sully 


0.4099 


0.4039 


0.5631 




Todd 


0.4110 


0.4001 


0.4308 




Tripp 


0.4096 


0.4023 


0.4431 




Turner 


0.4148 


0.4050 


0.4260 




Union 


0.4132 


0.4045 


0.3910 




Ziebach 


0.4118 


0.4067 


0.6179 


Wisconsin 


Buffalo 


0.4095 


0.4060 


0.4122 



83 

^ 78 

ERIC 



Appjndix Table B.l conU 












Giii!6d 


GiniTO 


GiniSO 




Clark 


0.4080 


0.4055 


0.4116 




i^iaWiuru 


0.3747 


0.4486 


0.4072 




Id/UXill 


0.3781 


0.4025 


0.3829 




VJicUIl 


0.3962 


0.3999 


0.3885 




lOWa 


0.3929 


0.3820 


0.3845 






0.3956 


0.3864 


0.3773 






0.3981 


0.3820 


0.4213 






0.3963 


0.3796 


0.3719 




Richland 


0.4032 


0.3820 


0.3960 




Trempealeau 


0.3945 


0.3861 


0.3876 




Vernon 


0.3941 


0.3830 


0.4133 




Waushara 


0.3979 


0.3847 


0.3970 



o 

ERIC 



AppOMfix Table Condatioa Coffiideiit Matrix for lac^ 

Fktdiciiir Varialiks, North Cealral Regiob, 




GINI60 


PHS60 


LGFARM 


PMANEMPo 


PSEREMP6 


GINI60 






-.320*** 


4 AAA 


142** 


PHSoO 






i4 gg AAA 


< AA 
-.139** 


.321*** 


LGFARM 








-.050 


.112 


PMANEMPo 










.075 


PSEREMPo 












PW0MLP6 












PCPOP56 












PCRETIRe 












PCMAIN6 












PCUNEM6 












PCGOVX6 













♦ ps .05 N = 397 
ps .01 
ps .001 



Apfiemfix Table BJ. Comlatioa CodTidcnft Matrix for Incnne Di^^ 

IVedktor Variables, North Ccatnd Rcgioo, 197t. 





GIN170 


PHS70 


LGFARM 


PMANEMP7 


PSEREMP7 


GINI70 




-.310*** 


-.257*** 


-.155** 


-.124** 


PHS70 






,456*** 


-.247*** 


.156** 


LGFARM 








-.096 


,069 


PMANEMP7 










.100* 


PSEREMP7 












PW0MLF7 












PCPOP67 












PCREnR7 












PCMAIN7 












PCUNEM7 












PCG0VX7 













♦ ps ,05 N = 397 
♦* ps ,01 
*♦♦ ps .001 



ERIC 



80 



AppCBiSx Table BwZ coaL 










PWOMLF6 


PCl»OPS6 


PCRETIRe 


PCMAIN6 


PCUNEM6 


PCGOVX6 


-.048 


-.101* 


-.162*** 


.319*** 


.142** 


-.521*** 


.217*** 


.310*** 


,251*** 


-.361*** 


-.323*** 


.558*** 


.067 


.199*** 


-.042 


-.385*** 


-, 164*** 


.324*** 


.409*** 


.336*** 


.298*** 


.047 


.460** 


-.211*** 


.438*** 


.411*** 


.080 


-.085 


.069 


,183*** 




.267*** 


.381*** 


.081 


.203*** 


.027 




— 


-.079 


-.375*** 


.079 


.175*** 








.208*** 


.158** 


.039 








- 


.219** 


-.354*** 












-.138** 















Appendix Table B3 cooi. 


PWOMLF7 


PCPOP67 


PCREnR7 


PCMAIN7 


PCUNEM7 


PCG0VX7 


-.180*** 


-.151** 


-.157** 


.188*** 


-.056 


-.257*** 


.152** 


.044 


.261*** 


-.471*** 


-.436*** 


.547*** 


.059 


.070 


-.046 


-.334*** 


-.204*** 




.490*** 


.506*** 


-.020 


.184*** 


.532*** 


-.267*** 


.459*** 


.110 


.250 


.127** 


.096* 


.033 




.399*** 


.273*** 


.165*** 


.271*** 


.056 






-.288*** 


-.032 


.269*** 


-.060 








.061 


-.004 


.143** 










.380*** 


-.381*** 












-.251*** 















88 



81 



Appeadix Table B.4. Comtation Cocffioeat Mktrix for Idoqsk 




GIN180 


PHSSO 


LGFARM 


PMANEMP8 


PSEREMP8 


GINI80 






-•279*** 


AAA 

-.363*** 


-.054 


PHS80 






,432*** 


-.249*** 


K ^ AAA 

•214*** 


LurAKM 










.U/9 


I'M AIN CM ro 












KdCIvCMr^o 
























PCPOP78 












PCRETIRS 












PCMAIN8 












PCUNEM8 












PCGOVX8 













* ps .05 N =» 397 
*♦ ps .01 
ps .001 



87 



ApiMndix Tabte B.4 coat 










PWOMLF8 


PCPOP78 


PCRETIR8 


PCMAIN8 


PCUNEM8 


PCG0VX8 


-.340*** 


-.312*** 


-.007 


.256*** 


-.172*** 


-.057 


.006 


. 174*** 


.198*** 


-.339*** 


..426*** 


.484*** 


.017 


-.113* 


-.082 


-.365*** 


-.184*** 


.324*** 


.463*** 


,5 10*** 


-.177*** 


.029 


.436*** 


-.440*** 


All*** 


-.081 


.223*** 


,122** 


.016 


.210*** 




.302*** 


.101* 


,m*** 


.256*** 


-.191*** 




— 


-.370*** 


-.047 


.390*** 


-.315*** 








.353*** 


-.077 


.241 








— 


.186*** 


-.262*** 












-.23 1*** 















{ 



ERIC 



83 



Appends Table B^. 


Student FnronmcBls, Expeoditiro for FAiratinn, aad Education 
Ripcwitures per Studnit, United States, 1959-19^2. 




Students Enrolled in Public and 
Private Schools 




Public and Private School Expenditures 
(in 1967 Constant Dollars) 


Year 


K-12 


Higher Ed. 


Total 




K-12 


Higher Ed. 


Total 




thousands 






billion dollars 














1982 


44,743 


12,426 


57.169 




41.7 


26.7 


68.4 


1980 


45,949 


12,097 


58.046 




41.4 


25.3 


66.7 


1978 


47,636 


11.259 


58,895 




46.5 


25.3 


71.8 


17 fO 


49,484 


9,731 


59,215 




46.4 


25.0 


71.4 




50,053 


8.518 


58,571 




43.1 


23.2 


66.3 




50,744 


7,800 


58.544 




44.3 


23.3 


67.6 


1970 


51.272 


7.136 


58,408 




39.3 


21.3 


60.6 


1968 


51,174 


6,802 


57,976 




34.5 


18.3 


52.8 


1966 


48,780 


5,526 


54,306 




30.9 


15.6 


46.5 


1964 


46,957 


4,234 


51,191 




26.7 


12.2 


38.9 


1962 


44,547 


3,726 


48.273 




23.0 


9.4 


32.4 


I960 


42,012 


3,216 


45,228 




20.3 


7.6 


27.9 


1958 


38,996 


3,284 


42,280 




18.1 


5.2 


23.3 


1956 


36.106 


2,996 


39,102 




15.5 


4.2 


19.7 


1954 


33,396 


2,515 


35.911 




13.0 


3.5 


16.5 


1952 


30,554 


2,302 


32,856 




10.5 


2.9 


13.4 


1950 


28,660 


2,659 


31,319 




9.2 


3.0 


12.2 



89 



Appendix Table R>5 coat 




Expenditures per Studeat 
(in 1967 Ctnstant Dollars) 




K-12 


Higher Ed. 


Total 












932 


2,149 


1,196 




901 


2,091 


1,144 




976 


2.247 


1,219 




938 


2,569 


1,206 




861 


2.724 


1,113 




873 


2,987 


1,155 




766 


2,985 


1,038 




674 


2,690 


911 




633 


2,823 


856 




569 


2,881 


760 




516 


2,523 


671 




483 


2,363 


617 




464 


1,583 


551 




429 


! 402 


504 




389 


2, 92 


460 




344 


1,260 


408 




321 


1,128 


390 



Appeadb Table B.C. Ntnber off Fuim, Fana Iscooie and Gonnmcdt Fi;^^ 

Saki CXw, VmUd States, 19i»-19S2. 



Fann Sales 


Selected 






Year 






Variables^ 




lyoU 


1962 


1964 




$100,CXX)+ 


Number &nns 


(K) 


23 


29 


32 


43 


Fann income 


($ mil) 


OyUOU 


7,967 


9,014 






Gov*t. pajrments 


($ mil) 


30 


90 


118 


3 JO 


$40.000-$99.999 


Nun^bo' fmos 


(K) 


90 


106 


114 


143 


Fann in^mc 


($ mil} 


5,420 


6,550 


7,108 


V,JOl 




Gov't payments 


($ mil) 


77 


210 


260 




$20,000-$39.(X)0 


Number* ianns 


(K) 


227 


254 


268 


304 


Fann income 


($ mil) 


6,474 


7,450 


7,954 






Gov't, payments 


($ mil) 


111 


309 


412 




$10,000-$19,999 


Number &rms 


(K) 


497 


493 


482 


445 


Farm inccHoe 


($ mil) 


7,389 


7,608 


7,604 






Gov't, payment-' 


($ mil) 


1 fin 

159 


417 


563 




$5,000-$9,999 


Number farms 


(K) 


660 


589 


534 


476 


Farm income 


($ mil) 


5,125 


4,740 


4,359 






Gov't, payments 


($ mil) 


144 


320 


371 




Less than $5,000 


Number farms 


(K) 


2,466 


2,221 


2,027 


l,o4o 


mini uivfjux? 


fS mil) 


4,4S9 


4,157 


3,804 


3,791 






min 

\w al*»«/ 


181 


401 


457 


548 


TOTAL 


Number farms 


(K) 




3,692 


3,457 


7S7 






(S mil) 


34,957 


38,472 


39,843 


47.128 






(% mil) 


702 


1,747 


2,181 


3,277 


$100,000 -f 


Number fiuins 


(%) 


0.6 


0.8 


0.9 


1.3 


Farm income 


/ OtN 
{%) 


I /.J 


20.7 


22.6 






Gov't, payments 


/ fit \ 

{%) 


4.3 


5.2 


5.4 


in 


$40.000-$99,999 


Number farms 


(%) 


2.3 


2.9 


3.3 


4.4 


Farm income 


(%) 




17.0 


17.8 


1Q Q 




Gov't, payments 


(%) 


II.O 


12.0 


11.9 




$10,000-$39,999 


Number farms 


(%) 


18.3 


20.2 


21.7 


23.0 


Farm income 


(%) 


39.7 


39.1 


39.0 


33. V 




Gov't payments 


(%) 


3S.3 


41,6 


A A 1 

44. / 




T tKafl cin nnn 


Itlllljcsi jMriiiaf 


f %) 


78.9 


76.1 


74.1 


71.3 




Farm income 


{%) 


27.5 


23.1 


20.5 


16.6 




Gov't, payments 


(%) 


46.3 


41.3 


38.0 


29.0 


Total^ 


Number farms 


(%) 


100.1 


100.0 


100.0 


100.0 




Farm income 


{%) 


100.0 


99.9 


100.0 


100.0 




Gov't, payments 


(%) 


100.1 


100.1 


100.1 


100.0 



Source: Economic Indicators of the Farm Sector: National Financial Sumary, 1985. NadcMial Economics 

Division, Economic Research Service, U.S. E>epartment of Agriculture. ECIFS 5-2. November 1986. 
Tables 27, 30 and 31. 



• Farm income includes cash receipts, net Commodity Credit Corporation Loans, direct government payments, 
and other farm-related income. 

* Totals may not sum to 100.0 pcrcait because of rounding. 



ErJc 86 




Apfieada Table B.€ ODot 



Year 



1968 


1970 


1972 


1974 


1976 


1978 


1980 


1982 


45 


53 


79 


151 


164 


212 


271 


324 


13,994 


18,402 


26,930 


49,581 


53,977 


71,719 


97,932 


108,401 


399 


530 


759 


149 


229 


1,150 


568 


1,685 


149 


165 


207 


330 


324 


347 


354 


357 


9,900 


11,885 


14,976 


22.401 


23,098 


25,492 


26,072 


25,123 


608 


708 


862 


136 


231 


1,059 


414 


1,065 


306 


302 


305 


330 


308 


292 


281 


267 


9.778 


10,515 


10,706 


10,872 


10,063 


9,753 


9,291 


8,275 


770 


826 


814 


100 


117 


397 


146 


355 


415 


362 


347 


329 


309 


295 


288 


278 


7,032 


6,627 


6,371 


5,530 


5,080 


5,025 


4,903 


4,329 


710 


671 


630 


67 


60 


159 


59 




439 


372 


353 


324 


311 


316 


311 


302 


3.802 


3,505 


3,284 


2,718 


2,581 


2,834 


2,779 


2.486 


422 


400 


373 


42 


43 


l3o 




ITT 


1,717 


1,695 


1,569 


1,331 


1,081 


973 


928 


873 


3,671 


3,834 


3t415 


2,624 


2,369 


2,460 


2,318 


1,958 








IT 








lid 


3.071 


2,949 


2,860 


2,795 


2,497 


2,436 


2,433 


2,401 


48.166 


54,768 


65,682 


93,726 


97,168 


1 17,283 


143,295 


150,570 




1 TIT 












3 492 


1.5 


t.8 


2.8 


5.4 


6.6 


8.7 


11.1 


13.5 


29.0 


33.6 


41.0 


52.9 


55.6 


61.2 


68.3 


72.0 


11.5 


14.3 


19.2 


28.1 


31.2 


38.0 


44.2 


AO 

48.2 


4.8 


5.6 


7.2 


11.8 


13.0 


14.2 


14.6 


14.9 


20.6 


21.7 


22.8 


23.9 


23,8 


21.7 


18.2 


16.7 


17.6 


19.0 


21.8 


25.6 


31.5 


35.0 


32.2 


30.5 

— 


23.5 


22.5 


22.8 


23.6 


24.7 


24.1 


23.4 


22.7 


34.9 


31.3 


26.0 


17.5 


15.6 


12.6 


9.9 


8.4 


42 7 


40 3 


36.4 


31.4 


24.2 


18.4 


15.9 


14.3 


70.2 


70.1 


67.2 


59.2 


55.7 


52.9 


50.9 


48.9 


15.5 


13.4 


10.2 


5.7 


5.1 


4.5 


3.6 


3.0 


28.2 


26.4 


22.6 


14.9 


13.1 


8.7 


7.6 


6.9 


100.0 


100.0 


100.0 


100.0 


100.0 


99.9 


100.0 


100.0 


100.0 


100.0 


lOO.O 


100.0 


100. 1 


100.0 


100.0 


100.1 


100.0 


100.0 


100.0 


100.0 


100.0 


100.1 


99.9 


99.9 



.^2 



87 



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fK9 



95 



North Central Regional Center for Rural Development 



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ERIC 



NORTH CENTRAL REGIONAL CENTER FOR RURAL DEVELOPMENT 

Iowa State University 
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1 f i n Ames, Iowa 50011 

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