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A Conceptualization and Analysis of the

Distributional Impacts of Alternative
Agricultural Credit Policies

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A CONCEPTUALIZATION AND ANALYSIS OF THE DISTRIBUTIONAL
IMPACTS OF ALTERNATIVE AGRICULTURAL CREDIT POLICIES

BY

Glenn Darwin Pederson

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Economics

1980

ABSTRACT

A CONCEPTUALIZATION AND ANALYSIS OF THE DISTRIBUTIONAL
IMPACTS OF ALTERNATIVE AGRICULTURAL CREDIT POLICIES

BY

Glenn Darwin Pederson

Concern over the distributional impacts of agricul-
tural credit has grown in View of; (1) the increased demand
for financial capital on farms in developed countries and
(2) the committment of massive resources to governmentally
sponsored and funded credit programs in developing coun-
tries. This study was made in response to the need for a
more thorough understanding of the distribution of benefits
from credit. It focused on a firm-level analysis of the
financial impacts which arise under various conditions on
loan extension, use, and repayment. Those financial impacts
include changes in current household income, consumption
levels, and growth in operator's net worth.

In the absence of an adequate theoretical foundation
the following three objectives were formulated to provide a
basis for policy evaluation. First, develop a conceptuali-
zation of the process which carries the distributional con-
sequences of credit policies. Second, formulate a theoret-
ical-mathematical model of the relationships between credit
institutions and the farm firm-household. Third, construct
a numerical simulation model to evaluate the financial,
distributional impacts of specific credit policies over

time.

Credit policies, rules, and practices theoretically;
affect the distribution of potential benefits from credit
by establishing rights over various financial returns. In
addition real and potential costs are associated with the
use of credit. The distribution of net benefits from cre-
dit depends upon the ability of firms in different resource
situations to gain access to credit and productively use
it over time. A modified cash flow analysis was performed
for nine firm-household resource situations over a fifteen
year period using a computer simulation model which was
developed for corn grain farmers in southern Michigan.

Numerical results were obtained under different lend-
ing criteria and rules concerning access to and repayment
of credit. Three single criteria for making investment
credit available were contrasted; (l) ability to make the
required down payment out of available liquid assets, (2)
ability to repay out of net reinvestment income, and (3)
ability to bear financial risk as indicated by the borrow-
er's debt/asset position.

No single lending policy provided all farm situations
with the greatest growth and income potential. Large,
well-established farms obtained relativelv greater growth
in income and equity when the net capital ratio was empha-
sized. Small and intermediate sized units captured greater
benefits when income-generating capacity and cash for down

payment were utilized.

Three credit rules were varied within the context of
a basic lending model to evaluate their relative distribu-
tional effects. Small and intermediate sized farms ob-
tained relatively greater benefits in the form of increased
net worth from an extension of the loan repayment period.

A reduction of the down payment percentage requirement led
to qualitatively similar results. Primarily due to model
construction these two credit rule changes were capitalized
into land values. A maximum allowable debt/asset ratio
limitation was most restrictive for small, highly—leveraged
operations as expected.

Consumption behavior modifies firm growth potential
to a significant degree. Household consumption expendi-
tures directly reduce net reinvestment income and the rate
at which the farm business potentially expands.

An implication for lenders and policymakers is that
over time the size distribution of farms may be signifi-
cantly changed by credit policy actions. Conditions for
loan extension and repayment greatly influence borrowers'
benefits. The analysis suggests that time spent negoti-
ating these aspects of the loan agreement has a potentially
large return for the farmer using debt financing for ex-
panSion.

A third implication occurs during periods of rapid
price increases, when rapid cost increases decrease net
income. The greater need for increased cash flow is es-

pecially problematic for small operations. Off-farm

earnings become essential to supplement reduced farm in-
come to meet expanding household requirements, debt-service

committments, and other cash needs.

DEDICATION

To my father, Harvey. His
memory remains a constant
source of personal inspiration
and gratitude

ii

ACKNOWLEDGMENTS

During my graduate study several faculty members have
given encouragement, advice, and direction beyond that needed
to satisfy the requirements of their position. To these men
I owe a debt of gratitude.

The greatest debt of gratitude is due Dr. John R. Brake
who served as my curriculum and thesis advisor. His assis-
tance, counsel, and understanding were invaluable to me and
helped me attain a primary objective —- that of becoming a
better researcher and economist.

A special thanks is due to Dr. Lindon Robison whose in-
sights and criticisms of this manuscript greatly improved its
quality. I am also indebted to the other members of my the-
sis and guidance committees, Dr. James Shaffer, Dr. Stanley
Thompson, and Dr. Norman Obst for their support.

I would like to acknowledge the generous financial sup-
port of the Michigan State University Agricultural Experiment
Station and the Department of Agricultural Economics.

No undertaking that is worthwhile is also easy. In rec-
ognition of that fact I want to thank my wife, Mary, and my
children, Patrick and Karyn, for their understanding during
my graduate work and completion of the thesis. A special
thanks to my mother, Dorothy, whose attitude of perseverance
shaped my character and career.

iii

TABLE OF CONTENTS

CHAPTER I. Introduction

CHAPTER II.

2.1
2.2

IntrOduction O O O O O O I O O O I O 0

Historical Development of the Literature on

Distribution . . . . . . . . . . . . . .

A Selective Review of the Literature

1.1 Research Objectives . . . . . . . . . . . . . .
1.2 Lack of a Theoretical Foundation . . . . . . .
1.3 Research Methodology . . . . . . . . . . . . .
1.4 Analytical Framework . . . . . . . . . . . . .
1.5 Format of the Dissertation . . . . . . . . . .

2.3 Pure Theories of Personal Distribution . . . .
2.3.1 Individual Choice Theory . . . . . . . .
2.3.2 Inheritance Theory . . . . . . . . . . .
2.3 3 Human Capital Theory . . . . . . . . . .

2.3.4 Public Choice Theory . . . . . . .

2.4 Applied Research on Personal Distribution . . .

2.5 Overview of the Literature on Personal
Distribution . . . . . . . . . . . . . . . . .

2.6 Literature on Financial Theory and Credit . . .

page

ooqoxuwp

10

11
l4

14
15
l6
17

18

22
23

2.6.1 Static Approaches . . . . . . . . . . . .

2.6.2 Partially Dynamic Approaches . . . . .

2.6.3 Synthetic Approaches to Modeling Firm
Growth and Expansion . . . . . . . . . . .

2.7 Literature on Credit Policies and Institutions .
2.8 Summary . . . . . . . . . . . . . . . . . . . . .
CHAPTER III. Statement of the Economic Problem

3.1 Introduction . . . . . . . . . . . . . . . . .
3.2 Restatement of the General Problem . . . . . . .

3.2.1 The Problem of Access to Initial Resources
and Information . . . . . . . . . . . . .
3.2.3 The Problem of Cumulative Impacts . . . .

3.3 Financial Impacts of Short Term Credit . . . . .

3.3.1 Assumptions Underlying the Short Run
Model . . . . . . . . . . . . . . . . . .

3.3.2 Limitations and Uses of the Short Run
Model . . . . . . . . . . . . . . . . . .

iv

page

3.4 Cumulative Impacts of Credit . . . . . . . . . . 50

3.4.1 Income Redistribution Effect on Credit . . 51
3.4.2 Asset Redistribution Effect on Credit . . 53

3.5 Additional Comments on the Nature of the
Research Problem . . . . . . . . . . . . . . . . 56
3.6 Summary . . . . . . . . . . . . . . . . . . . . . 59

CHAPTER IV. A Conceptualization of the Distribution

Process
4.1 Introduction . . . . . . . . . . . . . . . . . 62
4.2 A General Framework for Analysis and Design . . . 63
4.3 Outline of the Conceptual Framework . . . . . . . 65

4.3.1 Benefits and Costs of Credit . . . . . . . 66

4.4 The Distribution Process . . . . . . . . . . . . 70

4.4.1 Size and Other Financial Characteristics

of Farms . . . . . . . . . . . . . . . . . 72
4.4.2 Liquidity . . . . . . . . . . . . . . . . 72
4.4.3 Leveragability . . . . . . . . . . . . . . 74
4.4.4 Price-sensitivity . . . . . . . . . . . . 76
4.4.5 Size Distribution of Farms . . . . . . . . 77
4.4.6 Credit Distribution, Asset Distribution,

and Rights Distribution as a Distribution
Process 0 O O O O I O O O O O O C O I O O 78

4.5 Credit Policies and Their Implementation . . . . 81
4.6 Summary . . . . . . . . . . . . . . . . . . . . . 85

CHAPTER V. A Mathematical Model of the Distribution
Impacts of Credit

5.1 Introduction . . . . . . . . . . . . . . . . . 87
5.2 Model Participants and Their Economic Behavior . 88
5.2.1 Farm Firm-Households . . . . . . . . . . . 88
5.2.2 Lending Institution . . . . . . . . . . . 89
5.2.3 Government . . . . . . . . . . . . . . . . 91
5.3 A Systems View of the Representative Farm
Firm-Household . . . . . . . . . . . . . . . . . 92
5.4 Specification of the Theoretical RelatiOnships . 95
5.5 Production and Cost Relationships . . . . . . . . 95
5.6 Investment and Financing Relationships . . . . . 98
5.7 Investments and Value of the Firm . . . . . . . . 103
5.8 Household Consumption Activities . . . . . . . . 106
5.9 Commodity Market . . . . . . . . . . . . . . 108
5.10 Capital Market and Credit Policy Relationships . lll

CHAPTER VI. The Numerical Model

6.1
6.2

6.4

Farm Resource Situations . . . . . . . . . . . .
The Simulation Model . . . . . . . . . . . . . .

6.2.1 Production and Cost of Production . . . .
2 The Price Distribution . . . . . . . . . .
3 Valuation of Land . . . . . . . . . . . .
4 Value of the Firm and Household
Consumption . . . . . . . . . . . . . . .
.5 Asset Acquisition . . . . . . . . . . .

Data Requirements and Sources . . . . . . . . . .

6.3.1 Farm Resource Initializations . . . . .
6.3.2 Structural and Behavioral Parameters . . .
6.3.3 Credit Policy Parameters . . . . . . . . .
6.3.4 Additional Model Parameters . . . . . . .

Strategy for Policy Evaluation . . . . . . . . .

CHAPTER VII. Review and Analysis of the Numerical

7.1

Results
Results from the Basic Model . . . . . . . . . .

.1.1 Cash Flow of the Basic Model . . . . . . .
.1.2 Variations of the Basic Model . . . . . .
.1.3 Firm Growth in the Basic Model . . . . . .
.1.4 The Influence of Rising Land Values on
Firm Growth . . . . . . . . . . . . . . .

Distributional Impacts of Alternative Lending
criteria I O O O O O O O O O O O O O O O O O O 0

7.2.1 Lending According to Value of Assets
Controlled (Model I) . . . . . . . . . . .
7.2.2 Lending On the Basis of Ability to Make
the Down Payment (Model II) . . . . . . .
7.2.3 Lending According to Ability to Generate
Income Sufficient for Repayment (Model
III) . . . . . . . . . . . . . . . . . . .
7.2.4 Equal Weighting of the Repayment and
Risk-bearing Capacity Criteria (Model

IV) . . . . . . . . . . . . . . . . . . .

Sensitivity of Distributional Impacts to Initial
Equity Positions . . . . . . . . . . . . . . . .
Impacts of Alternative Repayment Policies . . . .

7.4.1 Effect of Increasing the Length of Loan
(Model VI) . . . . . . . . . . . . . . . .
7.4.2 Reduction of the Down Payment
Requirement (Model VII) . . . . . . . . .
7.4.3 Effect of Liberalizing the Down Payment
Requirement and Extending the Length of
Loan (Model IX) . . . . . . . . . . . . .

vi'

page

113
116

118
119
120

121
121

122

123
125
127
129

129

132

133
138
140

143

145

145

148

151

154

155
160

161

163

165

7.4.4 Impacts of Raising the Maximum Allowable

Debt/Asset Ratio (Model VIII)

7.4.5 Comparison of the Relative Distributional
Impact of Variations on the Basic Model

7.5 Summary of the Effects of Other Model

Parameters . . . . . . . . . . . .

7.5.1 Effects of Changing the Household Consump-

tion Function (Model X) . .

7.5.2 Effects of Alternative Assumptions About

Investment Behavior and Productive

Opportunities . . . . . . .

7.5.3 Effect of Increases in the Rate of Land
Value Appreciation and Accompanying Changes

0 O O O

in the Level of Prices and Interest Rates.

CHAPTER VIII. Summary and Conclusions

8.1 Review of the Conceptual Approach and Underlying

Assumptions . . . . . . . . . . . .
8.2 Summary of the Numerical Results .
8.3 Conclusions . . . . . . . . . . . .
8.4 Implications and Limitations . . .

8.4.1 Implications for Lenders and
makers . . . . . . . . . . .

8.4.
8.4.
Research . . . . . . . . . .

APPENDIX C O O O O O O O O O O O O O I O

BIBLIOGRAPHY . . . . . . . . . . . . . .

vii

Policy-

2 Some Implications for Borrowers . .
3 Limitations and Areas for Further

page

166

168

173

175

176

179

184
186
192
195

195
196

197
201

213

7.10

LIST OF TABLES

Initial Conditions in Three Michigan Corn
Producer Resources Situations . . . . . . . . . .

Annual Cash Flow of the Basic Model in Selected
Years for 70 Percent Initial Debt/Asset Ratio by
Firm Situation 0 O O O I O O O O O O O O I O O 0

Annual Cash Flow of the Basic Model in Selected
Years for 30 Percent Initial Debt/Asset Ratio by
Farm Situation . . . . . . . . . . . . . . . . .

Model Numbers and Values Assigned to Items in
the Basic Model and Variations of the Basic
MOdel O O I O O O O O O O O O O I O O O O I O O 0

Income, Assets, and Debt Statement by Farm
Situation for Selected Years, Model V . . . . . .

Income, Assets, and Debt Statement by Farm
Situation for Selected Years, Model I . . . . .

Income, Assets, and Debt Statement by Farm
Situation for Selected Years, Model II . . . . .

Income, Assets, and Debt Statement by Farm
Situation for Selected Years, Model III . . . . .

Sensitivity to the Initial Equity Position and
Debt Servicing Obligation of the Firm by Farm

Situation 0 O O O O O O O O O O O O O O O O O O 0

Comparison of Basic Model Results with Alterna-
tive Lending Criteria and Credit Policy

Variables by Farm Situation . . . . . . . . . . .

Comparison of the Basic Model Results with
Results Generated Under Alternative Parameter

Assumptions . . . . . . . . . . . . . . . . . . .

viii

page

124

134

135

139

141

146

149

153

156

170

174

5.2

5.3

LIST OF FIGURES

page

Causal Loop Diagram of the Distribution Process
for Considering Alternative Credit Policies . . . 71

General Block Diagram of Representative Firm
and Firm-Household Financial Structure . . . . . 93

Nonland Cost/bushel Versus Tillable Acres . . . . 101

Theoretical Short Run Marginal Cost and Average
Variable Cost Curves of the Firm ... . . . . . . 101

The Simulation Model . . . . . . . . . . . . . . 117

ix

CHAPTER I

Introduction

The past three decades have marked a period of sub-
stantial structural change in U.S. agriculture. Changes
in financial structure have been of special significance.
To accomodate the process of structural change, financial
institutions and policies were required to adapt to the
changing economic conditions. This process of development
through structural and institutional change has implica-
tions for development strategies in developing countries,
as well as the organization and control of U.S. agricul-
ture in the future. These implications arise in part from
the distributional impacts which either directly or in-
directly flow from deliberate policy choices.

Agricultural policy analysts have found considerable
evidence for this structural change in the decline of farm
numbers, increase in average farm size, increase in spe-
cialization and output of farm operations, substitution
among factors of production, and shifts toward more pur-
chased inputs. All of these factors have contributed to
the need for increased financial capital and credit.

Changes in financial structure have accompanied the
general pattern of structural change. Growth in the phys-
ical size of farms through acquisition of land and durable
assets has changed the size, structure, and liquidity of
farm assets and liabilities. Meanwhile, the percentage of

1

capital financed internally from farm earnings and savings
has gradually declined. As these changes in financial
structure of farms have occurred and as the distribution
of assets has become more skewed, questions about the dis-
tributional impacts of credit have taken on increasing
importance.

The importance of these questions derives from a need
to know more about the process by which credit distribution
leads to other distributional consequences. A study of
alternative credit policies would be useful in providing
that needed understanding.

There are four general policy questions which relate
directly to the nature of such a study. This study will
attempt to provide answers to the following questions,

1. Does credit have important distributional impacts?

2. How do credit policies, rules, and practices
affect the distribution of benefits from credit?

3. How do changes in credit policies, rules, and
practices bring about a redistribution of those
benefits?

4. What credit policy design and implementation con-
siderations are most effective in the redistribu-
tion of benefits from credit?

The first question asks if in fact credit is a deter-

minant of distribution. The second question seeks to
identify the process. The third question asks how the

process works. The fourth question asks how the process

can be most effectively designed and managed at the policy
level.

As an illustration of the kinds of policy choices
being referred to above, the following comparison can be
made. A dichotomy is noted between the traditional lend-
ing criterion which emphasizes the risk-bearing capacity
of the farm (i.e., the net capital ratio) and an alterna-
tive lending criterion which emphasizes the debt-servicing
capacity of the farm (i.e., expected repayment potential).
Under identical conditions, but where the distribution of
assets and productive opportunities of farms systematically
vary, these two credit policies are hypothesized to lead
over time to quite different distributions of credit, and
ultimately to quite different financial impacts.

There is clearly a need to describe and analyze the
benefits of credit and the extent to which the rules and
policies of credit extension and various borrower circum-
stances affect the distribution of benefits among borrowers.

A realistic evaluation of the distributional impacts
of credit policies begins with an identification of the
forms in which these impacts are realized. Both monetary
and nonmonetary impacts are likely to occur. To keep the
study of these impacts analytically tractable the focus
will be upon the monetary or financial impacts. Nonpecu-
niary effects may well be important considerations, but

including them greatly increases the level of analytical

complexity without significantly adding to a better under-
standing of the distributional process.

Two forms of distributional impacts are identified.
First credit is potentially of value in augmenting the
flow of returns to the farm enterprise. The use of credit
provides leverage and leverage acts as a multiplier with
respect to the marginal value product of productive re-
sources. Second, the use of credit has potentially favor-
able impacts by enhancing the stock of assets which can be
controlled. These stock and flow dimensions represent the
two primary means by which credit policies and their dis-
tributional impacts can be linked. The emphasis here is
on the use of credit, although credit held in reserve will

also be considered in the analysis.

1.1 Research Objectives

 

The analysis of the distributional impact of credit
policies, rules, and practices represents an important
part of the evaluation of credit policy implementation and
design. Such an analysis should provide a better under-
standing of the institutional and economic factors involved
in the distributional process. This research is oriented
toward an assessment of alternative credit policies. This
orientation has yielded the following set of objectives:

1. To develop a conceptualization of the distribu-

tional process which carries the distributional

consequences of alternative credit policies.

2. To formulate a theoretical-mathematical model of
the relationships between credit institutions and
the farm firm-household. This model will accomo-
date alternative lending criteria and explicitly
consider the effects of different initial levels
of equity, downpayment requirements, terms of re-
payment, taxes, and consumption requirements.

3. To construct a numerical simulation model consis-
tent with the above theoretical model to evaluate
the financial, distributional impacts of specific

credit policies over time.

1.2 Lack of a Theoretical Foundation

 

An obstacle to the evaluation of the distributional
impact of credit policies is the lack of a unified theory
of the distributional process. Distribution theories have
traditionally been concerned with functional distribution
(as it has been incorporated into contemporary growth
theories). More recent has been the concern with personal
distribution theories, essentially all of which are piece-
meal or partial theories.

The growth models which have been advanced in recent
years are theoretical-mathematical models of growth and,
therefore, should not be considered as theories of growth
nor as theories of distribution. These models are static

equilibrium models with dynamic exogeneous elements and do

not serve as dynamic theories of the distributional pro-
cess (H. Johnson, 1973).

The personal theories of distribution come closer to
being dynamic theories. Most of these theories have been
developed in recognition of the need for endogenizing sev-
eral of the variables which were formerly left exogenous
(Sahota, 1978).

The lack of a theory of financial institutions com-
pounds the problem of evaluating the distributional impact
of credit policies. The need for such a theory becomes
apparent when we consider that credit policies are inter-
preted through various lending institutions and the various
rules and practices they follow in the distribution of
credit and loans.

The lack of a theoretical foundation has not prevented
policy analysts from undertaking analysis of the impacts
of financial constraints and strategies on the financial
and physical growth of the farm. The use of dynamic pro-
gramming and simulation models has provided a number of
useful qualitative insights into the growth process at the

micro-economic level.

1.3 Research Methodology

 

This research is problem-solving in nature. The
theoretical work presented here is required to pass the
tests of coherence (internal consistency) and correspond-

ence (external consistency). These tests require that the

conclusions follow from the assumptions and propositions
advanced, and that the results agree with our experience
in the area of agricultural credit policy and programs. A
simulation model will be constructed to numerically make
the consistency checks. In addition the tests of clarity
and workability are necessary. To achieve clarity we re-
quire that the approach and results be communicable. Al-
though no specific prescriptions are made, the conclusions
which result should pass the test of workability if the

implied actions are taken.

1.4 Analytical Framework

 

An evaluation of agricultural credit policies along
the lines already identified requires a synthetic analyti-
cal approach which is consistent with the financial as-
pects of the distributional process. A modified cash flow
approach to the research problem is both consistent with
that process and essential to an analysis of the antici-
pated financial impacts.

This modified cash flow approach at the farm firm—
household level is comparable to the sources and uses of
funds statement. The sources and uses of funds accounts
for flows to and from the farm business and serves as a
means for going from one balance sheet to a subsequent one.
Both cash withdrawals and injections are considered. In
credit, as an example, new borrowings provide a major new

source of operating and investment funds while the repayment

of debt represents a use of those funds. Other important
uses of funds are cash withdrawals for household consump-
tion and income taxes. This modified cash flow approach
is described in the mathematical model and employed in the

development of a numerical simulation model.

1.5 Format of the Dissertation

 

The next chapter provides a selective historical re-
view of the literature in the areas of distribution theory,
financial theory, and credit policy. This review empha-
sizes those elements of the existing body of theory which
most directly relate to the research problem.

Chapter III is a formal statement of the economic
problem under investigation. The chapter also identifies
the distributional impacts of credit.

The conceptualization of the distribution process is
presented in Chapter IV. This chapter looks at the dis-
tributional process as a working financial system.

A theoretical-mathematical model of the distribution
process is specified in Chapter V. The mathematical model
employs a modified cash flow approach.

In Chapter VI a numerical simulation model consistent
with the mathematical model is constructed and used to
determine the distributional consequences of alternative
credit policy choices.

Chapter VII provides for a review of the basic results

of the simulation model under alternative credit policies,

scenarios about inflation, technological change, and other
exogenous factors.

Chapter VIII presents a brief summary of the primary
research results and conclusions concerning the conceptual

approach.

CHAPTER II

A Selective Review of the Literature

2.1 Introduction

 

The previous chapter identified the need for a unified
theory of the distributional process. The economic litera-
ture contains an expanding number of alternative theories
of distribution. These theoretical contributions have been
partial theories which have not been integrated into a
comprehensive theory of distribution.

Given the proliferation of theories of distribution,
what principles might be employed in the selection and/or
construction of a theoretical approach to distributional
issues?

Distributional theories are judged by their
ability to explain and predict what has occurred,

what is, and what will be. But it is also impor-

tant to know their implications for how the eco-

nomic game should be played. If alternative

specifications do not have different policy im-

plications, the choice between any two alterna—

tives is ultimately not very important. In that

case, no matter which theory is correct, the same

actions and policies will be optimal... Which of

our current policies are based upon incorrect

distributional assumptions and need to be replaced?

How should individual economic actors change their

actions if they believe in alternative theories

of distribution? (Thurow, 1975, p. 182)

The concern which Thurow expresses appears to emphasize a
need to develop theories which make a difference in which

policy choices are made.

10

11

2.2 Historical Development of the Literature on
Distribution

 

The evolution of the literature on distribution has
progressed along two generally distinct paths. The first
and earliest branch of thought was concerned with func-
tional distribution. Functional distribution focuses on
how the economic returns are distributed among the factors
of production. The first political economist to apply
abstract and theoretical analysis to derive basic principles
of functional distribution was David Ricardo. The theoreti-
cal work of Ricardo, Adam Smith, and J.S. Mill provided for
the original separation of distribution theory from value
theory. This separation was not accomplished in Marxian
theory.

The classical version of functional distribution was
greatly altered by the emergence of marginal productivity
theory as developed by Alfred Marshall and other neoclas-
sical theorists. Marginal productivity theorists contended
that the theory of factor prices and the theory of func-
tional distribution comprise only one aspect of the gen-
eral economic theory of pricing. This theoretical devel-
opment represented a major shift away from the classical
approach which regarded distributional issues as problems
of class distribution. The break from the classical per-
spective was not complete, however, since the marginal
productivity theory retained many of the institutional

ideas of the classical thedrists.

12

As already stated, the marginal productivity theory
has merged with modern growth theories. These growth
theories were built on the classical theoretical principle
that if there are diminishing returns, due to the quantity
of one of the factors being fixed relative to the others,
there is a limit to the increase in output which will be
realized from increasing the relative amounts of the vari-
able factors.

The emergence of the marginal productivity theory of
functional distribution gave rise to a skepticism about
the need for a theory of distribution distinct from the
general theory of prices. Johnson (1973) made the follow-
ing justifications for a separate distribution theory.

First, a literature exists containing many
special problems and pieces of analysis. Second,

from a historical and cultural point of view, a

folklore of distribution theory has grown up,

particularly associated with the Marxian tradi-

tion, with which economists frequently have to

deal. Thind,economistshave recently become

interested in the problems of economic growth

and capital accumulation - the '1ocus classicus'

of theoretical work on these problems has been

distribution theory. Fourth, economists have

recently become concerned with problems of in-
equality of personal income distribution and

poverty. (p- 3)

The second and most recent branch of thought has been
in the area of personal distribution. Personal distribu-
tion refers to the distribution of, say, income among per-
sons, families, or households. Modern economic literature
is replete with writings in this particular area. This

proliferation may be explained in two ways. First, not

all of the recent work has been theoretical in nature. A

13

great deal of research has been undertaken to empirically
investigate problems of personal distribution (or, maldis-
tribution). Second, the development of theories of per-
sonal distribution have been partial theories in recogni-
tion of the fact that personal distribution problems are
complex and defy simple explanation.

In the classical approach to distribution there existed
an equivalence of functional and personal distribution
since the ownership of factors of production (land, labor,
capital) correspond closely with the three general social
classes of the time (landlords, laborers, and capitalists).
The distribution of income shares among these three classes
led essentially to the same distributional pattern as might
be obtained if income had been allocated to the factors
themselves.

The high degree of correlation between factors and
ownership by class which held for classical theory is not
found in the modern economy. As a partial consequence,
there has been a fragmentation of the theoretical argu-
ments which have been formulated to explain and analyze
the secular changes which have occurred in personal dis—
tribution over time.

The following two sections present a selective review
of the literature on pure theories of personal distribution

and applied work on problems of personal distribution.

14

2.3 Pure Theories of Personal Distribution

 

Existing theories of personal distribution can be
classified in a number of useful ways. For our purposes
they can be arranged into two basic classes as suggested
by Sahota (1978); (l) theories which are based on the be-
lief that people and societies can consciously determine
the relative income positions of their members and (2)
theories that believe that these positions are preordained.
This section will concentrate on the former class of pure
theories. Four theories will be reviewed in roughly chron-

ological order.

2.3.1 Individual Choice Theory

The individual choice theory of distribution as
written by Milton Friedman (1953) focuses on individual
risk preferences and chance opportunities. Individual
choice affects personal distribution in two ways. First,
there may be differences in the profiles of cash income
over time, or the relative combinations of cash and non-
cash advantages which compensate individuals for selecting
one action choice over another. Second, the possible al-
ternatives which an individual may consider differ in the
probability distribution of income they promise. The
theory applies under certainty or uncertainty.

Friedman's view of the personal distribution process

is consistent with the Bernoullian expected utility

15

hypothesis. In this framework the individual makes choices
involving risk so as to maximize his expected utility of
income. It is Friedman's contention that the personal
distribution is largely produced by individuals who seek

to optimize their set of risk-return preferences as speci-

fied by their utility functions.

2.3.2 Inheritance Theory

 

The inheritance theory of distribution concentrates
on the importance of prOperty and the intergenerational
transfer of wealth in contrast with other theories which
look primarily at earned incomes. James Meade (1964), a
major proponent of the inheritance theory approach, con-
tends that two factors contribute to greater skewness in
property and incomes from prOperty, than found in labor
earnings. First, the rich are better able to save a high
proportion of their current income. Second, they are able
to obtain a high rate of return on the property they own.

Mead's inheritance theory identifies "fortunes" or
endowments as genetic and social factors, and inherited
property itself. The observed pattern of incomes, savings,
and accumulation of property result from an interaction of
these endowments and feedback on these endowments. Feed-
back provides for both higher rates of return on larger
property and higher propensities to save out of current

income. The emphasis of Meade's work lies in the link

16

between savings behavior and the rate of growth of income
from property.

The inheritance mechanism remains an important compo-
nent of the long run distribution process. More theoreti-
cal and applied work has potential for contributing greatly

to an integrated theory of personal distribution.

2.3.3 Human Capital Theory

The most widely recognized theory of personal distri-
bution has evolved from the placement of human skill dis-
tribution within the capital theory framework - the human
capital theory of distribution. The human capital theory
is not particularly new, the "old vintage" theory of human
capital originated with Adam Smith in the treatise on The

Wealth of Nations (1892). The "modern vintage“ of the

 

human capital theory is largely credited to T.W. Schultz.
Under Schultz (1963, 1971) and Zvi Griliches (1974)
the human capital framework was used to explore the sources
of increased agricultural productivity and growth. Gary
Becker (1962, 1967) and others developed further the gen-
eral theory and earnings distribution theory of human cap-
ital. The modern theory of human capital is characterized
by the principle that individuals make intertemporal opti—
mizing decisions in investing in education. The develop—
ment of the theory served to broaden capital theory to in—

clude human investments. Becker (1967) attempted to extend

17

the theory as an analytical approach which could consider

both earnings and property income.

2.3.4 Public Choice Theory

The public choice theory of personal distribution
represents a fourth theoretical framework. Actually, this
is not so much a theory of distribution as it is a theory
of redistribution. We will concentrate on that part of
the public choice theory which deals with the issue of
distributional bias of public finance.

The distributional effects of certain forms of tax
policy have been demonstrated theoretically. However, for
most taxes and almost all categories of public expenditure
there are virtually no theoretical works on their net re-
distributional effects. In the absence of a pure public
choice theory of personal redistribution, some concepts
and principles have been advanced which would be relevant
to such a theoretical approach.

G. Stigler (1970) presented a formal statement of
Director's law which is basically a philosophical View of
the public expenditure process. Under Director's law pub-
lic expenditures primarily benefit those coalitions who
control the state and its powers of taxation and spending.
Stigler claims support for this proposition in farm policy,
minimum wage laws, social security, public housing, and

tax exempt institutions.

18

A set of concepts advanced by Hochman (1974) and
others looks at the transitional effects which occur as a
consequence of a change in public policy. Rule changes
and changes in institutions lead to a redistribution of
the benefits and costs as controlled by those rules and
institutions. No consistent set of theoretical principles
can at this time be distilled from the diverse collection
of works which comprise the suggested public choice theory

of distribution.

2.4 Applied Research on Personal Distribution

 

Developing parallel to the pure theories reviewed
above has been an expanding body of applied, empirical
work on personal distribution. The genesis of applied
work on the size distribution of personal income was
Pareto's (1897) empirical findings which has since become
known as Pareto's Law. It basically states that the num-
ber of income receiving units and the size of income are
log-linearly related, therefore, the size distribution is
loqnormal.

More recently other statistical - theoretical studies
have been done in search of a descriptive model which
yields the best statistical fit to the observed size dis-
tributions. Salem and Mount (1973) made an empirical com-
parison of the lognormal and gamma distributions and found
the gamma density function to provide superior fit to U.S.

personal income data. Thurow (1970) advocated the beta

19

distribution as the most flexible and found it to provide
excellent statistical fit to U.S. household time-series
income data.

A less descriptive, more analytical track has been
followed in the models which have attempted to develop a
synthesis of the factors which influence the distribution
process in a growth and development context. In his sur-
vey of the literature Cline (1975) suggested that the
question be divided into two parts; the influence which
growth has on the distribution process, and the influence
which distribution (or, redistribution) has on the growth
process. Four studies shall be reviewed which deal with
the effects of growth on distribution.

Bruce Gardner (1968) advocated a long run, equilibrium
model of personal distribution. Gardner classified the
determinants of distribution as "long run" when they re-
lated to resource ownership, and "short run" when the con-
tributed to transitory variations in rates of return to
comparable resources. The two-levels of analysis allowed
Gardner to consider aspects of the marginal productivity
theory (in the short run analysis), and aspects of changing
ownership patterns and institutions (in the long run ana-
lysis) within the same model.

Gardner's model is useful in two ways. First, it
looks at the distributional process more realistically by
disaggregating distributional effects into equilibrium

effects and momentary disequilibrium or transitory effects.

20

Second, the model attempts to integrate the theoretical
tools of the marginal productivity approach with various
facets of the theories of personal distribution reviewed
above.

In the context of a neoclassical growth model Joseph
Stiglitz (1969) investigated the implications of various
assumptions about savings, inheritance, labor skills, and
reproduction for the distribution of wealth and income
among groups. Stiglitz used a mathematical model to illus-
trate how certain economic forces tend to equalize wealth,
while others (class savings behavior, heterogeneous labor
force, primogeniture) tend to disequalize it.

The model which Stiglitz developed is interesting in
the synthesis it achieves between functional distribution
(in the growth theory context) and aspects of changing in-
stitutions and behavior.

Lee Martin (1975) makes the assumption that market
forces (supply and demandfactors) play an important role
in the inducement of institutional change and ultimately
income distribution. He developed a macromodel embodying
production, demand, human capital, and asset ownership to
determine the influence which agricultural development has
on distribution in a developed country.

Changes in production technology analytically lead to
growth rate expressions for agricultural output and the
marginal products of labor and land. Excess supply and

slowly expanding, highly inelastic demand create downward

21

pressure on output prices and returns to agriculture.
Government supply control programs have quite often biased
the distribution of benefits toward assets and away from
labor.

The model employed by Martin also represents a syn-
thesis. Although primarily a model concerned with func-
tional distribution, the model incorporates aspects of
human capital theory and the theory of public choice (as
they have been identified above).

Fei, Ranis, and Kuo (1978) decomposed the distribu-
tion of income into three independent effects in investi-
gating the interaction between growth and family income
distribution in a developing country. The analytical tech-
nique called for a factoring of the Gini ratio into the
following components: (1) a functional distribution effect
due to changes in the relative shares of capital and labor,
(2) a factor Gini effect due to changes in family asset
ownership patterns, and (3) a reallocation effect due to a
shift of the economy from primarily agricultural acitivities.
This factoring was accomplished by identifying types of
family income streams.

The model presented by Fei, Ranis, and Kuo synthesizes
aspects of functional distribution and changing asset own-
ership patterns. The authors see the advocated model as a
first step toward a more deterministic theory of distribu-

tion.

22
2.5 Overview of the Literature on Personal Distribution

The basic criterion identified at the start of this
chapter was to select a theory, or theoretical approach
which would make a difference in policy selection. That
criterion can be extended to applied work on the distribu-
tion process. The usefullness of any approach should be
assessed in terms of its intended purpose. A two-way
classification scheme is suggested below as a means of
identifying distribution studies as they relate to the

need to make better policy choices.

FUNCTION

Developmental Impact

 

Instrumental

 

Non-Instrumental

APPROACH

 

 

 

 

Developmental approaches primarily seek to identify
the major economic forces or processes at work which lead
to a particular distributional outcome. These approaches
perform a design function as they identify strategies and
alternative institutions which contribute, or act as a
barrier to the realization of a target distribution. De-
velopmental approaches focus on the macrodistribution pro-

cess at the sector level of analysis.

23

Impact studies perform a "what if" analysis function.
In the event that a particular policy or set of policies
is changed, impact studies attempt to determine the distri-
bution (or, redistribution) of benefits which results
either directly or indirectly. Although impact studies
have been used at the sector, subsector levels, they have
been more appropriate for the study of the microdistribu-
tion process at the individual, family, or household level.

The instrumental/noninstrumental distinction relates
directly to the problem of policy selection. Instrumental
approaches attempt to identify the instrumentalities for
change in the distribution process. Instrumental theories
focus on the leverage points in the personal distribution
process to provide policy makers with control variables
which can be used to direct or redirect the flow of bene-
fits. Noninstrumental approaches would not attempt to
identify specific control variables in the system.

We consider this particular research to perform an
impact analysis function, and to represent an instrumental

approach to the problem.

2.6 Literature on Financial Theory and Credit

 

The review of literature on the distribution process
has led to a better understanding of the complexity and
the dynamic nature of the process being studied. Analysis
of the impact of credit policies requires a dynamic approach

to modeling.

24

A dynamic approach to the distribution process con-
siders the influence of: (l) imperfect knowledge, (2)
changes in technology, (3) financial management, (4) changes
in institutional (including financial) structure, (5)
changes in human behavior, and (6) changes which occur
over time. A limited dynamic approach would consider some,
but not all, of the above aspects of dynamics.

We concur with Shubik's (1975) belief that, "economic
dynamics is 'financial dynamics'. The financial system is
the neural network and dynamic guidance system of the real
economy" (p. 185).

Shubik's major contention is that we live in a "pro-
cess world" and to begin to adequately model that world
there is a need for more adequate microeconomic models of
financial control. These models begin by differentiating
between the "real sector" of goods and services and the
"paper sector" which consists of money, ownership claims,
and contracts.

Shubik makes an insightful comment on the issue of
financial control and management.

Instead of guidance by the budget constraint the
world appears to run by predominant use of spot mar-
kets with trade in money, financial paper, and other
contracts which permit a limited extension into the
future. (p. 185)

A cash flow approach to planning embodies these ideas and
is essentially a partial dynamic approach to financial

management and control. For example, a projected cash

flow analysis represents a technique for financial

25

management over time under imperfect knowledge and chang-
ing financial institutions.

The whole area of financial planning and progress of
the firm is well-researched. The literature contains both
theoretical and empirical studies on the firm growth pro-
cess. We shall review static approaches and dynamic ap-
proaches to growth to determine how financial variables

and relationships influence that process.

2.6.1 Static Approaches

Three comparative static analytical contributions are
most relevant to our review of the literature on firm growth.
First, Baumol (1962) and later Halter (1966) used a simple
growth equilibrium model to assess the impact of financial
variables on the rate of growth of output.

Second, Baker and Hopkin (1969) formulated a mathemat-
ical model of levered growth to look at the impact of fi-
nancial variables on the rate of growth of equity capital.

The model developed by Baumol assumes both pure com-
petition and a linear, homogeneous production function
(this latter restriction eliminates the need for financial
management in the model). The firm is assumed to maximize
profits. Baumol divides costs into production costs and
costs associated with expansion of the firm. Present
value techniques and differential calculus are used to

analytically determine the effects of changes in interest

26

rates and the introduction of a government subsidy on the
firm's growth rate.1

A.N. Halter employed many of the same basic assump-
tions and techniques used by Baumol, only in the context
of a farm firm-household unit. Once again management
played no role and the rate of growth of output, and
hence revenues, was assumed constant over time.

Halter's hypothesis is that there exists a limit to
the rate of growth in any given period. Conceptually,
growth is defined as an internal process of the firm in
which the "productive opportunities" of the firm are ex-
ploited to expand its size. Halter used the same analyti-
cal techniques as Baumol to determine the impact of the
rate of earnings retention on the rate of firm growth.

Halter's view of growth as an internal process of the
farm firm appears to be inconsistent with his choice of a
static framework for analysis. Although borrowing as a
source of funds for expansion is explicitly part of the
model, Halter does not consider potential impacts of farmer

control over his borrowing strategy or the rates of return

 

lBaumol's use of present value techniques in the
purely static framework points to an interesting concep-
tual problem in the literature. Present values imply the
passage of time, since they represent a stream of cash or
income payments over time evaluated at a single point in
time. While it is necessary to use present values to de-
termine the consequences of changes in financial variables
over time, it is not strictly correct to call it a static
analysis of firm growth. The assumption that the revenue
stream is constant over time (as an annuity) is not suffi-
cient to make the analysis static.

27

earned on his investments. Consequently, the internal pro-
cess to which Halter refers is not really a process in the
dynamic sense.

C.B. Baker and John A. Hopkin demonstrated that a
mathematical expression for growth in equity could be de-
rived from an accounting identity. In this derived model
the rate of growth is a linear function of the rate of
consumption, and the financial leverage ratio.

Baker and Hopkin's model allows for a purely static
investigation of the impacts of changing any of the above
variables on the growth rate. This particular model is
useful in the wealth of information which can be gained
about the impact of financial variables on growth. Addi—
tionally, the growth expression is completely derived from
information which is available from the balance sheet, the
income statement, and the cash flow (sources and uses of

funds) statement.

2.6.2 Partially Dynamic Approaches

No completely dynamic financial theories of firm
growth exist. Several partially dynamic conceptualiza-
tions of the firm growth process have been advanced. We
shall undertake a selective review of the work which has
been done, either of a theoretical or applied nature.

The principles of two-period consumption and invest-

ment behavior first established by Irving Fisher (193m and

28

later generalized to the multiperiod case by Hirschleifer
(1958) represent an important first step toward dynamics.
Under the Fisherian approach the problem of intertemporal
investment/consumption choices is solved by equating the
rate of time preference (for consumption) with the rate of
return on the available investment opportunities of the
firm in the context of a perfect capital market.

Hirschleifer extended Fisher's analysis in two ways.
First, he dropped Fisher's assumption that borrowing and
lending rates were equal. Second, he generalized the two-
period case to a multiperiod analysis. In the first ex-
tension Hirschleifer shows that either the lending or the
borrowing activity is redundant depending upon the rate of
return on the investment opportunities. Somewhat less-
satisfying was Hirschleifer's resolution of the problem of
multiperiod analysis which he made theoretically equiva-
lent to the two-period case by assuming that all invest-
ment options of the firm were independent.

The theoretical contributions made by Fisher and
Hirschleifer to a dynamic approach to firm growth can be
summarized in two parts. First, they redefine the concept
of investment as not an end in itself but rather a dynamic
process for distributing consumption over some time hori-
zon. Secondly they look at the financing (lending/borrow-
ing) activities of the firm-household unit over time.
These activities represent cash transactions and the ana-

lysis constitutes a kind of optimizing cash flow approach.

29

Douglas Vickers (1968) attempted to integrate the
economic theories of production, capital, and finance to
formulate a broader theory of the firm. Vickers' theoreti-
cal proposition was that the availability and use of capi-
tal will influence the optimum relationships of the rela-
tive use of capital and other factors in the production
process. This situation in turn influences the growth or
expansion path of the firm.2

Vickers advocates an approach in which the objective
is to optimize the "economic position" of the owners of
the firm. This requires that the firm maximize the capi-
talized value of owner equity. The analytical approach is
essentially comparative statics (a single period optimiza-
tion problem is solved) with two dynamic elements. Imper-
fect knowledge is integrated through a risk-adjusted capi-
talization rate function. Financial control is exercised
over the capital structure to determine the optimal amount
of debt to employ.

Peter J. Barry and John R. Brake (1971) explore al—
ternative financial management strategies for farm firms
in various phases of growth. Financial strategies are im-
portant in long term problems of capital management in
activities such as investment, financing, reinvestment,

and risk management.

 

2This proposition was analytically shown by C.B.
Baker (1968).

30

To evaluate alternative financial strategies Barry
and Brake suggest that a cash flow approach is instrumen-
tal for analyzing the productivity of resources and the
liquidity position of the firm over time.

In addition, cash flow analysis aids in evalu-
ating the impact of both alternative investment
choices and alternative financing strategies used in
acquiring resources on liquidity position, income
expectations, and the present worth of the firm.
Hence, the growth process requires information about
the uses of farm income as well as the productivity
of resources. (p. 9)

The core of this growth process is acquisition of
control over durable assets which generate sufficient re-
turns to cover costs and add to the present value of the
firm. Barry and Brake suggest that the economic incentive
to growth may be in the existence of economies of size in
the production activities of the farm firm.

Peter Barry (1977a) identifies three general strate-
gies which have typically been used to finance control of
additional resources:

1. lease of fixed capital, and purchase of working

capital with credit,

2. invest in assets which appreciate in value over

time, and

3. attract equity capital from outside the firm.
Barry finds that the first financing strategy represents a

more austere consumption strategy than do the other two

alternatives.

31

Barry (1977b) contends that a truly dynamic framework
for study of firm growth and adjustment over time has been
lacking. He suggests that a more adequate theoretical
base for such work is provided by optimal control theory.

Barry recognizes that the mathematical requirements
of control theory restrict its complete adoption to growth
of the farm firm. He does see control theory as a power-
ful framework to aid in identification of the elements of
growth and conceptualization of the growth process. The
elements of growth include state variables (capital struc-
ture and organization of enterprises), and rate variables

(investments, rates of return, and cash withdrawals).

2.6.3 Synthetic Approaches to Modeling Firm Growth and
Expansion

The above review of dynamic approaches to farm firm
growth focused primarily on conceptualizations of the
growth process. Paralleling these theoretical contribu-
tions have been applied models which have employed pro-
gramming and simulation techniques. We shall review one
dynamic programming, one multi-period programming, and two
simulation applications. Irwin (1968) provides a review
of several such firm growth models.

Michael Boehlje and T. Kelley White (1968) develop a
dynamic programming model of a hypothetical Indiana farm
to investigate the interdependence of production and in~

vestment decisions over time. Growth, as measured by the

37.. E!!-

32

change in net worth and disposable income, is facilitated
by the generation of investable funds through retained
earnings and new borrowings. Boehlje and White make a
comparison of two optimizing criteria: (1) maximization
of the discounted stream of disposable income over the
planning period, and (2) maximization of net worth at the
end of the planning period.

Specifically, Boehlje and White find that under the
first criterion all the assets purchased are depreciable
assets, while under the second objective function the
major asset purchased is land. An inspection of the sha-
dow prices on credit restrictions and capital resources
reveals that in the early years expansion is limited by
institutional credit restrictions based on collateral
rather than the expected profitability of the investment.
Additionally, they find that optimal firm growth is not
scale growth, rather, substantial substitution of capital
for labor on the hog/corn farm modelled.

Richard Duvick (1970) applies a multiperiod program-
ing model to the stUdy of financing strategies on a Michigan
dairy farm. Duvick evaluates the effects of the following
financial variables on growth: (1) levels of beginning
equity, (2) downpayment requirements for short term and
long term loans, and (3) repayment terms on long term debt.
Alternative optimizing criteria, appreciation of land
values, investment credit, and lower milk prices are also

examined for their financial impacts.

33

Duvick finds that greater initial equity leads to
proportionally higher ending net worth. Initial equity
requirements and down payment requirements are found to
vary in direct proportion. Alternative lengths of repay-
ment have almost no impact on growth in net worth or con-
sumption. Flexibility is gained under longer repayment
periods and this facilitates ownership control over a
larger dairy operation. Various weighting schemes on the
objective function lead to only small differences in the
end of period position.

George Patrick and Ludwig Eisgruber (1968) construct
a simulation model of farm firm adjustment and managerial
behavior under uncertainty. Although the variances of
prices and yields do not enter the model, their expected
values are considered. Patrick and Eisgruber outline the
major elements of a behavioral theory of the farm firm
which integrates factors internal to the firm (goals, ex-
pectations, and resource endowment) and those external to
the firm (random events and government actions).

The simulation model is implemented to control for
capital structure and managerial ability of the operator
on a hypothetical Indiana farm. Patrick and Eisgruber find
that the level of managerial ability is an important factor
in determining the rate of financial growth. Limits on
long term loans are also influential in the timing of land
purchases. Farmers who were able to acquire land sooner

accumulated greater final net worth. Refinancing of long

34

term loans provides needed credit, but limitations on in-
termediate term loans is not an important growth factor.

A second application of computer simulation analyzes
the financial impacts of recent proposals to index govern-
ment price supports to the costs of production. The simu-
lation model is developedlnrMichael Boehlje and Steven
Griffin (1979). The model evaluates the impact of various
levels of indexation on the cash flow of the farm business,
debt-carrying capacity, financial leverage, and rates of
growth for farms in different financial circumstances.

The model explicitly considers the mean and variance
of the supported price distribution, and a risk—adjusted
capitalization rate. Three farm situations are analyzed
varying from a young farmer with low equity to a well-
established farmer with a sizeable operation and high
equity. Alternative policy simulations indicate that pro-
portionately more of the guaranteed cash flow benefits of
such a support program would accrue to farms which begin
at a lower leverage ratio position due to their greater
ability to acquire land. As a consequence, high-equity,
large resource base operators derive greater benefits in
terms of the value of their land and higher levels of con-
sumption than do similarly leveraged, but smaller opera-

tions.

35

2.7 Literature on Credit Policies and Institutions

 

The most comprehensive review of the role of small
farmer credit policies and programs was assembled in the

Agency for International Development Spring Review of Small

 

Farmer Credit (1973). We shall review three of the analyt-

 

ical papers in that series for the insights they provide
concerning the institutional aspects of credit policies,
programs, and practices.

C.B. Baker (1973) contends that small farm credit
programs in developing countries have failed to realize
their expectation for success because they have not been
designed with an adequate knowledge of small farmer be-
havior and the small farmer's financial environment.
Limited equity and restricted means of repayment (due to
low volume of cash flow) limit small farmers to higher
cost sources of credit. The small farmer must solve prob-
lems of cash flow and risk management without large flows
of cash. In addition to production credit small farmers
have a need for loans to finance consumption and marketing
activities as well.

Judith Tendler (1973) reviews the dilemma of multiple
goals which confront credit institutions and, therefore,
policy makers. She identifies three broad categories of
goals; (1) efficiency, (2) equity, and (3) institutional
viability. These goals take on varying importance in the

context of specific policy issues. For instance, in the

36

case of evaluating a borrower's repayment capacity the
efficiency goal requires the existence and adoption of a
profitable technology. However, when the repayment issue
is evaluated by some combination of equity and institu-
tional viability, Tendler suggests that institutional be-
havior will be determinant. This latter situation calls
for the use of equity-oriented criteria by lenders to pre-
vent the outright exclusion of less-established borrowers,
according to Tendler. She generally finds that small far-
mer programs suffer from goal addiction and that goals have
sometimes become overly fixed.

John R. Brake (1973) looks at the problem of evaluat-
ing credit institutions. Brake finds that no precise
evaluation is possible in View of the number of criteria
for success, and he suggests that clientele benefits and
institutional integrity are most useful. Relative to the
implementation of credit policies and programs Brake makes
the following assertions; (1) the institutional objectives
must be clear, (2) the lending institution needs to be
efficient, and (3) the credit institution should fit the
purpose and cultural situation.

Claudio Gonzalez-Vega's dissertation (1976) dealt ex-
tensively with the role which interest rate policies play
in overall credit policy. Gonzalez-Vega formulates the
"iron law of interest rate restrictions" which can be de-

scribed in the following way. The imposition of a ceiling

37

on interest rates on loans and the lowering of that ceiling
leads to a redistribution of the credit portfolio of formal
lending institutions away from small operators and toward
well-established, large operators. His maintained hypo-
thesis is that interest rates affect resource allocation

and income distribution.

2.8 Summary

 

Lack of a comprehensive theory of distribution neces-
sitates that one of two possible approaches be taken in
developing a framework for analyzing the distributional
impacts of credit. One approach would be to develop such
a theory. The formal requirements of the implied theory
are great and doubtless go beyond the scope of the problem
being addressed. A second strategy is to develop a concep-
tual approach which selectively draws from existing theory.
This latter approach allows for a consideration of various
structural and behavioral aspects of distribution and the
role which financial institutions play in the more general
distributional process.

There is a need to consider more directly the distri-
butional aspects of credit policies, rules, and practices.
These institutional factors are expected to affect long

term changes in the distribution of benefits from credit.

CHAPTER I I I

Statement of the Economic Problem

3.1 Introduction

 

The broad issue of distributional effects of public
programs has received increasing attention from policy
analysts in recent years. Bonnen (1969) and others have
contended that information on the distributional impacts
of public programs is an essential part of a comprehensive
review and evaluation of programs and policies. It seems
consistent that due to the quasi-public nature of several
major lending agencies in this country and the importance
of governmentally sponsored and funded credit programs in
developing countries, evaluation of credit policy distri—
butional effects is in order.

There are a number of views concerning the distribu—
tion of and benefits from credit, some of them opposing.
A systematic consideration of the underlying assumptions
is needed. Consider the following two views on the dis-
tribution of credit. First, credit is a means of self-
help by which a borrower potentially improves his income
and financial situation. While credit involves neither a
permanent transfer of funds nor an outright gift, it does
carry the connotation of providing financial assistance to
the less-well-off. Provision of credit on the basis of
expected income-generating capacity and ability to repay

38

39

redistributes access to and benefits from credit. An al-
ternative view is that debt is a resource like any other.
It is demanded in relation to its expected productivity
and is supplied by lenders in hopes of repayment. Unique
to this view, however, is the interpretation given to re—
payment capacity which requires that credit be distributed
in direct relation to the existing pattern of asset owner-
ship. Those who already have substantial assets are en-
titled to credit, and those without substantial assets who
most need credit cannot get it.

Partly in response to the above controversies concern-
ing credit, Brake and Melichar (1977) made several sugges—
tions for future research in the area of agricultural fi-
nance and capital markets. One suggestion was for addi—
tional evaluative and analytical research on how credit
institutions operate given the continous increase in the
capital and credit needs of American agriculture. A second
suggestion was that research be done on the distribution
of income and other benefits of credit programs to assist
policymakers in making informed choices among alternative
programs.

The problem area identified in this chapter is likely
to be of interest to two clientele groups: (1) government
planners and policy makers who design credit policies and
programs, and (2) the quasi-public and commercial lending

agencies which implement credit policies. Government

 

planners design credit programs and policies to facilitate

40

an overall development strategy. Questions of growth and
distribution are linked and must be dealt with at the pol-
icy level. Quasi—public and commercial lending agencies
which implement those policies are concerned with their
impact upon borrowers' ability to repay. Lending agencies
concerned with their performance cannot neglect the dis-
tributional consequences which are an implicit part of
their lending strategies.

Credit has been suggested to perform an allocative

 

function and a distributive function (Gonzalez-Vega, 1977).

 

When a loan in sufficient amount is provided to a farm
with productive opportunities but limited capital, an im-
provement in the allocation of existing resources within
that firm can potentially occur. Credit may, therefore,
facilitate greater allocative efficiency within the firm.
If the farmer is also able to realize a sufficient
gain in income to provide not only for a greater level of
consumption but also savings and/or reinvestment, the dis-
tribution of income may be changed. This distributive
property of credit is hypothesized to be more pronounced
within a dynamic situation where productive opportunities
of the firm improve due to technological change factors.
Control over additional resources as facilitated by the
loan allows for both a fuller exploitation of existing
productive opportunities and the possibility of improving
those opportunities through investment in new production

technologies.

41

The distributional gains to which we refer have an
important time-related dimension. Immediate distributional
effects flow from subsidized credit policies in which the
loan recipient receives a benefit through a lower interest
rate. If the market rate of interest is r and the actual
rate charged is i, then the amount of the transfer to the
loan recipient is proportional to the reduction in the
cost of credit (r-i).

The deferred distributional effects arise over time
from credit policies which facilitate within-firm changes
in financial management, production, and investment activi-
ties and a realization of greater net returns from farming.
The distributional gains which accrue as deferred benefits
are hypothesized to depend upon the quantity of credit a
firm can obtain and on other characteristics of the firm
situation. The distributional gains which accrue to bor-
rowers over time are the primary focus of this study.
Accordingly, the credit policies to be considered will
relate to the distribution of credit. Although, those

policies may imply different interest rates.

3.2 Restatement of the General Problem

The general problem of lack of knowledge about the
distributional impact of credit policies can be subdivided
into two problem areas for research. This redefinition of

the problem recognizes that firms differ in their beginning

42

resource base and in their abilities to use resources pro-

ductively.

3.2.1 The Problem of Access to Initial Resources and
Information

A low resource farmer may experience unsatisfactory
income levels either due to an inadequate resource base or
poor financial decisions. To explore this problem situa-
tion, consider a representative farm which makes decisions
on production, investment, and consumption interdependently.

A farmer's production activities may be limited by
several factors. From Hirshleifer's (1970) extension of
Fisher's model of intertemporal choice, three factors are
influential:

l. the farmer's internal production opportunity,

2. the farmer's initial endowment of inputs or re-

sources, and

3. the farmer's access to external financial resources.

The internal production opportunity set defines the
existing potential which a farm enterprise holds for the
profitable use of resources. The production opportunities
are a function of the situational elements of the farm
(institutional, biological, climatological, and technolog-
ical factors) including the farmer's intelligence capacity
and available family labor resources. A final important

element in the production opportunity is the riskiness of

43

the production activities and how the farmer interprets
the relative risk.

A useful summary of a farmer's production opportuni-
ties is obtained by deriving a gross returns function (by
interjecting prices into an evaluation of the production
function at various levels of the variable inputs). The
extent to which a farmer is able to realize the production
potential, and therefore potential returns, is greatly
affected by his control over resources -- his initial en-
dowment of the variable factors and his ability to obtain
external financing.

The initial endowment at the start of the production
cycle is equal to the current inventory of variable inputs
plus cash. The farm business competes internally for the
variable resources with the consumption requirements of
the household. In the absence of money and capital mar-
kets, the initial endowment defines the extent to which
the internal production opportunities of the farm can be
exploited.

To the extent that the exploitation of existing pro-
fitable production opportunities is determined by external
financing, access to credit is a determinant of the dis-
tribution of returns among farms. Credit policies designed
to improve the credit access of low resource farmers, theo-
retically, at least, remove the resource constraint. Given
sufficient financial management capacity, the additional

capital generates income in excess of debt servicing

44

requirements. The surplus can be allocated to increased
consumption and/or retained earnings.

The core of the growth process is acquisition and
control over resources which continue to generate returns
in excess of costs and thus add present value to the firm.
In this way, credit is often suggested as a means of over—
coming or offsetting an uneven initial distribution of
factor ownership. On the other hand, credit policies
which confer relatively greater benefits on rapidly growing,
large resource base farms may, over time, bias the distri-
bution of resource ownership and the distribution of income

in favor of the wealthy.

3.2.2 The Problem of Cumulative Impacts

The ability to use resources productively is certainly
an important factor to consider when we compare the growth
paths of farms over time. As already noted, growth is a
cumulative process and in that sense is dependent upon
past financial behavior and experiences Of the farm. Fi-
nancial growth of a farm business can be regarded as a
function of the financial strategy employed by the farm
operator. For example,\it has been shown that a strategy
in which the operator assumes a heavy debt load and the
amount of disposable income available to satisfy farm fam-

ily consumption is restricted, there will be an accelera-

tion in the growth of net worth (Boehlje and White, 1969).

45

An implication of the above observations concerning
growth is that farmers can manage credit and other finan—
cial resources to approximate their desired rate of growth.
That implication is essentially correct, but presents only
half of the picture of the growth process. The other half
of the equation depends upon how credit policies (more
specifically, lending criteria) take alternative financial
strategies and past growth performance into account in
distributing credit.

Different lending criteria place different emphasis
on the various financial assets, amount of equity, and
past rates of growth of income and net worth of the farm.
By changing the relative weights which alternative lending
criteria receive, credit policies implicitly alter the
economic value of certain financial characteristics of
individual assets in the farmer's portfolio. The econmomic
value of a financial asset, or portfolio of assets, de-
rives in part from the amount of credit which can be ob—
tained either due to its collateral value or its income-
generating, debt-servicing capacity.

It is hypothesized that credit policies which place
greater emphasis upon asset value for those assets which
have skewed distribution of ownership will redistribute
relatively greater amounts of credit towards large resource-
base farmers and confer relatively greater benefits on
those farms in the form of greater asset ownership and re-

turns from farming. Conversely, those credit policies

46

which deemphasize asset values, and increase the weight is
placed on repayment capacity of the farmer, will confer
relatively greater amounts of credit, and hence benefits
from the use of credit, on less-well-endowed, yet effi-
cient, farms.

How does a consideration of alternative firm growth
paths constitute a problem? Growth is a cumulative pro-
cess and it is not clear that the cumulative impacts of
credit policies which evolve over a number of years are of
the same relative orders of magnitude as the immediate im-
pacts of those policies. The recursive nature of firm
financial growth in conjunction with technological change
and other dynamic elements sufficiently obscures the de-
ferred distributional benefits from credit that these cum-
ulative impacts should be modeled and evaluated.

The two problem issues we have identified are to a
degree interrelated and in formulating credit policies
decisionmakers need to consider both aspects of the dis-
tributional problem. The hypotheses which have been pro—
posed require that we take a closer look at the theoreti-

cal bases of the problem issues which have been identified.

3.3 Financial Impacts of Short Term Credit

 

The first problem issue, access to resources, has
been analyzed within the context of access to operating

credit. Gonzalez-Vega (1976) employed a single period

47

model of production to examine a specific set of assump-
tions about farmers' relative internal productive oppor-
tunities and initial resource endowments. Within that
framework fixed and variable inputs play important roles
in the determination of access to, and benefits from credit.
Fixed inputs include family labor, human and physical
capital, technology and land resources. These fixed fac-
tors are among the determinants of each farmer's internal
production opportunity set. Variable inputs include seeds,
fuels, fertilizers, pesticides, and herbicides. Credit
and cash reserves are the primary means for purchasing
these variable inputs. The level at which variable inputs
are used determines the extent to which the productive
opportunity can be exploited during the single period of

production.

3.3.1 Assumptions Underlying the Short Run Model

Specific patterns of distributional impacts result
when certain assumptions are made concerning the conditions
under which production occurs. Two farmers are assumed to
produce an homogeneous product using both fixed and vari—
able inputs. Both farmers are assumed to attempt to maxi-
mize net returns. Initially, neither farmer is able to
attain that maximizing level of production since both
farms possess less than the necessary endowment of the
variable inputs. The initial endowment of these inputs is

assumed to vary systematically between farms.

48

Gonzalez-Vega suggested, for instance, that there is
such a systematic variation in productive opportunities
and initial resource endowments between farms in Latin
America.3 A large farm operator theoretically faces a
greater overall productive opportunity due to his set of
fixed inputs (better land, more productive technology, and
greater accumulation of human and physical capital). This
large farmer also quite possibly controls a significantly
larger initial endowment of the variable factors of pro-
duction than does the small farmer.

In the above context the small farmer's marginal rate
of return from additional units of operating inputs is
assumed to fall more rapidly than for the large farm oper-
ator, due to the farmer's limited capacity to use large
amounts of those inputs. This limited capacity to handle
large amounts of short term credit reflects a small endow—
ment of the fixed inputs and a relatively limited manage-
rial capacity. Under these assumed conditions Gonzalez-
Vega illustrated some basic distributional impacts using
marginal value product curves and various limitations on
access to operating credit.

A key underlying assumption of the short run model
concerns rural capital markets. These financial markets
are assumed to be highly fragmented. Frictions which
exist (due to risky lending situations, behavior towards

risk, interest rate restrictions, and customary patterns

 

3Op. cit.

49

and arrangements in lending) act as barriers to the free
flow of funds between lenders and certain potential bor-
rowers. Presence of these barriers theoretically place
upward pressure on the transactions and real costs of bor-
rowing.

It is these rural financial market characteristics
which bring about differential access to operating credit
across farms with different resource situations. Accom—
panying this pattern of restricted access to credit in
which the agricultural lender acts as a monopolist, credit
flows to large farm operators due to their superior initial
resource endowment. Gross and net returns on large farms,
as a consequence, are greatly augmented by credit but may

be little changed on small, subsistence units.

3.3.2 Limitations and Uses of the Short Run Model

Clearly, the short run model of production is rather
limited as an analytical approach to the broad question cf
distributional impacts from credit. It focuses upon oper-
ating credit and the one period production horizon. Addi-
tionally, several restrictive assumptions are made about
productive opportunities and resource endowments of farmers
and their rural financial markets to define the set of
distributional impacts. In this regard, a more sufficient
empirical base appears to be required to document and ver-
ify these borrower, lender, and financial market charac-

teristics.

50

One of the significant attributes of the short run
production model is, however, that it focuses on two key
aspects of credit (its extension, use, and repayment)
which may be important determinants of the distribution of

associated benefits. Productive opportunities and asset

 

position or endowment of the borrower are both important
borrower characteristics which represent criteria for
evaluating potential repayment capacity.

The short run model provides a useful point of depar—
ture for this study to broaden the set of factors which
might be taken into account. Conceivably, broadening the
analysis to incorporate; individual farm total resource
situations over a longer period of time, consumption, and
investment behavior will generate other distributional
effects which accompany the extension of both operating

and investment credit.

3.4 Cumulative Impacts of Credit

 

Some of the assumptions employed in the short run
model of production need to be relaxed to explore the
problem of cumulative impacts of credit in a partially
dynamic framework. The maintained hypothesis is that the
distributional impacts of credit are more pronounced within
a dynamic situation where; (l) productive opportunities
can be improved through investment in technologically im—

proved inputs, and (2) the abilities of farmers to exploit

51

those opportunities and realize certain other benefits
from asset ownership are contingent upon the credit poli-
cies which are implemented.

Two forms of cumulative distributional impacts of

credit will be identified: (1) an income redistribution

 

effect which follows from an extension of the short-run

model, and (2) an asset redistribution effect which has

 

not been considered up to this point. Both of these
effects are hypothesized to evolve through the growth and

credit distribution processes over time.

3.4.1 Income Redistribution Effect of Credit

Production opportunities are not stationary, there-
fore, growth rates are not expected to be constant over
time. The production function may shift upward due to the
adoption of improved inputs, or improvement in the mana-
gerial abilities of the operator.

The adoption of an improved technology requires that
a new set of factors be employed which differs in some way
from those formerly used. T.W. Schultz (1964) suggests
that the essence of technological change is the addition,
deletion, or change, of at least one of the factors of
production. Credit may facilitate three means of adoption
of technological change: (1) purchase of highly divisible
inputs, (2) purchase of complementary divisible inputs, or

(3) purchase of lumpy, indivisible inputs.

52

The increased need for credit in this context arises
from the increase in cash flow for purchasing and the in—
crease in credit reserves for risk-bearing. When highly
complementary, divisible inputs are required for success—
ful adoption, significant increases in cash outlays may be
necessary to acquire the entire package of inputs at a
discrete point in time (e.g., at seeding time). The need
for credit is somewhat more obvious in the case of indi-
visible inputs. Large cash outlays may be required to
invest in such items as; buildings, tractors, implements,
and improvements to land.

Adoption of technologically improved inputs and prac-
tices may also increase the riskiness of returns due to
greater variability in yields. To meet the possibility
that returns may be inadequate, credit reserves may be
substituted for cash reserves. The level of those credit
reserves is expected to vary depending upon what is assumed
about operator risk preferences.

Each farm may generate growth in net returns by two
means. First, the marginal productivity of the variable
inputs increases as the production function shifts upward
as new inputs are added to the firm. Second, when more
debt is employed, financial leverage is increased. Lever-
age acts as a multiplier with respect to the value margi-
nal product of added resources, therefore, the marginal

rate of return.

53

The expanded net returns to the farm may improve the
debt-servicing capacity of the farmer. However, ability
to service debts is contingent upon consumption expendi-
tures and taxes. Increased net returns may be insufficient
to meet all of the above uses of those funds. Depending
upon what is assumed about the consumption function (in-
creasing or decreasing marginal propensity to consume as
income increases), the cumulative income redistributional
impact of credit may be diminished or enhanced.

Parallel to the consumption behavior of the household
and the progressivity of the tax structure, the cumulative
impact of credit depends upon how debt-servicing capacity
is taken into account in the design of credit policy. In
the context of a technologically dynamic environment credit
policies which redefine a borrower's credit ability to
place greater weight on repayment capacity are hypothesized
to lead to potentially important cumulative income redis-

tribution effects.

3.4.2 Asset Redistribution Effect of Credit

The value of assets and the asset distribution are
not expected to remain constant over time. Several eco-
nomic forces may act to change the market value of an
asset. An important example is the increases in farmland
values during inflationary periods and changes which occur

in the distribution of asset ownership and control.

54

An increase in the price level is an important vari-
able to consider as it affects both the price of assets
and the growth process. It derives this importance by the
way it affects purchasing power of an asset or a source.
Boyne (1964) suggests that the magnitude and incidence of
changes in the purchasing power of a price-sensitive asset
arise from three potential sources; (1) changes in the dis-
count rate, (2) changes in the size of the expected income
stream. and (3) changes in the value of money.4 More re-
cently, Plaxico and Kletke (1979) illustrated the implica-
tions of using increases in the purchasing power of real
estate for growth in farm assets under three alternative
approaches to the valuation of capital gains. The mechan-
ism used was credit. Asset value appreciation represented
an improved equity position and reduced financial risk
which could be utilized to obtain more credit under their
assumption about lender behavior.

The amount of credit which a given set of farm assets
can service may be significantly affected by changes in
the price level depending upon one's assumptions. Price
changes may be net income increasing (output prices more
affected than input prices) or net income decreasing (in-
put prices rise faster than output prices). Generally
leverage, as shown by debt asset ratios, tends to decrease

as price-sensitive assets rise in value relative to the

 

4Price - sensitive assets include; real estate, dur-
able capital, livestock, and crop inventories.

55

debt used in acquiring them. Unless the change in prices
is of the net income decreasing variety, it may be that
total firm risk is not appreciably changed. While business
risk rises with the value of assets owned, financial risk
falls (due to the lower leverage position).5
Growth in farmland values increases the downpayment
requirement. Consider an amortized real estate loan for
which repayment is in the form of fixed annual installments.
The downpayment on the land purchased is the difference be-
tween the market value of the land and the sum of the dis-
counted stream of loan payments. As price levels increase,
assuming the market value of land escalates faster than
the amortized loan, the downpayment, which is theoretically
the difference beween those two amounts, must also increase.
Two financial impacts are worth summarizing with ref-
erence to increasing prices: If price changes are net in-
come decreasing a farmer may find a cash flow problem de-
veloping as his receipts cannot keep up with expenditures
for purchased inputs and consumption needs. A second fi—
nancial impact is the potentially increased risk when

price level changes are net income decreasing. It may be

 

5The above usage of the terms business and financial
risk makes the following distinction: Business risk re-
fers to the variability in income resulting from the type
of business in which the firm is engaged. Business risks
relate to the kinds of investments made, enterprise selec-
tion, and organization of the firm. Financial risk refers
to the probability of incurring relatively greater losses
as the proportion of borrowed capital relative to equity
capital increases (Nelson, Lee, and Murray 1973).

56

that few if any financial strategies are effective in
dealing with long term price level adjustments of the net
income decreasing type. Yet, it is expected that those
strategies which are successful involve some combination
of credit and managerial ability.

It may be that in an inflationary economic environment
that the firm which controls greater assets has a greater
capacity for bearing increased risk. Yet, even if this
were not necessarily true (say, managerial capacity and
value of assets are positively, but not perfectly, corre-
lated), the practice of lending on the basis of the magni-
tude of the net capital ratio is expected to have impor-

tant cumulative impacts.6

In a situation of increasing
prices, credit policies which redefine a borrower's credit
ability to place greater weight on risk-bearing capacity
by employing rules of thumb which emphasize asset values

are hypothesized to lead to important cumulative asset

redistribtuion effects.

3.5 Additional Comments on the Nature of the Research
Problem

 

The usefulness of credit, and a major part of its
distributional properties, has to do with the assumed
nature of the production function. A number of alterna-

tive assumptions may be consistent with observed data in

 

6The net capital ratio is calculated by dividing
total farm assets by total farm debt.

57

various development contexts.

1. Low income traditional farmers are on production
functions so poor that additional funds via cre-
dit will be consumed rather than put into produc-
tion.

2. For credit to be profitable for a small farmer
in a developing country, there must exist a pro-
fitable new technology for adoption. There must
be a new, higher production function to be attained.
If credit is used to purchase improved intermediate
inputs, their productivity may be highest on small
farms where a ready reserve of complementary labor
exists in greater amounts.

3. Better managers have higher return production,
investment alternatives than poor managers (i.e.,
good managers can use a given amount of credit
more productively).

4. Large size farm firms have higher return (and
probably numerically more) alternatives for in-
vestment than small, limited resource farms. If
land and credit are complements, a larger land
base may result in higher returns to credit
(when credit is used to purchase lumpy assets
such as machines and implements).

5. Large size farm firms and small, limited-resource
farms face the same production function, but the

latter do not control the resources needed to

58

operate at the optimal level; hence their returns
to use of credit will be greater than for large
size firms.

Small or limited-resource farmers will have the
greatest return to additional resources because
they are most likely to have an imbalance of fac-
tors of production. That is, they have a given
amount of labor with too little land and too
little operating capital for the labor to be used
effectively and earn a favorable rate of return.
Small or limited-resource farmers are less know-
ledgeable about new production technology, hence,
they receive lower returns to additional invested
capital and management. Credit coupled with edu-
cation will lead to higher returns for this group

than for large farm operations.

The above hypotheses illustrate the results that might be

expected under different assumptions concerning production

functions and investment alternatives of farm firms.

In a realistic representation, farmers probably face

dissimilar investment-return alternatives for increments

to credit because:

1.

some operators are aware of opportunities not
known to others,

substantially different initial resource endow-
ments provide different investment-return alterna-

tives,

59

3. ability to acquire lumpy inputs, as opposed to
additional divisible inputs, suggests producers
face discontinuities in investment opportunity
curves, and

4. differences in investment risk, risk-bearing
ability and risk preferences suggest different
investment strategies, and hence, returns.

The assumptions one makes about the perceived invest-
ment opportunities and consumption behavior of a given
firm-household clearly have important impacts on the even-
tual benefits, and distributional aspects, which accrue to

credit.

3.6 Summary

 

Controversy over the role of credit in promoting
growth and development has indicated that there is a lack
of knowledge concerning the distributional impacts of ore-
dit. That general problem has been disaggregated into two
researchable economic issues: the problems associated
with access to initial resources and information, and the
problems of cumulative impacts.

A short run model of production opportunities is use-
ful for looking at the first problem issue, but inadequate
for evaluating the role of credit over time. The need for
a partially-dynamic approach is motivated by two observa-

tions; (1) production functions, hence opportunities, are

60

not stationary over time due to technological change, and
(2) asset values, hence the distribution of asset ownership,
are not expected to remain stationary in the presence of
changing price levels. A growth model approach can be
utilized to evaluate both problem issues as they relate to
the distribution of credit and the distribution of benefits
from credit.

Two research methodologies could be followed in eval-
uating the role of credit. One methodology would use fi-
nancial data from individual farm firm-households over a
number of years and compare the income and net worth posi-
tions of these units assuming their objectives were the
same. Several limitations make that approach difficult to
implement chief of which is the lack of useful data. A
second methodology which potentially yields greater insights
into credit policy impacts is to build a synthetic model of
the growth process which can be implemented at the firm
level. This approach allows consideration of different
assumptions about initial resource positions production-
investment opportunities, and consumption behavior under
varied credit policies.

The synthetic methodology is consistent with the need
to develop a conceptualization of the distributional pro-
cess to evaluate credit policies. Moreover, the synthetic
approach allows the modeler to identify the instrumentali-
ties of distribution in the growth process. Those instru-

mentalities can then be utilized to design and implement

61

credit policies with more knowledge about what their po-

tential distributional impacts will be.

CHAPTER IV

A Conceptualization of the Distribution Process

4.1 Introduction

 

Theoretical economic literature provides no unified
theory of the distribution process. However, a couple of
theoretical approaches can be integrated to develop a use-
ful conceptual framework for analysis and design. Those
approaches are the approach of institutional economics and
the approach of marginal productivity theory. An integra-
tive approach provides greater insights and analytical
capabilities in the area of policy analysis.

Marginal productivity theory is useful as it provides
the functional relationships (vis. production, cost func-
tions) which are necessary for translating relatively
scarce resources into relative incomes.7 Marginal produc-
tivity theory cannot, however, determine those relative
scarcities. The use of marginal productivity theory can
lead to a determination of the distribution of income
within an existing distribution of ownership.

An institutional approach is useful since it explic—
itly considers how it is that institutional arrangements,
or rules of the game, lead to different distributional

patterns. This approach allows the ownership and control

 

7Marginal productivity theory in this usage is generic
for those theoretical approaches which focus on issues con—
cerning functional distribution.

62

63

of resources to be an important component of the analysis.
Ownership becomes a variable in the analysis to evaluate
the performance implications of alternative institutional
and policy designs. To the extent that institutional ar-
rangements affect the dynamic performance of the economic
system, these institutions may become important factors in
determining the distributional impacts of credit policies.
The following four general premises can serve as con-
ceptual guidelines in the formulation of an integrative
approach:

1. Distributional impacts are both definable and
measurable. Their consideration is a necessary
part of statements and decisions concerning credit
policies.

2. Distributional impacts represent a dynamic pro-
cess which should be modeled in a growth frame-
work.

3. Distributional impacts are functions of variables
which are themselves distributions.

4. A conceptual framework for evaluating distribu—
tional impacts should attempt to incorporate as~
pects of both personal and functional distribu-

tion within the same analytical framework.

4.2 A General Framework for Analysis and Design

 

Once credit policies have been determined for an eco—

nomic system what remains is a management problem, a problem

64

of control where credit is allocated by established rules
and practices among competing uses. However, in a situa-
tion where the credit policies and lending criteria are
not predetermined (they are the variables under analysis),
the problem is one of design in which alternative policies
need to be evaluated.

The "systems approach" advocated by C.W. Churchman
(1968) provides a general framework for the analysis and
design of alternative credit policies. This approach has
the capability for incorporating tools of analysis from
both the institutional approach and the approach of margi—
nal productivity theory. It also allows sufficient flexi-
bility to consider alternative credit policy designs. In
addition a systems approach provides some relative advan—
tages in going from a conceptual, theoretical model to an
empirical representation of the model.

This approach has commonly been employed to undertake
policy experimentation by first identifying the operative
system (its structure and behavioral interactions) and
then constructing a logical, mathematical representation
of it. Performance consequences of policy actions are
evaluated by simulating the system's actual behavior. The
approach places a great deal of emphasis on the construc-
tion of models which adequately capture the important ele—
ments of the real system.

As an integrative approach the systems view allows

the policy analyst to undertake analysis and design through

65

an iterative process. In the design mode the analyst must
determine the structural makeup of the Operative system.
System structure becomes the variable to be determined,
while system outputs and inputs are prespecified. In the
analysis mode the policy analyst must determine which sys-
tem outputs flow from a predetermined system structure and
set of inputs. Analysis and design functions can be alter-
nately executed to determine which set of policy actions

lead to which set of performances.

4.3 Outline of the Conceptual Framework

 

The conceptual framework which follows attempts to
build upon selected concepts and terms. To avoid unneces-
sary confusion, several terms are now briefly defined.

Credit refers to financial trustworthiness. It is
the ability to borrow. Credit distribution is believed to
be a function of lending decision rules and borrower charac-
teristics.

A credit poligy is a deliberate course or method of

 

action selected to guide and determine present and future
decisions with regard to credit allocation.

A credit practice is commonly applied policy or pro—

 

cedure.

A credit rule refers to a specific application of

 

policy.

66

A lending agengy is any financial entity (usually a

 

financial intermediary, but not necessarily so) which can
allocate funds for the purpose of creating a loan.

Debt service costs are required payments to fulfill a

 

loan contract. These payments include principal and in-
terest.

A property right is an established practice, law or

 

custom which helps an individual form certain expectations
which he can hold in his transactions with others (Demsetz,
1968).

Lending can similarly be defined as a transaction
which follows certain established practices, laws, and
customs which legitimize, or make binding, a credit trans—
action between financial entities.

The above definitions start to clarify the view that
the extension of credit is a special form of property right.
The process of lending includes all of the protective cove-
nants between the lender and the borrower. These agree-
ments preserve each party's rights and establish certain
conditions on control over financial assets which are

transferred in the process.

4.3.1 Benefits and Costs of Credit

In listing the potential benefits from credit under
the traditional arrangements, interest rates are assumed

to be set at market levels with a legal force backing the

67

credit contract. In this context credit is viewed as in-
cluding four rights which provide for the internalization
of the benefits.

First, the borrower gains the limited right to pur—
chase additional assets or to otherwise invest the loan
proceeds within limits specified in the loan contract.
Included are the rights to reallocate resources within the
firm, to purchase needed productive inputs, or to invest
the loan proceeds for business purposes subject to any
restrictive covenants specified by the lender in granting
the credit.

The second benefit is the right to income from such
assets and investments, or from related productivity in-
creases above debt service costs, which come from the
credit. This includes rights to future income from rein-
vestment of savings accomplished by the original purchase
or investment. Therefore, one must consider the dynamic
nature of investing, paying debt service costs, generating
savings, and reinvesting the earnings to generate addi—
tional income. This suggests that a growth model may be
advantageous for evaluating the cumulative benefits of
credit. Further, it suggests that debt service is an in-
fluential factor affecting cumulative benefits in that
repayment terms will affect returns over the debt service
commitment.

The third benefit is the right to appreciation or

depreciation of assets or investments so long as the terms

68

of the loan are met. In an inflationary situation this
right can have particular importance in terms of the capi-
tal gains or appreciation accruing to some investments as
well as for depreciation write-offs, investment credit and
other possible tax advantages.

The fourth benefit is the right to sell, lease, col-
lateralize or otherwise use the property acquired with
credit subject to loan repayment terms. This is to say
that one can realize a profit on the sale of the item pur-
chased by credit, and that profit accrues to the owner
rather than to the lending institution as long as loan
terms are met. Such capital gains are conceived as sepa-
rate from the income produced by use of the asset.

Finally, if the two assumptions made above are dropped
(i.e., that market rates of interest are charged and legal
force backs the loan contract) then the borrower may obtain
two additional rights. One is the right to a reduced cost
because interest is subsidized at below market rates.
Secondly, the borrower may realize a direct transfer of
funds from the lender through nonrepayment. These latter
two are not uncommon in small farmer credit programs in
developing countries.

There are at least three real or potential costs to
the borrower associated with the use of credit. First,
there is the interest cost for the use of borrowed funds.
This is a cost which is recognized by all. Second, a po—

tential cost is the increased financial risk which goes

69

with a higher leverage position (higher debt/asset ratio).
The third cost is the risk of loss of future access to
credit. This latter cost refers to the risk that one may
not be able to borrow in the future because of increased
use of credit now, or that business risk or business fail-
ure with a highly leveraged financial position will hinder
acquisition of additional credit.

Credit institutions and policies, therefore, affect
access to the rights to purchase additional assets or ser-
vices and to benefit from income, value appreciation, and
reorganization of productive assets. Property rights are
thought to play an important role as they affect both own-
ership and control of financial resources. The contractual
nature of credit arrangements indicates how the associated
property rights are taken into account. Credit arrange-
ments typically provide for:

1. transfer of property rights in case of default

(not meeting the terms of the loan),

2. terms concerning security of the loan and for
retirement of the legal-financial claim against
the property being used to secure financing, and

3. determination of the amount which can be borrowed
within prudent lending limits reflecting safety

margins for riskbearing.

70

4.4 The Distribution Process

 

The distribution process being advocated can be sum-
marized by means of a diagram as illustrated in figure 4.1.
The diagram represents a form of causal loop. A causal
loop diagram is a graphic model which identifies the vari-
ables believed to be significant in the system being mod-
eled. Arrows in a causal loop are employed to indicate
processes or components in the system. The diagram shown
is a form of causal loop since the variables which are
identified are not all strictly variables, but rather in
some cases, are concepts which represent underlying vari—
ables. A second modification of the standard causal loop
is that no signs (positive or negative) are included at
the arrowheads to indicate the direction of causation since
the stimulus in most cases can have different effects de-
pending upon the farm resource situation.

The distribution process as illustrated in figure 4.1
is consistent with the set of premises which were estab-
lished earlier. The framework incorporates aspects of
functional distribution (rates of return on productive
assets) and personal distribution (the distribution of
asset ownership). Also, the process has the potential for
integrating institutional parameters and aspects of the

marginal productivity theory of distribution.

71

 

ESTABLISHMENT OF PROP

 

ERTY RIGHTS IN CREDIT

 

 

RULES AND PRACTICES
FOR IMPLEMENTATION OF
CREDIT POLICIES

 

 

{7 SIZE AND OTHER
FINANCIAL CHARACTER?
‘L_ l! ISTICS OF FARMS

 

 

 

RATES OF RETURN ON
PRODUCTIVE ASSETS

 

 

ASSET OWNERSHIP
PATTERNS

 

 

 

 

DISTRIBUTIONAL
IMPACTS

 

(internalization of benefits from credit)

 

Figure 4.1. Causal Loop Diagram of the Distribution
Process for Considering Alternative Credit Policies

 

72

4.4.1 Size and Other Financial Characteristics of Farms

An important link in the distribution process as out-
lined in figure 4.1 is the impact which credit policies,
rules, and practices have on size and other financial
characteristics of farms. Three financial characteristics
of assets are of theoretical relevance to our conceptual
framework; (1) liquidity, (2) leveragability, and (3)

price-sensitivity.

4.4.2 Liquidity

Liquidity refers to a potential change in the value
of an asset depending on whether one has made a decision
to buy or sell that asset. That is one form of liquidity.
In that context liquidity is a time-dependent concept in
the sense that once one has decided to sell an asset the
present value of the asset may depend upon how long it
takes to find a buyer.

Baker and Hopkin (1969) used a definition of liquidity
which determines the degree of liquidity within the context
of the value of the firm.

We conceive the value of a firm to be the amount
that could be obtained from sale of the firm as a
complete aggregate. The sale of asset(s) within the
firm will in general reduce the value remaining in
the firm by as much as, or more than, receipts from
the separately sold asset(s). If the cash acquired
from separate sale equals exactly the loss in firm
value, one concludes that the asset(s) is(are) per-
fectly liquid. For those assets whose sale would
contribute less to cash than to the reduction of re-
maining firm value, the asset(s) is(are) less than
perfectly liquid. (p. 1058)

73

While the above operational definition of liquidity
applies to the sale of an asset, it is also important to
consider an operational definition of how credit contri-
butes to firm liquidity, given that many assets are incor-
porated into the firm's financial structure as a basis for
credit, and not in anticipation of separate sale. Opera-
tionallm then, we can distinguish between a gglg form of
liquidity and a credit form of liquidity.

The concept of "credit-liquidity" is not new. It was
first suggested by Barry and Baker (1971) as one important
means by which farm managers can reduce risk. Credit-
liquidity can be operationalized if we consider the value
of an asset from its potential sale.

The use of assets to obtain credit is expected to in-
crease the financial base of the firm by some proportion
of the potential sale value. If the credit reserves ac-
quired are equal to the value of the assets then those
assets are said to be perfectly credit-liquid. To the
extent that this is not the case the asset or porthlio
of assets are less than perfectly credit—liquid.

As suggested above the credit form of liquidity is
both more available and more frequently used. Liquidity
has value to the firm since we can assign it either an ex-
plicit (interest) cost or an opportunity cost. In the case
of credit-liquidity the value of the financial characteris-
tic is suggested to derive in part from rights established

under alternative credit policies, rules, and practices.

74

4.4.3 Leveragability

Leveragability is defined as the ability of the owner
to borrow against the value of an asset. For example,
farmland and farm machinery provide leverage while growing
crops typically do not. Leverage is increased when credit
is used to purchase an asset. That credit may be obtained
through a loan secured by another asset, or by an unsecured
loan.

Leverage and credit—liquidity are near perfect sub-
stitutes in the financial management of the firm. While
unused credit provides credit-liquidity, the exchange of
credit for loans decreases credit-liquidity and increases
financial leverage. This suggests that there are some im-
portant costs/returns trade-offs to be considered by the
farm manager. The costs associated with greater credit—
liquidity are interest costs of maintaining reserves and
opportunity costs of using those funds (the increased re—
turns to be gained by the use of leverage). The returns
associated with greater credit—liquidity are derived from
reduced exposure to risk (the increased costs which would
be incurred if a higher leverage position were taken in—
stead).

A frequently used operational definition of leverage
is the ratio of total debt to total equity. Of course
there are a number of such leverage ratios possible for a

firm, ranging from a lower limit of zero (no debt) to some

75

upper limit. The upper limit is expected to be a function
of both the type of loan being made (real estate, nonreal
estate, operating) and the amount of financial risk a
lender is willing to assume at a given rate of interest.

Within the observed range of leverage ratios what
factors can be identified which account for the observed
differences between firms? Real estate provides for po-
tentially greater leverage than nonreal estate. One pos-
sible explanation for this involves a consideration of
security requirements.

While collateral security is almost universally
required for real estate loans, little unanimity of
opinion exists among lenders concerning collateral
for nonreal estate loans. Some lenders make a large
percentage of their nonreal estate loans on an un-
secured basis; others make few such loans that are
not protected by specific collateral. There are also
wide differences geographically that seem to be a
matter of custom. (Nelson, et.a., 1973, p. 244)

As a consequence, those firms which control greater real
estate assets may theoretically derive greater leverage
potential.

Leverage is a financial characteristic which has value
to the firm. Although no explicit cost can be attached to
the use of leverage beyond interest charges, the use of
leverage does have an explicit return associated with its
use. The use of debt acts as a multiplier with respect to
the marginal value product of those resources which are

added to the firm. The economic value of leverage is ex—

pected to originate with the rights exercised by the owner.

76

Credit policies, rules, and practices place important re—

strictions on the use of those rights.

4.4.4 Price-sensitivity

Price-sensitivity of an asset depends upon how freely
the market value of an asset appreciates or depreciates
with changes in the general level of prices. Real estate,
farm machinery and equipment, livestock, and crop inven-
tories exhibit varying degrees of price-sensitivity. Farm
real estate is certain to be considered as the most price-
sensitive of the physical assets listed, although livestock
and crop inventories show greater price volatility.

Price-sensitivity is potentially valuable to farmers
operating subject to a capital constraint. Increases in
the price level and accompanying increases in value—appre-
ciating assets increase the purchasing power of those as-
sets relative to other sources. Equity increases become
the property of the asset owner. Traditionally these
equity increases are thought to be of value when they
accrue as capital gains when the asset is liquidated.

Yet, price-generated increases in equity potentially have
another value.

Land price-generated increases, for example, can be
viewed as increased reserves which improve financial rae
tios (reduce the degree of financial leverage) and/or in?

crease financial flexibility. In this way price level

77

increases can yield associated increases in equity value
on a flow or annual basis.8
An important precondition for price-sensitivity to
have a continuing impact of firm growth is that credit
policies be in existence to provide additional credit to
the borrower on the basis of the improvement in financial
ratios. If these credit policies were not enacted there
would be no monetary advantage to the farmer in addition
to the right to the capital gains he would receive at the
time of sale. Credit policies, rules, and practices are
once again influential in determining the rights to bene-
fits from credit. As long as the terms of the loan are
met asset value appreciation, potential or realized, is

the right of the owner as well as the right to be taxed at

the lower capital gains rate.

4.4.5 Size Distribution of Farms

The foregoing discussion of financial characteristics
of assets and firms in the context of credit policies has
relevance to the issue of size distribution of farms.
Although several measures of size are possible, value of
total assets controlled or alternatively equity are useful

to consider, as well as the composition of those assets.

 

8For further discussion of this point see, Plaxico
and Kletke, (1979), pp. 327-328.

78

It has been argued that the initial resource endow-
ment is a crucial factor in realizing the benefits from
credit. Prospects for growth in size of the farm opera-
tion are intimately connected to credit policies, rules,
and practices which may offset uneven initial distribution
of factor ownership. In the process of redistributing the
access to credit, credit policies effectively modify the
size distribution of farms, initially and perhaps for sub-
sequent periods. One motivation for increasing the size
of operation may lie in the empirical evidence that in-
creasing, or at least constant, returns to size may exist
over significant ranges of farm production activities
(Scott, 1977; Madden, 1967).

One possible explanation for differences in rates of
expansion and financial growth between large, high—equity
and small, low-equity farms might be the initial equity
position and the debt-servicing obligation rather than
farm size factors such as acreage. This points to an im-
portant consideration of initial leverage position versus
initial endowment of land resources and how credit poli-
cies affect farm growth rates under variations in each

factor.

4.4.6 Credit Distribution, Asset Distribution, and Rights
Distribution as a Distribution Process

Our conceptualization of the distribution process ad—

vocates that several distributions (viz. credit, asset

79

ownership, and rights) are variables which are germane to
the distributional impacts of credit policies, rules, and
practices. Moreover, those distributions are affected
either directly or indirectly by technological change and
changes in the levels of prices.

There exists a close relationship between property
rights and pecuniary externalities (external costs and
benefits which can be expressed in monetary terms and whose
effects can be transmitted through the market). Property
rights perform important allocative and distributive func-
tions as they facilitate the ability of firms and house-
holds to internalize those effects.

Changes in knowledge result in changes in produc-
tion functions, market values, and aspirations. New
technologies, new ways of doing the same things, and
doing new things - all invoke harmful and beneficial
effects to which society has not been accustomed ...
prOperty rights develop to internalize externalities
when the gains of internalization become larger than
the cost of internalization. Increased internaliza-
tion, in the main, results from changes in economic
values, changes which stem from the development of
new technology and the opening of new markets, changes
to which old property rights are poorly attuned.
(Demsetz, 1967, p. 350)

The importance of the link between technology and
credit has been recognized for some time. Tinnermeier
(1973) states that, "agricultural credit will be ineffec-
tive without technology which is profitable to the farmer."9

During technological change the formerly external benefits,

which accrue as increased output, are internalized by the

 

9Tinnermeier (1973), p. 94.

80

adopting firm through its borrowing and investing activi-
ties. Liquidity and leveragability of the firm's assets
are instrumental to the internalization of those benefits.

Price level changes have been shown to be related to
increases in the firm's equity. If the firm already owns
the price-sensitive assets it has effectively internalized
the benefits. However, a farmer who is just getting es—
tablished may lack ownership and control. One investment
strategy is to consciously seek out opportunities to in-
vest in value-appreciating assets. There are definite
limits to which that financing strategy can be pursued.
One important limitation is the cash flow required to ser-
vice the increased debt.

Price level and technological changes are two exter-
nal economic forces which redistribute costs and benefits
among firms and households. The maintained hypothesis
which serves as the basis for our conceptualization of the
distribution process is: In a dynamic economic environ-
ment characterized by technological change and price level
increases the distribution of monetary benefits (and costs)
is internalized in accordance with existing rights as they
are established or subsequently modified by credit poli-

cies, rules, and practices.

81

4.5 Credit Policies and Their Implementation

 

Although the conceptualization is sufficiently general
to be useful for evaluating several lending alternatives,
two primary lending criteria are under examination here.
These two criteria form the basis for several credit policy
alternatives. Those two criteria are repayment or debt-
servicing capacity and risk-bearing capacity of the firm.
The importance of these two criteria in credit policy
formulation is well recognized.

The conventional processes of achieving owner—
operatorship are being supplemented by various forms
of low-equity financing. Probably most critical in
our present situation is a need for more emphasis
on productivity of resources and repayment capacity,
in contrast to a traditional emphasis on equity of
the borrower. This development in lending practice
is of particular significance in that it still has
relevance when resource ownership and operatorship
are at least partially separated. Government pro—
grams (largely in Farmer's Home Administration)
have pioneered the development and application of
this concept and associated techniques to agricul—
tural credit. In the purely commercial credit field
it still has application but encounters legal and
institutional limits. (Staniforth, 1972, p. 58)

The loan contract is an instrument which interprets
credit policy. The contractual arrangement is an implemen-
tation of the particular credit policy design which is
chosen. This implementation is carried out by means of
several lending rules and practices. Four general rules
can be identified:

1. screening rules which select viable borrowers

from among all potential borrowers, that is to

say who qualifies for credit,

82

2. lending limitations which vary by type of loan,
security offered, amount of borrower assets, re-
payment potential, etc.,

3. terms of repayment, that is, length of repayment
period, schedule for repayment, options for re-
financing, etc., and

4. specification of loan uses where funding is tied
to specific use categories, (e.g., land or dwell-
ing improvement, purchase of machines and imple-
ments, storage facilities, operating capital,
etc.).

These four rules represent different, yet interrelated,
types of credit regulations. Shaffer (1979) suggests that
such regulations structure the opportunity set, modify be-
havior and shape performance in important ways. The distri-
bution process outlined above is such a regulatory process.
The rules and practices of lenders regulate the distribu-
tion of credit using the four types of rules identified.

Shaffer identifies five types of regulations two of
which are relevant here. Lenders allocate credit according

to custom and standard operating procedures. Statutory

 

law has to be considered in the case of usury laws, which
place legal limits on the rate of interest which can be
charged, and auxilliary legislation such as "truth in lend—
ing" laws. Our main concern here is with informal rules
and practices (the customs and standard operating proce—

dures) which often become formalized in the loan contract.

83

Lending limitations represent frequently used standard

operating procedures of lenders for dealing with risk.
Interest rates on farm loans from the cooperative

Farm Credit System, rural banks, and other lenders

may increase to build reserves for expected loan

losses, but these rates seldom vary among borrowers.

The brunt of risk pricing for farm debt is expressed

in terms of loan limits that differ among borrowers -

a more inefficient and less effective pricing mech-

anism. (Barry and Fraser, 1976, p. 294)

These standard operating procedures have been accompanied
by lenders' requirements for higher margins of borrower
equity and additional security.10

Rules of thumb are frequently used by lenders to im-
plement these credit policies. These rules provide for a
simple, uniform means of making decisions about when and
when not to lend. For example a loan application can be
approved or disapproved on the basis of the borrower's net
capital ratio (total assets/total debt), or debt:equity
ratio (total debt/total net worth).

It is customary for a loan contract to include a sche-
dule of repayments indicating both the amount of each in-
stallment and the date on which it is due. The terms of
repayment reflect lending custom in the sense that amorti—
zation schedules, length of repayment, and even options to
refinance a loan often reflect the same business practices

and lending arrangements which have traditionally been

used. For example, real estate mortgages traditionally

 

10Barry and Fraser (1976) suggest that these and
other conservative lending practices have increased the
effective costs of debt.

84

have a life of 20 to 30 years with equal amortization pay-
ments over the life of the loan. Variations to extend the
length of loan or allow for an accelerated or deferred
loan payment schedule are possible, but not frequently
employed.

A second aspect of the terms of repayment is the
amount of down payment which is customarily required of
the purchaser to secure financing of an asset. It is well
recognized that the larger the down payment, the smaller
are the necessary installments to amortize the loan. Some
borrowers may not have the cash to make a conventional
down payment of, say 20 percent. These same borrowers may,
however, have sufficient income—generating potential to
make the required repayments if a lower percentage down
payment were instituted.

Specification of loan uses represents another customs
ary lending practice as observed by Baker (1968).

We found primary lenders (banks and production
credit associations) to prefer loans that are (l)
self—liquidating and (2) asset-generating.

A self-liquidating loan is one in which the
proceeds are so used that the payoff occurs within
the maturity of the loan. An example is a ones
year loan for nitrogenous fertilizer. An assets
generating loan is one in which the proceeds are
used to add an asset that can be formally pledged
as a lien in support of the loan. A machinery
loan has this property. (p. 519)

Lenders' preferences for loans with these two characteris-
tics indicate that the specification of loan uses may be a

more subtle means of regulating the distribution of credit

among borrowers. Although no explicit regulation is in

85

force, the lender's discretionary control over credit for
certain uses represents an important lending custom. Pref-
erence for both self-liquidating and asset-generating as-
pects may be interpreted as being favorable to loans which
have the potential for both debt-servicing and risk-bearing,

although this need not be the case.

4.6 Summary

 

A conceptualization of the distribution process lar-
gely reflects what is taken into account. A "systems
approach" integrates aspects of institutional economics
and marginal productivity theory of distribution.

Our conceptual framework considers the benefits and
costs of credit as they are internalized through rights to
income, assets appreciation or depreciation, and other
financial benefits. It is useful to View credit policies
as effectively modifying the distributions of credit,
assets, and ultimately rights to benefits from the use of
credit as suggested in the causal loop diagram presented.
The maintained hypothesis is that the benefits and costs
of credit in a dynamic environment are internalized accord-
ing to the exercise of existing rights, as they are estab-
lished or subsequently modified by credit policies, rules,
and practices. Implementation of credit policies through
enactment and institution of lending rules and practices
leads to important modifications of intended credit policy

distributional impacts.

86

The distribution process identified is functionally
similar to other regulatory processes which employ customs
and standard operating procedures to implement policies.
The distribution process is evolutionary in nature as
lending rules and practices gradually change over time.

To analyze the distributional impacts generated under al-
ternative credit policies there is a need to identify the
major participants, their motivations and behavior, and
specify in greater detail the interactions between partici-

pants in the distribution process over time.

CHAPTER V

A Mathematical Model of the Distributional
Impacts of Credit

5.1 Introduction

 

The previous chapter outlined a conceptualization of
the process by which credit has distributional impacts.
Credit is hypothesized to have two major impacts: (1) an
income redistributional impact and (2) an asset redistri-
butional impact. Both impacts are hypothesized to be more
pronounced in a dynamic environment where productive op-
portunities and the distribution of assets change over
time. In addition the cumulative impacts of credit poli—
cies are expected to be quite different depending upon the
degree to which those policies emphasize the value of
assets controlled or income-generating capacity of the
borrower. In this chapter a theoretical model will be
developed which can be used to illustrate those differences.

Dynamic elements of the distributional process, as
identified, will be emphasized in the theoretical model.
Those elements include; (1) changing commodity price dis-
tributions and price expectations, (2) growth in the value
of assets, (3) technological change, and (4) changes in

the availability of credit.

87

88

5.2 Model Participants and Their Economic Behavior

Major components of the theoretical model include;
(1) the participants, (2) the markets in which they inter-
act, and (3) the assets which they control. Three general
types of participants are identified in the model; (1)
representative farm firm-households, (2) a lending insti-
tution, and (3) a government. These actors participate in
three markets; (1) a commodity market (a real market), (2)
a market in physical assets (also a real market), and (3)
a market in financial assets (a paper market). Three
types of assets are considered in the model; (1) real es-
tate, (2) all other productive physical assets (machinery,
equipment, and crop inventories), and (3) cash. The fi-
nancial market consists of transactions in cash, ownership
claims, and contracts. The physical assets market is a
market in durable capital and nondurable inputs. Finally,

the model has only one commodity — corn.

5.2.1 Farm Firm-households

Farm firm—households are complex, integrated economic
decisionmaking units which undertake several activities to
simultaneously meet the current needs of the farm business
and household. They are also planning units making deci—
sions about future activities with limited information
about what economic conditions will prevail as those plans

are executed.

89

In each period the needs of the business and household
compete for basically the same set of resources. Each
firm-household is assumed to derive utility from consump-
tion and net worth.

Firm growth is achieved through numerous decisions
and economic activities which place varying weights on
consumption and net worth in the utility function. Con-
sumption and net worth in turn are complex variables which
depend upon preferences for liquidity, risk preferences,
and minimal or subsistence requirements.

Firm-households participate in all three markets.
Their portfolios consist of ownership claims to physical
assets, crop inventories, and cash reserves. The economic
activities of firm-households include:

1. production and sale of corn,

2. investing in physical, productive assets from

earnings,

3. borrowing and repaying loans,

4. earning off-farm income,

5. paying income and social security taxes, and

6. consuming durable and nondurable goods from after-

tax income.

5.2.2 Lending Institution

The credit institution participates in the market for

11

financial assets. It determines and implements credit

 

11For our purposes this lending institution (continued)

90

policies in accordance with several broad objectives. Two
suggested objectives are; (1) to minimize the costs of
lending activities and (2) to remain viable as a lending
institution. Those two goals may conflict when specific
credit distribution policies are considered. Allocating
large amounts of credit to a few borrowers may minimize
the costs of lending. Yet, in a risk situation where re-
payment is not guaranteed the lender may become insolvent
if a few such borrowers default by nonrepayment of their
loans. Conversely, a policy of allocating smaller amounts
of credit to all borrowers drives up transaction costs for
the lender.

Economic activities of the lending institution are
initially assumed to be quite simple. The lender provides
cash balances in exchange for credit. In turn repayment
is received for the principal amount loaned plus an inter-
est charge. The lender's portfolio consists of cash and

financial contracts.

 

11(continued) abstracts from individuals and other
private lenders who finance the transfer of real estate
and some nonreal estate through contract for deed or other
informal means of financing. That is not to imply that
private individuals are of no consequence to the distri-
butional impacts which are under investigation. These
individuals and other private lenders held approximately
40 percent of farm real estate debt in the U.S. from 1960
to 1978 (Board of Governors of the Federal Reserve System,
1977, pp. 14-15). Their role has been somewhat less sig-
nificant in the area of farm nonreal estate debt. The
concern here is with formal lending institutions such as
commercial banks, Federal Land Banks, Production Credit
Associations, and the Farmers Home Administration. The
lending institution being described is representative for
those formal lenders.

91

The behavior motivations of the lender are expected
to be different from those of the borrowing farmer. Un-
like the farmer, the lender does not participate in profit
expectations. Also, the lender may be asked to finance
the acquisition of a long-term asset with loans of a shorter
maturity. The payoff period for the lender may in this
case be significantly shorter than that for the borrower

(Baker, 1968).

5.2.3 Government

Government participates in two of the three markets -
the commodity market and the financial market. A highly
simplified set of behavioral assumptions is made about the
government. Its objective is to stabilize returns from
farming for farm firm-households. To accomplish that goal
the government uses a support price program which operates
to set a floor price and a ceiling price for corn. Support
prices are operationally set to a percentage of the cost
of production (excluding land charges) plus an arbitrary
percentage rate of return on land to cover taxes and main-
tenance costs.

To finance its commodity programs the government
collects income taxes from farm households in each period.
In addition to cash (in the form of tax receipts) the gov-
ernment portfolio consists of contracts with farms for de-
livery of the crop inventories which are being stored on

farms.

92

Our description of the participants and their behav-
ioral characteristics is purposely simplified in both be-
havior and organization. This degree of abstraction is
thought necessary to focus on the essential distributional
impacts of credit policies in an otherwise complex dynamic

economic environment.

5.3 A Systems View of the Representative Farm Firm-house-
hold

 

Representative farms have already been briefly iden-
tified in terms of their behavioral characteristics and
financial activities. There is still a need to consider
the firm-level financial structure within which those be-
havioral interactions occur.

Figure 5.1 is a block diagram which illustrates the
financial structure for a representative farm. Block dia-
grams symbolically use blocks to denote physical and be-
havioral processes, or sets of interactions. The arrows
connecting the blocks represent financial flows. Inputs
(stimuli) are shown as arrows into a block, and outputs
(responses) are indicated by arrows which leave a block.
With the exception of the production process the connecting
arrows are cash flows. In this way the diagram is useful
as a modified cash flow approach to the modeling of the
impacts of credit.

Decisions about which processes to include in the dia-

gram were based upon economic theory and other modeling

93

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

loan
cash from loan: ] limitations
, Investment
pr1ce of Jase Process . ' ,
. l
:cash from 8 '
Input sav1ngs f'\ 3 Capital
Market : m :3 ' Market
3 s -
| 3.? a l i
“L . .
l 1;: | a
u o u
1 Production "4 3 e {13,
Process % I a)» p
I 0 “:2;
I o
u
l as!
Commodity 1% cost of product1on' Accounting
Market I gross receipts Process 18'
I l ‘ I’
4 A '
I disposable :
I income m '
Consumption 8
' Process ex enditures l
I Off-farm I
Earnings
I Endggenous___ J

 

 

 

 

 

 

 

Exogenous

Figure 5.1. General Block Diagram of Representative Firm and
Firm-Household Financial Structure

94

considerations. For example, theory of the firm suggests
that the rate of investment will affect farm production
capacity and the rate of output. Similarly, consumption
levels affect the availability of funds for reinvestment
in the farming enterprise. Each block in the diagram rep—
resents one or more functional relationships or equations
which relate inputs to outputs.

The block diagram provides for an emphasis on finan-
cial structure, yet requires that a certain amount of de-
tail be omitted. Processes such as production and invest-
ment are structurally different from market processes such
as the commodity and financial markets. Conceptually, the
diagram may be inadequate for a detailed consideration of
farm-level structure. It does, however, identify the major
linkages between internal processes of the farm and cer-
tain key markets.

Boundaries of the farm firm-household are indicated
by the broken line. The boundaries illustrate the extent
to which managerial decisions and control are exercised in
the allocation of resources within the farm. In this for—
mulation the exogenous factors and policy parameters are:
El (credit policy limitations, interest rates), E2 (gov—
ernment programs), E3 (weather, technological progress),
E4 (price levels, changes in price levels), and E5 (taxes,

changes in price levels).

95

5.4 Specification of the Theoretical Relationships

Numerous alternative theoretical models could be spe-
cified and used to illustrate various aspects of the con-
ceptual approach described in Chapter IV. To integrate
the production, investment, and consumption activities of
the firm-household unit with credit policies and government
support price policies, however, requires a somewhat unique
set of model relationships. What follows is a mathematical
description of the theoretical relationships which were
identified in figure 5.1. The theoretical arguments draw
from several sources: Boehlje and Griffin, 1979; Henderson

and Quandt, 1971; Vickers, 1968; and others.

5.5 Production and Cost Relationships

 

A simple production function can be specified for a
farm producing a single output, Q, from two variable inputs,
X1 and X2, and a fixed level of service inputs, F, from a
durable factor as follows:

(5.1) Q = f (X1. X2, F)

The rate at which services are extracted from the

durable during the year is given by the function,

(5.2) F = F (x3, IIS)
where X3 = a variable input
I = level of net investment during the year
S = beginning stock of the durable in the cur-

rent year.

96

Net investment is chosen instead of gross investment since
the rate of depreciation is not explicitly considered.

The variable cost function, C, is defined for varying
levels of X1 and X2 as the least-cost combination of those
inputs for given levels of Q and F.12

(5.3) C = C (O. F)

Variable costs as specified in equation 5.3 comprise
an important part of the total production costs. Total
production costs include other costs which are independent
of the level of output. An expression for the total costs
of production is,

(5.4) TC = VC + MC + CC + MGT + LC

where, TC = total production costs

VC = variable costs

MC = machinery ownership costs
OC = overhead costs
MGT = management costs

LC = land charges

Equations 5.3 and 5.4 represent static relationships
which hold for the current period.3 A dynamic total cost

relationship can be specified as,13

 

12The variable cost function can be analytically de-
rived from the production function, the cost equation (for
the variable inputs) and the expansion path. See Henderson
and Quandt (1971, p. 84) for such a derivation using a two-
factor Cobb-Douglas production function.

13Dynamic in this context refers specifically to the
fact that current values of the variable depend upon past
values of that variable.

97

(5.5) ret = TCt_1 (1. + 91)

where 61 = a parameter for the annual rate of increase

of total costs, 0 §_61

The production relation in equation 5.1 could, alter-
natively, be interpreted as a function of the productive
assets of the farm. Reformulating equation 5.1, the pro-
duction relation for the t-th period is,

= ¢ A (1. + ez)t'l

t

total productive assets in period t, in

where, A
dollars
= (nondurable + durable + real estate)
82 = a parameter to account for rate of tech-
nological change in the form of increased
yields per unit of input, 0 i 82
¢ = yield in bu./acre
Total productive assets, A, represents a set of inputs
which is combined in those proportions which are necessary
for the firm to operate along its theoretical expansion
path. A necessary condition for the firm to operate along
that growth path is,
(5.7) ~dXi/dxj = Pj/Pi
Equation 5.7 simply states that for a firm to produce a
desired level of output in a least-cost way, it should
combine those inputs in such a way as to equate the ratio

of the marginal products to the ratio of the input prices.

98

This result from static production theory assumes that all
net investments occur out of the firm's own internal fi-

nancial resources.

5.6 Investment and Financing Relationships

 

Consider now the possibility that the firm cannot ob-
tain all the productive assets it needs without a limit.
Rather, the firm must rely on borrowed funds to finance
(at least partially) the purchase of the needed inputs.
The new optimizing condition 13,14

P. + m.

(5.8) -dXi/dxj = —l————1

O + 0
P]. m].

where, mi, mj = the marginal costs of finance for each

unit of input acquired using borrowed
funds, for the i-th and j-th inputs,
respectively.

If the ratio mj/mi equals the ratio Pj/Pi’ the same result

obtains as in equation 5.7. There exists, however, the

opportunity for a significant departure from the expansion

path combination of inputs, depending upon lender responses

to, and credit policies concerning, loan requests to fi—

nance the needed investments. In this way credit rules

and practices become potentially important determinants of

the marginal costs of financing.

The most readily recognized factor affecting the mar-

ginal cost of financing is the rate of interest charged on

 

l4C.B. Baker (1968), pp. 508-513.

99

the loan. Interest rates may vary by type of loan, and
the kind of assets being financed (e.g., loans to finance
purchase of operating inputs versus those to purchase dur—
able inputs).

In addition to these explicit cost factors we could
consider the effects of loan limits. Suppose that the
rate of interest for all loans is fixed at the market rate.
Lenders may exercise varying loan limits depending on the
type of loan. Credit may be absorbed more rapidly in the
purchase of certain lumpy, indivisible assets. If unused
credit has value to the firm, the amounts of each input
purchased by the firm will reflect the implicit costs
associated with loss of liquidity.

There are other potential costs of financing; (1) the
added risk which accompanies a higher leverage position,
and (2) the added risk that future credit may not be avail-
able if credit is exhausted earlier.

Credit policies which alter the effective cost of
inputs acquired by the firm are expected to lead to changes
in the productive organization of the firm. The assumption
of equation 5.6 that production occurs with optimally-
combined sets of assets appears quite restrictive when
considered in the context of various credit policies and
associated financing costs. An alternative to scale ad-
justments is to use additional variable inputs to substi-

tute for land. Some important substitutes for land are

100

biological and chemical inputs (e.g., new seed varieties,
fertilizers, pesticides, and herbicides).

Figure 5.2 illustrates the long run variable cost
function of equation 5.3 as farm size (number of tillable
acres) increases the variable costs decline initially at a
rapid rate and then at a gradually slower rate. This cost
curve is consistent with that found on Illinois farms by
Scott (1977), and Mueller and Hinton (1975) for corn.
These economies of size are eventually exhausted according
to Scott.

In the context of credit policies which place limita-
tions on the amount of credit available to purchase land,
machinery and other lumpy inputs an important alternative
is the purchase of land substitutes. In any given period
the producer can apply more of the variable inputs and
move along the short run marginal cost curve which becomes
the individual firm's supply curve. Theory of the firm
suggests that the profit-maximizing producer moves along
the upward sloping portion of the marginal cost curve to
the point where the marginal output cost just equals the
market price. Figure 5.3 illustrates the firm's short run
marginal cost curve and the theoretical profit-maximizing
level of output for the perfectly competitive producer.

An explicit expression can be derived for the marginal
cost curve in figure 5.3. Quantity produced is defined as
the yield per acre, Y, multiplied by the number of acres

cultivated, L.

101

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102

(5.9) Q = Y - L
Yield can be expressed as a function of the amount of vari-

able inputs used per acre.

(5.10) Y = Y x8
o
where, Y0 = some initial level of output per acre, a
constant
X = variable inputs per acre, measured as the
variable assets used
8 = a yield response parameter, 0<B<l

Solving a cost equation for X, substituting into equation
5.9, then solving for the cost variable, C, we have the
short run cost function. The marginal cost function is

derived by differentiating with respect to the level of
output; 11) 3C _ PX Q l-B/B

. was]
Since a dollar of variable inputs has a price of one,
equation 5.11 can be simplified and equated to the price
of the output to obtain the profit-maximizing level of

'k *
variable inputs, X , and the associated yield, Y ,

* , B/l—B
(5.12) Y Yo (PQBL)

*
(5.13) X

*
(Y Arc)“B
Cost functions such as in equations 5.3 and 5.11 can
be usefully combined to illustrate a firm level growth
process. While a firm may continue to expand by acquiring
more acreage it may not always have the financial resources
to do so. If credit policies restrict a firm from making

these lumpy investments in land the farmer may elect to

103

use available liquid assets and operating credit to pur-
chase greater variable inputs to substitute for land. The
costs of increasing yields and total output with variable
inputs involve higher cost alternatives as input substitu-
tion becomes more difficult. This suggests the upward
sloping marginal cost function as in equation 5.11.

The behavioral assumption underlying the implied
ratchet-like growth process is that the farmer determines
first if he is able to purchase additional land with equity
capital and long term credit. If those financial resources
are not sufficient to acquire land, then he utilizes his
liquid assets and available operating credit to move up
the short run marginal cost curve toward the profit-maxi-
mizing level of output using variable inputs to increase
yields. The extent to which the farmer is able to purchase
the additional variable inputs to increase yields depends
upon his available liquid assets and short run credit
policies. Each year the farmer is faced with the same set
of investment alternatives. In implementing this behavior
it will be assumed that the farmer is not able to invest

in both land and increased variable inputs in a given year.

5.7 Investments and Value of the Firm

 

The value of an asset or the firm in the current per—

iod, V0, can theoretically be found by capitalizing the

104

expected future stream of income at the appropriate rate.15

If we consider just the income stream and ignore the value
of the asset or firm at resale, we have the valuation for—
mula,

(5.14) V = E(Rt)/kt

t
where, E(Rt)

the expected return in period t

kt

The expected income stream is defined as

(5.15) E(Rt) =E(Pt) - TCt] ° Qt - rDt

the capitalization rate

where, E(Pt) = the expected price in period t
TCt = total production costs in period t
Qt = output level in period t
rD = the interest on debt in period t

t

The capitalization rate, k is specified as a func-

t,
tion of several other rates following the specific func-
tional form developed by Lee (1976). The general function-

al relationship is,

 

15If R is the expected return in each period and r is
the interest rate on borrowed funds, then current value is
comprised of two parts; (1) the discounted value of the
asset or firm when sold, Vo/(l+r)n, and (2) the discounted

earnings, R R R
‘1>S=71—:77+W2+°°-+n+—r7n

Since equation 1 is the sum of a geometric series, it can
be simplified. An equivalent expression for S is,

(2) S = R [1 - (l + r)-€] /r

Substituting this value for S into the two-part valuation
formula and taking the limit, we have an expression similar
to that in equation 5.14,

(3) Vo = R/r
with the interest rate, r, substituting for the capitaliza-
tion rate, k.

(5.16) k

where, CC

t

MTR

DP

GN

I

GLV

Lee developed

a

105

k (CC, MTR, r, DP, GNI, GLV)

the opportunity cost of capital, a per—
centage

the marginal tax rate, a percentage

the interest rate, a percentage

the down payment required on a real
estate loan

the expected rate of growth in net income
the expected rate of growth in land

values

discounted cash flow approach to arrive at

a formula for the maximum bid price for land. The maximum

bid price is the maximum price that a prospective buyer of

farm land could afford to pay if he based his decision to

invest or not to invest on net present value calculations

as suggested by Barry, Hopkin, and Baker (1979, Chapter 13).

The opportunity cost of capital variable, CC, is a

function of the variability of output price and the risk

free rate.
(5.17) C

where, Vl

C

2
2 6/0 pt

the risk free rate

MAX (V1, V

the maximum allowable opportunity cost
of capital

variance of the price expectations dis-
tribution

a parameter to account for risk aversion

of potential land purchasers, 6 > O

 

106

Since potential land buyers are assumed to be risk averse
the maximum opportunity cost of capital variable is reduced
to account for output price risk. The effect of increasing
0 (while holding V2 constant) is to decrease the after-tax
cost of capital, CC, which in turn increases the maximum

16 This

bid price for land in the model developed by Lee.
appears to be a logical result. A decrease in the oppor-
tunity cost of capital would increase the likelihood that
the yield or internal rate of return on land would exceed

the opportunity cost of capital making land an acceptable

investment alternative.

5.8 Household Consumption Activities

 

Expenditures for family living are significant to the
model of distributional impacts of credit for two reasons.
First, consumption both directly and indirectly affects
the ability of the farm to acquire additional productive
assets. Living expenses act as a direct drain on cash
flow and, thereby, reduce the debt-servicing capacity of
the firm in the current and future periods. Secondly,
family expenditures reflect the standard of living the
farm family can expect to achieve as expansion occurs.

Considerable controversy has surrounded the household
consumption function. Keynes (1936) noted a tendency in

cross-section data for the marginal propensity to save to

 

16Op. Cit.

107

rise as income increased. The work of Modigliani and
Brumberg (1954) and later Friedman (1957), in attempting
to reconcile the apparent inconsistency between aggregate
time-series data, and cross-section and budgetary data,
showed that such a relationship cannot be derived from
cross-section data. Modigliani and Brumberg pointed out
that such differences in the propensities to consume could
reflect different net worth positions of households at
varying levels of income and stages of the life-cycle.

In their investigation into the distributional effects
of the financial system in Mexico, Reynolds and Corredor
(1976) hypothesized that the cross-sectional savings func-
tion is quite nonlinear at low levels of income, but be-
comes linear as incomes rise over time. As a result mar-
ginal propensities to save converge across income levels
as incomes rise.

Although little research has been done on a consump-
tion function which is easily used in farm growth models,
Brake's (1968) empirical study of rural farm household
consumption provides some useful information. Brake fitted
a Cobb-Douglas type function to published data using after-
tax income and family size as regressors. The functional
form fitted to the arithmetic means of the data classes
gave a reasonably good fit. Results indicated that the
elasticity of current consumption out of after-tax income

is approximately 0.59.

108

Two aspects of this empirical work provide input into
our modeling effort. First, the function was fitted to

after-tax, disposable income. Second, the functional

 

form suggests that the marginal propensity to consume out
of after-tax current income declines as incomes increase
for the rural farm household.

The basic form of the household consumption function
to be used is consistent with Brake's findings. A subsis-
tence level of consumption is specified along with a log-
linear part using after-tax income,

_ a
(5.18) HCt - Max. (St’ Yt)

where, HC household consumption in period t

t
St = the minimum or subsistence level of con—
sumption for a family of four
Yt = after-tax income
a = a parameter for the elasticity of family

consumption out of current after-tax

income, 0 < a < l.

5.9 Commoditv Market

 

A government price support program is operative in the
commodity market. Price expectations are directly affected
as the support price prOgram modifies the price distribu—
tion. The price support activity provides for a dynamic
price adjustment mechanism in the commodity market. Gov-

ernment is assumed to follow current farm policy programs

109

which include provisions to increase the support levels
for feed grains depending upon increases in costs of pro-
duction. Those increased costs of production reflect
changes in input prices and production technology. This
support price adjustment process reflects an indexation of
support prices to the rate of inflation.

The support price adjustment mechanism is specified
as,

(5.19) SLt = G (H, TCt)

where, SLt the support price level per unit of

output
H = a policy variable which specifies which
costs are to be included and at what
levels
TCt = production costs per unit of output
The policy variable is specified as to the percentage of
total costs of production which will be supported by the
price program. Included in the support program is a man-
dated return to land.

Price support programs modify the price distribution
by truncating the lower tail of the distribution at the
support level. The modified distribution of price expec-
tations is comprised of two parts, the portion above the
support price (the first integral), and the portion below

the support price (the second integral),

110

cc

* 2
(5.20) E(P ) = Jf N(p' Pt’ 9 *) Pdp
t t
SL

t SL

+ t N(p P* 02 ) dp SL
I t, p; t
0

where, E(Pt) the price expectation in period t

*

Pt = the expected price for the normal dis-
tribution of prices assuming no support
_ * t-l
P = the price variable, a random variable
op*2 = the variance of the normal distribution
t

of prices
= (NP;)2
The price expectations model in equations 5.20 and
5.21 assumes that the unmodified price distribution is
normal, although other price distributions could be speci-
fied.17

The variance of the price expectation is defined as

the second moment of the modified price distribution,

2 _ m * 2 2
(5.21) 0p — f N(p. Pt. 0 *> p dp
p
t t
SL
t L

+ t N(p P* o 2) dp SL2 - E(P2)
o ' t, p; t 1:

Price supports have been shown to have an impact on
asset values over time, particularly land prices (Reynolds

and Timmons, 1969). The indexation of price support levels

 

17The technique which will be used to redistribute
the truncated portion of the price distribution is to
stack those probabilities at the support level. This is
the procedure used by Boehlje and Griffin (1979).

111

to costs of production provides a direct linkage between
governmental support activities and the escalation of land
values. Boehlje and Griffin (1979) have illustrated the
potential financial impacts from alternative percentage
levels of support of production costs. Indexed support
prices are expected to increase the guaranteed cash flow
of the farm business and reduce the financial risk. Bid
prices for land and other durable assets increase. Addi-
tionally, the improved equity position which results from
land value appreciation increases debt-carrying capacity,

financial leverage, and the rate of firm growth.

5.10 Capital Market and Credit Policy Relationships

 

Two forms of credit will be considered in the numeri-
cal analysis - investment credit and operating credit.
Investment credit is defined as credit used to acquire
land and the machinery complement to land. Operating
credit applies to those uses of funds which require cash
purchases. Operating loans are usually used to purchase
seeds, fertilizer, fuel, pesticides, and hire additional
labor and other resources.

As suggested earlier, investment credit and operating
credit are subject to credit limitations based upon (1)
risk-bearing capacity of the farm (i.e., the net capital
ratio) or (2) the repayment, debt-servicing capacity of

the farm (i.e., expected repayment capacity). Consistent

112

with those policy choices the following constraints are
placed upon the current availability of debt capital,

(5.22) D : plAt

t
(5.23) Dt : p2E(Rt)
where, At = total assets in period t
At = 0t (equ1ty) + Dt (debt)
E(Rt) = expected annual income of the firm

available for debt-servicing (after
consumption and taxes).

The dynamic nature of these and other credit policy
choices is reflected in the movement of individual firms
along the identified cost curves. From an initial resource
position to a position in a subsequent period the movement
of a firm along the cost curves represents a firm growth
process. As the firm continues to gain control over addi-
tional resources through the availability of credit the
distributional benefits of credit are realized. This dy-
namic financial adjustment process is conditional upon the
indexation of credit availability either to the net capital
ratio and risk-bearing capacity or the firm's expected re-

payment capacity.

CHAPTER VI

The Numerical Model

6.1 Farm Resource Situations

 

Three farm situations will be analyzed using the firm-
household systems framework. Those three farms differ
according to two resource categories - physical resource
endowment and financial resource endowment.

In the physical resource dimension the major consid-
eration is size of farm operation in terms of tillable
acres. Acreage farmed represents an important aspect of
size for a couple of reasons. First, the ability to gen-
erate output and farm income in an amount sufficient for
current consumption and investment needs is partly con-
strained by the available land base. Second, the alloca-
tion of farm operator and family labor between on-farm and
off-farm employments is likely to require a decision which
is influenced by number of acres per laborer. This issue
of on-farm versus off-farm employment is certainly a com—
plex issue. All that is being suggested is that size of
farm operation is likely to be a key variable in a consid-
eration of resource use and availability across farms.

Initial financial resources in terms of total farm
assets reflect the land base of the farm since real estate
comprises the majority of all farm assets. Yet, the land

base does not fully describe the financial situation of

113

114

the farm firm. Various equity positions are possible
ranging from zero net worth to complete equity ownership.
In addition to the initial equity position the debt servic-
ing obligation of the farm needs to be specified as an
initial condition of resource control.

It is assumed that yields and land price do not sys-
tematically vary by size of farm. Based upon available
data from Michigan Telfarm business summaries the former

. . 18
assumpt1on 1s reasonable.

The latter assumption is some-
what harder to substantiate, empirically. Yet, if land is
valued initially on the basis of its expected yield, the
equal-price assumption is consistent and reasonable. These
and other initial conditions are summarized later in this
chapter.

Farm type A represents a small land base farmer, typ-
ical of theyoung, beginning operator. Due to the recent
establishment of the farming operation the farm operator
is assumed to have limited initial equity. Therefore, a
70 percent debt/asset ratio (30% equity) is initially
assumed in this resource situation.

Farm type B is more typical of an average Michigan
cash grain operation. While all three farm situations

represent growing farms this average size unit is in what

might more accurately be labeled the growth stage of the

 

18Yields on farms with less than 400 acres, 400 to
800 acres, and over 800 acres showed no systematic varia-
tion by size of farm for 1977 or 1978, either on rented or
owned land.

115

firm life cycle.19 The land base of farm B is 320 acres

as an initial condition. Financially, farm B is in a
somewhat lower risk position than farm A. Farm B's opera-
tor initially has 50 percent equity in the operation.

Farm type C is representative of a well-established
farmer with a 640-acre land base. The size of operation
is intended to reflect a somewhat more advantageous physi-
cal and financial resource position. It is assumed that
farm C has a relatively low debt obligation with a 30
percent debt/asset ratio.

Annual debt service which represents payment of prin-
cipal and interest on outstanding debt is assumed to be
approximately equal to 10 percent of total farm debt.
Although various assumptions about the debt-service load
could be made, the initial assumption will be that these
costs are equal in proportion across farms.

To initialize the marginal tax rate variable it was
necessary to calculate income after expenses on each farm
for the initial year of the simulation and then find each
farm's marginal rate in the tax table. During subsequent
iterations of the model the 1979 U.S. tax table is auto-
matically used to calculate both the amount of Federal in-

come tax due and estimate the marginal tax rate for each

 

firm.20

19The life cycle of the firm is usually categorized
by establishment, growth, consolidation, and disinvestment
phases.

20

Commerce Clearing House, Inc., 1978.

116

6.2 The Simulation Model21

 

Figure 6.1 illustrates the major components of the
simulation model and the associated data requirements.
Sequencing of the components represents a logical process
where decisions or outcomes from preceding processes are
required as inputs into subsequent, or related processes.
The diagram appears to emphasize seriability of the activ-
ities, yet this aspect of the model is not strictly correct.
Time is incremented only after all components of the model
have been executed. If in fact all activities were com-
pleted strictly on the basis of information generated in
the current period, problems of simultaneity would arise.
In operation the model carries forward certain "state"
variables for use in a subsequent time period. An example
of this lagging of a state variable would‘be the acquisi-
tion of assets after net returns and value of the firm
have been determined. Newly acquired inputs then become
available for use the following year.

There exists a close correspondence between the simu-
lation model components in figure 6.1 and the processes
which were identified in the block diagram for the repre-
sentative firm-household unit in figure 5.1. To illustrate

that correspondence we shall sequentially go through figure

 

21The simulation model presented here is adapted from
an already existing model developed by Boehlje and Griffin
(1979). Certain modifications were made upon that model
to generate the numerical results which are reported in a
subsequent chapter of this study.

1

Computation Sequence

 

17

Data Requirements

 

 

 

Initialize Model
Parameters

.____J

 

Determine Production/
Cost of Production

 

 

Budget Information,
Rates of Inflation,

 

l

Generate Price
Distribution

I

Input Quantities

 

Initial Price
Distribution,

 

I

Mean and Variance

 

[Calculate Net Returns

 

Input Quantities and

 

1

[Valuation of Land

,Costs

1

 

tion, Tax Rates,

 

Terms of Loans

1

 

Determine Family
Consumption

 

Functional Form of
Family Consumption

 

l

[Asset Acquisition

Requirements

Rate of Capitaliza- 1

 

Q

Flow Limitations;

 

 

Figure 6.1.

I I

Debt/Equity and Cash
Y

Asset Complementarit

The Simulation Model

a.

v.

118

6.1 briefly describing the set of processes which are in-
volved and the subroutine(s) of the simulation model which
accomplish the indicated computations. The main program

and subroutines being referenced are listed in Appendix A.

6.2.1 Production and Cost of Production

Instead of modeling the productionprocess in figure
5.1 itself, by the use of production functions, the simu-
lation model employs cost curves to estimate variable and
fixed costs for each farm situation. To estimate variable
costs per bushel of corn for farms of varying size a quad-
ratic function was fit (using ordinary least squares) to
published Illinois farm budget data (Scott, 1977). The
estimated equation is,

2

(6.1) VC 107.24 - 2.139 A + .04015 A

R2 = .989, s.e.(at 640 acres) = .06

The coefficients are in cents per bushel of corn and the
independent variable, A, is hundreds of acres under culti-
vation. The variable cost function in equation 6.1 exhib-
its economies of size in corn production.

A similar function was fitted to the Illinois data to
estimate the fixed or ownership costs per bushel for farms
of varying size. The estimated equation is,

2

(6.2) PC 46.56 - 1.462 A + .03425 A

R2

.983, s.e.(at 640 acres) = .031

119

Ownership costs in this context refer to machinery owner-
ship costs and general farm overhead (primarily building
maintenance). Adding equations 6.1 and 6.2 together yields
an estimate of total non-land costs (in cents per bushel)
of corn.
(6.3) NC = 153.8 - 3.601 A + .0744 A2
By comparison with other published estimates on Lake States
and Corn Belt cash grain farms equation 6.3 appears to yield
reasonable estimates of the cost function coefficients
(Black, 1979).

Equations 6.1 - 6.3 are used throughout the simulation
program but are listed in theihitializations section. Ad-

justments are made to the Illinois data to make the cost

estimates comparable to Michigan corn grain budgets.

6.2.2 The Price Distribution

To generate an expected price and an associated vari-
ance of price consistent with government support program
objectives and the theoretical framework of Chapter 5, the
simulation employs a subroutine, PEM, and two auxilliary
functions, FUNCX and SIMPI.

Function FUNCX provides the functional form of the
initial price distribution (in this case a normal density)
to SIMPI. Function SIMPI is a Simpson's integration al—
gorithm which numerically integrates the area under the

price density function between any two bounds. In the

120

preceding chapter it was shown that the government support
program truncated the price distribution at the support
price. Function SIMPI integrates the lower portion of the
truncated density function and that area is then "stacked"
at the support price by subroutine PEM. Once the modified
density function is constructed PEM again uses Simpson's
integration to compute the first and second moments of the
modified price distribution between the support price and
the release price for corn.

Since the support price is set at a percentage of the
non-land cost of production the simulation model replicates
the interaction between the production process and commodity

market in figure 5.1.

6.2.3 Valuation of Land

Land is valued in the model by a higher level of in-
teraction between the capital market, the investment pro-
cess, and the input market in figure 5.1.

The simulation model employs subroutine MAXBID based
upon Lee's capital budgeting framework for investment in
land. Subroutine MAXBID receives variables such as annual
net returns, interest rates, marginal tax rates, and the
opportunity cost of capital for each farm situation and
determines the maximum bid price a farmer would pay under

those economic conditions.

121

The land valuation aspect of the simulation models
only the demand side of the land market. A critical assump-
tion under which the model operates is that land is avail-
able but to get it the price of land is bid to its maximum
price before it is acquired. Therefore, land always car-
ries its potential market price. One might be concerned
with this aspect of the model if deflationary conditions

were to be modeled.

6.2.4 Value of the Firm and Household Consumption

As illustrated in figure 5.1 the consumption process
and the accounting process are directly related. Both
processes and the off-farm earning activity are modeled in
subroutine FAM.

Subroutine FAM has an income section and a balance
sheet section. The income section calculates gross re-
ceipts, net farm income, income after taxes and household
consumption expenditures. Additionally, the income sec-
tion computes the debt service load and net reinvestment
income. The balance sheet section determines total assets,

total debts, equity, and the rate of growth of equity.

6.2.5 Asset Acquisition

Productive assets can be of two basic types - land
and its machinery complement or operating inputs. There-

fore, two acquisition activities are modeled. These

122

activities represent interactions between the input market,
investment process. capital market. and the accounting
process in figure 5.1.

Land acquisition occurs in subroutine LAM which con-
tains three primary real estate loan options; (1) the net
capital ratio (acres 1), (2) availability of liquid assets
for down payment (acres 2), and (3) ability to generate
sufficient income for loan repayment (acres 3).

If the operator's financial position and credit poli-
cies do not allow a land purchase, subroutine VAM is called
to provide the option to purchase additional operating in-
puts from liquid assets. Subroutine VAM allocates operat-
ing credit if liquid assets are insufficient to purchase
the desired level of variable inputs per acre. Operating
loans are made on the basis of ability to generate net

income to repay the loan.

6.3 Data Requirements and Sources

 

A conjectural simulation model such as described in
the proceeding section has the advantage of requiring
little in the way of input data for it to operate. An
associated disadvantage is that the model may be highly
sensitive to the data which is provided and, therefore,
values for key variables must be chosen to reflect the
actual situation and with an understanding of how each

variable is used. Three classes of data‘ must be provided;

123

(1) initial farm resource situations, (2) structural and

behavioral parameters, and (3) policy parameters.

6.3.1 Farm Resource Initializations

Initialization of each farm situation is summarized
in table 6.1. Assets for farm type B are based upon the
average Michigan cash grain farm business summary for 1978
(Kelsey & Johnson, 1979). The summary data reflects 67
farms in southern, lower Michigan. Total assets reflect
farm capital owned with land valued at $910 per acre.22
Farm types A and C are initially set at one-half and
twice the total assets of farm B, respectively. Debt-
service costs in each farm situation are set at approxi-
mately 10 percent of total liabilities. In fact debt-
service costs could vary greatly due to type of financing
of each farm (short, intermediate, or long term), the cor-
responding interest charges, and associated lengths of
loan. The equal percentage of debt-service costs provides
a useful benchmark for comparing alternative assumptions
about these loan maintenance costs.

Off-farm earnings in each farm situation reflect wages,
salaries, interest earnings, dividends, pensions, Social

Security benefits, and rental income from non-farm property

 

22The price of land is set at $910 per acre since
that represents the best available published estimate of
average farm real estate values in Michigan in 1978 (USDA/
ESCS, 1979).

124

Table 6.1.

Producer Resources Situations

Initial Conditions in Three Michigan Corn

 

 

 

 

 

Farm A Farm B Farm C
Production Situation:
Size (tillable acres) 160. 320. 640.
Yield (bu./acre) 95. 95. 95.
Nonland cost of production
($/bu.) 1.87 1.81 1.71
Total cost of production ($) 2.20 2.15 2.05
Financial Situation:
Land price ($) 910. 910. 910.
Assets ($1000) 203.6 407.1 814.3
Liabilities ($1000) 142.5 203.6 244.3
Equity ($1000) 61.1 203.6 570.0
Annual debt service ($1000) 13.6 20.3 26.3
Other Items:
Marginal tax rate (%) 22 32 50
Off-farm income ($) 8500. 5500. 4500.

 

125

as reported in the 1974 Census of Agriculture.23 Each

 

off-farm income figure in table 6.1 is the estimated geo-
metric mean for farm families with off-farm incomes by size

of farm (acreage).24

6.3.2 Structural and Behavioral Parameters

The structural variables which are of concern are
those which relate to the cost structure and production
activities of each farm. Cost parameter estimates were
obtained from published 1977 FEDS budget data (Krenz,

1978). Based on an expected yield of 95 bushels per acre:

1. total variable cost per acre (TVC) = $100.70

2. ownership costs per acre (OWN) = $32.97

3. overhead cost per acre (OVH) = $23.37

4. land charges per acre (XLC) = $47

5. land taxes per acre (XLTAX)25 = .016/$ market value

6. management charges (as percent of items 1-4), (XMC)

= 7 percent

7. general farm overhead (to include minimum family

consumption expenditures), (OVHEAD) = $10,000.

 

23
1977.

24The computed geometric mean is brought up to a 1978
basis by inflating the 1974 extimate by an inflationary
parameter of 6 percent per year.

25This value represents an average tax rate for agri-
cultural land in southern Michigan in 1978. (Michigan
State Tax Commission, 1978).

U.S. Department of Commerce, Bureau of the Census,

126

In addition to the above cost parameters four other
structural variables must be assigned values. The minimum
land parcel which can be purchased (ACMIN) is 40 acres.
Land can be acquired in single acre increments once the 40
acre minimum parcel has been purchased. Short-run produc-
tion response to added operating inputs is determined by
the output elasticity parameter, BETA. It is initially
set at .05, but can realistically vary between zero (no
response) and .075 (a response which increases current
yields to roughly twice the long-run yield level). The
government price support program contains two policy para-
meters. Percentage of support of cost of production (PCT)
is set at 90 percent of the non-land costs of production.
The return to land (RETL) is set at 1.5 percent of land
value. Both policy parameters could be varied from one
simulation to another but initially these values appear
consistent with the 1978 program.

Two behavioral variables are of major importance.
First, the consumption response of the farm family to in-
creased income after taxes is determined by the elasticity
parameter, GAMMA. Initially this elasticity is set at .95,
which means that for each 1.0 percent increase in after-
tax income the level of consumption expenditures increases
by .95 percent with .05 percent going to savings. Other
assumptions could be made about the magnitude of GAMMA.
Second, the liquidity preference of the farm operator is

specified in parameter, DELTA. Since liquid assets are

127

theoretically held for transactions and/or precautionary
purposes each farm operator is assumed to make available
only DELTA-percent of each year's beginning liquid assets.
Initially, DELTA is set at 50 percent. The assumption which
is common to both elasticity of consumption and liquidity
preference is that these variables are constant across
farm-household types. In fact these parameters could be
uniquely specified to reflect different behavioral charac-

teristics on each farm.

6.3.3 Credit Policy Parameters

Several credit policy variables are of great importance
to an evaluation of alternative lending criteria. There are
three sets of credit variables to consider; (1) interest
rates, (2) length of loan parameters, and (3) acquisition
constraints.

Interest rates on real estate mortgages are lower
than the rate charged on machinery loans and short-term
operating loans. Farm real estate interest rates are set
at the 1978 average Federal Land Bank rate of 8.75 percent.
The FLB rate represents an intermediate choice since it is
typically below rates charged by life insurance companies
yet above rates which exist on seller-financed real estate
loans. Machinery loans and operating loans are charged an
annual rate of 10 percent. That interest rate represents

an average rate charged by Production Credit Associations

128

and rural banks on non-real estate farm loans in 1978.26

Length of loan parameters are used to compute the
amortization payments on real estate and non-real estate
loans. Each firm's investment planning horizon (in years)
is used to compute the debt service costs on real estate.
This planning horizon varies between 20 and 30 years. A
second parameter is the number of years to amortize machin-
ery and other debts. These non real estate loans are
assumed to be repaid in 7 years. Operating loans are
assumed to be repaid within one year.

To effectively constrain the acquisition of assets
two credit policy parameters are manipulated (both in real
lending situations and in the context of the simulation
model). A debt/asset ratio ceiling is assigned for all
farms in their borrowing activities. Farms below the
ceiling are permitted to borrow for land and machinery
purchases, those above the ceiling cannot. Since the
ceiling is defined by the total debt/total asset ratio the
constraint is also binding on operating loans. The debt/
asset ceilhxgis initially set at 50 percent.

A second acquisition constraint is the down payment
requirement. Percentage down payment may vary by type of

loan (long-term, intermediate-term) and in conjunction with

 

26Although interest rates vary over time and between
lending institutions, the term structure of those rates
remains relatively constant. The particular values chosen
were compiled from USDA published data (USDA/ESCS, 1978).

129

the repayment schedule. Down payment may be an effective
barrier to firm growth by means of credit. This is ex-

pected to be especially true for lumpy investments. Ini-
tially the down payment is set at 25 percent of the asset
purchase price. This percentage will be varied to deter-
mine how sensitive the firm growth and distributional im-

pacts are to it.

6.3.4 Additional Model Parameters

In addition to those parameters identified above there
are some rates of growth and change which should be men-
tioned. Direct costs per bushel increase by six percent
per year in the basic model. Land prices grow at an annual
rate of five percent. Corn market prices are assumed to
increase by two percent each year to reflect an outward
shifting demand curve. The expected growth in net income
per acre is one percent per year. Finally, corn yields
are expected to increase along a long term trend line at a
rate of one percent per year. This latter increase in

yields reflects a constant rate of technological progress.

6.4 Strategy for Policnyvaluation

 

Iknumber of hypotheses have been advanced relating to
the anticipated distributional impacts of credit. Our
working hypothesis is that benefits and costs of credit

use within a dynamic environment are modified by credit

130

policies, rules, and practices. More specifically, lending
criteria redistribute the benefits and costs of credit when
technological change and price level increases are jointly
considered. These benefits and costs of credit are ex-
pected to vary between farm resource situations under dif-
ferent assumptions about; consumption behavior, investment
decisions, production alternatives, interest rates, and
down payment requirements.

In order to systematically evaluate alternative lend-
ing criteria to extract the greatest amount of information
about the above behavioral, structural, and policy vari-
ables a basic model is initially constructed. That basic
model uses values which were previously set in this chap-
ter. Numerical solutions to the basic model will be used
as the norm to which other solutions will be compared.

Variations on the basic model will be undertaken by
changing individual parameters while holding all other
parameters at their initial (basic model) levels. A simple
comparison of numerical results for the same lending cri-
terion under alternative parameter settings provides a
useful test of model sensitivity. It also provides in-
sights into the financial and distributional impacts which
evolve over time.

The basic model and all subsequent variations on that
model are essentially deterministic models. While the

price of corn is a random variable (a distribution of corn

131

prices exists about which expectations are formed), there

is no purely random or stochastic element in the model.27

 

27The potential for including such a random element
exists in the determination of short run yields.

CHAPTER VII

Review and Analysis of the Numerical Results

7.1 Results from the Basic Model

 

The numerical simulation model is an analytical tool
which provides a means for evaluating the distributional
impacts by farm situation of variations in lending criteria,
initial equity and debt load, repayment terms, consumption
behavior, and other key variables which affect firm growth.
A major objective of the simulation exercise is to identify
some of the necessary conditions for credit to have certain
redistributional effects. Hopefully, the results will in-
dicate the relative importance of various credit design
and implementation considerations, which is a major objec—
tive of the study. Results are drawn from selected years
to indicate the magnitudes and rates of growth of the per-
formance variables.

Initially, the basic model is reviewed in greater de-
tail to observe the growth and distribution processes as
they evolve. Summaries of the basic model cash flow, in-
come statement, and balance sheet are compared at 5 year
intervals. Variations of the basic model are reviewed and
contrasted on the basis of their cumulative effects by

looking at values in year 15 of the simulation.

132

133

7.1.1 Cash Flow of the Basic Model

Our basic analytical approach was to employ a modified
cash flow approach to evaluate the financial impacts of
credit. Tables 7.1 and 7.2 present summaries of the cash
flow situation on each farm over time. Credit is allocated
to each farm up to the minimum of either repayment capa-
city, risk-bearing capacity, or ability to make the required
down payment.

Total cash available is composed of initial cash on
hand, money borrowed during the year, and gross income
(gross farm receipts, off-farm earnings, plus other in-
come). Cash expenditures include cash farm expenses, in-
vestments, debt service costs, family consumption, income
taxes, and savings.

In table 7.1 gross income rises gradually onall farms
through year 10, as both price of corn and corn yields
increase. Gross income moves up sharply during the last
5 years as prices increase more rapidly and farms expand
in size. Borrowing, which primarily represents interme-
diate and long term debt for acquisition of land and ma-
chinery, occurs during the expansion years (years 12 to
15). The large farm (farm C) alone uses operating credit
in most years. Year 11 was the highest in this regard,
when $20,500 of short term credit was used to supplement

cash reserves.

134

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136

On the cash outflow side farm operating expenses in-
crease throughout the period due to rising costs of produc-
tion inputs. Operating costs make significant jumps in
those years when farm expansion occurs. Family consumption
expenditures set a relatively constant pattern on all farms.
Consumption on farm A is at or just above the minimum sub-
sistence level in most years. This result supports the
generally made assertion that small land base operations
find it difficult to generate sufficient disposable income
when a heavy debt load is also carried.

Taxes and savings show somewhat similar patterns of
increase. Farm C's tax burden rises sharply in the later
years along with income. The two smaller farms show lower
taxes in part due to operating costs and debt service costs
which cut into earnings. Savings fall during the expansion
years on farms A and B as one would expect, yet farm C
continues to increase its level of savings throughout the
last 5 expansionary years. Farm C's savings pattern is in
response to the increase in net returns per acre, which
escalate sharply in years 10 to 15. Withdrawals for taxes
and family consumption directly influence the amount of
reinvestment income or savings of the firm-household units.
Since investment tax credits are not considered in the
model the taxes paid each year are overstated for all
farms. Other factors such as propensity to consume are

equally important, since they significantly alter an

137

operator's or a lender's percentage of future repayment
capacity and ability to expand.

Ending cash balances generally reflect the process of
expansion, then consolidation, then expansion again. On
farm A expansion occurs in years 12 to 15. Farm B depletes
cash throughout the period as it uses cash to purchase
additional operating inputs. The reduction of cash re-
serves from year 10 to year 15 is also due partly to ex—
pansion which occurs in the intervening years. Farm C
starts out with large cash reserves but depletes them
rapidly as yields and returns are boosted by acquisition
of additional operating inputs in the early years. Cash
is kept low on the large farm as expansion occurs in years
12 to 14.

Generally, none of the farms reach the point where
additional short term credit is needed to meet current
debt obligations on time.28 Farm B is close to depleting
its cash reserves in year 15 when major enlargement of the
farm occurs.

The seventy percent initial debt/asset (D/A) ratio is
the most severe debt-servicing situation in the model. A
simple comparison with the corresponding entries in table

7.2 reveals that the cash flow situation is more flexible

 

281n the context of a deterministic model such as
this, low cash balances present no special problem. Yet,
in a situation where considerable variation occurs in
gross receipts (due to yield or price variability): low
cash balances could easily translate into cash flow short-
ages.

138

when the debt load and debt service costs are reduced
across farms of varying size, as one might expect. This
general result allows for firm expansion to occur signifi—
cantly earlier in all cases and allows all farms to achieve
a larger terminal size of operation.

In table 7.2 the lower debt load (greater equity)
position leads to significant increases in consumption
expenditures and savings by the firm-household unit. The
magnitude of change in these and other variables underscores
the importance which initial financial position of the firm
potentially has in leading to a realization of the benefits
associated with growth, holding the initial size of each

farm constant.

7.1.2 Variations of the Basic Model

Several modifications are made in the basic model to
determine the importance which key variables have for level
of income, capital accumulation, debt position, and overall
growth of each firm. In table 7.3 several models are iden-
tified along with the values each of the items take for
those simulations. The basic model, which generated the
preceding cash flow results and which will be used as the
standard for comparison, is model V. Models I-IV repre-
sent the primary lending alternatives under consideration;
(1) A1 refers to lending on the basis of the net capital

ratio and value of assets of a borrower (i.e., risk-bearing

1:39

Table 7.3. Model Numbers and Values Assigned to Items in the Basic Model and Variations
of the Basic Model

 

Items varied in the model

 

 

 

Down Maximum
Lending Length
Model Number criterionl/ of loan payment allowable Other
required D/A ratio
years percent percent

Io Al N 025 050 ..

II. A2 N .25 .50 --

III. A3 N .25 .50 --

IV. 80 N .25 .50 -

v‘g/ AMIN N .25 .50 -

VI. AMIN N + 10 .25 .50 --

VII. AMIN N .10 .50 -—

VIII. AMIN N .25 .75 -

rx. AMIN N + 10 .10 .50 --

X. AMIN N .25 .50 Linear consumption function

KI. AMIN N .25 .50 Increase yield response to
oper. inputs

XII. AMIN N .25 .50 Land values appreciate at 62

XIII. AMIN N .25 .50 Land appreciates at 82, inter-
est rates increase to 122

XIV. A2 N .25 50 (real estate), and 142 (non-
real estate), cost of inputs

XV. A3 N .25 50 rise 81

XVI AMIN N .25 50 Purchase land only if the bid
price is greater than or equal
to the market price

a/

- A1 refers to lending on the basis of value of assets or risk-bearing capacity.
A2 refers to lending to all borrowers who can make the required downpayment.
A3 refers to lending on the basis of ability to repay the loan out of reinvestment income.
80 refers to a lending strategy which applies equal weight to criteria A1 and A3.
AMIN refers to lending on the basis of the most limiting situation, the maximum of A1, A2,or A3.

32lThis is the basic model to be used in subsequent model comparisons.

140

capacity), (2) A2 denotes lending on the basis of the
borrower's ability to make the required down payment out
of his liquid assets, (3) A3 represents the situation
where loans are made on the basis of ability to generate
excess income which can be used to repay the loan (i.e.,
repayment capacity), and (4) E0 is a mixed lending rule
which applies equal weight to criteria Al and A3.

Models VI through IX explore variations in length of
loan, down payment requirement, and maximum allowable debt/
asset ratios. Essentially all four of these variations of
the basic model represent relaxations of the conditions
for either receiving credit or repaying the loan. Models
X through XVI explore alternative assumptions about con-
sumption, production, and investment activities. These
latter model specifications also perform sensitivity tests

on key parameters in the numerical model.

7.1.3 Firm Growth in the Basic Model

In table 7.4 the growth experiences of the three firm-
household units are summarized for comparison across farm
resource situations. Farm A starts from the highest D/A
ratio and represents a young farm operator's financial
situation. Farms B and C start with greater initial equity
and lower D/A ratios.

Annual net income per acre illustrates a severe cost-

price squeeze for farm A in years 1 through 10. The decline

141

 

 

 

 

 

 

 

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142

in net returns per acre is reflected in the falling bid
price for land, even as the capitalization rate decreases.
That trend is reversed in year 12 when the farm expands by
52 acres, thereby making more efficient use of fixed pro-
ductive resources as it moves downward along the long run
cost curve. Neither of the other two farms experience
such a prolonged decline in net income per acre.

Reduction of the capitalization rate for land primarily
reflects the effects of the government price support pro-
gram. The support price theoretically truncates the ex-
pected price distribution and thereby reduces the variance.
With a fall in the variance of price the opportunity cost
of capital falls due to decreased risk, and as the cost of
capital falls so does the capitalization rate on land.

The guaranteed cash flow aspect of the support program is
effectively capitalized into the bid price for land.

Land bid prices reflect the annual net incomes gener-
ated in each farm situation. Farm 0 generates substan-
tially higher net returns per acre and is capable of pay-
ing more for land in this framework. By year 15 the mar-
ket price for land has more than doubled going from $910
to $2030. In real dollar terms, however, there is no
change in the market price. In this same period farm size
has increased significantly. Farm A more than doubled its
acreage, farm B increased more than 2.5 times its initial
size, and farm C grew more than 4.5 times its starting

acreage.

143

By looking briefly at the summary data from the bal-
ance sheets it is clear that all farms have greatly in-
creased both their total assets and net worth positions.

A majority of the increase in total assets and net worth
in the farm operation is accounted for by the increase in
land and the value of land. Final total land value of
each farm increases approximately 2.2 times due to land
value appreciation alone. Net worth on farms A and B in-
crease more than 6 times over the fifteen year period
while farm C's equity capital increases more than eight
times its initial value. The debt/asset ratios of farms

A and B decrease dramatically by year 15 due primarily to
appreciation of land assets which inflate total assets and
each farm operator's net worth. Farm C's debt/asset ratio
remains near .30 since unit C increased its acreage and
land-based debt throughout the period. The reduction in
the D/A ratio between years 10 and 15 is again a reflec-

tion of land value increases.

7.1.4 The Influence of Rising Land Values on Firm Growth

A number of references have already been made to the
role of increasing land prices in the basic model. Land
value appreciation has two primary sources in the basic
numerical model. First, government commodity price sup-
ports serve to increase the operator's expected cash flow

by (1) raising the expected output price and (2) exerting

144

downward pressure on the capitalization rate (by reducing
the variance of the output price distribution). Second
buyers' expectations of land price increases are explicitly
included in the capital-budgeting subroutine in which the
maximum bid price of land is determined.

Escalating land values, by whichever means, lead to
two somewhat opposing conditions. The stream of unrealized
capital gains from land raise a firm's net capital ratio
and provide an increased borrowing base for continued ex-
pansion. Running contrary to that effect is the necessity
for cash flows (specifically, net reinvestment income) to
keep pace with the rise in land prices. Otherwise, ability
to service the greater debt load becomes a constraint to
further expansion.

In order to evaluate how sensitive firm growth in the
basic model is to buyers' expectations a run was made with
that expectation parameter set equal to zero. Bid prices
for land by the end of the period were higher than they
were at the start of the simulation, but were unchanged in
real terms. Those land prices were significantly below
bid prices generated in the basic model. When compared
with the basic model results farm sizes were sharply re-
duced on small and intermediate-sized farms where initial
D/A ratios were initially higher. Those farms with low
initial D/A ratios (30 percent) dramatically increased in
size. Apparently, the reduction in land price-generated

equity increases greatly diminished the borrowing base of

145

highly leveraged firms. Meanwhile, relatively cheaper
land augmented the financial resources and ability of
other farm units to expand their sizes of operation. In
this regard smaller, 160-acre farms with adequate equity
participated in firm expansion along with larger, 640-
acre operations indicating that cash flow was adequate for

expansion regardless of initial farm acreage.

7.2 Distributional Impacts of Alternative Lending
Criteria49

7.2.1 Lending According to Value of Assets Controlled
(Model I)

When risk-bearing capacity is the sole criterion for
lending, all farms increase both in size of operation and
operator net worth. Table 7.5 reports on the growth of
the three representative farms. Lenders allocate credit
on the basis of total assets and in direct proportion to
the difference between the firm's current debt/asset ratio

30 It is antic-

and the maximum allowable debt/asset ratio.
ipated that farms with high net capital ratios (low D/A
ratios) such as farm C will capture the greatest benefits

from credit when lenders use this criterion.

 

29Comparisons of nominal values generated under various
lending alternatives in this section represent comparisons
with those values obtained in the basic model unless other-
wise stated.

30The risk-bearing capacity criterion is operational-
ized in the numerical model as f lows- a lender sets avail-
able investment credit equal to l.-d/ times total borrower
assets. Here, d is the borrower's current D/A ratio and
is the maximum allowable D/A ratio (.5 in the basic model).

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147

Comparison of the values in table 7.5 with those in
table 7.4 provides evidence that farm A is able to achieve
slightly larger size of operation. This growth occurs, as
before, in the last 5 years when the firm's total assets
inflated sufficiently for the D/A ratio to fall and credit
became available. Expansion of farm A is inevitable at
some point in time due to asset value appreciation, as long
as operator A continues to meet the service costs on his
initial indebtedness. This result is an illustration of
the third benefit from credit, identified in Chapter IV as
the right to appreciation of assets or investments as long
as the terms of the loan are met. Farm A's equity increases
slightly to $350,000 ($234,600 in real terms) under the
risk-bearing criterion as total land value rises by more
than total farm debt. The fact that current income and
family consumption are essentially unaltered by the change
in lending rule indicates that most of the impact of the
rule change is on the firm's debt-asset structure.3

Farm B's experience under the lending rule change is
similar to that of farm A. Expansion occurs earlier on
farm B since it starts from a lower initial D/A ratio.
Farm size increases by 84 acres when the lender considers
just assets controlled and the operator's net capital

ratio. Total equity grows by 7 percent to just over $1.4

 

31Since consumption occurs totally out of current
income the increase in net worth has no immediate impact
on the level of consumption expenditures. A modification
of the basic model would be to allow for consumption out
of increases in equity as well.

148

million (just over $.6 million in real dollars) 86 percent
of which is accounted for by real estate.

Farm C shows the most dramatic effects of the change
in lending criterion. Changes occur in both income state-
ment and balance sheet statistics. Cultivated acreage
expands to 3795 acres by year 15. That size of operation
is larger than the minimum long run average cost acreage.
Since economies of size are exhausted the annual net in-
come per acre declines by $25 per acre. Resultant changes
occur in disposable income and consumption expenditures.

The bid price for land also declines with the fall in
net income per acre. The lower bid price is not sufficient-
ly large, however, to offset the larger farm size and both
total assets and operator net worth increase dramatically.
Farm C's equity in the final period grows to $5.77 million
($2.55 million in real dollars) for a 20 percent increase

over the basic model result.

7.2.2 Lending on the Basis of Ability to Make the Down
Payment (Model II)
The distributional impacts of this lending criterion
are substantially different from those we have just seen.
In table 7.6 the summary data indicate that farms A and B

obtain substantial benefits from the change in lending rule.

 

32Ability to make the required down payment on land
and machin ry is defined in the numerical model by the ex—
pression; total liquid assets/(price of land and machinery
per acre times percentage down payment) .

149

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150

Although net income per acre on farm A does not change
the size of operation expands by 304 acres in year 15. One
important change does occur in net income per acre. Farm
A no longer receives a negative net return as it had for-
merly. Expansion occurs earlier for farm A under this
lending criterion and the larger size of operation leads
to reduced costs per acre. Consequently, income after
taxes increases by nearly $30,000 and family consumption
doubles to $23,800. The combination of a substantially
larger operation and slightly lower land prices translates
into a $475,000 increase in equity for operator A. The
increase in equity is reflected by the 4 percent increase
in the rate of growth. Operator A also achieves a lower
risk position with a .35 D/A ratio in year 15.

Farm B obtains a substantial increase in benefits from
the rule change. By year 15 farm size has grown by an ad-
ditional 700 acres. The increase in farm size leads to
economies of size and a corresponding increase in net re-
turns per acre. Disposable income rises by $20,000 in
year 15 and family consumption is higher in years 10 through
15. The higher land price and increased farm size are the
primary contributors to a one million dollar increase in
net worth by the final year. Equity grows at an annual
rate of 16.2 percent over the 15 year period, the largest
growth rate of all three farms.

The impact of the rule change on farm C is a mix of

financial gains and losses. Farm size has again increased

151

but is only slightly higher than in the basic model at
3070 acres. Net income per acre has again fallen indi-
cating that the farm is operating on the upward-sloping
segment of the long run cost curve. Both after-tax income
and family consumption expenditures are lower in the later
years than their levels in the basic model. Total opera-
tor's net worth is up slightly to $4.87 million ($2.15
million in real dollars) due to a combination of larger
farm size and lower farm debt. The D/A ratio is essen-
tially unchanged from the basic model, but is lower than

the corresponding value in model I.

7.2.3 Lending According to Ability to Generate Income
Sufficient for Repayment (Model III)

Credit is extended under this policy option in propor-
tion to repayment capacity of the borrower. Repayment
capacity is operatinally defined as the amount of income
after taxes and consumption which is available to service

debts.33

Defined in this way, the availability of credit
is highly dependent upon the consumption behavior of the

household.

 

33A borrower's ability to repay a real estate loan
was determined in the numerical model by the following
expression:

(current net reinvestment income times present value

factor for that annuity taken N years into the

future)
The present value factor is the one determined by the
mortgage rate of interest on real estate loans.

152

The pattern of distributional impacts is quite similar
to that in model II. In table 7.7 farm A experiences lower
levels of consumption on somewhat lower disposable income.
Size of operation is 201 acres larger than in the basic
model, and expansion occurs considerablv earlier. The
effects of both aspects of growth is to raise the value of
total farm assets and operator's net worth by the fifteenth
year. Net worth on farm A increases by over 50 percent as
a result of the credit rule change.

As in model II, the financial benefits which accrue
in this lending model (model III) are relatively greater
for farm B than for either of the other two farm situations.
Farm B increases current disposable income by nearly
$22,000 in year 15. Family consumption also responds to
the change in income under the rule change. This illus-
trates the second benefit from credit which is the right
to the income from the assets and/or investments acquired
with credit. Greater income and consumption as benefits
arise from the implied growth process. Improved access to
credit over time promotes larger farm size and associated
cost economies in production. These adjustments lead to
gains in net returns per acre and net farm income. Farm B
nearly doubles its size of operation and increases final
net worth by 82 percent over the value achieved in the
basic model. The debt/asset ratio remains at .35 due to
rapid expansion of the farm operation throughout the period

being modeled.

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154

Financial growth of farm C in model III is also simi-
lar to that in model II (where availability of liquid
assets for the down payment determined access to credit).
Current income less taxes and consumption expenditures are
somewhat lower than reported in the basic model. Larger
average farm size raises gross income but reduces income
after operating and overhead costs are deducted. For the
first time annual net returns per acre are less on the
large farm than they are on farm B. A main contributor to
this result is the general shape and position of the long
run cost curve. Under the repayment capacity policy farm
C achieves an operation of 3661 acres which is 669 acres
larger than the final acreage in the basic model. As.a
result equity in the farm operation increases by $820,000
over the basic model. The growth rate on equity reflects

this increase.

7.2.4 Equal Weighting of the Repayment and Risk—bearing
Capacity Criteria (Model IV)

As an alternative to employing any of the above cri—
teria exclusively, it is useful to consider combinations
of the above lending policies which provide lenders with
more loan security and provide borrowers with potentially
greater financial benefits. One such possiblity would be
to employ a rule which gives equal weight to value of
assets controlled and ability to generate income for loan

repayment.

155

Results from model IV indicate that the distributional
impacts of this lending rule modification are substantially
the same as obtained under the repayment capacity criterion
(in model III). Farm A's operation reaches 506 acres by
year 15. Total farm equity capital grows to $692,000
($305,900 in real dollars). Little change occurs in farm
A's current income situation.

Farm C again shows substantial increase in size of
operation by expanding to 3739 acres, up 747 acres from
the basic model result. Total equity of operator C in-
creases by $1 million (in nominal terms) over the basic
model value.

Farm B is the apparent gainer in relative terms from
the rule change. Farm B's disposable income, family Con-
sumption, size of operation, and final net worth are all
greater than the basic model results. Farm B's financial
growth parallels that achieved under the lending rule con—

sidered in model III.

7.3 Sensitivity_of Distributional Impacts to Initial
Equity Position

 

 

Models I-V provide some insight into the magnitude of
the financial impacts on farms of different size and ini—
tial equity positions. To determine how important the
initial equity position is to these impacts a comparison
is made of the growth paths of the three farms when the

debt/asset ratio alone is varied. Table 7.8 contains

156

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157

final period values on costs and returns, income, consump-
tion, and balance sheet items.

Results of the change in beginning operator equity
are substantial for all farms indicating that financial
leverage and debt-servicing obligation play as important
a role as farm size, depending upon which performance vari-
ables are being evaluated.

Farm A's nonland and total costs of production are
lower under the reduction of the debt load due primarily
to the increased size of operation. Annual net income also
increases reflecting the reduction in variable costs per
acre. The higher net income per acre is reflected in the
bid price for land, which inflates by 24 percent for farm
A given the change in D/A ratio. Corresponding to the in-
crease in land price and size of operation farm A's final
net worth increases to $1.24 million ($.55 million in real
dollars) which is over 8 times greater than the initial
equity of the firm. Some of the more dramatic impacts of
the change are the increases in net income, income after
taxes and family consumption expenditures. These benefits
to the firm-household unit occur in response to farm expan-
sion and the increase in cash available for consumption at
an earlier point in time.

Financial growth of farm B parallels that experienced
by farm A. Acreage under cultivation on farm B expands to
1525 acres with a corresponding drop in costs of produc-

tion and a doubling of net returns per acre. Operator's

158

net worth increases to $2.34 million. Net income, after--
tax income and family consumption of unit B increase the
most of any farm situation. Disposable income grows to
nearly six times the value achieved under the lower equity
position.

Simple comparison of farm size illustrates that farm
C expands by an additional 2170 acres when the debt load is
reduced from 70 percent to 30 percent of total assets.
Even though farm size is beyond the minimum cost point by
the fifteenth year, net returns per acre are also substan-
tially higher. Final net worth is also dramatically higher
at $4.8 million when the D/A ratio is initially at 30 per-
cent.

The rates of growth in equity for all three farms are
somewhat lower under the higher debt/asset ratio. This
result is not apparently consistent with the Baker-Hopkin
(1969) model of leveraged growth. According to the Baker-
Hopkin hypothesis, as long as the marginal rate of return
on capital exceeds the marginal cost of using nonequity
capital, the owner may increase his level of income by in-
creasing his financial leverage. By reinvesting some of
this income in the farm business or repaying farm debts
with the suprlus, net worth will increase over time. The
implication is that farms with a higher leverage ratio,
other factors held constant, can expect their equity to
grow at a somewhat faster rate under the assumptions of
the static, linear model. In fact, Table 7.8 indicates

that over time the Baker-Hopkin result need not hold.

159

A closer examination of the first few years of growth
in the basic model shows that the annual rate of growth in
equity is greater for the more highly leveraged operation.
But over time that pattern changes across different lever-
age positions as farms expand in size. Several factors
may be responsible: (1) consumption behavior, (2) asset
value appreciation, (3) production opportunities, or (4)
taxes. Unlike the Baker-Hopkin model our basic model in-
corporates a non-linear consumption function. As incomes
increase with farm size, a larger proportion of the addi-
tional income is plowed back into the farm business. Con-
sequently, the larger farm with the lower leverage ratio
expands acreage and net worth faster as land appreciates
in value and propensity to consume out of current income
falls.

A third factor is the nonlinear cost function. Baker
and Hopkin assume linear production relationships, hence
no economies of size. Farm expansion does lead to econo-
mies of size in the model developed here. The associated
increases in net returns per acre along with the guaranteed
cash flow effect of the government support program are soon
capitalized into the price of land and lead to accelerated
growth in net worth.

A fourth and final influential factor is the nonlinear
income tax function. The federal income tax schedule is

progressive and that should reinforce the leverage effect

160

which Baker and Hopkin hypothesize (since interest expenses
are written off against taxable income).34
Results from table 7.8 indicate that farm size and
financial leverage are important factors in explaining the
observed differences in expansion rates and financial
growth under alternative lending criteria. Comparison of
growth effects across farms with the same debt/asset ratio
and across leverage ratios for the same size of farm lead
to the following two observations. First, adjustments in
financial leverage have relatively greater impact on the
performance variables in the balance sheet (total farm
assets and operator net worth). Second, changes in farm

size (holding the D/A ratio constant) have relatively

greater effects on current income and consumption.

7.4 Impacts of Alternative Repayment Policies

Three variations of the basic model are explored to
evaluate what distributional impacts arise when the length
of loan and the down payment requirement are altered. The
expectation is that a lengthening of the loan repayment
period will generally reduce annual debt service costs. A

reduction in debt payments should increase reinvestment

 

34A final observation worth noting about the Baker-
Hopkin hypothesis is that it is closely followed under
lending criteria such as in models II and III. This is
not the case in model I where the net capital ratio is the
lending criterion.

161

income and promote firm growth, even though the final sum
of principal and interest payments is greater. Similarly,
a reduction in the down payment requirement is expected to
promote firm expansion. A lower down payment theoretically
makes expansion easier for all farms. Depending upon a
firm's ability to generate sufficient income to maintain
the loan contract, the reduced percentage of down payment
should improve the growth potential of even low-resource
base farms. Once again, comparisons are made with the

results obtained in the basic model situation.

7.4.1 Effect of Increasing the Length of Loan (Model VI)

In model VI the repayment period of all three farms
is increased by ten years. As expected, debt service costs
declined and net reinvestment income increased in all three
representative farm situation. Farm size acreage increased
by 45 acres. Farm C actually decreased in size but that
seems consistent with the increase in land prices. With a
greater amount of reinvestment income available, firms bid
up the price of land, making firm expansion progressively
more difficult. Other cumulative effects contribute to
the observed changes in farm size. The ability of farm B
to expand to a larger Operation means that net income per
acre is also greater and land can be bid higher.

When compared with the basic model, operator's net

worth increases for farms A and B reflecting growth in farm

162

acreage and increased liquid assets. Operator C's equity
is reduced by about $400,000. Disposable income and family
consumption expenditures are at lower levels for all three
farms by year 15.

A comparison of farms with equal initial size, but
different initial D/A ratios, indicates that higher lever-
aged farms generally increase in size while farms with
lower leverage ratios do not expand as much when length of
loan is increased. This may be expected since relatively
greater benefits in the form of reduced annual debt pay-
ments should go to those operators furthest in debt. The
impact of the change on consumption and final net worth is
mixed. Owner equity increases on the highly leveraged
farms but falls somewhat on farms with lower D/A ratios
when compared with the basic model. This result is attrib-
utable to changes in farm size and the price of land. Dis-
posable income and consumption are‘little changed by year
15 for those smaller farms with higher initial equity
(lower leverage ratio). No significant changes occur in
disposable income or consumption between large farms with
identical initial size but different beginning leverage
ratios.

As a general result, lengthening the loan repayment
period promotes greater farm expansion for small and in-
termediate size farms during the 15 year period. Addition-
ally, relatively greater benefits are conferred on highly

leveraged firms when compared with identically sized

163

operations. Both impacts follow from a reduction in annual

debt service costs.

7.4.2 Reduction of the Down Payment Requirement (Model VII)

The percentage down payment is reduced from 25 percent
to 10 percent in model VII. Liberalizing the down payment
requirement plays a key role in two somewhat Opposing pro-
cesses. It affects the ability of a farmer to successfully
arrange financing of a land purchase, since he must have
the initial equity to meet the requirement. The percentage
down payment also affects the land bid price. It is an-
ticipated that the former effect is stronger in the early
years, before the change in down payment requirement has
greatly affected the availability of liquid assets for
making the down payment. In the later years higher land
prices are expected to dilute the expansionary effect of
the change.

A review of the results from model VII indicates that
the above two effects do occur but that their impacts are
not the same for all farm situations. Farm B expands more
rapidly, in the early years (years l-9) and slowly in years
10 to 15 due to the anticipated land price increases. Farm
A is not able to increase farm acreage in the early years
due to its debt load which exceeds the maximum allowable
D/A ratio. In the expansion years farm A is unable to

acquire as many acres as in the basic model due to the

164

inflated price of land. Farm C is also unable to expand
to the acreage achieved in the basic model due to land
price increases which are accelerated by the reduction in
down payment requirement.

A review of the other impacts which result from lib-
eralizing the down payment shows that final operator net
worth is reduced for all three representative farms. This
is due both to smaller farm sizes and increased indebted-
ness when compared with the basic model. The increase in
indebtedness is directly attributable to the lower down
payment. Disposable income and family consumption are
little affected by the change in equity requirement.

Effects of the reduced down payment across firms with
different leverage ratios parallel those experienced by
the three representative firm—households. Expansion does
not occur in the early years on highly leveraged operations
due to the maximum allowable debt/asset ratio. Lower lev-
eraged firms benefit initially from the lower percentage
down payment as was the case for farm B. The 320- and 640-
acre farms with initial D/A ratios of 30 percent do not
expand to the size achieved in the basic model due to the
escalation of land prices, as a result of the credit rule
change. The 160 acre farm expands slightly by year 15.
Disposable income and consumption are generally reduced
from their basic model levels across farms with different

initial equity positions.

165

Generally, the credit rule change which permitted a
lower percentage down payment led to an escalation of land
bid prices and a decline in farm size, operator equity and
level of family consumption. The immediate benefits of
increased farm size were not available to highly leveraged
units due to the maximum allowable D/A ratio. Expansion
in the later years was arrested by the increase in the

price of land.

7.4.3 Effect of Liberalizing the Down Payment Requirement
and Extending the Length of Loan (Model IX)

In model IX an attempt was made to examine the joint
impact of liberalizing both conditions for extending
credit. The effects were only slightly different from
those observed for either of the individual rule changes.
The combined effect is to increase size of farm B by 100
acres to 984 acres and to reduce the final acreage on both
farms A and C when compared with the basic model. Owner
equity increases slightly for operators A and B, but is
down for operator C from the basic model level due to a
lower value for real estate. Family consumption remains
at the minimum level for unit A, but declines for both
representative units B and C. The lower disposable income
on farms B and C in models VII—IX is largely due to the
increased debt service costs on land incurred as farm size
increases. Generally, the impacts of changing both credit

factors is similar to those found when only the length of

166

loan was increased, holding all other credit conditions

constant.

7.4.4 Impacts of Raising the Maximum Allowable Debt/Asset
Ratio (Model VIII)

Lending limits can take many forms. Some are absolute
limits defined in dollars per borrower. One application
of this latter type of constraint is the establishment of
a maximum allowable D/A ratio. In model VIII the distri-
butional impacts of raising the maximum D/A ratio from .50
to .75 are examined.35

It is logical to expect that the main beneficiaries
of such a rule change would be those farm units with D/A
ratios greater than the maximum. But that conclusion may
be incomplete. Other farm units with significantly lower
D/A ratios find that they too can borrow up to the higher
maximum. This latter right broadens the distributional
impacts of the change in credit policy.

Relatively greater benefits do go to farm A in the

form of greater gross income, disposable income, total farm

assets, total equity and rate of growth in equity. By the

 

35This issue of maximum allowable D/A ratios has also
been examined by Thompson and Hanson (1979). In that study
it was found that cash grain farms typically had maximum
feasible D/A ratios between .20 and .35 in the historical
period 1966-75. Additionally, it was shown that these
debt ratio ceilings were highly sensitive to length of
loan in the case of cash grain farms, where real estate
comprises a large part of total farm assets. These maxi-
mum D/A ratios were commonly in the .40 to .65 range for
mixed cash grain-livestock operations.

167

fifteenth year fram A had doubled the size it had attained
in the basic model. Corresponding to the increase in
acreage was a dramatic rise in owner equity to $1.04 mil-
lion ($460,000 in real dollars) by year 15. Annual growth

36 This latter

in net worth was 18.9 percent in that year.
rate of growth reflects appreciation of land values and a
higher rate of return on the farm operation. Finally,
farm A moves to a lower D/A ratio which suggests a lower-
risk financial position.

Farm B also realizes substantial benefits from the
higher D/A ratio maximum. Farm size reaches 1624 acres,
or twice that achieved in the basic model. Operator B's
net worth reaches over $2 million, double the basic model
result. In addition to those significant changes on the
balance sheet, current disposable income and family con-
sumption reach levels of $51,300 and $49,800 by the fif-
teenth year.3‘7

Owner's net worth and family consumption are both
lower on farm C as a result of the change in credit limi—

tation when compared with the basic model. Factors con-

tributing to the decline are lower net income per acre and

 

36The estimated rate of growth in equity reflects the
rate at which owner's net worth grew over the entire pre—
ceeding period.

37The increase in net farm income and income after
taxes reflects the indirect effects of the credit policy
change which speeds the expansion process and increases
net returns per acre.

168

reduced price of land. The lower net returns per acre
result from a lower price for corn which is $3.44 per bu-
shel in the basic model but $3.24 in model VIII in year
15. The lower price for corn itself reflects lower costs
of production and support levels. These lower costs in
turn arise from larger farm size on average, larger farm
size which was spurred by the change in credit policy.

The lower price of land on large farms such as unit C also
reflect the lower net returns per acre.

A comparison of the impacts of the credit policy change
across farms with different initial leverage ratios, but
identical initial acreages, reveals that on the basis of
increases in net worth and levels of family consumption
the greatest benefits went to farm-household units with
initial D/A ratios of .5 and above. Relatively smaller
benefits went to firmrhouseholds with lower initial D/A
ratios. Apparently, the D/A ratio ceiling was even more
restrictive on firms with low net capital ratios such as

farms A and B.

7.4.5 Comparison of the Relative Distributional Impact of
Variations on the Basic Model
Preceeding sections have examined the impacts of
changes in various aspects of extension and repayment of
credit. Most of the above analysis was on the basis of
absolute changes or deviations from the basic model. To

better grasp the relative distributional impacts of these

169

changes the values of selected performance variables are
compared at the end of year 15 by the use of index numbers.
Table 7.9 summarizes each of the above nine models for the
purpose of comparison. Each of the index numbers repre-
sent percentages of the initial value for that variable.
All index numbers were computed after current dollar values
were deflated and, therefore, represent constant dollar
comparisons over time.

The performance variables chosen are usefully broken
into two classes -- those relating to the current income
situation (disposable income, consumption expenditures,
and net reinvestment income) and those which reflect changes
in the asset position of the firm-household unit (total
assets, total debts, and net worth). The following com-
ments will assess the relative impacts according to those
two categories whenever possible. Changes in these two
categories of performance variables constitute the income

redistribution and asset redistribution effects alluded to

 

 

in Chapter III.

In the case of farm A the greatest income benefits
occur under the credit policy Which emphasizes ability to
make the required down payment (model II) and the greatest
benefits in the form of assets occur when the allowable
maximum on D/A ratio is raised (model VIII). A closer ex-
amination of the results from model II indicates that the
increase in income was due to the increased use of operat—

ing inputs and short-term credit to boost yields. Lending

170

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171

up to the fifteenth year, however, current income and con-
sumption expenditures were not significantly different from
those in the basic model. If that event is disregarded,
the increase in maximum D/A ratio leads to the greatest
overall benefits for farm A.

The two least satisfactory policies to pursue from
operator A's perspective would be the basic model lending
strategy in conjunction with a reduction in the down pay-
ment requirement. The benefits of expansion in the early
years are prohibited because of the D/A ratio ceiling and
in the later years land prices become prohibitively greater.
Two lending criteria which do promote firm expansion and
growth in net worth are ability to repay (model II) and
available cash for down payment (model III), which was
expected.

Farm B clearly receives the greatest benefits from
credit when repayment capacity is the criterion (model III).
Unit B similarly does well under a policy which provides
equal weight to risk-bearing capacity (determined by the
firm's D/A ratio and value of assets) and ability to repay
the loan (model IV). One interpretation of-this result is
that operator B has a relative advantage in his ability to
generate income. This appears to be consistent with farm
B's ability to expand, move along the long run cost curve,

and increase net returns per acre.

172

The policy of reducing the down payment holding all
other credit conditions unchanged results in somewhat
slower growth on farm B. Again, this slower growth is
explained to a significant degree by the higher land price.

On farm C it appears that there is no single lending
alternative which is clearly more advantageous. Depending
upon the relative weights (or utility) the family places
upon consumption versus net worth, the basic model or a
combination of credit policies (such as in model IV) may
lead to greater benefits for well-endowed farms. The im-
plication appears to be that while farm C has a relative
advantage in total assets, unit C's ability to generate
revenues to repay a loan is also quite good.

Significantly less beneficial to farm C are attempts
to improve access to credit by lengthening the loan matur-
ity and reducing the down payment (as in models VI, VII,
and IX). Within the structure and limitations of the
model these credit policy adjustments contribute to higher
land prices and greater indebtedness on farm C.

In summary, it appears that no single credit policy
alternative is "best" for all farms given the limited
ability of the simulation model to depict the actual growth
and distribution processes at work in the real world.
Model IV, which provides for an equal weighting of value
of farm assets and D/A ratio, and ability to generate in-

come for repayment, comes closest to providing relatively

173

greater benefits across farms of unequal initial size and
equity endowments.

Some final comments are in order on the relative fi-
nancial impacts of the various credit policy alternatives.
There is likely to be a high degree of complementarity
between policy actions. For instance, an increase in the
length of loan is likely to stimulate growth to a greater
degree when the lending criterion is repayment capacity
(as in model III). Another example would be a reduction
in the down payment requirement in association with lending
on the basis of available liquid assets for making the down
payment. Of course, all of these policies stimulate greater
expansion on highly leveraged farms if they are enacted

jointly with an increase in the maximum allowable D/A ratio.

7.5 Summary of the Effects of Other Model Parameters

 

In Chapter III several factors were identified which
play key roles in the determination of distributional im-
pacts from credit use. This section provides a summary of
the effects which result from changes in those behavioral
and structural model parameters. All comparisons referred
to in the following sections are with the basic model re-
sults unless otherwise stated. Selected numerical results
from these model sensitivity tests are reported in table

7.10.

174

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175

7.5.1 Effects of Changing the Household Consumption
Function (Model X)

An assumption of the basic model was that the house-
hold consumption function was logarithmic with a constant
consumption elasticity equal to .95. An alternative assump-
tion used by Boehlje and Griffin (1979) and suggested else-
where is that the consumption function is linear or becomes
linear as income increases. In model X a strictly propor-
tional consumption function is specified in which 75 per-
cent of income after taxes is consumed, regardless of in-
come level.

As expected, the linear consumption function leads to
an increase in family consumption expenditures on all farms
with a resultant decrease in net reinvestment income.

There is a corresponding decrease in farm size by the fif-
teenth year for all farms. Debt service costs are also
lower for all three representative farms.

By year 15 total farm equity is generally lower, lower
by $100,000 on farm A, $240,000 on farm B, and $727,000 on
farm C (as shown in table 7.10). The annual rate of growth
of equity is also reduced indicating that consumption is
indeed a drain on a firm's financial resources and tends
to retard the growth process. The percentage reduction of
equity, across farms with different initial sizes but iden—
tical beginning D/A ratios, is greater for small farms than

for larger sized operations. No discernible pattern emerges

176

across farms with identical size but different financial

positions.

7.5.2 Effects of Alternative Assumptions About Investment
Behavior and Productive Opportunities

In the basic model investment behavior and investment
opportunities pivoted on the decision or ability to buy
land and its machinery complement. Models XI and XVI
explore the growth and distributional implications of
changing the rate of return on operating inputs and of
changing the decision rule for buying land, respectively.

In former versions of the basic model it was assumed
that farm operators acquired land at the market price for
land, if they could arrange the financing.38 In model XVI
a farm operator is assumed to purchase land only if his
maximum bid price is equal to or greater than the market
price. This decision rule in effect states that the net
present value of the decision to buy land must be positive
when budgeted using the mortgage interest rate on real
estate debt.

Results of the numerical analysis indicate that most
farms do not expand in size during the lS-year period.

Farms with initially high leverage ratios, such as farm A,

do not increase in size regardless of initial size of firm.

 

38The market price is computed as the weighted average
of the individual firm maximum bid prices in each year.
The assumption is that there is a single real estate market.

177

Those farms with leverage ratios of 1.0 (debt equals
equity) were able to expand farm size. An exception to
that pattern was farm B. Again in the low D/A ratio group
the 160-acre and 640-acre units expanded, yet the 320-acre
farm remained unchanged. Farm C was the only farm situa-
tion which actually expanded to a size of operation greater
than it had in the basic model.

Accompanying farm C's increased size in year 15 were
increases in operator net worth, disposable income and con—
sumption expenditures. Higher income for farm C is par-
tially attributable to the higher price for corn. Farm B
experienced a reduction in final net worth, level of con-
sumption expenditures and net reinvestment income when
compared to the basic model results. Although operator
A's net worth is reduced, unit A's disposable income,
family consumption, and reinvestment income are up over
basic model levels. Land bid prices on both farms A and B
are lower under the change in investment decision rule.

An additional outcome in model XVI was an increase in
the use of operating inputs which in effect increased farm
revenues by raising corn yields. Farms A and B also great-
ly reduced their financial leverage as land appreciated in
value and as they continued to make the annual debt pay-
ments.

To determine what changes in distributional impacts
might result from a change in the rate of return on oper-

ating inputs the coefficient on the short run yield response

178

function was increased from .05 tO .075 in model XI. That
slight adjustment has the potential for approximately
doubling the short-run yield response tO additional Oper-
ating inputs.

A review Of the results from model XI showed no con-
clusive result from the change in yield response parameter.
Farms A and B and other small, highly-leveraged farms bene-
fited in the early years Of the simulation when farm expan-
sion was not possible. Those benefits took the form Of
increased disposable income, family consumption, and rein-
vestment income. However, in the later years when the in-
crease in reinvestment income should have aided firm growth,
it did not. Farm size, operator equity, disposable income,
consumption, and reinvestment income are reduced in year
15 from their basic model levels as shown in table 7.10.
Farm C is unaffected by the change in yield response par-
ameter.

One explanation for the unexpected decline Of farms A
and B is the lending criterion which is operating in the
basic model. Purchase Of additional Operating inputs must
occur out Of available liquid assets. These same liquid
assets are required to make the down payment on land. In
the early expansion years when liquid assets are the lim-
iting financial resource, firm growth is reduced. Since
firm growth is retarded in the early years, the other
benefits Of asset ownership (increased equity and greater

net returns) do not accrue tO the small farm units.

179

7.5.3 Effect Of Increases in the Rate Of Land Value Appre-
ciation and Accompanying Changes in the Level Of
Prices and Interest Rates

Our assumption to this point has been that land prices
appreciate at an annual rate Of 5 percent per year. The
recent experience in Michigan and elsewhere in the U.S. is
that land prices have increased in excess Of 10 percent in
some years. In models XII and XIII through XV we examine
what the cumulative distributional effects Of increasing
the rate Of land value appreciation are, given that farms
start from different initial size Of Operation and/or
equity position. In addition models XIII - XV assume that
costs Of production inputs also rise faster and that there
is a corresponding rise in interest rates.

The expectation is that an increase in the rate Of
land value appreciation (as in model XII), all other fac-
tors constant, will lead tO significant benefits for large
farm Operators in the form Of greater net worth and lower
relative debt position, therefore, lower financial risk.
Levels Of disposable income and consumption may or may not
increase depending upon the ability Of each farm tO in-
crease in size and realize greater net returns per acre.

Operator's final net worth is increased across all
nine farm resource situations when compared with the basic
model. Comparison Of the three representative farms in
table 7.10 reveal that operator A's final net worth in-

creased slightly more in relative terms than either Of the

180

other two farms. This result appears to be consistent

with the Baker-Hopkin hypothesis in view Of farm A's higher
initial leverage position. Farm size in year 15 is reduced
relatively more on farm C where a reduction Of 641 acres
occurs from the basic model level. Farms A and B are also
reduced by the escalation in land prices, but by smaller
percentages. Disposable income, family consumption and

net reinvestment income are lower on all farms due primar-
ily to the reduction in farm size. Debt service costs, as
a percentage Of gross farm income, increase slightly for
farms B and C due tO the need to finance higher priced
land, even though farm size is significantly lower.

Rate Of growth in Operator's net worth varies by ini-
tial farm size and equity. Smaller, 160-acre operations
generally increase their net worth relatively more than
320-acre and 640-acre units. Across farms Of varying size
but identical leverage positions, however, the pattern
indicates that lower leveraged units on average increase
their net worth relatively more than those units with some-
what higher leverage ratios. This latter result is largely
explained by the relatively faster expansion Of the lower
leveraged units in the earlv years. when they are not
effectively constrained by the D/A ratio maximum. In the
later years these same units receive relatively greater
benefits on average due tO the increase in appreciation in

assets and owner equity.

181

In models XIII through XV land is assumed to appreci—
ate at 8 percent each year. Cost Of purchased inputs and
consumption goods are also assumed to increase each year
by 8 percent. Interest rates are raised to 12 percent on
real estate loans and 14 percent on nonreal estate loans.
Both rates are in nominal terms. In effect the real rates
Of interest are not changed from their values in the basic
model. Corn prices are still assumed to increase at a 2
percent annual rate. The opportunity cost Of capital is
assumed tO increase tO 9.5 percent.

The above situation of increases in costs and interest
rates without a corresponding rise in corn output price is
expected tO lead to reduced cash flow for household con-
sumption and for reinvestment in the farm business. Fac—
tors contributing tO the expected cash flow problem are
increased price Of land and reduced farm size. Table 7.10
provides numerical results for models XIII through XV under
three alternative lending criteria.

Generally, the results indicate that land prices and
corn prices are significantly higher by year 15. Net re-
turns per acre are not greatly changed for farms A and B,
but are actually higher in all three models for farm C.

The rate Of capitalization for land is significantly lower
for all farms in all three models. Lower capitalization
rates result from joint increases in the expected growth

in land values, interest rates, and the Opportunity cost

182

Of capital, all Of which tend to increase the price Of
land when net returns per acre are held constant.

As expected, all farms experience a reduced cash flow,
especially the l60-acre and 320-acre units. Disposable
income and net reinvestment income on farm A are severely
reduced and become negative during several years. Farm A's
problem with regard tO cash flow are less severe in model
XIII where credit is extended according to the minimum Of
the three basic lending criteria. In that model firm
growth is reduced and along with it the debt service costs.

Cash flow is not as problemmatic on farm C where size
Of Operation appears tO be sufficient to generate an ade-
quate level of revenues to meet expanded consumption re-
quirements and debt-servicing costs.

Farm size is significantly smaller in most farm situ-
ations than Observed in the basic model. An exception to
that trend was farm A in model XIV, where the operation
increased by 40 acres. Operator's net worth increased in
all 9 farm resource situations as expected due to the more
rapid growth in land prices. Also, leverage ratios de-
creased dramatically over the lS-year period due tO the
rapid growth in equity and assets and relatively smaller
increase in farm debts.

Comparison Of the relative impacts on growth in oper-
ator's net worth across farm situations indicates that
Operators with lower initial leverage positions, regard-

less Of farm size, increased their equity relatively more

183

under the lending criteria which employed minimum credit
capacity and ability tO repay out Of current income (models
XIII and XV, respectively). This result is consistent
with that found in model XII where growth on these farm
units occurred in the early years before land prices were
able tO dampen the expansion process. Conversely, Opera-
tors which had higher initial D/A ratios, regardless Of
size, experienced relatively greater growth in net worth
when ability to make the down payment was the lending
criterion.

NO consistent pattern emerges concerning increases
in net worth across farms of identical initial size but
different levels Of equity. Farms starting with 320 acres
experience relatively less growth in net worth regardless
Of which lending rule is followed. Small, l60-acre units
on average do relatively better in lending models XIII and
XV, but increase net worth by a greater amount in model
XIV where the basis for lending is the ability to make the

down payment out Of cash reserves.

CHAPTER VIII

Summary and Conclusions

lm
1"

Review Of the Conceptual Approach and Underlying
AssumptiOns

 

This study has been undertaken in response to the
need for additional knowledge about the distribution Of
benefits from credit. It focused upon a firm-level
analysis of the distributional impacts which are likely tO
arise under various conditions on loan extension and re-
payment. The research approach confined the class Of dis-
tributional impacts tO just the monetary and financial
impacts. Consistent with that class Of impacts the two
performance dimensions used to evaluate alternative poli-
cies are: (1) changes in current household income and con-
sumption levels, and (2) growth in Operator's net worth.
These two performance criteria are assumed tO be important
arguments Of the household utility function.

A modified cash flow approach was developed to simu-
late and evaluate the potential distributional impacts
which result under alternative credit policies, rules, and
practices. This approach was useful in a number of ways.
First, it allowed for a comparable means of evaluating
specific financial impacts across credit policy choices or
Options. Second, the approach was useful in identifying
systematic variations in initial resource situations Of
representative firm-household units and associated

184

185

distributional impacts. Third, it facilitated an examina-
tion Of several assumptions about structural and/or behav-
ioral aspects Of farm firm-household units, or general
economic conditions, and what roles they likely play in
the determination of these financial impacts.

The modified cash flow approach was placed within a
growth model framework. What resulted was an analysis Of
two ongoing processes - a growth process and a distribu-
tional process. At this juncture there were two possibil-
ities for analysis: (1) the effect which growth has on
distrubution, and (2) the effect which distribution has
on growth. One hypothesis which has been advanced is that
credit is a means Of offsetting an unequal initial distri-
bution Of factor ownership. Our approach accordingly ex-
amined the effect which distribution, more specifically
credit distribution, had on financial growth Of the firm-
household unit and ultimately on the distribution Of bene-
fits from credit.

Our systems view Of the representative firm-household
initially made certain simplifying assumptions about be-
havior. Consumption expenditures Of each household rep-
resented a four-person family unit with a minimum consump-
tion requirement.

Operators' decisionsix>invest were confined tO on-
farm Opportunities. Acquisition Of land and its machinery
complement were motivated by; (1) cost economies Of size

and (2) increases in Operator's net worth as land appreciates

186

in value. If financing Of the land purchase decision
could not be Obtained, the Operator was assumed tO utilize
liquid assets (subject to a liquidity constraint) and Op-
erating credit, as needed, tO purchase additional Operat-
ing inputs to raise yields and net returns per acre.
Although Optimizing decisions with regard tO investment
and production alternatives were not explicitly considered,
they could be incorporated. The systems view and cash
flow approach are compatible in their emphasis on finan-
cial flows tO and from the farm business including:
revenues generated on the farm, income from Off-farm
sources, use and repayment Of debt, consumption with-
drawals, payment Of taxes, and reinvestment Of earnings.

Additionally, government was assumed to intervene in
the commodity market. Government did not actively manage
the commodity program but did act tO support the market
price Of corn at 90 percent Of the nonland cost Of produc-
tion. A dynamic price adjustment mechanism Operated tO
shift the commodity price distribution upward corresponding
tO increases in demand and modifications introduced by the
price support program.

Lenders were assumed to employ some basic criteria in
their allocation Of credit. They were motivated tO use
these standardized Operational rules to reduce costs Of
allocating credit and reduce the likelihood Of nonrepayment
of the loan. TO minimize this risk it was initially

assumed that lenders use a mixed strategy Of providing

187

investment credit up to the most limiting financial char-
acteristic Of the borrower. Other credit policy emphases
as well as modifications in the terms Of repayment and

down payment were explored tO evaluate certain assumptions
about the impacts Of liberalizing the conditions under

which credit is extended.

8.2 Summary Of the Numerical Results

A computer simulation model was developed for corn
grain farmers in southern Michigan consistent with the
above approach and assumptions. The model provided 15
years Of simulated firm-household growth starting from 9
initial resource situations. Summary statements which fol-
low are interpretations Of the simulated firm-level impacts.

1. Three single lending criteria were contrasted with
the results Obtained in the basic growth model. Distribu-
tional impacts under all three lending criteria were sensi-
tive tO initial farm size and Operator's initial equity po-
sition. NO single credit policy provided all farm resource
situations with the greatest potential for growth.

When risk-bearing capacity Of the borrower was empha-
sized and that capacity was interpreted by reference tO
value of assets and borrower's net capital ratio, units
such as farm C received relatively greater financial bene-
fits from credit. Those benefits tOOk the form Of increased
current income (both before and after taxes), higher levels

Of consumption and reinvestment income, and growth in owner's

188

equity in the farm business. Farm C represented a well-
established Operator who initially owned 640 acres and
started from a low D/A ratio position. Farms A and B, both
less-well-endowed farms with higher but different initial
D/A ratios, realized small increases in Operator's net worth
but not as a direct result Of the change in credit rule.
Appreciation Of land values gradually led to improvements in
each firm's equity position, and thereby, improved access

to investment credit. NO significant changes from the basic
lending model occurred in consumption or net reinvestment
income on farm A or farm B.

Changes in credit policy which increased the weights
attached to either borrower's repayment capacity or ability
to make the required down payment led to a redistribution
Of benefits. Farm units A and B (with initial sizes Of 160
acres and 320 acres, respectively) exhibited improved
growth potential. Although farm A still experienced low
levels Of consumption due to low cash flow, there was
slightly greater reinvestment income available. Operators
A and B both substantially increased their net worth by the
end Of the lS-year period while Operator C's net worth and
disposable income were somewhat reduced from their levels
in the basic lending situation. Farm B Obtained signifi-
cantly greater benefits from credit as gross income, dis-
posible income, consumption expenditures, and reinvestment
income all increased. Farms A and B appeared tO have a
relative advantage in Obtaining credit when the lending

agency emphasized income-generating capacity.

189

2. An examination Of the financial impact Of extending
the loan repayment period illustrated that representative
farms A and B Obtained relatively greater benefits from the
rule change. The predominant impact was upon each Opera-
tor's net worth. In effect the model converted the longer
repayment period into an increase in the demand for land
and, consequently, higher land prices. The implicit assump-
tion was that the reduction in annual debt service cost and
associated increase in net reinvestment income is quickly
used tO bid up the price of land. These higher land prices
generally led tO reductions in farm sizes when compared
with the normal repayment period policy.

Significant in a different way was the general reduc-
tion in disposable income and consumption level variables
which resulted across farm units. This appeared to be a
reflection Of decreased farm size and lower net returns per
acre when compared with the basic lending situation. The
credit rule change also led to slightly higher debt loads
and D/A ratios indicating generally higher risk financial
positions.

3. The financial impacts which resulted from a liber-
alization Of the down payment requirement were qualitatively
quite similar tO those generated by an extension Of the
loan repayment period. Once again, by assumption the bid
price on land rose when the percentage Of down payment was
reduced. Implicitly, the lower equity requirement made

more liquid assets available for purchasing add-on units

190

Of land. This effect also illustrated that if a buyer can
suddenly borrow 90 percent of the purchase price where he
could formerly borrow only 75 percent the increase in fi-
nancial leverage has value if the Opportunity cost Of
capital exceeds the after-tax rate of interest on the
mortgage.

The down payment rule change made available greater
expansion in the early years. Farms A and B were constrained
by the D/A ratio limitations during those years and, conse-
quently, attained smaller size Of Operation by the final
year when contrasted with the basic model result. Both Of
these farm units also experienced lower disposable incomes,
consumption levels, and reinvestment incomes. Farm C
achieved smaller farm size and reductions in net worth,
disposable income, and family consumption expenditures as
a result Of the reduced down payment requirement. Changes
in the current income situation on all three farms were
attributable tO a significant degree tO increased debt
service costs which accompanied the heavier debt load.

Concurrently longer repayment periods and lower down
payment requirements appeared tO accentuate the growth re-
tarding impact Of higher land prices. Farms A and B re-
ceived the primary benefits from the simultaneous change in
those credit rules in the form Of increased owner equity.

4. By far the most expansionary credit policy adjust-
ment was the increase in maximum allowable debt/asset ratio.

Both units A and B experienced substantial farm growth, and

191

increases in disposable income and net worth. Conversely,
farm C did not achieve the levels Of disposable income,
consumption, and net worth than it had when the D/A ratio
ceiling was more restrictive. One interpretation Of this
result is that risk-averse behavior whether by borrower or
lender is a significant deterrant tO more rapid farm growth
on farms where financial capital is a limiting factor.

5. In general the credit policy alternatives evalu~
ated here had relatively greater distributional impacts
across firms with different initial equity positions. Ini-
tial farm size, as measured by acres under cultivation, was
not as critical a factor in determining the distributional
impacts across firms. Greater sensitivity Of the model to
the initial equity position reflects the underlying behav-
ioral and structural assumptions inherent in the model.

6. Consumption behavior modifies firm growth poten-
tial in important ways. Household consumption expenditures
directly reduced net reinvestment income and the rate at
which the farm business potentially expanded. The amount
withdrawn for consumption was frequently greater than in-
come available for reinvestment. Linear and log-linear
consumption functions were assumed. As expected, all
farms experienced more limited growth under the linear
consumption function case. Higher income farms such as
unit C were affected relatively more by the change in
functional form. Since consumption was assumed tO Occur

out Of disposable income, the income tax schedule also

192

played an important role in modifying the levels Of con-
sumption expenditures across farm-household resource
situations.

7. When the rate Of land appreciation was acceler-
ated to 6 percent without an accompanying rise in interest
rates, levels Of owner equity increased. The general pat-
tern indicated that farms with lower leverage positions,
regardless Of initial farm size, improved their relative
net worth positions. Disposable income, family consump-
tion expenditures and net reinvestment income were lower
on all farms when contrasted with conditions involving
less rapid land price increases. Consequently, smaller
farm size and increased debt service costs contributed tO
the reduction in current income levels.

When the acceleration in land prices was matched by
an equal increase in interest rates but not in commodity
prices, the change in relative prices was net income de-
creasing. By comparison with the basic model results both
land prices and the expected price of corn were higher
than they were under less inflationary conditions. Farm
size and net cash flow were generally reduced. Small
units such as farm A had severe cash flow problems in
several years.

8. Decisions tO buy land were evaluated under two
alternatives. If the buyer was willing to accept a lower
rate Of return than the discount rate used tO budget the

investment decision, he was generally able tO expand his

193

Operation regardless Of farm size or initial equity posi-
tion. If small landbase Operators were not willing tO

accept lower rates Of return on the investment, they were
not able tO successfully bid for land. Large farm Opera-
tors continued tO successfully bid for land due to their

higher annual net returns per acre.

8.3 Conclusions

 

Four policy questions were posed relevant to the
issue Of distributional impacts Of credit. In response tO
those questions the following statements can be made with
reference to the conceptualization which has been developed
and application Of the numerical model.

1. Within the limited scope Of this analysis, credit '
(its extension, use, and repayment) is expected to have
important distributional impacts at the firm-household
level. Those impacts are financial in nature and can be
identified by changes in current income and/or in net
worth.

2. Credit policies, rules, and practices affect the
distribution Of potential benefits from credit by estab-
lishing four rights for effectively internalizing the
financial benefits. The four rights include;

1. a right tO invest the loan proceeds or acquire
additional assets with the loan proceeds,
2. a right to the income from those assets or

investments,

194

3. a right tO the appreciation or depreciation
Of the assets or investments, and

4. the right tO sell, lease, collateralize, or
otherwise use the property acquired with

I credit.

In addition there are some important real and potential
costs associated with the use Of credit. The distribution
Of net benefits from credit depends upon the ability Of
firms in different resource situations tO gain access to
credit and productively use it over time.

3. Changes in credit policies potentially redistri-
bute the benefits from credit by changing the relative
weights (or emphases) which are attached to the initial
resource endowments or anticipated productive opportuni-
ties Of borrowers. The redistributional impacts vary
across firm-household units in view Of existing variations
in initial resource endowments and the cumulative changes
which occur in productive Opportunities when economies Of
size are possible.

4. Policy design and implementation considerations
which appear to have the greatest redistributional impacts
are those which redefine the criterion or basis for lend-
ing. Low resource base, highly leveraged firms Obtain
relatively greater financial benefits when more emphasis
is placed upon repayment capacity Of the borrower. Con-

versely, large resource base firms with low leverage ratios

195

Obtain relatively greater financial benefits when value Of
assets and net capital ratio are given greater weight.

It is likely that the traditional emphasis on value
Of assets as collateral results from an increased risk
aversion, in View Of revenues that do not cover debt ser-
vice costs, and from the high costs of acquiring more re-
liable information concerning borrowers' repayment capaci-
ties. Collateral requirements appear to restrict credit
availability tO those who already own sufficient assets.
The lack Of acceptable collateral need not, however, indi—
cate an inability tO repay the loan. Repayment capacity
is conditional upon productive opportunities, consumption,
reinvestment, and other behavioral responses Of the bor-
rowing unit.

The implementation Of liberalized lending rules
appeared tO affect the form in which distributional im-
pacts are potentially realized. Extension Of the loan
repayment period and reduction Of the down payment were
effectively capitalized into land values and reflected in
Operators' net worth positions. This result followed from
assumptions which were used in model construction. Yet,
it raises an important question on how tO implement poli-
cies for redistributing credit, given that lending rules
which increase net reinvestment income may well serve tO
stimulate bidding for land resources with no direct impact

on ability to generate income.

196

8.4 Implications and Limitations

 

Whenever a study Of this type is undertaken, a number
Of implications may follow. These implications are pre-
scriptive in nature, indicating what should or ought to be
done. This particular study has direct implications for
lenders and policymakers, and indirect implications for

borrowers.

8.4.1 Implications for Lenders and Policymakers

Within lending policies, rules, and practices exists
the potential for modifying certain aspects Of the struc-
ture Of agriculture. Although this study does not look
explicitly at the size distribution Of farms, some in-
sights can be Obtained from the numerical results.

For instance, lender reliance on value Of assets and
net capital ratios in allocating credit over an extended
period Of time greatly aids potential growth Of initially
larger resource base farms. This is illustrated in lend-
ing model I where the large-farm/small-farm acreage ratio
went from 4:1 to 10:1 in just 15 years. By comparison
this farm size ratio went tO just 5:1 when lenders employed
a policy which emphasized a borrower's income-generating,
repayment capacity (model II). The inference is that the
size distribution of farms becomes more skewed under the

former lending policy.

197

An aspect closely related tO structure is the ability
Of farmers in different resource situations tO bid for
land and other productive assets. In all lending models
presented large farm Operators were able to make higher
bid Offers on land, given that land was being valued on
the basis Of capitalized net returns per acre. If this
situation were tO persist with scarce land resources being
bid away from small Operators the trend toward fewer and
larger farms may be accelerated, or at least continue.

The most general welfare implication Of the study is
that credit confers certain potential benefits tO those
who have access tO it. Lenders and policymakers who design
and/or implement credit policies might well consider their
distributional impacts over time. These impacts arise not
only when interest rates change. They arise when the cri-
teria for lending and/or the rules concerning loan repay-

ment are altered.

8.4.2 Some Implications for Borrowers

Conditions for loan extension and repayment appear tO
be influential in determining borrowers' benefits. In
lieu Of interest rates which vary little between lenders,
the analysis suggests that time spent negotiating aspects
Of the loan agreement has a potentially large return for

the farmer using debt financing for expansion.

198

The analysis indicates that expansion Of farm size
remains difficult during periods Of rapid price increases.
One Of the primary conStraints is the need for increased
cash flow. Higher input costs coupled with higher interest
rates reduce repayment capacity and the ability Of an Oper-
ator tO take on additional debt. The analysis indicates
that this problem Of inadequate cash flow is most severe
on small farming Operations. On these units Off-farm
earnings are essential tO supplement existing farm earn-
ings.

A third implication Of the analysis is that taxes and
consumption outlays alter the potential for farm growth,
especially in the early years of expansion. Consumption
expenditures reduce repayment capacity and, therefore,
debt-carrying capacity. A beginning farmer with low ini-
tial equity may find that his growth potential is substan-
tially improved by judicious management Of both tax liabil-

ity and consumption outlays.

8.4.3 Limitations and Areas for Further Research

Emphasis has been placed upon an evaluation Of alter-
native lending policies and rules in the conceptual
approach. The numerical model was used as an illustration
Of that conceptual approach. Certain limitations Of the
numerical model need to be recognized. These limitations

represent useful areas for extending the applicability Of

199

model and associated concepts tO the problem Of financcial
impacts.

First, a single cropping activity was used tO illus-
trate the impacts Of credit, yet additional cropping or
production alternatives could usefully be incorporated.

In this way more information about existing on-farm pro-
duction and investment Opportunities in various farm re-
source situations could be included.

Second, it is likely that the distributional impacts
Of lending policies and rules are modified in important
ways by firm-household behavior and Operator goals. The
numerical model used does not have the capability Of eval-
uating growth potential under alternative Operator goals.
This capability plus an ability tO consider additional
cropping activities could be developed by the use Of a
linear programming component in the simulation model. The
LP component would also provide a means for explicitly in-
cluding leasing arrangements as an Option for establishing
control over productive assets.

Another behavioral aspect relates tO Operators' in-
vestment decisions. The model would be greatly improved
by utilizing an investment decision framework. Currently,
land is the focus Of the investment decision. However,
the decision to buy land could be weighted against other
investment and production alternatives in terms Of expected

rate Of return.

200

In conjunction with this decision framework the land
market needs to be modified tO reflect competitive condi-
tions where land may be bid away from other farm units.
Certain instances Of farm expansion in the analysis do not
reflect land availability limitations which characterize
local real estate markets.

A final limitation is the high degree Of sensitivity
Of the model tO variations in parameters which affect the
bid price for land. Two such parameters are the Opportun-
ity cost Of capital and the expected rate Of growth in
land values.

The conceptual issues raised concerning the distribu-
tion process, as it has been identified, need further con-
sideration and development. A careful development Of that
process will provide; (1) a more thorough understanding Of
how policy and behavioral parameters modify credit policy
actions and (2) additional hypotheses on which to perform

empirical tests.

APPENDIX

C
C DEFINE FUNCTION STATEMENTS
C PRESENT VALUE OF A UNIFORM SERIES
PV HUS XII a (1. O-( O+XI)**(-N))/XI
C COST OF PRODUCTION SCHEDULE (EXCLUDING LAND) WITH ECONOMICS OF SIZE
BUCOST<ACI CDIFF) I CDIFF+(153. 8-3. 6014*AC+ 0744*AC5AC)/100.
OUNCST(ACIODIFF) I ODIFF+(46.56-1.46212!AC+.03425*AC*AC)/100.
C TVCOST(ACITDIFF) 8 TDIFF+(107.24-2.1393*AC+.O4015iACsAC)/100.
E *ii INITIALIZATIONS iii
C EXPDgTEDA INELSTIDN RATE IN THE PRICE OF LAND (TO BE CAPITALIZED)
C PEREENZ RSCI9PST/ OF PRODUCTION" TO USE AS SUPPORT LEVEL
C MANDATED RETURN TO LAND IN C-O-P CALCULATIONS
DATA RETL/.015/
C EXPECTED GROWTH IN NET INCOME PER ACRE (TO BE CAPITALIZED)
DATA AGNI/.01/
C EXPECTED INFLATION (OR GRONTH IN DEMAND) IN CORN (OUTPUT) PRICES
DATA EINF/102/
C OPPORTUNITY COST /OF CAPITAL
C DATA OPCC/.
CALL OPENMS(11IIUN11I50IO)
C NUMB%§FOF ALTERNATIVE SETTINGS FOR SELECTED PARAMETERS
IGNI - l
IPCT - 1
IRETL a I
IEINF a 1
IOPCC ' 1
C NUMEE: OFqFARM TYPES UNDER STUDY
-
INITIALIZE THE FARM DATA FILE
CALL ZEROF(NOF)
IZERO = 0
AZERO = O
SIGL I O
ZBARL - O
XINC 8 O.
EFF I O
MAXF = 9
r IFTYPE a 3
00102 J = lIMAXFIIFTYPE
C AGGREGATION HEIGHTS FOR THE INDIV. FARM PART. TO THE NAT"L LAND MKT.
GwL(J) = 10/3.0
AGGHL(J+1) = 20/3. 0
CGHL<J+2) a .70/3. 0
C AGGREGATION WEIGHTS FOR THE INFIV. FARM PART. TO THE NAT"L C-O-P
AGGW “C(J) I 10/3. 0
AGGHC<J+I) - .20/3.0
AGGHC(J+2) I .70/3.0
C 102 CONTINUE
DO 1102J I lIMAXF
ACOST(J) a O.
C1102 ALAND(J) I O.
TAREA = 1. 0
PRO a 2 i
C EXPECTED PR RI E OF CORN PER BUSHEL

201

The Computer Simulation Model

PROGRAM HAGIC(INPUT=256IOUTPUT=256ITAPEIIITAPE5=INPUTITAPEO=CUT
PUTITAPE9=255ITAPEIO=256ITAPE12=756ITAPE13=256ITAPE14=256I
TAPE15=256ITAPEIO'ZDOITAPE17=256ITAPE18=256ITAPE19=256I
TAPEZO=256ITAPE21=256ITAPEZ2=256ITAPE23=256ITAPE24=256I
TAggggfigggITAPE26'256ITAPE278256ITAPEZOBZDQITAPEZ9-256I

A t )

own-cm

ER
DIMENSION AINF(IO)I AONI(10)I APCT(10)IAOGNL(I O) IAGGNC( (IO)
DIMENSION RETL(10)I ALAND(10) IACOST(IO)IXPS(110) ICAPR(10)
DIMENSION EINF(10)I OPCC(10)IIUN11(50)
COMMON/Al CONSTIPILEIXINC SLIUPIZBARISIGMA
COMMON/INTI MMIIISH
COMMON/LAND/ ANIICCIDPIGNIIINFIIRIMTRITSTARIPBARITIN
COMMON/TEMP/ SIGLIZBARLITAREA
COHHON/COST/ TVCIOUNCIXHSIXLCICOPIYLDIEXPRIEFFIOPERCAIYLDSRI
1 XINCOHICOPEXPITAXES
lCOMMON/ASSET/ ACRES.XLANDPIOTHERAIASSLIGITASSETI DEBT. DEBTLI
DEBTOIEGUITYIDSERI SDAIEXINC BDAIDDPI XNET EGOI OPCRED
lCOMMON/DATA/ ACREOIOTHEOIOPTIICLASSIIPH

0

00000000000

()

I”)

(I

0 00000000

000000000

.202

ZBARO a 2.1
278T = a2. 2

ZP 70
STAggsgDa aDEVéATION OF CORN PRICE EXPECTATIONS

SIGMA I STDP*ZBARO
PROPERTION OF EXPECTATIONS PILED

YIELD OF LAND IN BUSHELS OF CORN
YLDO 8 95.
YLDSR 8 0.

RATE AT HHICH YIELDS ARE EXPECTED TO INCREASE
YLDINF - 1.01

PRODUCTION COST DATA/l
TOTAL VARIABLE COSTS (FERTILIZERI PESTICIDESI FUELI ETC.)

TVCO = 100. 7/Y LDO

OUNERSHIP COSTS (MACHINERYI EQUIPMENTI ETC.)
ONNO a 32. 97/Y YLDO

OVERHEAD COSTS IN CENTS PER BUSHEL OF CORN
CVHO = 28. 37/ YLDO

MINIMUM GROSS FARM OVERHEAD (INCLUDES FAM. CONSUMP.)
OVHEAD = 10000.

BUDGETED LAND CHARGE
XLCO = 47. lYLD

AVEgAgSDPRIgEoOF LANDI HIGH GRADEI/l

LANDL TAXES BASED ON A PERCENT OF CURRENT LAND VALUE

01
MANASEMENTO CHARGE AS A PERCENT OF COST-OF-PRODUCTION(LESS MGMT)

07
COPO 3 TVCO + OVHO + OHNO + XLCO
M80 3 COPO*XM C
TOTAL COST OF PRODUCTION (INITIAL CONDITION)
COPO = COPO + XMSO
BASE IONA BUDGET FARM SIZE (HUNDREDS OF ACRES)
BASEAC = 3.80
BACRES = BASEACiIOO.
MINIMUM PARCEL ONNHICH TO ESTIMATE A LAND BID PRICE

cosggogng038CTION DIFFERENTIAL BETWEEN ILL. DATA AND IONA BUDGETS. //
- COPO - BUCOST(BASEAC CDIFF)

ODIFF E SNNO - ONNCSTIDASEAC ODIFF)
INFERTTON-OFV DIRECTVgggTSBQERAgUSBEEF(NOT COMPENSATED FOR av YIELD)
REAECEETATEIMSRTAGE INTEREST RATE BASED ON AVERAGE FLB RATE

IR 08
DOWN PAYMENT REQUIRED IN BUYING LAND
DP 8 .25

DDP 8 DP
ACCUSCCY ngERANCE FOR INTEGRATION PROCEDURE
NUMBER AOF aYEQRS TO BE SIMULATED

IY
OPPORE§95TY COST4OF CAPITAL FOR A LONG-TERM RISK-FREE INVESTMENT
. a
INTEgESTa RATE FOR MACHINERY AND OTHER DEBTS

IR
NUMgEs OF7YEARS TO AMORT. MACHINERY AND OTHER DEBTS

TE(9I777)
TE(10I777)
1

DO 2 JJ BIINOF

READ MARKET PARTICIPANT CHARACTERISTICS
READ(3I1) OPTIICLASIIPHIMTRIACREOIOTHEOIDEBTIDSERISDAIBDAI
1 TSTARIEXINC

HRI
NRI

fi 2 IRH
.

IPH PLANNING HORIZON OF THE FIRM IN YEARS
MTR MARGINAL INCOME TAx RATE

ACREO NUMBER OF ACRES INITIALLY OWNED AND OPERATED BY THE FIRM
OTHEO VALUE OF NON-LAND ASSETS OF WHICH 60 PERCENT ARE LIQUID
DEBT INDEBT NESS OF THE FIR

DSER EARLY DEBT SERVICE OF THE FIRM IN INTEREST AND PRINCIPLE
SDA £=EBT TO TOTAL ASSET RATIO FOR THE INITIAL CONDITION
BDA DEBT TO TOTAL ASSET RATIO AS AN ACQUIST ION CONSTRAINT

TSTAR CAPITAL GAINS TAX RATE

000

000

00

0000

0

203

EXINC EXTERNAL OFF-FARM INCOME

EXPECTED INFLATION RATE IN THE PRICE OF LAND//
NF I AINFI (II NF)
INFP I 100. *INF
EXPECTED GRONTH IN NET INCOME FROM LAND/l
ON NI I AGNI(IONI)
GNIP I 100. *GN I
PERCENT OF THE COST-OF-PRODUCTION SUPPORTED BY GOV"T PROGRAMS
PCT I APCT<IPCT)
PCTP I 100. *PCT
IF<PCT. E0. 0. ) PRO I Q
HANDATED RETURN TO LAND AS A PERCENT OF CURRENT LAND VALUE
RETLD I RETL(IRETL)
RETLDP I RETLD*IOO.
CCO I OPCC<IOPCC>
CCOP I CCO*IOO.
GRONTH IN EXPECTATIONS OF CORN PRICES PER YEAR
EXINF I EINF(IEIN NF
EX INFP I (EXINF-I. O)*100
INRT I IDA + JJ
IPRT I IDA + NOF + dd

URITE(IHRT.4) OPT.ICLASSaPCTP.RETLDPaEXINFP.CCOP
HRITE(IPRT.4) OPT:ICLASSoPCTP:RETLDP:EXINFP.CCOP

*** INITIALIZE OUTPUT VARIABLES ***

XLANOP I PLANO
PBAR I PLANO
PSTAR I PLANO
XPS<JJ) I PLANO
PSTARP I O.
ALANDL I PLANO
INITIALIZE THE ASSET AND LIABILITY POSITION OF THE FIRM

ACRES I ACREO

AC1OO I ACRES/100.

YLD I Y

ONNC I OHNCST(AC100:ODIFF)

OVH I OVHO

TVC I TVCOST(ACIOO.TDIFF)
TOTAL LAND VALUE

LA NOV I ACRES*XLANDP
TOTAL VALUE OF 8THER ASSETS

OTHER I OTHE
TOTAL VALUE OF OTHER. NON-LIQUID. NON-LAND ASSETS
OTHERA I ACRESIONNC*YLD*PVUS(XMIR MYR)
1 . (1. 0+PVUS(XMIR:MYR)*XMIR)
TOTAL VALUE OF LIQUID ASSETS
ASSLIQ I OTHER-OTHERA
TOTAL ASSETS
TASSET I TLANDV + OTHER
DEBT TO TOTAL ASSET RATIO
DER I SOA
DEBT I TASSETISDA

OPCREO I O.

OPERCA I 0.

E00 I TASSET - DEBT
TOTAL EQUITY

EOU ITY I EGO
TOTAL DEBT SERVICING COMMITTNENTS
83551: DEgTL/PVUS(IR.IPH) + DEBTO/PVUS(XMIR:MYR)

ZBARO
SIGMA I STDPIZBAR

SM I SIGMA
DIREET COSES EgUag TOTAL VARIABLE COSTS PLUS OHNERSHIP COSTS
I +
CALCULATE THE RETURN TO LAND AS MANOATED
XLC I RETLDIXLANDP/W WO
MANAGEMENT CHARGE IS ACONSTANT PERCENT OF COST OF PRODUCTION
XMS I XMC*(DC + XLC + OW )
CALCULATE THE COST OF PRODUCTION (INITIAL)
COP I DC + XLC + XMS + OVH
XNONLC I DC + XMS + OVH
CALCULATE ANNUAL NET INCOME ABOVE DIRECT COSTS AND TAXES
OVHN I OVHEAD-(OVH*ACRES*YLD)
IF(OVHN. LT. O.) OVHN I 0.
OVHN I OVHN/(ACMINIYLD)

0

0

00

0000

0

00

00

204

IF(OVHN.GT.OVH) OVHN I OVH

ANNUAL NET INCOME I GROSS MARGIN - TAXES - OVERHEAD (IF NEEDED)

881 IcééZ7S-(TVC+ONNC+OVHN))iYLD)~XLTAX*XLANDP
I

CALL MAXBIO(PSTARO)

SUPRL I PRO

BAR
CALCULATE THE IMPngIT CAPITALIZATION RATE

CRATE I ANI/PST
CRP I CRATEIIOO.

DISPLAY ZERO YEAR INITIAL CONDITIONS

HRI
9761FORMAT(”OYR NL. COST L. COST C.O.P. E(:)“'

3

E

WRITE

777 FORMA
1 T

X

E

TE(IHRT:976)

5X.“STD(P) SUP. P A. CAP.RATE .L.”:

2 2X.8BID.P.L. O. C. CAP”)

IYR
HRIT .6 J-XNONLC XLCaCOP. Z7BJS"ISUPRIANI CRP PSTARaPSTAR01CC
RT )UoXNONLC.XLC COP Z7S.SMoSUPR.ANI; CRP PSTAR:PSTARO:CC
OYR ACRE TOTLANDV OTHERA TOTASSET LANDDEBT ”.

EBT EQUITY D/A EGROH OSERV FCONS ":

)NET LIQASS OPCREO")

T
RT 777

RT 17)IYR ACRES.TLANDV.OTHER TASSET:DEBTL:DEBT:EQUITY:
aGRONTHaDSER.FCONS.XINCLT:AZERO.ASSLIQ:OPCR ED

0"

1
HRITE(9.17) IYR ACRES.TLANDV.OTHER TASSET.DEBTLoOEBToEQUITY.

ER:GROHTH.DSER:FCONS:XINCLT.AZERO.ASSLIQ.OPCRED

1
1HRIT§ééObé7) IYR.ACRES.TLANDV.OTHER.TASSETuDEBTL.DEBT:EQUITY.

OUTH:DSER.FCONS:XINCLTpAZEROoASSLIQ:OPCREO

CALL PUT(JJ)
CONTINUE
URITE(30.9)

91FORMAT(1HO.T4:*J*:T7a*UJiaTIS.*YLD*.T22:*YLDSR*.T31.*TVC*;

T409 *0“~C*o T49: *XLC*: T580 *X"S*J T67! *COP’: T74: *OPERCA*I
T83:*XINCDN*.T93:*CUPEXP*:TIC4:*TAXES*)

*** BEGIN TIME LOOP ***

DU 11 J ' 1:1YEAR

DO 111 KFARM I 1. NOF
CALL GET(KFARM )
I ACRES/100.

AC 00
DIRECT COSTS MAY BE INFLATED (DEFLATED) (OVER TIME

OUNC I OHNCST<AC100.0DIFF) * DCINF

TVC I TVCOST(AC100.TDIFF) i DCINF**(d-1))
DC I TVC + OUNC

YLDINF I 1.01

YLD I YLDOIYLDINF ** (IYR-1)

LAND CHARGE I F (RETURN ON LAND. LAND PRICE) CROP YIELD)

XLC I RETLD * XLANDP / YLD
XMS I XMC * (DC + OVH +

LC
COST-OF-PRODUCTION I F (DIRECT COSTS. MGMT CHARGE. LAND. OVERHEAD)

111

XNONLC I DC + XMS + OV H
COP I DC + XMS + XLC + OVH
ACOP I ACOP + AGGHC(KFARH)*COP
ACOST<KFARM) I COP
CALL PUT(KFARM)
CONTINUE

ACOP / (DCINF**(IYR-1)

XA ACOP
THE SUPPORT LEVEL IS BASED ON A NATIONAL COST OF PRODUCTION

201

HRITE(6o 201) ACOP. XACOP

FORMAT(“ONATIONAL COST OF PRODUCTION IS CALCULATED AS: $“.F7. 3.
EFLATED CID-P $".F7. S)

URITE(6.976)

SUPR I PCT * ACOP

SUPPORT PRICES ARE HELD TOP AT LEAST INITIAL LEVEL
IF( (SUPR. LT PRO) SUPR RO
SUPPORT PRICES ARE INFLEXIBLE DOHNHARO

(SUPR. LE. SUPRL) SUPR I SUPRL
SUPRL I SUPR

‘l'§*****fi***********§********I-*4}**§*

205

g *** PRICE EXPECTATIONS MODEL iii

C YEARLY ADJUSTMENT PROCESS FOR MEAN OF ORIGINAL EXPECTATIONS DISTR.
ZBA AR I ZBARO * EXINF *4 (d-1)

C MAN OF UNDERLYING PRICE EXPECTATIONS CAN NOT BE HELD"

C ZPCT OF THE NATIONAL C-O-P
ZLOH I ZPCT * ACOP

C IF(ZBAR. LT. ZLOU) ZBAR I

YEAngPngUSTMENT PROCESS FOR 0VAR. OF ORIGINAL EXPECTATIONS DISTR.
C SET THE GOVERNMENT SUPPORT LEVEL

SL
C SToggg; RELEASE POINT (TRUNCATING THE UPPER PART OF THE DIST)
a
RP I TSL *0 RPPCT
CALL PEM(PROPP.TOL.FM:SM.RP)

*‘D‘I‘I‘D‘R‘IO‘I‘I‘D‘I‘I‘D‘I'I‘I{*Q‘Ii‘iflfl'§***§***§
ALANDP I 0.
DO 112 dd I 1 NOF
CALL GET(dd)

THE EXPECTED PRICE EGUALS THE FIRST MOMENT OF THE MODIFIED

EXPECTATIONS DISTRIBUTION.
EXPR I FM
OVHN I OVHEAD - (OVH. i ACRES * YLDO)
IF(OVHN. LT. O. ) OVHN IO.
OVHN I OVHN/(ACMIN i YLDO)
IF(OVHN. GT. OVH) OVHN I OVH

ANNUAL NET INCOME I GROSS MARGIN - TAXES - OVERHEAD (IF NEEDED)
ANI I (EXPR*YLD)-((TVC+ONNC+OVHN)*YLDO)-(XLTAX I XLANDP)

LINEAR PARAMETER RELATING THE OPPORTUNITY COST OF CAP. TO PRICE RISK
SET THE OPPORTUNITY COST OF CAPITAL GIVEN THE INITIAL PRICE RISK
AND RISK-FREE RATE.

CCMAX I

C

C

C

00

00

0000 0

10
CO I OPCC(IOPCC)
C2 I 01/160.
C1 I (CCMAX - CCO) * SIOP2*SIGP2

ADdUST THE OPPORTUNITY COST OF CAPITAL BY THE PRICE RISK
CC I CCMAX - CC1/(SM*SM)
IF(CC.LT.RISKFR) CC I RISKFR

{Qiifiiflm‘l LAND PRICE MODEL §*§§“**§"*

DETERMINE THE MAXIMUM BID FOR ADDITIONAL LAND
PBAR I ALA NDL
CALL MAXBID(PSTAR)
CAPITALIZATION RATE K I I/V
CRATE I ANI/PSTAR
CAPR(dd) I CRATE
C CALCULATE LAND VALUE GIVEN INDIVIDUAL”S RISK-UTILITY FUNCTION
PSTARP I ANI/CAPR(dd)
XPS(dd) I PSTARP
XLANDP I PSTARP

CALL PUT(dd)
ALAND(dd) I PSTARP
ALANDP I ALANDP + AGGHL(dd)*PSTARP
112 CONTINUE
CCRF I CC1/(CCMAX-RISKFR)
CCRF I SGRT(CCRF)
HRITE(6.699) CCRF
699 FORMAT(“ STD(P) AT RISK-FREE RATE OR LOHER:".F7.3)

LAND PRICES ARE INFLEXIBLE DOHNUARD
IF(ALANDP.GT.ALANDL) ALANDL I ALANDP
DO 113 dd I1.NOF
CALL GET(dd)
INRT I IDA + dd
dK I dd + NOF
XLANDP I ALANDP
C***********O**iifi******* FIR” ASSET MODEL oi§ifia§§§I§§***§*******

CALL FAM (XMIRaMYR.IR:IPH:XLTAX:DCINF.YLDO:dadK)

C§I§§§§*§§i§§*§§§§¢§§ LAND ACQUISTION MODEL *§§*§§§§§§§§§Q****Q§**
CALL LAM(XMIR.MYR.IR.IPH.YLDO.XPS(dd))

CRP I CAPR(dd)I100.
NONLC COP XLC
C DISPLAY RESULTS OF YEAR SIMULATED

HRITE<IWRTo b) d.XNONLC.XLC COP:EXPR.SM:SUPR.ANI;CRP.
1 XLANDP.XPS(dd):CC

0000 00

0

00

206

URITE(5.6) J.XNONLC. XLC.COP.EXPR.SM.SUPR.ANI4CRP.

1 (L‘NDP XPS<JJ) cc _

wRITE<30.9913) J.JQ.YLD.YLDSR..vc.ou~c.XLc.xns.COP.0PERCA.
NCOH.COPEXP.T

XI AXES
99131FORHAT(1H :2I3a7F9. 3.4F10. 2)

no 000 n n 0 0 n 0

0000

00

(1

CALL PUT(JJ)

1&3 CONTINUE
CUT

-OFF YEARLY ITERATION IF LAND VALUE EXCEEDS $20 THOUSAND/ACRE
IF<PSTAR. GT. 20000. GO,TO 10

11 CONTINUE
10 CONTINUE

HRITE(6:2)
ENDSTOECUTION OF THE PROGRAM

FORMAT STATEMENTS‘

é EggSA;E?S.II.I3oF4.0oFS.0oE7.0.ES.O.F8.0:3F4.0.F5.0)
A
4 FORHAT(“1FARN LEVEL.IHPACTS OF GOVERNMENT PRICE SUPPORT POLICIES
1 GLENN PEDERSON“//
2" FARM: ”IAA." SALES CLASSz“. /
3“ PERCENT OF COST-OF-PRODUCTION SUPPORTED BY GOVT: ” F6.2/
4“ HANDATED RETURN TO LAND (PERCENT OF CURRENT VALUE): "F6. 2/
3” EXPECTED INFLATION IN PRICES RECEIVED FOR CORN F6. 2/
INITIAL-STATE OPPORTUNITY COST OF LONG-RUN 4CAPITAL; “oF6. 2])
6 FORMAT(1H oI2.4F10.3.F10. 4.3F10.3a2F10.0-F110
17 ggghAT(1H :IZIF6. 0.6F10. OIFB. 4pFS. 2.6F8. 0)

3

SUDROUTINE LAH(XMIR.MYR.IRaIPHIYLDOaBIDP)
LAND ACQUISTION MODEL

AL
COHMON/ASSET/ ACRES.XLANDP.OTHERA.ASSLIOITASSET: DEBT.DEBTL4
DEBTOIEQUITY DSERoSDAuEXINCoBDAuDP XNET EGO OP CRED
COHHON/COST/ TVC OHNC XHSaXLCoCOPoYLDaEXPR EFFOOPERCADYLDSRO
I XINCOH1COPEXP.TAXES
PVUS(XI.N) I (I.O-(1.0+XI)*i(-N))/XI
OPERCA I O.
OPCRED 3 O.
YLDSR a O.
DER 3 DEBT/TASSET
IF(ASSLIG.LE.O.) RETURN
IF(XNET.LEHO.) RETURN
BUYHIN a 40.
XHOA 8 OHNC § YLDO * PVUS(XHIR:HYR) * (1.0-XHIR)
AVDOL 8 (EDA-DER) * TASSET/EDA
DEBT/EQUITY LENDING CONSTRAINT
ACRESI 8 AVDOL/(XLANDP + XHOA)
CASH LIQUIDITY CONSTRAINT
CR E52 8 ASSLIG/((XLANDP + XNOA) * DP)
CASHc SURPLUS TO SERVICE LAND DEBT
RESB 8 XNET * PVUS(IR: IPH)/((XLANDP * XHOA)*(1. O-DP))
NUMBER OF ACRES FEASIBLE TO BUY ‘
BUY 8 AHINIIACRESI.ACRESEIACRESO)
IF<BUY.LT.BUYHIN) GO TO 10
IF(BDA. LT DER) RETURN
ACRES 8 ACRE S * BUY
INCREMENT INDEBTNESS AND LIQUID ASSETS
DEBTL 8 DEBTL + (I. 0-DP)*XLANDP§BUY
ASSLIQ a ASSLIQ - DP*XLANDP§BUY
RETURN
10 CALL VAN(XMIR.MYR.IR.IPHIYLDO)
RETURN

SUBROUTINE VAH(XHIR:HYR:IR.IPH:YLDO)
VARIEBLEIASSETS MODEL

COHHCN/ASSET/ACRES.XLANDP,0THERA.ASSLIG.TASSET‘DEBT
I DEBTL.DEBTD.EGUITY DEER SDA.EXINC.BDA.DDP.XNET. EG0.0PCRED
1COMNCN/COST/ TVC OHNC XMS.XLC COP.YLD EXPR.EFF.OPERCA.YLDSR.
(INCSM CCPEXP TAXES
1PVUSiXI N) a (1.0-(1. O+XI)§*(-N)) XI
BETA IS A VIELD RESPONSE PARAMETER FOR ADDITIONAL VAR. INPUTS

E: A 35
DELTA IS A ' IGUIDIT‘! PREFERENCE PARAMETER FOR WORKING CAPITAL

0 0 000 00 00 O

00

207

DELTA I 5

DER I DEBT/TASSET

IF(ASSLIQ. LE. 0.) RETURN

IF<XNET. LE.O RETURN

AVDOLA I ((BDAEBEg)*TASSET/BDA)/ACRES

ASSLIQ
YLDMAX IS POTENTIAL OUTPUT AT “HIGH MC IS SET EQUAL TO) E(P)
YLDMA AX I YLD * (EXPR*BETA*ACRES) ** (BETA/(1. O-BETA
OPCMAX IS THE LEVEL OF OPERATING INPUTS CORRESPOND. TO) YLDMAX
OPCMAX I (YLDMAX/YLD) ** (1. O/BETA)
GROSS RETURNS PER ACRE
GRMAX I YLDMAX * EXPR
OPERCA I AVLIQ/ACRES
IF(OPERCA. GT. OPCMAX) OPERCA I OPCMAX
IF(OPERCA. LE. 0. OPERCAI
21 I ALOG(YLD) + BETAGALOG(OPERCA)
YLDSR1 I EXP(ZI )
GRAI I YLDSR1*EXPR
IF(GRA1-GRMAX) 20.10.30
10 YLDSR I YLDSRI
GO TO 30
20 SHORT I OPCMAX - OPERCA
OPCRED IS OPERATING CREDIT
IF(XNETA. GE. SHORT) OPCRED I SH HORT
OPERAT. CREDIT IS CONSTRAINED BY THE AVAIL. OF LIQUID
ASSETS FOR REPAY MENT
IF(XNETA. LT. SHORT) OPCRED I XNETA
IF(OPCRED. LT. OPCRED IO.
OPERCA I OPERCA: + OPCRED
IF(OPERCA. LE. 0. OPERCA I 1.0
22 I ALOG(YLD) + BETAIALOG(OPERCA)
YLDSR2 I EXP(22)
YLDSR I YLDSR2
30 CONT NUE
UPDATE LIQUID ASSETS OF THE FIR
ASSLIQ I ASSLIQ - ((OPERCA - OPCRED)*ACRES)
EEBURN

SUBROUTINE FAM (X"IRI"YRIIRIIPHOXLTAXODCINFIYLDOIIYRIIFARH)
FIRM ASSET MODEL

REAL MTRIIR.INF.INFP.XREAL<13)

INTEGER T

1COMMON/ASSET/ ACRES.XLANDP.OTHERA.ASSLIQ.TASSET.DEBT.DEBTL:

DEBT O EQUITY . WERaSDAoE XINC: BDAnDDP XNEToEQOoOP CRED

1COMMON/COST/ TVC.OHN NC.XMS.XLC. COP;YLD.EXPR.EFF:OPERCA.YLDSR.

I XINCOM :COPEXPvTAXES

COMMON/LANDIANI.CC.DP.GNI:INF.XR.MTR:TSTAR.PBAR:T.N

PVUS(XI.N) I (1.0 - (1.0+XI) **(-N))/XI

GAMMA I .93

IN I 6

IHRT I IFARM + II

INFP I 100 * INF

INCOME SECTION

IF(YLDSR. LE. 0. ) YLDSR I YLD
TOTAL GROSS INCOME EQUALS CROP INCOME + EXTERNAL EARNINGS

XIN NCOM I EXPR*YLDSR*ACRES + EXI NC
CASH OPERATING EXPENSES I DIRECT COSTS + TAXES + INTEREST

COPEXP I ACRES*YLDO*TVC + (XLTAX*XLANDP*ACRES) + IR*DEBTL +

1 XMIR*(DEBTO + OPCRED) + OTHERA/MYR
TAXABLE INCOM

TINC I XINCOM-COPEXP+(XMIRIASSLIQ)

EXEMP I 4000.

TINLEX I TINC - EXEMP
INCOME TAXES

TAXES I O.

IF(TINLEX.GT 0.) CALL TAX(TINLEX)
INCOME LESS TAXES

XINCLT I TINC-TAXES

FAMILY CONSUMPTION
FCONSM I 5000.*DCINF**(IYR-1)
IF(XINCLT.LT.O.) GO TO 27
FCONS I XINCLTiIGAMMA

27 CONTINUE
IF(XINCLT. LT. O. ) FCONS I FCON SM
IF(FCONS. LT. FCONSM) FCONS I FCONSM

DEBT SERVICING (PRINCIPLE ONLY)
DSER I DEBTL/PVUS(IR.IPH)

 

208

C NETx INVESTMENT INCOME 0R SAVINGS

COO

1x

1

NE O:cgégCLT-FCONS-DSER'iWIR*DEBTL)-(OPCRED*XMIR)+OTHERA/MYR -

BALANCE SHEET SECTION

TLANDV I ACRESIXLANDP

ASSLIQ I ASSLIQ+X NET

OPERC I (OPERCA -OPCRED)*ACRES

IF(ASSLIQ. LT. O. DEBTO I DEBTO-ASSLIQ

IF(ASSLIQ. LT O. ) ASSLIQ I O.

OTHERA I ACRESIOUNCIYLDO*PVUS(XMIR MYR)/(1.0 +
PVUS(XMIR.MYR)*XMIR)

OTHER I ASSLIQ + OTHERA + OPERC

TASSET I TLANDV + OTHERA + ASSLIQ + OPERC

DEBTL I DEBTL-DEBTL/PVUS(IR:IPH)+(IR*DEBI%

)
DEBTO I DEBTO - DEBTO/PVUS(XMIR.MYR) + ( IRfiDEBTO)

TA
EQUITY I TASSET-DEBT
GROW NTH O.
IF(EQUITY. GT. 0. AND. EQO. GT. 0.
GRONTH I ALOG(EQUITY/EQO)*100. IIY YR

1
URITE(IHRT.10) IYRaACRESoTLANDVoOTHER.TASSET.DEBTL.DEBT.EQUITY;

DER.GROUTH.DSER.FCONS:XINCLT.XNET.ASSLIQ.OPCRED

I
1URITE(9.10) IYR.ACRES.TLANDV.OTHER.TASSET.DEBTL.DEBToEQUITYnDER.

GROUTH.DSER.FCONS.XINCLT:XNETaASSLIQbOPCRED

IO FORMAT(1H o12;F6.0:6F10.0oFS.4-FS.2.6

()0

Ci"

-33 3 s

XINFL I 1.0/(DCINF ** (IYR - 1))

TLANDV*XINFL

OTHER*XINFL

TASSET*XINFL

DEBTL*XINFL

DEBTIXINFL

EQUITY*XINFL

DER

GROWTH

DSER*XINFL
FCONS*XINFL
XINCLT*XINFL
XNET*XINFL
ASSLIQ*XINFL

) IYR.ACRES:XREAL

Olllll

FUNCTION FUNCX(XaSIGMA.XBAR)
REAL NEXP PF
COMMON/INT/MM1.ISH

SQRTF(X) I SQRT(X)

NEXPF(X) I EXPF(-X)

IF(ISU) 10:20:30

0TH R I 1.0

0 40
OTHER I XIX
FUNCXZI (éIO/SGR£F(6. 2832*SIGHA**2))*NEXPF((x-XBAR)**2/

O
FUNCX I FUNCX * OTHER
RETURN

SUBROUTINE PEM(PROPP TOL:FM.SM.RP)
COMMON/A/CONSTIPILE:XINCoSLoUPoZBAR.SIGMA
COMMON/INT/MMI ISH
COMMON/TEMP/SIGL.ZBARLoTAREA

INITIALIZATIONS
MM1 I O
NITERSI 1

XI 10
C PROPD -- PROPORTION PLACED UPON THE MEAN OF THE DISTRIBUTION

PROPD I 1.0-PROPP

XL I ZBAR-STD*SIGMA

UP I ZBAR+STD*SIGMA
IF(XL.GT.(SL-SIGMA)) XL I SL-SIGMA
IF(XL.LT.O.) XL I 0.

CH.

C
C

IF
10

m
or

cm

C if".

C III".

IF

Cfiii

OO

00

IF

8388

10

F

209

59%

) RP UP

SIGL .AND. ZBARL. EQ. ZBAR) GO TO 10

EA U DER THE UN-STANDARDIZED NORMAL CURVE
I

N

(ZBoSI. TOL:XLX.UPX)
T HAS NOT OCCURRED DUMP ALL PARAMETER VALUES
1) CALL EXITX

mNmubaunHHzmcmeH
....
:
m>> Hmpfit

HHWZOFFOFUH

000 MRE
REA BELOW THE SUPPORT PRICE

SIMPI(ZB SIoTOLoXLXoSLX)
aPgle‘l'CON NST

LE
OPP NE.O.) PILE I PILE*PROPP
I CONST*((TAREA-PILE)/(TAREA-OPILE))
HEIAREA ABOVE THE RELEASE PRICE

S
L
I
Z
X
E
X
1.
A

VIHHOmmO

225

X

I O.
.LT.UPX)PILE1 I SIMPI(ZB:SI.TOL.RPX.UPX)
I PILEIICONST
1 I PILE1
ROPP.NE.O.) PILE1 I PILE1*PROPP
T I CONST*((TAREA-PILEl)/(TAREA-OPILE1))
THE AREA OF THE RESULTING DISTRIBUTION
SIMPI(ZB SI TOL:SLX.RPX)+PILE+PI LE1
TgE FIRST MOMENT OF THE RESULTING DISTRIBUTION
SIMPI(ZB:SI:TOL.SLX.RPX)+PILE*SLX+PILE1*RPX
GICAL RESULT HAS NOT OCCURRED DUMP ALL PARAMETER VALUES
LT. ZBAR) CALL EX XITX
THE SECOND MOMENT OF THE RESULTING DISTRIBUTION

I 1

I SIMPI(ZB SI TOL.SLX.RPX)+PILEISLX*SLX+PILE1*RPX*RPX
(SM-FM**2) LE. GO TO 20
I SQRT(SM-FMII2)
OGICAL RESULT HAS NOT OCCURRED DUMP ALL PARAMETER VALUES
SM. GT. SIGMA) CALL EXITX

TO 30

CALL EXITX

CONTINUE

CONTINUE

RETURN

END

?ZZAHFAE£O
U Omwrmm
NHUHIHHmN

HNH)WH“)WOH01H1HHOH
UWOI
3

gmnw zmumnommnnnmz
AF A tZAF £2

(M~)Uha
O“ Zfl

SUBROUTINE EXITX

WRITE<6o10)

FORMAT("OEXITX HAS BEEN CALLED.")
253W"

FUNCTION SIMPI(XBAR:SIGMA.TOL.XMIN.XMAX)

SIMPSON": RUEEI : EXAMPLE FOR NORMAL DENSITIES (ZELLNER.P.404)

REAL EXP
COMMON/INT/MM1:ISW
COMMON/A/DUMMY(7)
DIMENSION 0(2)
ABSF(X) I ABS(X)

XPF(X) I EXPF(-X)
FLOATF(I) I FLOAT(I)
GAMMAF(X) I GAMMA(X)
LOGF(X) I ALOG(X)
LGAMAF(X) I ALOGF(GAMMAF(X))
SQRTF(X) I SQRT(X)

SIMPI I O.

IF(XMIN. GE. XMAX) GO TO 65
H I (XMAX-XMIN)/2. O

E I FUNCX(XMIN.SIGMA.XBAR)
G I FUNCX(XMAX.SIGMA.XBAR)
C I H*(E+G)

()0

0000000000000000 00

OO

00

10
13

20
23

33

45

35

65

210

0(2) I 3.*C
N I 1
MINITR I 64
HAXITR I 5000
S I 0.
DO 15 L I1.N
S IS+FUNCX(XHIN+(FLOATF(2*L-1))iH.SIGHA.XBAR)
0(1) I c+4.*H*8
C I (C+D(1))/4.
0(2) I 0(1)
N I 2*N
H I .SIH
IF(N-HINITR) 10.20.20
8 I o.
00 25 L I1.N
S I S+FUNCX(XMIN+(FLOATF(2*L-1))IH.SIOHA.XBAR)
IF(N-HAXITR) 30.30. 45
0(1) I C+4. *HiS
IF(ABSF(D(1)-0(2))—3. *TOL) 40.40.35
C I (C+D(1))/4.
0(2) ID(1)
N I 2IN
H I .SIH
GO TO 20
B I 0(1)/3.
GO TO 55
NRITE(6.50)N.C.S.0(1)
FORMAT(I10. 0.5
B I D(1)/3.
CONTINUE
CONTINUE
SIMPI I B
RETURN
END
:UNCTION EXPF(Y)
8
IF (X.0T.160. .OR. x.LT.-160.) GO TO 5
GO TO 6
CONTINUE
HRITE(6.7)

FORMAT("O***WARNING*** X > 160 OR X C -160".10X." X I“.F12.5)
IF(X.GT.160.) X I 160.

IF(X.LT.-160.) X I -160.

EXPF I EXP(X)

RETURN

END

SUBROUTINE MAXBID<PSTAR)

gGM £2: DETERMINE THE MAXIMUM BID PRICE FOR LAND GIVEN CERTAIN PARAMETERS

LEE.WARREN F. AND NORMAN RASK “INFLATION AND CROP
ROFITABLITY HOW MUCH CAN FARMERS PAY FOR LAND?“
AJAE 58 (1977). 994-990

ANI -- EXPECTED ANNUAL NET RETURNS PER ACRE BEFORE TAXES

CC -- BUYER“S OPPORTUNITY COST OF CAPITAL AFTER TAXES

DP - PROPORTION OF PURCHASE PRICE PAID DOWN

GNI -- EXPECTED ANNUAL RATE OF GROWTH IN ANI

INF - EXPECTED ANNUAL RATE OF INFLATION IN LAND PRICES

IR -- NOMINAL RATE OF INTEREST CHARGED ON THE MORTGAGE

MTR - BUYER“S MARGINAL INCOME TAX RATE (FEDERAL AND STATE)
- BUYER"S PLANNING HORIZON IN YEA ARS

PBAR .. AVERAGE PRICE PER ACRE FROM RECENT SALES OF COMPARABLE PARCELS

PSTAR -- MAXIMUM BID PRICE

T -- AMORTIZATION PERIOD OF LOAN IN YEARS

TSTAR -- CAPITAL GAINS TAX RATE

REAL M;R.IR.INF
COMMON/LAND] ANI. CC. DP. GNI. INF.IR.MTR. TSTAR. PBAR.T.N

PSTAR I PBAR
IF(ANI.3E.O.) RETURN

01388UNTED- VALUE OF ANNUAL NET INCOME ADJUSTED FOR INCOME TAXES

10

1. N
SUM1 I SUM1 + (((ONE+GNI)/(ONE+CC))**1)9ANI*(ONE-MTR)
CONTINUE

PRESENT VALUE OF EXPECTED CAPITAL GAINS WHEN SOLD IN THE N-TH YEAR

ALPHA I (((ONE+INF)/(ONE+CC))**N)*(ONE- TSTAR)

00

00

211

SUM1 I SUM1 + ALPHA*PBAR

DO 20 I II. T
lSUM2 I SUM2 +(ONE/((ONE+CC)**I))*((ONE+IR)**(T- I+1)-ONE)
/(IR*(ONE+IR)**(T-I+1)
201 CONTINUE

SUMO I DP + (ONE-DP)*((((ONE+CC)§§T)-ONE)/(CC§(ONE+CC)§§T))

1 I ((IR*(ONE+IR)**T)/((( ONE+ +IR)**T)-ONE))
2 - ((ONE-DP)*MTR*IR§((IR* *(ONE+IR)**T)/(((ONE+IR)*§T)-ONE))*SUM2)
3 - (TSTAR/((ONE+CC) MI W))

PSTAR I SUM1/SUMO
IF (PSTAR.LE.O.) GO TO 25
RETURN
25 CONTINUE
NRITE(6.26) SUM1.SUM2.SUM3.ALPHA
26 FORMAT("ISUM1.SUM2.SUM3.ALPHA I“.4F16.6)
NR1TE(6.27) ANI.CC.DP.GNI.INF.IR.MTR.N.T
27 S?3gAT("OANI.CC.DP.GNI.INF.IR.MTR.N.T I“.7F12.6.214)
END

SUBROUTINE TAX(DUMMY)
REAL MTR
DIMENSION BTAXR(16). PCT(16). ARG(16)

COMMON/LAND/ ANI. CC. DP.GN I.INF.IR. MTR. TSTAR. PBAR.T.N
COMMON/ASSET/ ACRES.XLANDP. OTHERA.ASSLIG.TASSET.DEBT. DEBTL.
D EQUITY. DSER SDA.EXINCo BDA.DDP.XNET. E00.0PCRED
COMMON/COST/ TVC.OUNC.XMS.XLC.COP.YLD.EXPR.EFF.OPERCA.YLDSR.

XINCOM.COPEXP.TAXES
DATA ARG/O..3400..5500..7600..11900..16000..20200..24600..
29900..35200..45800..60000..85600..109400..162400. 213400. /
DATA BTAXR/0..O. 05345..OS289..11798..14156..16203..1S313.
.20739. 23187.. 27773..32797..39138..43459..50163..545521
DATA :CT/O. ..;3?.16..IS..21..24..28..32..37..43..49..54..59.

001OI"1.16
IF(DUMMY.GE.A??({) 00 TO 10
)

Murat-bu

)
TAXES I BTAXR )IARG(I-1) + PCT(I-1)*(DUMMY-ARG(I-1))
MTR I PCT(I-1
RETURN
10 CONTINUE
TAXES I BTAXR(16)*ARG(16) + PCT(16)*(DUMMY'ARG(16))

)
COMMON/LAND/ FLAND( LAND(2)
COMMON/DATA! RDATA( DATA(3)
INDEX I 4*(IFARM-1)+1
CALL HRITMS<11. FASSET. 17.1NOEX.-1)
INDEX I INDEX 1
INDEX I INDEX 1
CALL NRITMS(11.:FLAND.11.1NDEX.-1)
INDEX I INDEX
CALL HRITMS(11. RDATA.5.INDEX.-1)
INDEX I INDEX + 1

RETURN
END
SUBROUTINE ZEROF(N)
DIMENSION A(17)
D0 10 J I 1117
A(J) I 0.
IO CONTINUE
12 J . 10"
DEX ' 4*(J-I) I
CALL. HRITHS(11. A. 17. INDEX. ‘1)
INDEX 3 INDEX 1
CALL URITMS(11:A.13»INDEX1'1)
NDEX I IN E I

I D X
CALL NRITMS(11. +A.11. INDEX.-1)
INDEX I INDEX + 1
CALL NRITMS<11.A.5.INDEX.-1)
INDEX I INDEX + 1

12 CONTINUE

no

*EOR
*EOF

212

RETURN
END

D‘AA

IN FARH

CALL READfl9(11+FA-17:INDEX)
INDEX I INDEX 1

CALL READHS(11: +FCI1301NDEX)
INDEX I INDEX +

CALL READH8(11a FL:11:INDEX)
INDEX I INDEX 1

CA AREADHS(11:RDo5oINDEX)
INDEX - INDEX

RETURN

BIBLIOGRAPHY

BIBLIOGRAPHY

Books, articles and special reports:

Baker, Chester B. "Role of Credit in the Economic Devel-
opment of Small Farm Agriculture," in A.I.D. Spring
Review of Small Farmer Credit, Vol. XIX, June 1973,
pp. 41-70.

 

Baker, C. B. "Credit in the Production Organization of
the Firm," American Journal of Agricultural Economics,
V01. 50, NO. 3' 1968' pp. 507-520.

Baker, C. B. and G. D. Irwin. Effects of Borrowing from
Commercial Lenders on Farm Organization, Illinois
Agricultural Experiment Bulletin 671, Champaign-
Urbana, 1961.

 

Baker, C. B. and J. A. Hopkin. "Concepts of Finance
Capital for a Capital-using Agriculture," American
Journal of Agricultural Economics, Vol. 51, December
1969, PP. 1055-1064.

Barry, Peter J. "Theory and Method in Firm Growth Re-
search" in Economic Growth of the Agricultural Firm,
Technical Bulletin 86, Washington State University,
Pullman, February 1977 (a), pp. 7-15.

 

Barry, Peter J. "Strategies for Growth" in Economic Growth
of the Agricultural Firm, Technical Bulletin 86,
Washington State University, Pullman, February 1977_
(b): PP. 23-32. '

 

Barry, P. J. and C. B. Baker. “Management of Financial
Structure: Firm Level," Agricultural Finance Review,
Vol. 37, February 1977, pp. 50-73.

 

Barry, P. J. and C. B. Baker. "Reservations on Credit
Use," American Journal of Agricultural Economics,
Vol. 53, No. 2, May 1971, pp. 222-227.

Barry, Peter J. and Donald R. Fraser. "Risk Management in
Primary Agricultural Production: Methods, Distribu-
tion, Rewards, and Structural Implications," American
Journal of Agricultural Economics, Vol. 58, 1976,
pp. 286-295.

213

 

214

Barry, Peter J., John A. Hopkin, and C. E. Baker. Financial
Management in Agriculture, Interstate Printers, Inc.,
Danville, Illinois, 1979.

 

 

Barry, Peter J. and John R. Brake. Financial Strategies
and Economic Decisions of the Firm, Agricultural
Economics Report No. 185, Dept. of Agricultural Eco-
nomics, Michigan State University, East Lansing,
February 1971.

 

 

Baumol, William J. "On the Theory of Expansion of the
Firm," American Economic Review, Vol. 52, No. 2,
December 1962, pp. 1078-1087.

 

Becker, Gary S. Human Capital and the Personal Distribu-
tion of Income: An Analytical Approach, Institute of
Public AdminIStration and Department of Economics,
University of Michigan, Ann Arbor, 1967.

 

 

Becker, Gary S. "Investment in Human Capital: A Theoret-
ical Analysis," Journal of Political Economics, Vol.
70, No. 5, Pt. 2, October 1962, pp. 9-49.

 

Black, J. Roy. Cost of Producing Corn, Staff Paper No. 17,
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